Natural Resources for Human Health (NRFHH)
Natural Resources for Human Health covers a wide range of natural science disciplines. Such fields can be at the all aspects of works bearing on natural compounds of plants, algae, microbes and animals. View more 1.7 CiteScore 2024 2.0 CiteScore Tracker 2025 0.21 SJR 33% Acceptance Rate Open Access
A Riemannian Hypergraph Spectral-Temporal Network with Directed Effective Connectivity for Parkinson’s Disease Recognition from Resting-State EEG
The electrophysiological changes that accompany Parkinson’s disease are expressed not only in the power of individual rhythms but also in the direction and geometry of the coupling between cortical regions, information that undirected and Euclidean descriptors only partially retain. This work introduces a Riemannian Hypergraph Spectral-Temporal Network, termed RHST-Net, that combines three complementary views of resting-state electroencephalography. Directed effective connectivity is first estimated for five rhythmic bands with phase transfer entropy, which captures the asymmetric flow of information between electrodes. In parallel, band-wise spatial covariance matrices are treated as points on the manifold of symmetric positive definite matrices and are projected to a Euclidean tangent space through a log-Euclidean mapping, producing geometry-aware descriptors. The electrode array is then modelled as a hypergraph whose hyperedges couple functionally related sensor groups, and a Chebyshev spectral convolution propagates information over this higher-order structure. A temporal convolutional network with dilated causal filters models the non-stationary evolution of connectivity, and a squeeze-and-excitation module recalibrates the five band streams before classification. On the public University of California San Diego resting-state dataset the network reaches 97.85 percent accuracy, a 97.80 percent F1-score and an area under the curve of 0.998 under a stratified protocol, and it preserves 90.36 percent accuracy under a subject-independent protocol. Ablation and interpretability analyses confirm that directed beta-band coupling over sensorimotor cortex drives the decision..
Accreditation versus Ranking: Two Different Pathways to Institutional Excellence in Health Sciences Higher Education—A Narrative Review
Background: Accreditation and institutional ranking have emerged as the two principal mechanisms for evaluating quality and performance in higher education. Although frequently used interchangeably, they differ fundamentally in their objectives, evaluation frameworks, and institutional implications. Their complementary roles within health sciences education remain inadequately synthesized. Objective: To critically compare accreditation and institutional ranking in health sciences higher education and develop an integrated conceptual framework illustrating their complementary contributions to institutional excellence. Methods: A narrative review was undertaken using peer-reviewed literature, policy documents, educational reports, and review articles published predominantly between 2012 and 2026. Evidence was synthesized across major themes including quality assurance, governance, curriculum, faculty development, research performance, internationalization, accountability, innovation, and institutional outcomes. Comparative thematic analysis was performed to identify conceptual distinctions, areas of convergence, and strategic opportunities for integration. Results: Accreditation consistently emerged as an internally driven quality assurance mechanism emphasizing governance, curriculum quality, faculty competence, regulatory compliance, continuous quality improvement, and patient safety. In contrast, institutional ranking primarily evaluated external competitiveness through research productivity, citation impact, academic reputation, internationalization, innovation, and graduate employability. The review demonstrates that neither framework independently defines institutional excellence. Instead, accreditation establishes the foundation for a sustainable quality culture, while ranking reflects the external manifestation of institutional maturity and academic competitiveness. A novel conceptual framework is proposed, integrating institutional inputs, accreditation, continuous improvement, quality culture, ranking performance, and multidimensional outcomes into a unified pathway toward institutional excellence. Conclusion: Sustainable excellence in health sciences higher education requires strategic integration of accreditation and ranking rather than prioritization of either system alone. The proposed framework offers a comprehensive roadmap for institutional planning, quality enhancement, and global competitiveness while providing a foundation for future empirical validation across diverse higher education settings..
Does Infrastructure Investment Pay Off Differently by Governance Type? A Small-Sample Heterogeneous-Effects Analysis of NAAC-to-NIRF Trajectories
A recurring policy question in Indian higher education is whether the same quality-assurance investment yields different competitive payoffs depending on an institution's governance type: whether it is centrally funded, State funded, or a self-financing Private/Deemed university. This paper examines one specific version of that question empirically: does a university's NAAC-assessed Infrastructure and Learning Resources score (Criterion 4) predict a subsequent gain in its NIRF overall ranking score, and does that relationship differ across governance types? Using 48 Indian universities for which both a NAAC criterion-level snapshot and at least two subsequent NIRF ranking cycles (2016-2025) were available, we split institutions into higher- and lower-infrastructure groups by a median split on Criterion 4 and estimated the average and type-moderated association with annualised NIRF score change, first with an unadjusted comparison of means, then with a covariate-adjusted linear model, and finally with an explicit treatment-by-governance-type interaction term. We found no statistically detectable overall association between infrastructure standing and subsequent NIRF gains (adjusted effect = 0.37 points/year, SE = 0.49, p = .45), and no statistically detectable heterogeneity by governance type (largest interaction term p = .66). The only robust predictor of annualised change was the institution's own baseline NIRF score, which was negatively associated with subsequent gains (b = -0.06, p = .047), consistent with a regression-to-the-mean or ceiling effect rather than any effect of infrastructure investment itself. Because the governance-type subgroups are small (as few as 6 Central universities with usable longitudinal data), we treat these null results as inconclusive rather than as evidence of no effect, and we set out the sample size a confirmatory replication would need. All analyses were conducted in Python..
Comparative Genome Mining Reveals Biosynthetic Potential of Endophytic Bacteria from Ethnomedicinal Plants of Manipur as Sustainable Natural Resources for Human Health Applications
Background: Plants have always been a great source of medicine for us, and even with new ways of making drugs, they still are [1]. Some bacteria, called endophytic bacteria, live inside these plants and can make lots of different helpful chemicals for human health [2]. These chemicals are often similar to the ones the plants themselves make, or they can work well together with them. This is really interesting because it means these tiny living things can help us find new medicines. They can even make chemicals that are just like the ones the plants make, which is really useful. So, scientists are paying more attention to these interesting endophytic bacteria and the plants they live in, because they might hold the key to new and exciting medicines Objectives: Recent breakthroughs in sequencing and mining microbial genomes have changed the way we discover special compounds, called metabolites, by letting us predict what a microbe can make before we even try to isolate it. Our research looks at five types of bacteria that live inside plants used in traditional medicine in the Indo-Myanmar Biodiversity Hotspot and to sequence their whole genome and report [3]. We want to see what kinds of useful compounds these bacteria can produce and if they can be a new, sustainable source of natural products for improving human health. Methods: To understand how certain bacteria can help us, we looked at the complete DNA sequences of five special kinds of bacteria that live inside plants. We used various computer programs such as antiSMASH [4] and MIBiG [5] to find groups of genes that work together to make useful compounds. These compounds might be able to help people feel better. We compared the biosynthetic potential found in these bacteria to see what kinds of useful compounds they might make. We were looking at bacteria called Cellulosimicrobium cellulans MUKR5 [3], Enterobacter hormaechei ST4 Bacillus subtilis ST2, Bacillus paralicheniformis ST3, and Bacillus velezensis ST5. These bacteria were isolated from plants used in traditional medicine in Manipur which lies in the Indo Myanmar Biodiversity hot Spot region. We already had sequenced the whole genome of these bacteria earlier through Next Generation Seqencing and were interested to look at what useful things these bacteria could make to be used for human disease therapy. Results: The process of comparative genome mining has revealed a stunning array of biosynthetic gene clusters, including non-ribosomal peptide synthetases, type I and type III polyketide synthases[6], ribosomally synthesized and post-translationally modified peptides, terpene biosynthetic pathways, siderophores, betalactones, thiopeptides, and hybrid PKS-NRPS clusters. One particular isolate, Bacillus velezensis ST5, stands out for its exceptionally rich biosynthetic architecture, boasting twelve predicted biosynthetic regions that encode a range of clinically relevant metabolites, such as difficidin, bacillaene, macrolactin H, surfactin, fengycin, bacilysin, and bacillibactin [8-18]. Meanwhile, other isolates have been found to possess complementary biosynthetic capabilities, encoding metabolites like enteromycin, alkylresorcinol, lankacidin C, aerobactin, bacitracin, lichenysin, pulcherriminic acid, and carbapenem-related compounds [19-27]. MIBiG analysis has also identified a multitude of biosynthetic core enzymes, tailoring enzymes, regulatory proteins, and transporter proteins that underpin the complex process of secondary metabolite biosynthesis. This diverse array of biosynthetic gene clusters and associated enzymes highlights the remarkable potential of these isolates to produce a wide range of valuable compounds. Conclusion: In short, the study shows that bacteria living inside plants used in traditional medicine are a great source of new medicines. By looking at the genes of these bacteria, we can find new ways to make drugs to fight diseases. This is a powerful tool for finding new medicines and can help us make new treatments for many diseases, including ones that are hard to treat. These bacteria are like a treasure chest of new medicines, and studying them can help us discover new ways to fight diseases like cancer, infections, and more.
Transformer-Based Deep Learning for Cardiovascular Disease Classification Using Raw Photoplethysmography Signals
Cardiovascular disease remains the foremost cause of death worldwide, and elevated blood pressure is its largest modifiable contributor, yet many hypertensive adults remain undiagnosed because sphygmomanometry requires clinical contact. Photoplethysmography offers an inexpensive optical alternative already embedded in wearables, but published classifiers depend on handcrafted descriptors or time-frequency images, and most are validated with splitting schemes that allow recordings from one participant to appear in both training and test partitions. This work introduces CPA-Former, a cardiac-phase-aware Transformer consuming the raw pulse together with its first and second derivatives, combining a multi-derivative tokenizer, a positional encoding defined on cardiac phase rather than absolute time, cross-derivative co-attention, and segment-level attention pooling. It is evaluated on the public PPG-BP database of 219 participants under strict subject-independent cross-validation, alongside four deep baselines and classical references trained identically. The proposed model attains 45.60% accuracy on four-stage staging and 71.66% on binary screening. The central finding is negative and arguably more useful than a headline number: architectures spanning two orders of magnitude in parameter count prove statistically indistinguishable on this cohort, and none convincingly exceeds handcrafted morphology features. Repeating the identical experiment with folds formed over recordings rather than participants inflates accuracy substantially, offering a parsimonious explanation for the far higher figures reported elsewhere on this dataset.
Graph Neural Network-Based Intelligent Resource Allocation in RIS-Assisted 6G Networks
Reconfigurable intelligent surfaces are among the strongest candidate technologies for sixth-generation wireless networks because they shape the propagation environment using nearly passive elements rather than additional radio chains. Realising that promise requires solving a joint beamforming and phase-shift problem that is non-convex, tightly coupled and must be re-solved whenever the channel changes. Classical alternating optimisation needs hundreds of iterations per channel realisation, which is incompatible with millisecond coherence times, and neural networks tied to one array geometry must be retrained whenever the network size changes. This work proposes HRG-Net, a heterogeneous relational graph network that represents the downlink as a bipartite graph over user and surface-element nodes and performs message passing along cascaded-channel edges. Because aggregation is permutation equivariant in both node types, one trained model transfers across network sizes it never saw. A model-driven output layer anchors the solution on regularised zero-forcing directions and learns only the power split, the surface phases and a residual correction, and the whole network is trained without labels by maximising achievable sum rate directly. Simulations of a multi-user multiple-input single-output downlink show that HRG-Net attains 108.8% of the weighted minimum mean square error sum rate while requiring 16 times less computation per channel realisation.
Cloud-Assisted Deep Learning Framework for Real-Time Cotton Leaf Disease Diagnosis using Android Application
Early and accurate detection of cotton leaf diseases is essential for improving crop productivity, minimizing yield losses, and supporting sustainable agricultural practices. Traditional disease diagnosis methods depend on manual field inspection by experts, which is time-consuming, subjective, and often impractical for large-scale cotton cultivation. To address these limitations, this research presents an intelligent Android-based cotton leaf disease classification system that integrates mobile image acquisition, cloud-based storage, and algorithm-driven disease classification. In the proposed framework, cotton leaf images are captured or selected using an Android mobile application and securely uploaded to the cloud using the ImgBB image hosting service. The cloud platform provides scalable image storage and acts as an interface between the mobile application and the classification engine. Uploaded images are retrieved from the cloud and preprocessed through resizing, normalization, and enhancement to ensure consistency with the trained dataset. Disease classification is then performed using machine learning and deep learning algorithms, with emphasis on a transfer learning–based CNN–Transformer–LSTM hybrid model trained on cotton leaf images representing multiple disease classes and healthy leaves. The classification module automatically identifies cotton leaf diseases such as Alternaria leaf spot, bacterial blight, and grey mildew, along with healthy leaf conditions, and returns the predicted class with confidence scores to the Android application. By offloading computationally intensive tasks to the cloud, the system minimizes mobile device resource usage while maintaining high classification accuracy. Experimental evaluation on the cotton leaf dataset demonstrates strong performance in terms of accuracy, precision, recall, and ROC–AUC values, confirming the robustness of the proposed approach under varying image capture conditions. The developed Android–cloud integrated system provides a practical, user-friendly, and scalable solution for real-time cotton leaf disease diagnosis. It enables farmers and agricultural practitioners to obtain rapid diagnostic feedback using smartphones, facilitating early disease management and reducing dependence on expert intervention. The proposed framework can be extended to support additional crops, real-time model updates, and integration with IoT-based agricultural monitoring systems, making it a valuable contribution to smart and precision agriculture.
Adoption of AI-Driven Social Media Marketing Tools and Their Influence on Cosmetic Consumption of Women in Chennai
An AI marketing tool is an application or a platform that uses AI technology to improve marketing activities and make data-driven decisions. AI-driven tools such as personalized advertisements, Chabot’s, recommendation algorithms, and influencer analytics are highly shaping the way consumers discover and purchase cosmetic products. These tools control AI algorithms, natural language processing, machine learning, and data analytics to automate processes, analyze data, and provide insights to enhance cosmetic marketing strategies and campaigns. This study explores how AI-powered social media marketing tools influence the cosmetic consumption patterns of women in Chennai. The data collected from women across different age groups in Chennai; the study analyzes the relationship between exposure to AI-driven marketing strategies and changes in buying behavior. The research also examines various factors such as Awareness & adoption of AI-driven social media marketing, AI-personalization & consumer engagement, Influence on purchase intention & consumption behavior, Brand-wise perception of AI-driven cosmetic marketing, trust and satisfaction. The study indicates that the brand Lakme, by its active usage of AI-powered marketing tools on social media platforms, plays a major role in purchase decisions and holds strong consumer trust and loyalty. The study also indicates that platforms such as Instagram with AI-based marketing play an important role in shaping the young consumer’s buying behavior, especially when the content is relatable, affordable and trustworthy.
Understanding Kerala Ayurveda's Brand Formation: The Serial Mediating Roles of Affection, Connection and Passion in the Relationship between Service Quality and Clinical Excellence
Purpose – This study investigates the comprehensive brand formation process in Kerala's Ayurveda healthcare sector. It examines a holistic framework where service quality, destination image, convenience, and perceived value influence clinical excellence and satisfaction, serially mediated by emotional brand attachment dimensions (affection, connection, and passion), ultimately leading to brand formation and loyalty. Design/Methodology/Approach – A structured questionnaire comprising 16 multi-item constructs (64 Likert-scale items) was administered to 143 patients across multiple Ayurveda hospitals, clinics, resorts, and massage centers in Kerala. The study employs descriptive statistics, full correlation analysis, and serial mediation analysis utilizing all questionnaire variables to test the hypothesized comprehensive framework. Findings – The results validate the serial mediation pathway (Service Quality → Affection → Connection → Passion → Clinical Excellence → Brand Formation). Additionally, destination image and perceived value emerge as significant antecedents, while treatment satisfaction and overall satisfaction act as parallel mediators bridging clinical excellence with brand loyalty. The comprehensive model explains 68.4% of the variance in brand formation. Practical Implications – The study provides actionable insights for administrators, policymakers, and tourism stakeholders on leveraging comprehensive service design, emotional engagement, and destination branding to strengthen Kerala's position as a global Ayurveda leader. Originality/Value – This research is the first to empirically examine a fully integrated model of Kerala Ayurveda brand formation, utilizing all dimensions of patient perception—from service quality and destination image to emotional attachment and loyalty.
From Flare to Freedom: Managing Erythrodermic Psoriasis (Kitibha Kushta) through Panchkarma (Ayurvedic Detoxification) and Polyherbal Therapy: A Case Report
Background: Erythrodermic psoriasis is a form of psoriasis that affects more than 90% of the body's surface area. It has systemic complications like thermoregulatory dysfunction, fluid imbalance, and secondary infections. Contemporary treatments, such as systemic immunosuppressants offer symptomatic management, but they produce adverse effects and cause relapse. Ayurveda classifies this condition under Kitibha Kushta, a type of Kshudra Kushta (minor skin disorders), and favours a approach of detoxification (Shodhana) followed by palliation (Shamana). Case Presentation: A 36-year-old male presented with a 4-year history of generalized erythematous, scaly patches with severe pruritus over the entire body. He had scoring of 45.2 on the Psoriasis Area and Severity Index (PASI) and 25 on the Dermatology Life Quality Index (DLQI). This indicated severe disease with an extremely large impact on quality of life. The patient was managed with a 90-day integrated Ayurvedic protocol including Deepana-Pachana, internal and external oleation (Snehana), whole-body sudation (Sarvanga Bashpa Sweda), therapeutic purgation (Virechana) with Trivritta Leha, triphala kwath and manuka phanta, post-purgation diet (Samsarjana Krama), and oral polyherbal formulations including Kaishor Guggul, Patolkaturohinyadi Kashay, Manibhadra Gulam, Aarogyavardhini Vati, and Tab. Allerco, and topical Psorolin oil. Outcomes: At the end of the 90-day treatment, the PASI score reduced from 45.2 (severe) to 5.6 (mild) at Day 17 and to 0 (clear) at the last follow-up. The DLQI score improved from 25 (large effect) to 6 (moderate effect) at Day 17 and reached 0 (no effect) at the 90-day. Complete clinical improvement was achieved with no relapses. No adverse effects were noticed throughout the treatment. Conclusions: This case shows that a planned Ayurvedic approach combining Panchakarma with individualized polyherbal therapy may result in full and lasting recovery in severe erythrodermic psoriasis.
Environmental Preference or Economic Concern? Public Perception of Ethanol Blending Policy in India: An Empirical Study
India recently met its government target to roll out 20% ethanol- blended petrol (E20), five years ahead of schedule of the earlier plan of reaching E20 by 2030 as per National Policy on Biofuels, 2018. While this pushes India closer to its goal of phasing out fossil fuels and increasing energy security, it also poses fresh questions about its impact on fuel efficiency, vehicle compatibility and the legal regime regulating ethanol-blending. This paper assesses India’s legal regime for Ethanol Blended Petrol (EBP) Program, drawing comparisons from the laws governing ethanol-blending in United States of America, Brazil and European Union(EU). India follows a framework of government policies and administrative actions, rather than having a separate law regulating ethanol-blending programs. As a supplementary study, we conducted a survey of 125 respondents on their awareness about E20 petrol and concerns relating to environment, economy, fuel efficiency, vehicles etc., along with their trust for government and other institutions dealing with ethanol-blended fuels. Our data suggests that concerns are primarily attributed towards implementation of the program, lack of awareness about it and lack of trust. Only minor concerns were attributed to ethanol-blending directly. With these conclusions, we propose that India adopt a separate Biofuels Act which clearly lays down timelines for rollout of ethanol-blending program, vehicle compatibility requirements, consumer protection requirements, independent testing and net-public disclosure of all information related to ethanol-blending programs along with price support and penal provisions to instill trust in the general public and have an improved legal regime for future endeavors.
Local Flap Reconstruction in Plantar Soft-Tissue Defects of the Foot: Surgical Outcomes from a Prospective Observational Study
Background: Plantar soft-tissue defects of the foot resulting from trauma, diabetic foot ulcers, infections, and post-oncological resections present significant reconstructive challenges because of limited local tissue availability, weight-bearing requirements, and the need for durable coverage. Local flaps provide a reliable reconstructive option while preserving function and achieving acceptable aesthetic outcomes. Aim: To evaluate the effectiveness and outcomes of local flap reconstruction in plantar soft-tissue defects of the foot. Materials and Methods: A prospective observational study was conducted among 30 patients with plantar soft-tissue defects of the foot managed using local flap reconstruction between December 2024 and December 2025. Flap selection was based on defect size, location, and vascular anatomy. Advancement, rotation, transposition, interpolation, propeller, and intrinsic muscle flaps were used. Patients with extensive composite defects, critical limb ischemia, active osteomyelitis, or peripheral vascular disease were excluded. Outcomes assessed included flap viability, complications, functional recovery, and patient satisfaction. Results: Complete flap survival was observed in 26 patients (86.7%). Partial flap necrosis occurred in 4 patients (13.3%), and wound infection developed in 3 patients (10.0%). No total flap loss occurred. All complications were managed conservatively with antibiotics, wound care, and debridement when necessary. Most patients achieved stable wound coverage and satisfactorily resumed weight-bearing activities. Conclusion: Local flaps are a safe, effective, and dependable option for selected plantar soft-tissue defects of the foot. Careful patient selection and meticulous surgical planning yield high flap-survival rates, acceptable complication profiles, and satisfactory functional outcomes.
Noise-Induced Hearing Loss in Mining: Biological Mechanisms and Emerging Pharmacological Otoprotection
Noise-induced hearing loss (NIHL) is a major occupational health problem, especially within the mining industry, where workers are frequently exposed to hazardous levels of occupational noise from drilling, blasting, crushing, and heavy machinery. While engineering controls, hearing protection devices, and audiological surveillance are key preventive measures, they may not completely prevent cochlear injury. This review focuses on the epidemiology, biology, and novel pharmacological preventive and mitigating otoprotective strategies for mining-associated NIHL. Excessive noise exposure causes oxidative stress, mitochondrial dysfunction, inflammation, excitotoxicity, synaptic damage, and hair cell injury, thereby providing a biological rationale for studying otoprotective therapies. Experimental studies have demonstrated promising effects of antioxidants, including N-acetylcysteine, D-methionine, and ebselen, as well as anti-inflammatory, metabolic, and regenerative therapies, but human studies remain limited and inconclusive. Limitations in effective drug delivery to the cochlea, the long-term safety of repeated drug administration, the lack of validated biomarkers, and the absence or near absence of mining-specific pharmacological trials are significant barriers. Pharmacological intervention should thus be considered investigational, additional to, and not a substitute for comprehensive hearing-conservation programs. Future studies should prioritize longitudinal occupational research, biomarker-guided risk assessment, mining-specific clinical trials, and clinically meaningful hearing outcomes.
Patterns, Predictors, and Expenditure Associated with Tobacco Use among Rural Adults in Jharkhand: A Cross-Sectional Study
Background: Jharkhand carries one of India's highest tobacco burdens, yet little is known about what rural users consume, what sustains their use and what it costs them. Objectives: To describe the forms, frequency, age at initiation and duration of tobacco use, and to assess domain-based predictors and expenditure among current tobacco users. Methods: A community-based cross-sectional study was conducted among 212 adults aged more than 15 years in the rural field practice area of the Rural Health Training Centre, RIMS, Ranchi. The current tobacco users were assessed for the mode, frequency and form of use, age at initiation, duration of use and expenditure; eight behavioural predictor domains were scored from 0 to 1. Results: Of 212 participants, 94 (44.3%) were current tobacco users. Smokeless tobacco predominated: 85 (90.4%) used it daily, whereas 36 (38.3%) smoked, only 11 (11.7%) daily. Khaini was the commonest product, used daily by 50 (53.3%). Exclusive smokeless use (61.7%), dual use (28.7%) and exclusive smoking (9.6%) were the patterns observed. Mean age at initiation was 18.0 years for smoking and 22.4 years for smokeless tobacco, with mean durations of 7.6 and 25.8 years. Misinformation (0.893) and peer pressure (0.882) scored highest, followed by self-medication (0.615) and parental influence (0.516); social rewards (0.274) scored lowest. Mean expenditure was Rs. 12.87 daily and Rs. 2760.64 yearly. Conclusion: Tobacco use was predominantly daily, smokeless, early-initiated and long-standing, sustained by peer networks, family modelling and beliefs that tobacco revitalises and relieves tension, at a recurring cost to modest household budgets.
Automated Detection, Quantification and Grading of Tuberculosis Bacilli in Ziehl-Neelsen Sputum Smear Microscopy: A Structured Review of Methods, Datasets, Multi-Centre Learning and Robustness
Sputum smear microscopy remains the frontline diagnostic test for pulmonary tuberculosis in high-burden settings, yet it is slow, demands a trained microscopist and reports limited sensitivity. Deep learning has been applied intensively to this task, but the literature has grown without a shared benchmark, a shared metric definition or a shared statement of the clinical target. This review organises the 2022-2026 literature into a six-category taxonomy spanning acquisition and image formation, detection and segmentation, quantification and bacillary load grading, learning under limited supervision, multi-centre privacy-preserving learning, and robustness. Primary studies, seven reviews and every reachable public dataset are compared in eight tables. Three findings emerge. Reported performance is inflated by task substitution, since patch classification studies report accuracies near ninety-nine percent whereas detection studies report mean average precision near fifty percent when averaged across localisation thresholds. Quantification is largely unsolved, because clumped bacilli are annotated away rather than resolved and only one study automates the five-point World Health Organization load scale. Multi-centre and robustness evidence is almost absent, with one of one hundred and fifty-two artificial intelligence tuberculosis studies reporting a domain-shift analysis and no published federated learning study on sputum smear microscopy. We formalise the evaluation gap, propose a foreground-density heterogeneity measure for federated smear analysis, and set out a research agenda centred on grading-aware, multi-centre and reliability-audited models.
Early Prediction of Diabetic Retinopathy using a Multimodal Deep Learning Framework Integrating Neural Network
Diabetic retinopathy (DR) is one of the most common diabetes-related causes of preventable vision loss that is equally important in diabetes management for proper diagnosis and treatment at an early stage to save one's vision. This study introduces a combined multimodal deep learning approach for early detection of DR using the fusion of retinal fundus images and structured clinical data. The framework uses an artificial neural network with the feature extraction ability of convolutional network EfficientNet-B3 to process clinical variables, such as age, duration of diabetes, glycated hemoglobin (HbA1c), fasting blood glucose, blood pressure, body mass index, cholesterol level, and family history. The extracted visual and clinical features are integrated in a multimodal feature integration layer to further enhance classification accuracy. Empirical results indicate high accuracy (97.20 %), precision (96.90 %), recall (96.50 %), specificity (97.80 %), F1 score (96.70 %), as well as area under the ROC curve (AUC = 0.989), outperforming conventional image-based deep learning models. The results suggest that multimodal learning has a significant effect on the prediction of early DR and can act as an efficient clinical decision support system for artificial intelligence-based large-scale screening and prompt intervention.
Evaluating the Effectiveness of Multi Interventional Strategies for Children with Hearing Impairment
People with normal hearing abilities can perceive signals, understand spoken words and languages, and actively engage in communication with others. However, individuals with hearing impairment struggle to hear spoken sounds and may face difficulties in interacting with others. Hearing impairment presents unique challenges, affecting speech and language development, academic success and social integration. Early identification and early intervention are critical to mitigate these challenges. Curriculum adaptation is a key strategy in addressing the educational needs of children with hearing impairment ensuring inclusivity and fostering academic and non – academic skills. This study, focuses on developing a total communication intervention package to enhance moral values and communication skills in children with hearing impairment. This study conducted in a special school in Coimbatore with 15 participants, the study aimed to evaluate the impact of the intervention on variables such as age, gender, type and level of hearing loss. The objectives included assessing children’s understanding of moral values, developing an enhancement package and determining its effectiveness in fostering moral awareness. The hypotheses tested the differences in moral values before and after the intervention among various demographic variable. The findings revealed the significant improvements in communication and moral understanding, highlighting the effectiveness of intervention in promoting holistic development. These efforts aim to empower children with hearing impairment enabling their fullest potential and thrive in various facets of life.
An Innovative Treatment for Florid Cellulitis Leg (Multiple Mini-Fasciotomies - MMF)
A new innovative surgical approach, Multiple Mini Fasciotomies (MMF), is described to hasten resolution of florid cellulitis of leg and foot. Simplicity of the procedure and the prompt favorable response within 12-24 hours, in an apparently hopeless situation, is considered very useful in minimizing the morbidity and complications of sepsis. Though the number of cases (n) is not very large, consistent improvement in all cases who underwent this procedure, is being reported.
In Vitro Antioxidant Activity and Total Phenolic Analysis of Grewia Multiflora Juss
This study was conducted to evaluate the antioxidant activity and total phenolic content of Grewia multiflora Juss. extracts obtained using solvents of different polarities, namely chloroform, acetone, methanol, water and also methanol (70%). Fresh leaves of Grewia multiflora Juss. (5 kg) were collected from the Meru Matha area near the Subarnarekha River in the biodiversity region of the Similipal forest, Mayurbhanj district.The extracts were prepared and their percentage yields were determined. Antioxidant activity was assessed using the DPPH free radical scavenging assay, while total phenolic content was estimated by the Folin–Ciocalteu method. Among the tested extracts, the methanol (70%) extract exhibited the strongest antioxidant activity, with the lowest IC50 value of 95.08. This activity correlated with its highest total phenolic content (115.48±3.72mg GAE/g) and greatest extraction yield (17.35%). In comparison, the methanol extract showed moderate antioxidant activity with an IC50 value of 205.96, a total phenolic content of 108.47±3.12 mg GAE/g, and a lower extraction yield of 9.18%. The chloroform and acetone extracts demonstrated the weakest antioxidant activity, which corresponded with their low phenolic content and minimal extraction yield. These findings suggest that solvent polarity significantly influences the extraction efficiency, phenolic composition, and antioxidant potential of Grewia multiflora Juss., indicating that the plant may serve as a promising natural source of antioxidant compounds.
Medication-Related Osteonecrosis of the Jaw Associated with Monoclonal Antibodies and Small-Molecule Inhibitors: A Literature Review
Medication-related osteonecrosis of the jaw (MRONJ) has evolved from an uncommon clinical entity to a well-recognized, multifactorial complication predominantly linked to antiresorptive and antiangiogenic therapies. While bisphosphonates were the first drug class linked with M.R.O.N.J., subsequent research has identified several newer therapeutic agents, specifically monoclonal antibodies (mAbs) and small-molecule inhibitors (SMIs), as significant contributors. The increasing use of these agents in oncology and metabolic bone disorders necessitates a thorough understanding of their mechanisms and association with MRONJ. This review synthesizes evidence from 25 key studies, encompassing randomized controlled trials, cohort analyses, pharmacovigilance reports, and expert consensus guidelines. Our objective is to present an evidence-based framework highlighting the incidence, how the process works, risk modifiers, signs and symptoms, diagnostic protocols, and management strategies linked to MRONJ induced by mAbs and SMIs.
Prevalence of Various Anatomical Variants of the Nasal Cavity and Paranasal Sinuses using Multidetector Computed Tomography among Patients Presenting with Chronic Sinusitis
Background: Chronic rhinosinusitis (CRS) is a common inflammatory disorder of the nasal cavity and paranasal sinuses. Anatomical variations may influence sinonasal drainage and contribute to disease pathogenesis. Multidetector computed tomography (MDCT) plays a crucial role in identifying these variations and guiding management. This study aimed to determine the prevalence of sinonasal anatomical variants using MDCT and evaluate their association with disease severity and demographic factors in patients with CRS. Methods: This hospital-based cross-sectional study included 150 patients with clinically and radiologically confirmed CRS. MDCT scans were performed to identify anatomical variants. Statistical analysis was conducted using Chi-square and Kruskal–Wallis tests, with a p-value <0.05 considered significant. Results: Deviated nasal septum (DNS) was observed in 81 patients (54.0%), paradoxical middle turbinate in 79 (52.7%), agger nasi cells in 79 (52.7%), Haller cells in 77 (51.3%), and concha bullosa in 76 (50.7%). Maxillary and ethmoid sinus involvement was most common (90; 60.0%). No statistically significant association was found between anatomical variants and disease severity: DNS (χ²=2.00, p=0.368), agger nasi cells (χ²=0.43, p=0.806), concha bullosa (χ²=1.02, p=0.601), Haller cells (χ²=2.57, p=0.277), frontal cells (χ²=3.50, p=0.174), maxillary septation (χ²=3.74, p=0.155), accessory ostium (χ²=0.48, p=0.786), uncinate pneumatization (χ²=0.01, p=0.994), and paradoxical middle turbinate (χ²=1.38, p=0.503). No significant gender-based differences were observed, e.g., DNS (χ²=0.000, p=1.000). Age (H=3.845, p=0.146) and symptom duration (H=0.715, p=0.700) were also not associated with severity. Conclusion: Anatomical variants are highly prevalent in CRS but are not independently associated with disease severity. MDCT remains essential for preoperative evaluation and surgical planning.
Efficacy and Safety of Shatavari (Asparagus Racemosus) Root Extract in the Management of Menopausal Symptoms: A Multicenter, Randomized, Double-Blind, Placebo-Controlled Trial
Introduction: Menopause is associated with vasomotor, psychological, and somatic symptoms that impair quality of life. The objectives was to evaluate the safety and efficacy of Shatavari root extract capsule improving menopause related hormonal changes and alleviating hot flushes and other climacteric symptoms. Methods: Multicenter, randomized, double-blind, placebo-controlled trial enrolled 70 peri- and postmenopausal women, who randomized to receive Shatavari root extract capsules (400 mg/day) or placebo for 56 days. The primary objective was to evaluate effects on hot flushes and other climacteric symptoms compared with placebo. The secondary objective was to assess effects on stress, sleep, mood, quality of life, safety and tolerability. Results: Baseline characteristics were comparable between groups. Shatavari significantly reduced menopausal symptoms (total MRS and somatic scores), improved quality of life (MENQOL) (total and psychosocial scores) and mood (total mood, tension-anxiety, vigor-activity scores) from Day 14 compared to placebo. Significant reduction in hot flush severity, other climacteric symptoms, stress, MENQOL, mood and improvement in sleep quality observed from Day 28. Shatavari significantly modulated serum estradiol (increased) and luteinizing hormone (decreased) from Day 14 and decreased cortisol from Day 42 compared to placebo. Follicle-stimulating hormone and testosterone remained stable. No meaningful changes in hematological, hepatic, or renal parameters, and adverse events low and comparable between groups. Conclusions: Shatavari root extract for 56 days was safe and well-tolerated and resulted in clinically improvements in vasomotor symptoms, psychological well-being, sleep quality, and quality of life in peri- and postmenopausal women, supporting as a non-hormonal, holistic for management of menopausal symptoms.
Bioactive Potential of Melaleuca Alternifolia: In Vitro Assessment of Antioxidant, Antimicrobial, and Anti-Parkinsonian Activities
Melaleuca alternifolia (tea tree) essential oil is a rich source of bioactive terpenoids with promising antioxidant, antimicrobial, and neuroprotective properties. The present study investigated the phytochemical composition, antioxidant, antimicrobial, and in vitro neuroprotective potential of M. alternifolia essential oil. Essential oil was extracted from shade-dried leaves by hydrodistillation using a Clevenger-type apparatus, yielding 0.53% (w/w) oil. Preliminary phytochemical screening and GC–MS analysis were performed to identify major constituents. Antioxidant activity was evaluated using the Ferric Reducing Antioxidant Power (FRAP) assay. Neuroprotective potential was assessed using the MTT assay on SH-SY5Y human neuroblastoma cells and the monoamine oxidase (MAO-A/B) inhibition assay. Antimicrobial activity was determined against Staphylococcus aureus, Escherichia coli, Aspergillus niger, and Candida albicans using the agar well diffusion method. Phytochemical screening confirmed the presence of steroids, flavonoids, phenols, tannins, saponins, glycosides, and proteins. GC–MS analysis identified 42 volatile compounds, with eugenol (43.52%), β-caryophyllene (19.51%), γ-terpinene dimer (17.72%), α-selinene (2.78%), and γ-muurolene (2.35%) as the predominant constituents. The essential oil exhibited potent antioxidant activity with a FRAP IC₅₀ of 0.01316 µL/mL. In the MTT assay, the essential oil demonstrated moderate cytotoxicity toward SH-SY5Y cells (IC₅₀ = 68.69 ± 0.05 µg/mL) compared with doxorubicin (48.62 ± 0.04 µg/mL). However, it showed negligible MAO inhibitory activity, with an IC₅₀ above the maximum tested concentration. The essential oil displayed concentration-dependent antimicrobial activity, with stronger antifungal effects against A. niger and C. albicans than antibacterial effects against S. aureus and E. coli.Melaleuca alternifolia essential oil possesses significant antioxidant and broad-spectrum antimicrobial activities together with moderate neuroprotective potential, supporting its potential as a natural therapeutic candidate for managing oxidative stress-associated disorders and microbial infections.
Shielding the Next Generation: Predictors of Parental Acceptance of the HPV Vaccine against Cervical Cancer: A Cross-Sectional Study from North Karnataka
Introduction: Cervical cancer remains the second most common cancer among women in India, despite being largely preventable through HPV vaccination. Parental acceptance is critical for vaccine uptake, particularly in rural areas where awareness, access, and sociocultural influences strongly shape health decisions. This study examines the sociodemographic, attitudinal, and contextual predictors of parental acceptance of the HPV vaccine in rural North Karnataka. Methods: A community-based cross-sectional study was conducted among parents of children aged 8–14 years in selected rural regions of Belagavi, Karnataka (October 2024–January 2025). Using systematic household sampling, 321 consenting parents completed a structured and interviewer-administered questionnaire assessing HPV-related knowledge, attitudes, perceived barriers, facilitators, and vaccine acceptability. Reliability testing yielded a Cronbach’s alpha of 0.80. Data were analysed using descriptive statistics, ROC curve analysis, and stepwise multiple logistic regression to identify independent predictors of high vaccine acceptability. Results: Perceived barriers were the strongest predictor of high acceptability (AUC = 0.735, p < 0.0001), followed by facilitators (AUC = 0.694, p < 0.0001), number of children (AUC = 0.701, p < 0.0001), and parental attitude (AUC = 0.652, p < 0.0001). Regression analysis showed significantly higher odds of acceptance among Muslim parents (AOR = 5.49, p = 0.012), parents with two to four children (AOR range 12.38–15.14, p < 0.05), those with positive attitudes (AOR = 4.37, p = 0.013), low perceived barriers (AOR = 17.11, p < 0.0001), and strong facilitators (AOR = 5.77, p = 0.0001). Higher knowledge levels were inversely associated with acceptance (AOR = 0.03, p < 0.0001). Monthly income also showed a negative association with acceptance. Discussion: Psychosocial and contextual influences—specifically perceived barriers, facilitators, and attitudes—play a more decisive role in HPV vaccine acceptance than demographic factors or knowledge alone. These findings align with global evidence indicating that misinformation, fear, and structural barriers reduce vaccine uptake, while strong provider recommendations and community support enhance acceptance. The inverse association between knowledge and acceptability suggests that increased exposure to misinformation or conflicting narratives may shape parental perceptions in rural settings. Conclusion: Improving HPV vaccine uptake in rural India requires targeted strategies that address perceived barriers, strengthen positive attitudes, and enhance supportive factors rather than focusing solely on knowledge dissemination. Implementing culturally sensitive, community-based, and provider-driven interventions could substantially increase HPV vaccine acceptability and contribute to cervical cancer prevention in underserved regions.
Integrated in Vitro, in Silico, and GC MS Evaluation of Annona Muricata Leaf Extract for Osteoporosis Therapy
Background: Osteoporosis is a progressive skeletal disorder characterized by reduced bone mass and microarchitectural deterioration, leading to fragility fractures. Current pharmacological interventions are limited by adverse effects and incomplete modulation of bone remodeling. Plant derived bioactives offer a promising alternative due to their multitargeted mechanisms and favorable safety profiles. Methods: The ethanolic leaf extract of Annona muricata Linn. (AMLE) was subjected to GC MS analysis to identify phytoconstituents. In silico docking and ADMET profiling were performed to evaluate binding affinities and pharmacokinetic properties of identified compounds against bone related proteins, including RANKL. In vitro assays employed MC3T3 E1 osteoblasts and RAW 264.7 osteoclasts. Cell viability was assessed by MTT assay, osteoblast differentiation by alkaline phosphatase (ALP) activity, and osteoclastogenesis by tartrate resistant acid phosphatase (TRAP) staining. Results: GC MS profiling revealed bioactive molecules such as neophytadiene, tocopherol acetate, and ergostadienol. Docking studies demonstrated strong interactions with RANKL and other bone remodeling proteins, with binding scores comparable to reference ligands. In vitro, AMLE maintained >90% viability up to 50 μg/mL. ALP activity was enhanced two fold, indicating osteoblast stimulation, while TRAP positive osteoclasts were reduced by ~60%, confirming anti resorptive activity. Conclusion: The integrated GC MS, in silico, and in vitro evidence highlights AMLE as a dual modulator of bone remodeling, capable of promoting osteogenesis while inhibiting resorption. These findings position Annona muricata as a promising candidate for plant based osteoporosis therapy, warranting further preclinical and clinical validation.
Application of the Bosch KTS 590 Scanner and its Influence on the Teaching Performance in Electromechanics at Public University
The research called Application of the Bosch KTS 590 scanner in the teaching performance of Electromechanics at UNE Enrique Guzmán y Valle, 2026, aimed to evaluate the effects of the application of the Bosch KTS 590 scanner in the teaching performance of Motive Power at UNE Enrique Guzmán y Valle, 2026. The approach used was quantitative, adopting a pre-experimental design, with a sample of 11 Teachers from the Motive Power study program. In this research, the degree of reliability of the instrument for measuring the teaching performance variable was determined using a Kuder-Richardson (KR-20) for the calculation of internal consistency, confirming the reliability of the instrument for the dependent variable with a value of 0.930. The result revealed that there are positive, relevant and significant effects of the application of the Bosch KTS 590 scanner on the teaching performance of the Motive Power of the UNE Enrique Guzmán y Valle, 2026.
Continuous Training in Pedagogical Practice in Students of a Public Pedagogical Institute
The purpose of this research was to determine how continuing education influences the teaching practice of students at the Pichari Public Pedagogical Institute, La Convención, Cusco, 2026. The study was conducted using a quantitative approach, using a non-experimental design and an explanatory level. The sample consisted of 80 graduates from the institute. The results demonstrated a statistically significant influence, leaving no room for doubt regarding the influence of continuing education on teaching practice (chi-square = 30.336; p = 0.000), explaining 38.7% of the variability in continuing education. Similarly, the study demonstrated a positive impact on didactic planning (41.4% influence; p = 0.000), while no significant effect was found on teaching delivery (2.9%; p = 0.431) and learning assessment (7.5%; p = 0.084). It was concluded that, while continuing education supports pedagogical practice in its planning dimensions, there are weaknesses in its impact on the implementation and evaluation of pedagogical practice that justify the redesign of comprehensive and contextualized continuing education programs. Research on continuing education recommended strengthening training strategies with a comprehensive approach that articulates theory and practice and contributes to improving the quality of future teaching performance.
Didactic Resources and Cooperative Learning, In the Achievement of Competencies in Higher Education Students Lima
The purpose of this study is to ascertain how cooperative learning and didactic resources affect the development of competencies in the training units for students in the automotive mechanics specialty's II, IV Cycle at the Antenor Orrego Espinoza public technological higher education institute in the district of Chorrillos, Lima, 2026. The current study is of a basic kind with a causal correlational descriptive level, a non-experimental design, and a sample of 135 students. The results are supported by the test of a particular general hypothesis and the descriptive analysis presented in tables and figures. The findings show that cooperative learning and didactic materials have a positive impact on students' acquisition of competencies in the training units of the Antenor Orrego Espinoza public technological higher education institute of the district of Chorrillos, LIMA-2026's automotive mechanics specialty in cycles II and IV.
Training Needs Assessment and Capacity Building of Fishery Extension Personnel in India: Socio-Economic Correlates and Predictors of Felt Training Need
Effective delivery of fisheries extension services depends critically on the technical competence of front-line workers, yet systematic, evidence-based assessment of their felt training needs remains scarce in the Indian fisheries-extension literature. This study assessed the training needs of 180 baseline Fishery Demonstrators (F.Ds) engaged in government, non-governmental and progressive-farmer-linked extension delivery, across eleven technical subject areas spanning culture, breeding, feeding, water and soil quality, disease management, capture fisheries, resource management, fish seed, marketing, extension methodology and official work. A structured interview schedule captured socio-personal characteristics and felt training need (most needed / needed / least needed) for each of 41 specific items, from which a Training Need Quotient (TNQ) and mean need-score were computed per item and subject area. Water and soil quality (TNQ = 80.84%) and disease management (TNQ = 79.72%) emerged as the highest-priority subject areas, while capture-fisheries techniques (TNQ = 62.78%) ranked lowest. Correlation analysis showed that service experience was significantly and negatively associated with TNQ of culture (r = -0.452, P < 0.01) and breeding aspects (r = -0.279 to -0.452, P < 0.05), while the number of in-service training programmes already undergone was positively associated with TNQ of water-and-soil-quality, marketing and fish-seed aspects. Regression analysis confirmed that service experience and number of prior in-service trainings were the most consistent predictors of felt training need across subject areas, together explaining 13-26% of the variance in different models. The large majority of respondents (83.3%) were satisfied with their present position, yet only 27% were satisfied with their present salary, and the largest respondent shares favoured two-week duration (31.7%), an equal 50:50 theory-practical balance (48.3%), and training delivered outside the home state (top-ranked location). These findings provide an empirical basis for designing need-based, competency-linked capacity-building curricula for fisheries extension personnel in India.
A Dragon Fruit Peel Mucilage-Based Fast-Dissolving Oral Film for Scopolamine Hydrobromide Delivery.
Background: Motion sickness is associated with sensory conflict and commonly causes nausea and vomiting. Scopolamine is effective for prophylaxis; however, conventional oral administration may be inconvenient during travel. The present study aimed to develop a fast-dissolving oral thin film (FDOF) of scopolamine hydrobromide using dragon fruit peel mucilage as a natural film-forming polymer.Materials and Methods: Films were prepared by the solvent-casting technique using dragon fruit peel mucilage, HPMC, sodium alginate, plasticizer, and surfactant. Twelve preliminary formulations were prepared and evaluated for film-forming characteristics. The optimized formulation was characterized for physicomechanical properties, surface pH, moisture content, transparency, drug content, disintegration, and in-vitro drug release. Results: Formulation F12 demonstrated excellent film formation, flexibility, peelability, and uniformity. It exhibited thickness of 0.112 ± 0.003 mm, folding endurance of 327.67 ± 3.51 folds, drug content of 99.33 ± 0.76%, disintegration time of 18.00 ± 1.00 s, and 91.80 ± 1.15% transmittance. Cumulative drug release reached 98.40% within 10 min. Conclusion: The optimized scopolamine hydrobromide FDOF demonstrated satisfactory mechanical properties, rapid disintegration, excellent drug uniformity, and rapid drug release, indicating its potential as a convenient oral dosage form for motion-sickness management.
Artificial Intelligence in Deep Vein Thrombosis: A Bibliometric Analysis of Diagnostic and Predictive Research
Background: Artificial Intelligence (AI) use has been growing in Deep Vein Thrombosis (DVT) and Venous Thromboembolism (VTE) studies for diagnosis, risk prediction, prognosis and clinical decision-making. Although the field has seen rising trends over the past few years, a thorough bibliometric analysis of the research in the field of AI-powered DVT is still lacking. Objectives: The aim of this study was to profile the global scientific landscape of the applications of AI in DVT and VTE by analysing the publication patterns, prominent contributors, collaboration networks, and research themes. Methods: A bibliometric analysis was performed on the literature retrieved from PubMed with a search made up to 18/05/2026. The structured Boolean search incorporating artificial intelligence, DVT/VTE and clinical terms identified 561 publications. VOSviewer (v1.6.20) and Biblioshiny (Bibliometrix R package) were used to analyze data. The bibliometric indicators used were scientific production, citation performance, co-authorship and institutional collaboration, country productivity, keyword co-occurrence, thematic evolution and citation mapping. Results: Scientific production also rising significantly in recent years, As a result of the increasingly wide incorporation of machine learning and computational science in the study of thrombosis. The United States and the United Kingdom were the top contributors, and the University of Maryland system came out as a significant hub of collaboration. Commonly used terms were “machine learning,” “deep vein thrombosis,” “venous thromboembolism,” and “artificial intelligence.” Key themes that developed included deep learning, natural language processing, imaging-based AI, and prediction of COVID-19-associated thrombosis. Citation analysis revealed basic research on VTE risk scoring and predictive modelling. Conclusion: while AI-powered DVT research is on the rise, there is a need for further prospective validation, explainable AI, and anticoagulation-focused applications.
Prevalence of Diabetic Nephropathy in the Urban Diabetic Population of Mysore
Aim: Diabetes Mellitus (DM) is becoming a major public health challenge in India, with increasing incidence in urban areas. Among its chronic macrovascular complications, Diabetic nephropathy (DN) significantly contributes to morbidity and development to end-stage renal disease (ESRD). Early identification of individual at risk is essential to prevent irreversible renal damage and improve clinical outcomes. The present study aimed to understand the prevalence of DN and identify the risk factors associated with diabetes complications in the urban Mysore diabetic community. Subject & Methods: A hospital based observational cross-sectional research was conducted out among 200 subjects diagnosed with type 2 diabetes(T2DM) from selected urban healthcare centres in Mysore. Diabetic nephropathy was diagnosed using established criteria, with data collected using structured questionnaires, clinical assessments, and laboratory investigations including HbA1c, and urine albumin. Statistical analysis was performed to find associations between nephropathy and demographic or clinical variables. Results: Among the 200 participants, 54 (27%) were found to have DN. A statistically significant associations was observed between nephropathy and older age (p = 0.001), extended duration of diabetes (p < 0.001), presence of hypertension (p < 0.001), poor glycaemic control (average HbA1c 8.8% vs. 7.8%, p < 0.001), and smoking (p = 0.03). Gender and BMI did not significantly associate with nephropathy. Conclusion: The current study revealed a higher prevalence of DN in the urban diabetic population of Mysore. Early screening, lifestyle modification, and aggressive management of blood pressure and glucose are suggested to reduce the burden of complications. These findings highlight the need for targeted public health interventions in urban public health strategies for diabetes management and complication prevention.
Appendicolith Characteristics as Predictors of Failure of Non-operative Management in Adults with Uncomplicated Acute Appendicitis: A Systematic Review and Meta-Analysis
Background: Non-operative management (NOM) with antibiotics is an accepted option for selected adults with uncomplicated acute appendicitis, but failure is more frequent when an appendicolith is present. Whether appendicolith size, position, number, or obstructive characteristics improve prognostic stratification remains uncertain. Objective: To evaluate appendicolith presence and characteristics as prognostic factors for NOM failure in adults with imaging-confirmed uncomplicated acute appendicitis, with intraluminal appendiceal air, age, and sex assessed as additional prognostic factors. Methods: The review was structured as a prognostic-factor systematic review and meta-analysis. Evidence was synthesized from PubMed-indexed records, primary publisher/full-text records, guideline repositories, and backward and forward citation searching through 12 August 2026 using prespecified adult eligibility criteria. Direct studies of NOM failure were separated from indirect radiological or severity studies. Random-effects meta-analysis was undertaken only for compatible effect estimates. Risk-of-bias interpretation incorporated RoB 2 for parent randomized trials and prognostic-factor domains aligned with QUIPS. Results: Appendicolith presence was the most consistent adverse prognostic marker. In the CODA antibiotic-treated cohort, appendicolith was independently associated with appendectomy within 30 days (adjusted OR 1.99, 95% CI 1.28-3.10). An exploratory synthesis of CODA and Vons data yielded a pooled RR of 2.00 (95% CI 1.54-2.60; I²=0%; k=2) for short-term NOM failure. At one year, pooled individual-participant trial data showed appendectomy in 48.7% of antibiotic-treated patients with an appendicolith versus 30.6% without. Evidence for specific appendicolith characteristics was substantially weaker. Larger size and proximal/base or obstructing location were associated with adverse outcomes mainly in treatment-selection, complicated-disease, or severity cohorts. Intraluminal appendiceal air was not associated with failure, while age and sex effects were inconsistent. Conclusions: Appendicolith presence is the best-supported imaging prognostic factor for NOM failure. Larger appendicolith size and proximal or obstructive location remain plausible risk refinements but are inadequately validated in strictly uncomplicated adult NOM populations. Intraluminal air, female sex, and older age are not established independent adverse prognostic factors.
Preoperative Systemic Immune-Inflammation Index, Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio for Predicting Postoperative Complications in Acute and Emergency Abdominal Surgery: A Systematic Review
Background: Emergency laparotomy carries substantially higher morbidity and mortality than elective abdominal surgery, and established prognostic tools such as POSSUM, P-POSSUM and the National Emergency Laparotomy Audit (NELA) risk model, although well validated, require multiple physiological and intraoperative variables that are not always available at the time an operative decision must be made [1,2]. Ratios derived from the routine complete blood count — the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and systemic immune-inflammation index (SII, calculated as platelet count × neutrophil count / lymphocyte count) — have been proposed as inexpensive, immediately available adjuncts for perioperative risk stratification [3,4]. Objective: To synthesise the published evidence on the association between preoperative SII, NLR and PLR and postoperative complications and mortality in patients undergoing emergency general and abdominal surgery, and to relate these findings to established surgical risk-prediction tools. Methods: A literature synthesis was undertaken with reference to the PRISMA 2020 framework [5]. Records were identified through structured searches of PubMed/MEDLINE, Scopus, Embase and Cochrane CENTRAL using combinations of inflammatory-marker terms (SII, NLR and PLR) and emergency/acute abdominal surgery terms. Eligible studies reported preoperative NLR, PLR and/or SII in adults undergoing emergency or acute-care general surgical procedures, with a postoperative complication, morbidity or mortality endpoint. Study quality was independently appraised by two reviewers using the Newcastle-Ottawa Scale (NOS) [6], with disagreements resolved by discussion and consensus. Because of heterogeneity in populations, outcome definitions, marker timing and cut-offs, findings were synthesised narratively rather than pooled in meta-analysis. Results: Ten primary observational studies met the eligibility criteria for the qualitative synthesis, spanning emergency abdominal surgery, appendicectomy, acute-care surgery and emergency subgroups within abdominal surgical cohorts, with sample sizes ranging from 102 to 570 patients. Additional reviews and meta-analyses were used only for background and contextual comparison and were not treated as included primary studies. Reported discriminative performance for postoperative complications was inconsistent: area-under-the-curve (AUC) values for SII and NLR ranged from approximately 0.55 to 0.72 across studies, with several cohorts reporting significant independent associations after multivariable adjustment and at least one large cohort (n = 570) finding no independent association for NLR (AUC 0.55–0.60). Elevated preoperative NLR was more consistently associated with infective and septic complications than with the full spectrum of Clavien-Dindo ≥III morbidity. Evidence for SII specifically in emergency (as opposed to elective oncological) general surgery remains limited to a small number of cohorts. Conclusion: SII, NLR and PLR are inexpensive, readily available markers that show a biologically plausible but inconsistent association with postoperative complications after emergency abdominal surgery. Current evidence does not support their use as stand-alone replacements for validated multivariable risk scores such as NELA or POSSUM; they may instead have value as low-cost adjuncts within composite risk models. Adequately powered, prospectively registered multicentre cohort studies with standardised outcome definitions and pre-specified cut-offs are needed before these indices can be recommended for routine perioperative risk stratification in emergency general surgery.
Influence of TECAR Therapy on Proprioception Functions in Cervical Radiculopathy
Objectives: This study examined whether combined TECAR therapy into a cervical proprioception protocol enhances pain and function in cervical radiculopathy secondary to disc bulge. Methods: 60 participants aged from 30 to 40 years old with cervical radiculopathy secondary to disc bulge. They were recruited and randomly allocated to two equal groups. The active TECAR group received radiofrequency TECAR therapy plus designed cervical proprioception protocol, while the sham TECAR group underwent sham TECAR p;us the same designed proprioception protocol. Interventions were conducted three sessions per week for 4 weeks. All outcomes were assessed at start and after completion of the treatment period, included cervical joint position error (JPE), cervical range of motion (ROM), pressure pain threshold (PPT), and Neck Disability Index (NDI) scores. Results: In both groups, outcome measures improved significantly over the 4 week intervention period,. However, more favorable enhancemment in the active TECAR group, with statistically significant larger decreases in cervical JPE and NDI scores and more ncreases in PPT and ROM compared with the sham TECAR group (p < 0.05). Conclusion: TECAR therapy plus designed cervical proprioception protocol produced greater enhancement in cervical pain and function in Cervical radiculopathy secondary to disc bulge.
Green Synthesis of Silver Nanoparticles using Indigenous Medicinal Plants: Physicochemical Characterization and Biomedical Evaluation
Biological synthesis of silver nanoparticles (AgNPs) is a sustainable, eco-friendly alternative to toxic chemical methods to fulfil the urgent global need for advanced therapeutic nanomaterials. Bioactive phytochemicals of indigenous medicinal plants serve as excellent natural reducing and capping agents. This paper reports the rapid single-step green synthesis of AgNPs using an aqueous extract of indigenous plant. The progress of the bio reduction process was monitored by UV-Vis spectroscopy and the synthesised nanostructures were characterised systematically by UV-Vis spectroscopy, Fourier Transform Infrared (FTIR) spectroscopy, X-ray Diffraction (XRD) and Transmission Electron Microscopy (TEM). Moreover, complete biomedical evaluations were carried out in vitro. The successful formation of AgNPs was confirmed by UV-Vis spectroscopy having a characteristic surface plasmon resonance peak at 430 nm. FTIR analysis identified the main plant-derived phenolics and proteins responsible for long-term stabilisation of the nanoparticles without the use of chemical additives. The XRD and TEM confirmed the formation of highly stable spherical face centred cubic nanocrystals with optimum size distribution of 15 to 30 nm. Biogenic AgNPs showed significant dose-dependent antibacterial activity against Escherichia coli and Staphylococcus aureus in biomedical assays and was found to be much superior to the standard antibiotics. In addition, they exhibited high free-radical scavenging activity in DPPH assays and showed selective, high-efficiency cytotoxicity against MCF-7 human breast cancer cells while preserving normal fibroblast viability. To summarise, this plant-mediated synthesis provides a scalable, economic and non-toxic approach to fabricate multifunctional bio-nanomaterials with significant translational potential in targeted antimicrobial therapy, oxidative stress management and advanced clinical nanomedicine.
A Hybrid Deep Learning Approach for Classifying EEG Time-Frequency Representation based on Data Augmentation and Enhancement
EEG (Electroencephalography) is a signal that records the electrical activity of the brain. This experimental signal is used to evaluate the electrical activity of the brain, which is used to detect possible problems in communication between brain cells. In addition, EEG can be used for Human-Computer Interaction (HCI), medical diagnostics, and psychological research. Despite its important applications, EEG signals are difficult to process and classify due to their non-stationary nature, high noise, and high variability in recording conditions. In the meantime, EEG Time-Frequency Representations have many advantages for better representation of the signal as input to a classification system based on deep learning models. However, the gap in these methods is the lack of sufficient data for training and also the lack of attention to various features of the EEG signal. To fill these gaps, a hybrid deep learning method is proposed in this research for classifying EEG time-frequency representations based on data augmentation and data improvement. The first innovation of this research is the use of a CNN+LSTM hybrid network. The use of a hybrid network allows for the simultaneous use of both sequence and spatial features for classification. The second innovation is the use of data enhancement techniques using the Efficient Sub-Pixel Convolutional Neural Network (ESPCN) method. The third innovation is the use of data augmentation techniques. Simulation results on the BCI Competition IV dataset show that the three innovations of the proposed method lead to an improvement in the classification process of EEG time-frequency representations, achieving an accuracy of 88.23%.
Laith Khalid Younis Al-Bahadli, Reza Mollaei Karami
Maternal Hypovitaminosis D and Iron Homeostasis Biomarkers: Correlation and Regression Analysis in Iraqi Pregnant Women
Background: Vitamin D and iron deficiencies frequently coexist during pregnancy, yet their clinical crosstalk remains debated. This study aimed to evaluate the diagnostic and predictive associations between maternal serum Vitamin D levels and key iron homeostasis biomarkers. Subjects and Methods: A cross-sectional study was conducted on 196 pregnant women (N = 196; mean age 26.8 ±6.1years). Serum Vitamin D, ferritin, total iron-binding capacity (TIBC), erythropoietin (EPO), and transferrin levels were quantified alongside socio-demographic and obstetric parameters. Data were analyzed using Spearman correlation, multiple linear regression, and receiver operating characteristic (ROC) curves. Results: Overall, 63.8% (n = 125) of participants exhibited Vitamin D inadequacy (<30ng/mL). Vitamin D3 showed no significant linear correlations with ferritin (r = 0.064), transferrin (r = 0.053), or EPO (r = 0.031), though a trend with TIBC emerged (r = -0.150, p = 0.069). Ferritin inversely correlated with EPO (r = -0.270, p = 0.010). After controlling for confounders, Vitamin D was not an independent predictor of any iron biomarker (all p > 0.05). ROC analyses confirmed poor diagnostic performance for all iron markers in predicting Vitamin D status (AUC = 0.413–0.587, all p > 0.05). Conclusion: Serum iron homeostasis biomarkers do not reliably reflect or predict maternal Vitamin D status, highlighting the need for independent screening of both micronutrients during antenatal care.
Sayfaddin Sadraddin Hamad, Esmail Salih Ibrahim Kakey
Parametric Evaluation of Heavy Metal Removal from Iron and Steel Wastewater Using Chlorella vulgaris: Linking Operational Conditions to Biomass Composition and Treatment Performance
The effects of pH, illumination duration, wastewater dilution, and carbon dioxide addition on heavy metal removal were investigated over seven days of application to real steel mill wastewater. The microalga was isolated from surface water samples obtained from Eflany Lake, which is a freshwater aquatic ecosystem located within the Karabuk Province in Turkey. The isolated strain showed 99% sequence identity to Chlorella vulgaris, confirming its assignment to this species. The molecular composition of biomass under different operating conditions using Fourier transform infrared spectroscopy (FTIR) revealed the biochemical interactions associated with metal concentration. During a period of seven days, the results showed that wastewater concentrations and treatment efficacy were inversely related. An 18-hour light period, pH 8, 30% wastewater concentration, and 500 mL/h CO₂ flow rate was identified as optimal, achieving the highest removal efficiency for metals (As: 45.0 %, Cd: 75.0%, Cr: 82.0%, Cu: 88.0%, Mn: 90.0%, Ni: 65.0%, Pb 94.0%, Fe: 85.0%). Under optimal conditions characterised by high elimination efficiency, an improved protein content was observed with strong Amide I/II extension. Furthermore, correlation of FTIR peak ratios with removal efficiency can produce predictive biomarkers.
How Interpersonal Skills of Caregivers Affecting the Process of Caring Children with Attention Deficit Hyperactivity Disorders
Nursing children with Attention Deficit Hyperactivity Disorder (ADHD) requires interpersonal communication to ptovide proper and optimal care for the recipient. This study was conducted to define and analyze how interpersonal skills influence the process of caring of children with ADHD using six categories of Heron’s framework. We utilised an earlier instrument developed by Burnard and Morrison’s findings to examine which type of Heron’s categories that owned by most caregivers of children with ADHD. Our findings shows that caregivers of children with ADHD perceived themselves to be most skilled in supportive and prescriptive. The least categories they perceived were catalyctic and cathartic. Discussion upon how caregivers perception function the most were offered. These findings could add new perspective to provide information of caregivers perceptions who provide care for children with ADHD.
Comparative Study of Cellulose and Methylcellulose Grafted with Acrylic Acid-Based Hydrogels for Enhanced Soil Water Retention and Nitrogen Fertilizer Delivery
Grafting of cellulose (CE) and methylcellulose (MC) with acrylic acid (AA) represent a promising way to enhance soil water retention as well as to transfer fertilizers in a controlled manner to plants. For the purpose of comparison, hydrogels were prepared from cellulose and methylcellulose by grafting them with acrylic acid, (CE-g-AA) and (MC-g-AA). For the purpose of mechanically strengthening them, they were cross-linked with N, N- methylenebisacrylamide (MBA). The following techniques were used to characterize the prepared hydrogels include FT-IR, TGA, XRD, FESEM, and EDS. The degree of swelling of the hydrogels was studied, through which it can determine the suitability of each hydrogel for use in the appropriate place, where the swelling can regulate the loading and releasing behaviors of hydrogel microspheres. As a result, it was observed the (CE-g-AA)/MBA hydrogel, due to its high degree of swelling, is suitable for loading water and help the soil retain water. While (MC-g-AA)/MBA hydrogel, because its swelling is weaker, is suitable for carrying fertilizer, where it was actually loaded with nitrogen fertilizer model KNO3, and its maximum loading percentage (Lmax %) = 19.3%. The study involves studying soil saturation with water and then water retention in soil, where the (CE-g-AA)/MBA hydrogel soil sample has retained water for 19 days compared to 13 days only for controlled sample. The release profile of KNO3-loaded (MC-g-AA)/MBA copolymer hydrogel after choosing three pHs medium for release includes pH 4, pH 7, and pH 9, it was found to be more regular and regulated at pH 7.
Quantitative PCR Analysis of FKS1 Gene Expression in Candida glabrata Isolated from Clinical Specimens in Al-Baghdad Governorate
The Candida glabrata has become a major opportunistic pathogen, and there is growing resistance to echinocandins, mainly through the changes in FKS1 gene. This paper took on the objective of quantifying the expression of FKS1 gene in C. glabrata isolates of Al-Diwaniyah Province, Iraq, and testing its relationship with antifungal susceptibility. 43 isolates were obtained in the urine and bloodstream and wound samples. They were identified by conventional and automated techniques. The expression of FKS1 was measured using quantitative real-time PCR (qPCR), by normalizing to the expression of the housekeeping gene of ACT1 in the 2−ΔΔCt method. Per CLSI guidelines, caspofungin, micafungin and anidulafungin antifungal susceptibility was also determined. High levels of FKS1 expression (>2-fold) were seen in urinary isolates and were strongly correlated with a decreased sensitivity to echinocandins (p < 0.05). Blood isolates recorded the highest resistance rates, in particular, to micafungin and anidulafungin. Prior antifungal exposure was noted to be the most important risk factor related to increments in gene expressions. The results of these studies indicate the involvement of FKS1 upregulation in echinocandin resistance and the importance of molecular surveillance and optimized antifungal stewardship measures to enhance clinical outcomes.
Underestimating a Catastrophe: U.S. Media Coverage of the Spanish Flu, August to December 1918
This historical study examines how the American press covered the influenza pandemic of 1918 and 1919 during the second and deadliest wave of the outbreak. It analyzes the coverage published in the New York Times and the Washington Post between the first of August and the end of December 1918, drawn from the archives of both newspapers, and reads that coverage against the scholarly literature on the pandemic, on wartime censorship, and on the naming and stigmatization of infectious disease. The study argues that the American news media underestimated the catastrophe of the pandemic so as not to distract the public from the news of the First World War, and that it stigmatized Spain in the name it gave the disease, associating the epidemic with a country whose neutral stance during the war had allowed its press to report the outbreak freely. Three roles emerge from the coverage across the five months examined: reassurance that the epidemic would bring no serious consequences, association of the disease with a foreign country, and, only late in the period, the raising of public awareness of the precautions that might limit the spread.
A Comparative Review Study of Different Types of Radiation Sterilization Methods
This review paper aims to gather information on various radiation sterilization methods to assist in determining which method is appropriate for each application. While the review discusses the primary principles, mechanisms of microbial inactivation, process parameters, material compatibility, and certain uses in the medical, pharmaceutical, and food and consumer product industries, it provides an analysis of the available peer-reviewed literature. Also provided is a detailed analysis of the pros and cons of each sterilization method, along with the impact of various laws and ISO 11137 sections. Review the safety and environmental concerns for each radiation sterilization method. Finally, the review discusses industry trends moving towards alternative methods of sterilization using e-beam and X-ray technologies.
A Component-Isolated Analysis that Confirmed the Absence of An Incremental Signal of the Triglyceride–Glucose Index in CVD Mortality.
Background: The triglyceride–glucose (TyG) index is widely proposed as a low-cost insulin-resistance surrogate associated with cardiovascular disease (CVD) outcomes. However, virtually all published TyG studies report univariate or partially adjusted hazard ratios, without isolating TyG's contribution against models that also contain its mathematical components — fasting triglycerides and fasting glucose. Whether TyG adds incremental predictive value when those components are already present remains unresolved. Methods: We developed logistic regression (LR) and Cox proportional-hazards models for CVD mortality on NHANES 2003–2018 (n = 16,322; 408 CVD deaths; median follow-up 8.2 years). Models used 23 features and were trained on a 70/15/15 stratified split. We performed component-controlled bootstrap ablation (2,000 iterations) comparing 23-feature models against 21-feature models with TyG and TyG-BMI removed but with triglycerides and fasting glucose retained. The frozen LR model was then applied to a temporally distinct NHANES cohort (1999–2002, n = 4,318) for transportability assessment with time-matched (10-year) and unrestricted analyses. Secondary analyses compared TyG with HOMA-IR and performed competing-risk regression. Results: TyG showed no incremental discriminative value when its components were already in the model (LR ΔAUROC −0.0002, 95% CI −0.004 to +0.003, p = 0.43; Cox TyG HR 1.069, p = 0.27). In the development cohort, the LR model achieved an AUROC of 0.853 (95% CI 0.814–0.890) and a Cox C-index of 0.888, with a clear TyG quartile gradient (Q1 0.91% to Q4 3.61% CVD mortality). The univariate TyG dose–response gradient was preserved across cohorts (7.4% → 13.0% across strata, p < 0.001), but model discrimination dropped substantially under temporal transport (AUROC 0.853 → 0.652 at 10 years; 0.597 unrestricted). TyG and HOMA-IR were equivalent univariately (AUROC 0.590 vs 0.586, r = 0.562). Cause-specific Cox models suggested a possible CVD-specific pattern for TyG (HR 1.203, p = 0.089), with no association for non-CVD mortality (HR 1.004, p = 0.95). Exploratory Platt recalibration improved calibration metrics but was fitted and evaluated on the same temporal cohort and requires independent confirmation. Conclusions: TyG is a useful univariate cardiometabolic biomarker but provides no incremental value as an additional feature in multivariable models that already contain triglycerides and fasting glucose. Temporal transportability of the prediction model is limited; calibration adjustment requires independent validation. These findings refine the appropriate role of TyG in CVD risk modeling.
Development of a Social Dimension of the Marriage Readiness Index for Public Health Screening Among Premarital Couples
Social readiness is an essential component of marriage readiness because it influences couples’ ability to establish healthy relationships and maintain family resilience. However, no standardized instrument has comprehensively integrated social readiness into a multidimensional Marriage Readiness Index. This study aimed to develop and validate the Social Dimension of the Marriage Readiness Index among premarital couples using Confirmatory Factor Analysis (CFA). A cross-sectional study was conducted among 280 prospective brides and grooms registered at four Religious Affairs Offices in Malang Regency, East Java, Indonesia, between May and July 2025. Participants were recruited using consecutive sampling. The social dimension included six indicators: spirituality, relational commitment, communication skills, family background, perceived social support, and history of domestic violence. Data were collected using a structured questionnaire and analyzed using CFA to evaluate construct validity and estimate each indicator’s contribution. All five indicators significantly contributed to the latent construct (p<0.001). Spirituality showed the highest standardized factor loading (0.903), followed by relational commitment (0.703), communication skills (0.634), family background (0.624), and social support (0.564). These findings indicate that spirituality is the strongest component of social readiness, while the remaining indicators collectively represent interpersonal and social preparedness for marriage. The Social Dimension of the Marriage Readiness Index provides a culturally relevant and psychometrically supported instrument for assessing premarital social readiness. This index may strengthen premarital screening, support targeted counselling, and inform public health strategies to promote marital stability and family well-being in Indonesia.
Evaluation of Hematological Parameters as Predictors of Disease Severity in Patients with Hematological Cancers: A Cross-Sectional Study from a Private Hospital in Baghdad City
Background: Hematological malignancies, i.e., leukemias, lymphomas, and multiple myeloma, are known to cause significant abnormalities in the numbers of blood cells found in peripheral blood. Several parameters derived from complete blood counts (CBCs) have been proposed as inexpensive, readily available biomarkers of prognostic significance and disease severity, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and mean platelet volume (MPV). Objective: To investigate whether or not there is a relationship between baseline hematological indices (i.e.,hemoglobin, total white blood cell count, absolute neutrophil count, absolute lymphocyte count, platelet count, NLR, PLR, MPV) and the clinical severity of hematological malignancies in patients who had been recently diagnosed with a hematologic malignancy and were attending a private hospital in Baghdad. Methods: This was a descriptive cross-sectional study that included 110 patients newly diagnosed with hematological malignancies (i.e., acute leukemia, chronic leukemia, non-Hodgkin lymphoma, Hodgkin lymphoma, and multiple myeloma) who were evaluated by ahematologist/oncologist at a private hospital between March 2024 and February 2025. Each patient’s disease severity was classified as mild/moderate or severe according to the clinical and laboratory staging criteria established for their specific hematological malignancy. Each patient provided a venous blood sample, which was prepared and analyzed with an automated hematologyanalyzer. Statistical comparison of hematological indices between disease severity groups was conducted using either the independent-samples t-test or the Mann-Whitney U test. Receiver operating characteristic (ROC) analysis was used to assess the strength of the relationship between patients' hematological indices and disease severity. Results: Of the 110 patients, 46 (41.8 percent) had severe disease. The severity groups differed significantly with respect to hematological parameters, with patients with a severe disease exhibiting significantly lower hemoglobin (8.6 ± 1.9 g/dL vs. 11.2 ± 2.1 g/dL, p < 0.001), lower lymphocyte counts, and significantly higher NLR (6.8 ± 3.4 vs. 3.1 ± 1.6, p < 0.001) and PLR (215 ± 88 vs. 142 ± 61, p < 0.001) compared to patients with a mild or moderate disease. Patients with a severe disease exhibited significantly lower platelet counts (98 ± 62 × 10⁹/L vs. 186 ± 84 × 10⁹/L, p < 0.001). The NLR had the highest area under the ROC curve (0.81) for identifying patients with severe disease compared with those with mild or moderate disease. The second best predictor was hemoglobin (0.78), followed by PLR (0.74). Conclusion: Simple, inexpensive hematological parameters, especially NLR, hemoglobin, and PLR, show a significant correlation with the severity of hematological malignancies and may serve as adjunctive tools for risk-stratifying patients in resource-constrained clinical settings, such as a private hospital in Baghdad.
The Effect of a Training Program with Venous Blood Flow Restriction on Muscular Strength and Skill Performance on the Rings Apparatus
This study aimed to investigate the effect of an eight week blood flow restriction (BFR) training program on muscular strength and rings skill performance among university students, compared with a traditional training program of equal duration and frequency. Thirty male students enrolled in Gymnastics (1) at Palestine Technical University – Kadoorie were randomly assigned to an experimental group (n=15) or a control group (n=15). Both groups were equivalent at baseline. The experimental group performed BFR exercises three times per week using calibrated limb cuffs with progressive pressure (120→160 mmHg), while the control group followed the traditional program. Outcomes included vertical jump, pull ups, 10 s push ups, 10 s sit ups, and rings routine score. Both groups improved significantly (p ≤ 0.05), but the experimental group achieved greater gains, particularly in pull ups (+41.20%), push ups (+21.62%), sit ups (+22.33%), vertical jump (+14.01%), and rings routine score (+94.44%). Posttest comparisons favored the experimental group across all measures. The study concludes that structured BFR training enhances strength and rings performance more effectively than traditional methods. It is recommended that BFR protocols be integrated into university gymnastics programs, with careful monitoring of cuff pressure and individual responses, and that future research explore broader populations to confirm generalizability
Pharmacogenomics and Personalized Medicine: Leveraging AI and Machine Learning for Optimized Therapies
To examine how artificial intelligence (AI) and machine learning (ML) can advance pharmacogenomics and personalized medicine by improving drug-response prediction, treatment selection, dosing, and clinical decision support, while identifying the major barriers to clinical implementation. This narrative review critically synthesizes current applications of AI-enabled pharmacogenomics across oncology, cardiovascular medicine, psychiatry, and other therapeutic areas. It evaluates classical ML, deep learning, transformer models, unsupervised learning, federated learning, explainable AI, knowledge graphs, multimodal data integration, and electronic health record–based clinical decision support. Clinical applications, implementation requirements, ethical concerns, regulatory issues, and future research priorities are comparatively examined. AI and ML can integrate genomic, multi-omic, clinical, environmental, and longitudinal data to identify gene–drug associations, predict therapeutic efficacy and adverse drug reactions, optimize individualized dosing, and support genotype-guided prescribing. Evidence from oncology, cardiology, and psychiatry indicates that these approaches may improve patient stratification and treatment selection. Their integration into electronic health records and clinical decision support systems can enable timely, actionable recommendations. However, clinical translation remains constrained by limited and ancestry-biased datasets, inconsistent phenotype definitions, missing data, overfitting, class imbalance, weak external validation, limited interpretability, workflow incompatibility, clinician training gaps, privacy risks, algorithmic bias, and evolving regulatory requirements. AI-enabled pharmacogenomics has potential to support safer, more effective, and individualized therapy. Broad clinical adoption requires diverse standardized datasets, externally validated and explainable models, interoperable health information systems, ethical and regulatory governance, clinician education, and interdisciplinary collaboration. Federated learning, multimodal architectures, and continuous-learning systems may enable equitable, real-time pharmacogenomic decision-making.
Anatomical Distribution of Lymph Nodes in the Rabbit Neck: A Comprehensive Morphological Study
The cervical lymph nodes are important structures of the lymphatic system that are involved in immune surveillance and the processing of antigen in the head and neck area. The objective of this investigation was to provide a complete anatomical and morphological description of the lymph nodes distributed throughout the neck region of the domestic rabbit (Oryctolagus cuniculus). Eight clinically healthy adult rabbits (4 male, 4 female) were used for dissection and morphological investigation. After euthanasia and fixation in 10% neutral buffered formalin, a careful systematic dissection was performed to locate, isolate, and characterize all lymph nodes in the cervical region. Each lymph node was recorded with reference to anatomical position, size, shape, color, and relationships to surrounding vascular and neurological structures. A total of 18-22 lymph nodes per individual was found in the cervical region. A scheme was established to provide for classification into mandibular, parotid, atlantal, axillary, and cervical groups depending upon anatomical location and associated structures. The mandibular nodes were consistently the largest, measuring from 8-12 mm. in length, while the smaller cervical nodes measured from 2-4 mm. This investigation will furnish detailed anatomical reference data important in veterinary clinical investigation, surgical planning and in establishing diagnostic baselines for lymphoproliferative disease. The systematic mapping and morphological characterization of cervical lymph nodes in the rabbit will furnish valuable information on normal lymphatic anatomy of this species and aid greatly in the establishment of more effective diagnostic and therapeutic procedures in veterinary medicine.
Firas Riyadh Muhammad, Nahidah Zaher Kamil
Emerging Influenza A(H3N2) Subclade K: A Narrative Review
Viruses spread rapidly by antigenic drift, with the impetus to mismatch vaccines and early season epidemics. One new variant of Subclade K in multiple regions in late 2024 and early 2025 appeared at a rapid rate, becoming the dominant contributor to influenza activity in multiple regions. This narrative review summarizes existing evidence regarding the subclade’s viral characteristics, epidemiological behavior, clinical relevance and public health implications, from a peer-reviewed literature, genomic surveillance data, antigenic assays, and initial vaccine effectiveness estimates from ECDC and UKHSA. In addition, subclade K contains multiple hemagglutinin substitutions that reduce antigenic similarity to the 2025–2026 vaccine strain, with serological tests showing minimal cross-reactivity and suggesting immune escape. Within the EU/EEA, the subclade has risen rapidly over the last year, although there is no evidence of increased intrinsic virulence. The burden of health care could increase if early season transmission is further delayed. Early estimates of vaccine effectiveness provide strong protection in children of 72–75 per cent, with protection reduced by adults to 32–39 per cent. Subclade K is a major evolutionary event which presents the need for increased genomic surveillance, timely vaccination and coordinated public health preparedness to mitigate its present and future consequences.
Pregnant Women’s Perceptions of Antenatal Care Services in Royal Medical Services
Background: The quality of antenatal care is determined by the availability of prenatal services for pregnant women, which contributes to positive pregnancy outcomes. Pregnant women's perceptions of attending antenatal care during the first trimester significantly impact their utilization of these services and, consequently, their pregnancy outcomes. Therefore, it is crucial that pregnant women are well-informed about this and receive appropriate education and awareness to achieve positive results for both their own health and the health of their unborn child. Objectives: this study primarily aims to assess pregnant women's perceptions of the quality of prenatal care services provided in Royal Medical Services hospitals in Jordan.. Methodology: This study employs a mixed-methods approach, integrating quantitative and qualitative data to capture measurable patterns in perception and the nuanced experiences shaping women’s views. Once both data types will be analysed independently, the results will merged to create a comprehensive narrative Results: responses indicated neutral to positive experiences. Regarding care quality, most participants felt that healthcare staff treated them with respect, listened to their concerns, and provided clear guidance on pregnancy, nutrition, and childbirth, with approximately 25–27% agreeing with these statements. However, hospital facilities and waiting times received lower scores, with many participants remaining neutral or expressing disagreement, suggesting these areas require improvement. Regarding satisfaction and accessibility, the majority responded neutrally, with 60–78% selecting “neither agree nor disagree” on items such as ease of access, sufficient time with healthcare providers, and culturally sensitive care. Notably.
Nadia Mohammad Wanes Alshraideh, Orouba Mohammad Saad Al Manaseer
Risk of New-Onset Diabetes with Thiazide Diuretics in Hypertensive patients: A Systematic Review and Meta-Analysis
Background: thiazide diuretics are often recommended as the initial treatment of choice for hypertension. Due to their effectiveness and cardiovascular benefits. However, concerns regarding metabolic adverse effects, and particularly new-onset diabetes mellitus (NODM), remain and may deny any long-term advantage. This review and meta-analysis compared the risk of NODM and other classified outcomes using thiazide and thiazide-like diuretics versus not using thiazide diuretics. Methods: Following the PRISMA 2020 guidelines, a systematic search of PubMed, Scopus, and EMBASE from January 2005 to 2025 was conducted for cohort and randomized controlled trials that reported outcomes of NODM. Extracted data included patient population, type of treatment, blood pressure, cardiovascular events, and adverse events. Combined odds ratios (ORs) were estimated using a random effects model. Results: Five studies were included in this review (four randomized trials and one cohort; >22,000 patients). There was no significant association in NODM with thiazide treatment, compared with patients not treated with thiazide (OR 1.21, 95%CI 0.91-1.59, p=0.19; I²=48%) A subgroup analysis showed mixed results, with one study demonstrating an increase in the incidence of diabetes while other studies showed no increase. Thiazide use did appear to confer an increase in risk of hypokalemia (OR 6.58, 95% CI 5.49-7.89, p<0.00001). Conclusion: Thiazide diuretics remain safe and effective in the treatment of hypertension. Although we found risk of hypokalemia and a possible risk of NODM, these risks did not translate into increased cardiovascular morbidity or mortality. Glucose and potassium should be monitored in high-risk patients; however, thiazide will remain a foundation in hypertension therapy.
Effect Of Inspiratory Muscle Training Combined with Low-Level Light Therapy on Pulmonary Function and Pain on Neck Pain Post Thyroidectomy Scar Wound in Premenopausal Females
Background: About 80% of patients experience neck pain and stiffness after having an open thyroidectomy, which is one of the most common endocrine surgical procedures. This procedure is typically done as a result of thyroid cancer. Purpose: The goal of the study was to examine the efficacy of a combination of inspiratory muscle training along with low-level light therapy for post-thyroidectomy neck pain among premenopausal women in terms of pulmonary function and pain, as well as wound healing. Methods: 60 female patients diagnosed to get open surgery of thyroidectomy, their ages between 35 and 45 years, and their Body Mass Index (BMI) ranged between 25 and 29.9 kg/m². They were randomized into three groups. Group (A) consisted of 20 female patients who received only traditional medical treatment daily at first week, then three sessions per week for an additional three weeks, while Group (B) consisted of 20 female patients who received traditional medical treatment and inspiratory muscle training daily at first week, then three sessions per week for an additional three weeks. Group (C) consisted of 20 female patients who received traditional medical treatment, inspiratory muscle training, and low-level light therapy daily for the first week, then three sessions per week, day after day, for an additional three weeks. FVC, FEV1, PEF, and MVV, as well as PImax during the spirometry test, visual analog scale, and photographic wound assessment, were assessed pre- and post-study. Results: Group A showed non-significant improvement pre our post-treatment in all outcome measurements, but both groups A and B showed significant improvement in all outcome measurements (pulmonary function, pain (VAS score), and wound healing) from pre-treatment to post-treatment measurements (P value < 0.001). Conclusion: Both inspiratory muscle training alone and inspiratory muscle training combined with low-level light therapy have a beneficial effect on improving pulmonary function, pain (VAS score), and wound healing in favor of the combination of inspiratory muscle training with low-level light therapy.
Impact of Klebsiella Pneumoniae and Ceftazidime Therapy on Diagnosis of Pancreatic Cancer Patients
Extensive research points to microbiota as a potential driver of pancreatic ductal adenocarcinoma proliferation. It was postulated that gammaproteobacteria, including Klebsiella pneumoniae, impact pancreatic ductal adenocarcinoma survival, and that ceftazidime therapy would mitigate this impact. The current study was a retrospective analysis of patients admitted to Al-Amal Hospital in Baghdad undergoing pancreatico-duodenectomy and preoperative treatment for locally advanced or borderline resectable pancreatic cancers. Pancreatic ductal adenocarcinoma diagnosed between July 2024 and January 2025, with available bile cultures, was included. A total of 77 patients were evaluated. Various associations were examined between tumor features, survival statistics, antibiotic use, and intraoperative bile culture results. Kaplan–Meier and Cox regression analyses were run to determine survival rates. End result: A 1.9-month decrease in progression-free survival (PFS) per species (95% C.I. -3.3 to -0.5) was associated with an increasing number of pathogen species detected in intraoperative bile cultures (P = 0.00009). In patients who tested negative for K. pneumoniae, adjuvant treatment with gemcitabine increased PFS (26.2 months vs. 15.3 months; P = 0.039). In contrast, for patients who tested positive, PFS was not improved (19.5 vs. 13.2 months; P = 0.135). Median overall survival (OS) improved with ceftazidime treatment, regardless of K. pneumoniae status (48.8 vs. 26.2 months; P = 0.006). Patients receiving ceftazidime who tested positive for K. pneumoniae had a median OS of 26.4 months vs. 18.8 months without it (P = 0.028). The results show that ceftazidime therapy improves survival and that K. pneumoniae may increase chemoresistance to adjuvant gemcitabine.
The Contribution of Serum Netrin-1 Levels in Early-Onset Preeclampsia Detection
Background: Netrin-1 is a laminin-related protein involved in angiogenesis, vascular remodeling, trophoblast invasion, endothelial protection, and inflammatory regulation, making it a promising candidate biomarker for early prediction of preeclampsia. Aim: To evaluate the role of maternal serum Netrin-1 measured during the first trimester as an early biomarker for predicting early-onset preeclampsia and to assess its relationship with maternal and neonatal outcomes. Patients and Methods: A hospital-based case-control study was conducted at the Department of Obstetrics and Gynecology, Tikrit Teaching Hospital, Salah Al-Din Governorate, Iraq, between October 2025 and June 2026. Ninety pregnant women with singleton pregnancies were enrolled during the first trimester (11–13+6 weeks of gestation). Forty-five women who subsequently developed early-onset preeclampsia constituted the case group, while forty-five normotensive pregnant women served as controls.. Results: Maternal serum Netrin-1 concentrations were markedly lower among women who developed EOPE than among controls (286.4±71.8 vs. 468.9±84.6 pg/mL, P<0.001). Women with EOPE experienced significantly poorer maternal outcomes, including earlier delivery, increased cesarean section rate, higher ICU admission, and increased incidence of HELLP syndrome. Neonatal outcomes were also significantly worse, with lower birth weight, lower Apgar scores, higher preterm birth, and increased NICU admission. Serum Netrin-1 demonstrated significant negative correlations with maternal body mass index (r=−0.311), systolic blood pressure (r=−0.671), diastolic blood pressure (r=−0.642), serum creatinine (r=−0.514), AST (r=−0.488), and ALT (r=−0.451), while significant positive correlations were observed with gestational age at delivery (r=0.557), birth weight (r=0.602), and platelet count (r=0.463) (all P<0.05). Multivariable logistic regression analysis showed that serum Netrin-1 remained an independent predictor of EOPE after adjustment for maternal risk factors (adjusted OR=0.982, 95% CI=0.974–0.990, P<0.001). Conclusions: Maternal serum Netrin-1 measured during the first trimester is significantly reduced in women who subsequently develop early-onset preeclampsia. Lower serum Netrin-1 concentrations are associated with adverse maternal and neonatal outcomes and remain an independent predictor of disease development. Serum Netrin-1 demonstrates excellent diagnostic accuracy and represents a promising biomarker for early prediction and risk stratification of early-onset preeclampsia.
Sarah Kinaan Omar, Masryia Rashad Hassein
From Being Unaware to Be Informed: Evaluating Changes in Breast Cancer Awareness After Educational Program Intervention: A Quasi-Experimental Study
Background: Educational programs and awareness campaigns have recently been implemented in Saudi Arabia to enhance women’s knowledge and awareness about breast cancer (BC) screening and diagnosis. Aim: This study aims to assess the knowledge and attitudes of women regarding breast cancer screening before and after an educational lecture. Methods: A quasi-experimental design was conduسثcted by pre- and post-testing their knowledge through a structured written questionnaire, it was conducted on a randomly selected women aged 20 to 60 years at high schools in Alhasa- Saudi Arabia. An educational lecture was presented to them for one hour to raise their knowledge level about BC. All data was collected and then extracted to be analyzed with SPSS program version 27.0. Results: From total 100 women as participants, most participants were aged 40–49 and highly educated, with limited prior awareness and moderate baseline knowledge. Recognition of breast cancer signs and risk factors varied, with strong awareness of hereditary risks but weaker understanding of lifestyle-related factors. Screening practices showed inconsistencies, particularly regarding mammography frequency and appropriate age. After the educational intervention, significant improvements were observed in overall knowledge, risk-factor awareness, confidence, and behavioral intentions. Statistical analysis confirmed highly significant gains across most domains, demonstrating the program’s effectiveness (p<0.05). Conclusion: Educational lectures and the awareness supporting programs can enhance ethe women’s knowledge about BC screening and diagnosis in Saudi Arabia.
Role of Gemfibrozil in Hyperlipidemia: A Focus on Liver Function, Tissue Integrity and Lipid Regulatory markers in Rats
Background: Gemfibrozil is a strong and trusted medicine used to lower cholesterol and triglyceride levels in the body. High levels of these can lead to serious heart and brain problems. This study looks at the possible protective effects of Gemfibrozil in treating liver problems in rats with high cholesterol. The research investigates different physical indicators and analyzes liver tissue using a microscope to assess the drug's efficacy. Subject & Methods: In this study, 30 male rats were utilized. Six rats were assigned to each of the five groups formed by dividing the animals. The study includes five groups of rats. The first one is the control group. The second group has rats that were made to have high fat levels and they were given a high-fat diet without any treatment. The third group includes rats with high fat levels in their blood that were given Gemfibrozil at a dose of 600 mg/kg/day. The fourth group consists of rats with high fats in their blood that were given Gemfibrozil at a dose of 1200 mg/kg/day. Finally, the fifth group has rats with high fat levels that were given Gemfibrozil in a dose of 1500 mg/kg/day. Results: The analysis revealed notable differences (P <0.05) in the average values of lipid profile components (TC, TG, HDL and LDL), liver enzymes (AST,ALT and ALP), and FGF21 between group 2 (high fat diet) and the other experimental groups when compared with control group1. These variations were found to be statistically significant. Following the administration of the medication gemfibrozil, which helps to restore normal liver function, these tissue alterations totally improve as it clearly seen in the results of liver function test, FGF21 and adiponectin levels. In Conclusion: The liver of rats experiencing high blood fat levels is shielded by gemfibrozil through its ability to reduce cholesterol and triglycerides. This contributes to avoiding liver injury and minimizing inflammation.
Tuqa Haitham Qasim, Shahad Mohammed Ali Salih
The Role of Social Media in Unhealthy Food Consumption and Childhood Obesity: A Review of Current Evidence
Social media has become an integral part of children's daily lives. In 2021, the number of global social media users reached approximately 4 billion people. Such personalized and borderless social media platforms are changing people's eating habits through news, posts, and advertisements. Children and adolescents are at a unique life stage that makes them highly sensitive to social influences. This makes them especially vulnerable to marketing strategies on digital media. In fact, the behavior of children and adolescents is greatly influenced by social media and its various users, including friends, influencers, and marketers. Commercial messages are incorporated into gameplay in "advergaming," making it difficult to distinguish between entertainment and persuasion. Concurrently, the global increase in childhood obesity coincides with the marked increases in the food and beverage industry's budget for marketing aimed at children. Nowadays, food and beverage companies can utilize marketing trends that pass for entertainment to reach children anywhere and at any time. Since most of the social media platforms do not fully regulate the promotion of unhealthy meals, the issue is getting worse. Children and teenagers are at risk of being exposed to unhealthy food marketing on social media platforms due to the absence of comprehensive government regulation. Hence, children and young adults are now bombarded with food promotions on these platforms. In this context, the General Authority for Statistics reported that 98.43% of Saudi youth use social media, highlighting the need to study how the use of these platforms and exposure to food marketing affect food consumption patterns and weight gain among this age group (from 5 to 18 years old).
Anticancer and Immunomodulatory Effects of Ocimum Sanctum Linn on Oral Squamous Cell Carcinoma—An In-Vitro Study
Background: Oral squamous cell carcinoma (OSCC) constitutes a major health burden in developing countries, with chronic inflammation and dysregulation of the tumour microenvironment (TME) playing an important role in carcinogenesis. Aim: To evaluate the anticancer and immunomodulatory effects of Ocimum sanctum on oral cancer and immune cell lines in vitro. Materials and Methods: KB oral cancer cells and RAW 264.7 macrophages were treated with different concentrations of standardised Ocimum sanctum extract. All investigations were conducted in triplicate to confirm reproducibility. Cytotoxicity was evaluated using MTT assay and IC₅₀ values were calculated. Apoptosis was evaluated by Annexin V–PI flow cytometry. Cytokine modulation was analysed using a cytokine assay. The study protocol was reviewed and approved by the Institutional Ethics Committee. Results: Ocimum sanctum demonstrated dose- and time-dependent cytotoxicity against KB cells with IC₅₀ values of 323.8 µg/mL at 24 hours and 292.7 µg/mL at 48 hours. Flow cytometric analysis showed predominant early apoptosis (76 ± 2%) at IC₅₀ concentration. Significant downregulation of pro-inflammatory cytokines TNF-α, IL 1β, CXCL 10, and IL 17 was observed in RAW 264.7 cells. Conclusion: Ocimum sanctum exhibits significant anticancer and immunomodulatory activity, supporting its potential as a complementary therapeutic agent in OSCC.
Evaluating the Effectiveness of AI-Driven Chatbots in Early Detection of Mental Health Disorders and Psychological Counseling
The rapid growth of Artificial Intelligence (AI) and the development of natural-language-processing and conversational-technology systems, such as chatbots, has opened up new avenues for providing mental health support in an easily accessible manner. Chatbots driven by AI allow users to communicate in a natural way with the technology, receive psychoeducational information from it, and enable the chatbot to detect potential signs of psychological distress. The technology is able to generate basic supportive responses to users, and assist them in seeking professional help. This research investigates the potential effectiveness of AI-powered chatbots in assisting in the early identification of mental health issues and in providing initial counseling. The research will evaluate how well these four variables influence a user’s perception of the effectiveness of the provided counseling: Early Detection of Symptoms; Emotional Responsiveness; Customization/Personalization; Accessibility. Descriptive and analytical data collection methods were employed via a cross-sectional study design. An additional focus point included within this research project was a survey of 229 participants who were either experienced with conversational-AI based systems or had some familiarity with them. The structured questionnaire used a five-point Likert scale to collect the participant's perceptions of their experiences with AI-assisted counseling. Frequency analysis, descriptive statistics, Cronbach's alpha reliability assessment, Pearson correlation, multiple regression analysis, independent samples t-tests, and One-Way ANOVA statistical analyses were performed to analyze the participant's ratings. As indicated by the results, there appears to be a relationship between each dimension and perceived counseling effectiveness. Specifically, customization/personalization and emotional responsiveness appear to have a stronger relationship to perceived counseling effectiveness than does early symptom detection. Multiple regression analysis demonstrated that when considered collectively, the dimensions identified above are significant predictors of perceived counseling effectiveness. The researcher found that AI-driven chatbots may be effective supplemental tools for raising awareness about mental health issues, performing preliminary screenings, educating consumers regarding mental health, and providing counseling support. However, chatbots cannot replace the role of licensed professionals including psychologists, psychiatrists etc. Therefore, researchers conclude that any use of AI-based mental health systems requires human oversight, safeguards against misuse of personal identifiable information, valid clinical applications, clear communication of risks/benefits, and reasonable mechanisms for escalating critical care situations.
Integrated Emergency Healthcare Management through Pharmacy, Medical Nurse, Medical Laboratory, Paramedics, Social Worker, and Medical Information Collaboration
Emergency healthcare requires rapid assessment, accurate diagnosis, timely treatment, effective communication, and coordinated decision-making. In emergency situations, delays or fragmentation between healthcare departments can adversely affect patient outcomes. An integrated multidisciplinary approach involving Pharmacy, Medical Nurse, Medical Laboratory, Paramedics, Social Worker, and Medical Information can create a coordinated emergency care pathway from pre-hospital response through emergency department management, stabilization, discharge, and community follow-up. Paramedics provide the initial assessment, stabilization, and transportation of critically ill or injured patients while communicating essential clinical information to the receiving healthcare team. Medical Nurses provide continuous bedside assessment, medication administration, monitoring, infection prevention, patient education, and coordination of clinical activities. Medical Laboratory professionals provide rapid diagnostic testing that supports early recognition of conditions such as sepsis, acute myocardial injury, metabolic disturbances, bleeding, and organ dysfunction. Pharmacy professionals contribute to medication reconciliation, emergency medication preparation, dosing optimization, antimicrobial stewardship, drug-interaction assessment, and medication safety. Social Workers address psychosocial needs, social determinants of health, family communication, financial barriers, safeguarding concerns, and discharge planning. Medical Information professionals facilitate the secure collection, integration, storage, and exchange of patient data through electronic health records, laboratory information systems, medication records, emergency documentation, and clinical decision-support systems. The effectiveness of this integrated model depends on structured communication, clearly defined responsibilities, standardized emergency protocols, real-time information exchange, multidisciplinary training, and continuous quality improvement. Digital technologies, including clinical decision-support systems, electronic medication management, telemedicine, artificial intelligence, and predictive analytics, can further enhance coordination and accelerate clinical decision-making. This multidisciplinary framework can improve treatment timeliness, reduce medication errors, strengthen diagnostic accuracy, improve patient safety, and facilitate continuity of care after emergency stabilization. This article examines the individual and collaborative roles of Pharmacy, Medical Nurse, Medical Laboratory, Paramedics, Social Worker, and Medical Information in integrated emergency healthcare management and discusses how their coordinated efforts can improve emergency response, clinical outcomes, patient experience, and healthcare system efficiency.
Medical Doctor, Sterilization Specialist, Medical Nurse, Medical Laboratory, Pharmacy, and Medical Information Collaboration for Comprehensive Infection Prevention and Patient Safety
Healthcare-associated infections (HAIs) remain a major challenge for healthcare systems and can lead to prolonged hospitalization, increased treatment costs, antimicrobial use, complications, and preventable patient harm. Effective infection prevention cannot be achieved by a single department; it requires coordinated action across clinical, sterilization, laboratory, pharmaceutical, nursing, and information-management services. A multidisciplinary collaboration involving the Medical Doctor, Sterilization Specialist, Medical Nurse, Medical Laboratory, Pharmacy, and Medical Information provides an integrated framework for preventing infections and improving patient safety throughout the healthcare journey. The Medical Doctor provides clinical leadership by identifying infection risks, establishing diagnostic and treatment strategies, supporting appropriate antimicrobial use, and coordinating multidisciplinary infection-control decisions. The Sterilization Specialist ensures appropriate cleaning, disinfection, packaging, sterilization, storage, traceability, and distribution of reusable medical devices. Medical Nurses translate infection-prevention policies into daily clinical practice through hand hygiene, aseptic techniques, isolation precautions, device care, environmental safety, patient education, and surveillance of infection-related signs. Medical Laboratory professionals provide microbiological diagnosis, antimicrobial susceptibility testing, pathogen identification, and surveillance data that guide infection-control interventions and antimicrobial therapy. Pharmacy professionals contribute through antimicrobial stewardship, medication safety, dose optimization, review of drug interactions, and monitoring of antimicrobial resistance patterns. Medical Information professionals provide the digital infrastructure required to collect, integrate, analyze, document, and communicate infection-related information. Electronic health records, laboratory information systems, pharmacy systems, sterilization tracking systems, infection surveillance dashboards, and clinical decision-support tools can enable timely identification of infection risks and improve communication among healthcare professionals. Integration of these departments supports early detection of healthcare-associated infections, improves sterilization quality, strengthens antimicrobial stewardship, reduces preventable errors, and enhances continuity of care. This article examines the individual and collaborative responsibilities of Medical Doctors, Sterilization Specialists, Medical Nurses, Medical Laboratory professionals, Pharmacy professionals, and Medical Information teams in developing a comprehensive infection-prevention and patient-safety framework. It also discusses quality assurance, surveillance, communication, digital technologies, challenges, and future opportunities for strengthening infection prevention across healthcare facilities.
Effects of Thai Foot Massage on Pain, Ankle Range of Motion and Somatosensory Function in Patients with Type 2 Diabetes Mellitus and Peripheral Neuropathy: A Randomized Controlled Trial
Objective: To evaluate the immediate and long-term effects of Thai Foot Massage (TFM) on pain intensity, ankle range of motion (ROM), and somatosensory perception in patients with type 2 diabetes mellitus (T2DM) and diabetic peripheral neuropathy (DPN). Design: Randomized controlled trial. Setting: Tehsil Headquarters (THQ) Hospital, Sheikhupura, Pakistan. Participants: Seventy-two patients with T2DM-associated DPN (aged 45–64 years, diabetes duration >5 years) were randomly allocated using lottery method, to intervention (n=37) and control (n=37) groups. Intervention: The intervention group received 20-minute TFM sessions on each foot on alternate days for 15 days in addition to routine diabetic care. The control group received routine diabetic care. Blinding was not feasible due to the nature of the intervention. Main Outcome Measures: Pain intensity measured using the Numeric Pain Rating Scale (NPRS), ankle dorsiflexion and plantarflexion ROM measured with a universal goniometer, and somatosensory perception assessed using the Ipswich Touch Test (IpTT). Outcomes were evaluated at baseline, immediately post-intervention, and at 2- and 4-week follow-up. Results: Repeated-measures ANOVA demonstrated significant improvements in the intervention group across all outcomes. Pain intensity showed a significant time effect (Pillai’s Trace=0.953, F=249.699, p<0.001), while no significant change was observed in the control group (Pillai’s Trace=0.070, F=1.430, p=0.252). Significant improvements were also observed in right ankle dorsiflexion (Pillai’s Trace=0.764, F=39.983, p<0.001), right plantarflexion (Pillai’s Trace=0.668, F=24.778, p<0.001), left dorsiflexion (Pillai’s Trace=0.803, F=50.262, p<0.001), left plantarflexion (Pillai’s Trace=0.765, F=40.208, p<0.001), and somatosensory perception assessed by IpTT (Pillai’s Trace=0.773, F=42.007, p<0.001). Improvements were maintained at 2- and 4-week follow-up assessments. Conclusions: Thai Foot Massage produced significant immediate and sustained improvements in pain, ankle mobility, and somatosensory perception in patients with DPN. TFM may be considered a safe and effective complementary intervention to enhance functional outcomes and symptom management in individuals with T2DM-related peripheral neuropathy. Trial Registration: NCT06066944. Limitations: The study was conducted at a single center with a relatively short intervention and follow-up period, which may limit generalizability and long-term interpretation of outcomes.
An Advanced Deep Learning Framework for Mapping Forest Fire Smoke and Air Pollution Impacts on Human Health Using Satellite Imagery
The smoke produced during forest fires is a large-scale plume that contains high levels of hazardous air pollutants (HAPs) that can affect a wide geographical area and cause environmental and public-health problems. Traditional monitoring systems, however, only monitor at a small number of spatial locations, and they are not always sufficient for determining population level smoke exposure. In this study, an advanced deep learning approach is suggested to extract information about forest-fire smoke, air pollution, and impacts on human health from multispectral satellite data and observations from the atmosphere. This framework combines the spatial feature extraction by Convolutional Neural Networks (CNNs) with the segmentation of smoke-plumes by U-Net and the spatiotemporal analysis of pollution by Long Short-Term Memory (LSTM) networks. Satellite-based aerosol optical depth, fire radiative power, land surface temperature, vegetation indices, PM2.5 and air quality observations are combined to define exposure intensity. The experimental results show that the mapping of smoke affected areas is accurate with 96.75%, precise with 96.18%, recalled with 95.62%, F1 score with 95.90% and AUC with 0.982, and IoU with 93.84%. The novelty of the approach is related to the integration of fire detection, segmentation, estimation of pollution and mapping of health risk in a given architecture. The framework delivers scalable identification of pollution hotspots and communities with high exposures. The results show its potential in the support of early health advice, environmental surveillance, data-driven disaster-response planning.
Cognitive Behavioral Therapy for Burnout and Governance in Executives with Learning Disabilities
Executive burnout may hinder some leaders’ ability to exercise reflective judgment; however, rigid work systems may increase cognitive challenges for some leaders with learning disabilities. This randomized wait-list study examined the impact of eight sessions of accessible work-based cognitive behavioral therapy (CBT) on the burnout levels and ethical governance of 120 executives with dyslexia and/or some form of dysgraphia or dyscalculia. Rating scales were administered prior to the intervention, immediately after the intervention, and at the three month follow-up. Intention-to-treat mixed-effects models indicated that levels of burnout were significantly lower post-CBT (post-intervention, b = -0.40, 95% CI [-0.47, -0.33], d = -1.16; follow-up, b = -0.30, 95% CI [-0.38, -0.23], d = -0.88). Scores of ethical leadership and vignette scores also improved. There was no support for ethical leadership mediation, and indirect pathways to vignettes with counter-burnout effects were supported. The burnout reduction effects were strengthened by organizational support. Accessible CBT may improve leaders’ well-being and governance behavior if paired with organizational supports, redesign of workloads, and the establishment of ethical governance infrastructures.
Correlation Between Histopathological Findings and Clinical Causes of Dental Implant Failure
Background Dental implant failure is a multifactorial clinical problem involving biological, mechanical, and prosthetic factors. Histopathological examination of failed implants may provide important information regarding the tissue changes underlying implant failure and may help distinguish biologically mediated from mechanically related complications. Aim To evaluate the clinical causes and histopathological characteristics of dental implant failure and to identify clinical and histopathological factors independently associated with biologically mediated implant failure. Materials and Methods This study evaluated 100 patients with dental implant failure. Demographic and clinical characteristics, including age, gender, smoking status, diabetes mellitus, history of periodontal disease, and implant location, were recorded. The clinical causes of implant failure were categorized as peri-implantitis, lack of osseointegration, mechanical overload, implant fracture, prosthetic complications, infection/abscess, and other causes. Histopathological examination assessed inflammatory infiltrates, fibrous connective tissue formation, bone resorption, necrotic tissue, foreign-body giant cells, and reduced or absent osseointegration. Associations between clinical causes and histopathological findings were evaluated using the Chi-square test or Fisher’s exact test. Spearman’s correlation analysis assessed relationships between histopathological characteristics and clinical severity. Multivariable logistic regression was performed to identify independent predictors of biologically mediated implant failure. Statistical significance was established at p < 0.05. Results Peri-implantitis was the most frequent clinical cause of implant failure (32.0%), followed by lack of osseointegration (24.0%) and mechanical overload (16.0%). Chronic inflammatory infiltrate was the most common histopathological finding (62.0%), followed by bone resorption (53.0%) and lymphoplasmacytic infiltration (48.0%). A significant association was observed between clinical failure causes and histopathological findings (probability (p) = 0.044). Reduced osseointegration demonstrated the strongest correlation with clinical severity Spearman’s rank correlation coefficient (r) = 0.61, p < 0.001), followed by bone resorption (r = 0.58, p < 0.001) and chronic inflammatory infiltrate (r = 0.52, p < 0.001). In multivariable analysis, reduced osseointegration (adjusted Odds Ratio (OR) = 4.76, p < 0.001), bone resorption (adjusted OR = 4.12, p < 0.001), chronic inflammatory infiltrate (adjusted OR = 3.26, p = 0.003), history of periodontal disease (adjusted OR = 2.74, p = 0.005), and smoking (adjusted OR = 2.18, p = 0.030) were significant independent predictors of biologically mediated implant failure. Conclusion Dental implant failure was predominantly associated with peri-implantitis and impaired osseointegration. Histopathological evidence of inflammation, bone resorption, and reduced osseointegration was strongly associated with clinical severity. Periodontal disease history and smoking were also significant clinical predictors of biologically mediated failure, emphasizing the importance of periodontal and risk-factor assessment in implant therapy.
Causal-GAT-LLM: A Hybrid Framework for Explainable Heart Disease Prediction using LiNGAM and Language Models
Heart disease remains a leading cause of mortality worldwide, necessitating accurate and interpretable prediction systems capable of handling complex, multimodal healthcare data. This study proposes a novel hybrid framework Causal Graph Attention Network with Large Language Model and Linear Non-Gaussian Acyclic Model (Causal-GAT-LLM-LiNGAM) for robust and explainable heart disease prediction. The approach begins by applying the LiNGAM algorithm to structured clinical data to uncover directed causal relationships among variables such as blood pressure, cholesterol, and smoking history. These causal graphs form the backbone of a Graph Attention Network (GAT), which learns to emphasize the most influential features through attention mechanisms. Simultaneously, a domain-specific Large Language Model (LLM), such as BioBERT or GPT-based variants, is employed to extract semantic embeddings and causal cues from unstructured clinical text, including physician notes and discharge summaries. These text-derived features are integrated into the graph to enrich node and edge representations. By utilizing attention weights and LLM-generated narratives, the combined model not only improves predictive accuracy but also offers human-interpretable reasons for risk assessment. Evaluated on benchmark datasets such as MIMIC-III and UCI Heart Disease, the proposed framework outperforms traditional GNNs and black-box models in terms of accuracy, AUC, and clinical interpretability. This research demonstrates the synergistic potential of causal inference, graph learning, and large language models in developing next-generation diagnostic tools for personalized cardiovascular healthcare.
Lesion-Context Spectral Prototype Learning for Cross-Dataset Benign-Malignant Classification of MIAS and DDSM Mammograms
Mammogram classification often obtains high within-dataset accuracy while remaining sensitive to acquisition style, background artefacts, and the size of the cropped lesion. This study proposes a lesion-context spectral prototype network (LCSP-Net) for benign-malignant classification using MIAS and DDSM mammograms. The method first removes non-breast background, performs robust percentile normalization and local contrast enhancement, and constructs paired lesion and surrounding-context crops from the available abnormality annotations. Three complementary representations are then learned: a lesion representation, a wider contextual representation, and a high-frequency wavelet representation. A data-dependent gate fuses these features, while a cross-dataset prototype term encourages benign and malignant embeddings from MIAS and DDSM to remain close within the same class and separated across classes. This design makes the contribution reside in the learning objective and evidence fusion rather than in a post-hoc attention map. Grad-CAM is therefore used only for interpretation after prediction. A patient-wise evaluation protocol is specified to prevent leakage between training and test images. Preliminary aggregate values supplied for this manuscript show accuracy increasing from 96.15% for the lesion CNN to 99.14% for the full ablation sequence; however, these values must be verified from locked raw predictions before submission. The reconstructed confusion matrix and illustrative Grad-CAM panel are explicitly marked as provisional and are not treated as independent experimental evidence. The resulting framework provides a reproducible route for testing whether lesion evidence, local tissue context, spectral detail, and cross-dataset representation consistency improve mammogram classification beyond single-stream CNN or transformer baselines.
RAGSleepNet: Dual-Store Retrieval for Evidence-Grounded Sleep Staging from Single-Channel EEG
Automatic sleep stage classification now approaches expert agreement on single channel electroencephalography, yet the resulting decisions remain opaque and cannot be audited against the rules clinicians apply. This work introduces RAGSleepNet, a retrieval augmented and explainable staging framework in which every decision is produced together with the evidence supporting it. A dual scale convolutional encoder followed by a transformer context encoder maps each 30 s epoch, with its twenty surrounding epochs, into a contextual query. The query addresses two retrieval branches: a non parametric exemplar memory holding embeddings of all scored training epochs, and a store of 12 clinical knowledge cards whose predicates are soft functions of 17 interpretable descriptors derived from American Academy of Sleep Medicine criteria. A gating network estimates how far the parametric head, the retrieved exemplars and the cards should be trusted for each epoch, and the three posteriors are combined in a log linear mixture. Subject independent five fold cross validation on 39 nights from 20 subjects of the Sleep-EDF Expanded database, comprising 42,307 scored epochs, yields 82.44 percent accuracy, 75.14 percent macro F1 and a Cohen kappa of 0.7588. Band stop probing shows that removing the frequency support cited by the card lowers the posterior of the predicted stage by 0.271, against 0.066 for a random band, confirming that the explanations are faithful.
Comparative Analysis of Simplified 1D Convolutional Neural Network Performance on Balanced ECG Datasets for Cardiac Arrhythmia Detection
Globally, cardiovascular illnesses continue to be the leading cause of morbidity and mortality. Among them, arrhythmia is a prevalent cardiovascular disorder that affects a significant portion of the population. Timely detection and precise classification of cardiac arrhythmias are essential for actual intervention and patient care. Convolutional Neural Networks (CNNs), a cutting-edge advancement in deep learning, have shown remarkable success in automating the identification of cardiac arrhythmias from electrocardiogram (ECG) data. However, the performance of these models can vary significantly depending on the class distribution of the dataset, with imbalanced datasets presenting a significant challenge. This study presents a comparative analysis of one-dimensional convolutional neural network (1D CNN) performance when applied to multiple balanced ECG datasets. It sheds light on the impact of data distribution on model effectiveness. In the balanced dataset, the 1D CNN achieved improved accuracy and F1-score to detect abnormal ECG patterns. The comparison was conducted on different batch sizes of 16, 32, 64, and 128. Among batch sizes 16 and 32, the BorderlineSMOTE data balancing on MIT-BIH 1D CNN mode yielded superior performance to other balancing methods based on the accuracy, F1-score and ROC-AUC Score. Similarly, in the case of an increase in batch sizes to 64 and 128, ADASYN oversampling applying on the specified model provided improved results compared to others. The accuracy and F1-score vary from a range of 0.9752 to 0.9804 and 0.8622 to 0.8994 respectively. These findings emphasize the need for careful consideration of medical dataset-balancing techniques while applying deep learning models.
Design and Simulation of Quadcopter for Planetary Expedition
Quadcopters were also called as quadrotor helicopter, is popularly known as Unmanned Aerial Vehicles (UAV). Quadcopters have been widely used in much of the civilian and military applications over the past few years due to its compact size and high stability. Numerous studies are being conducted to investigate the potential for employing these devices for different operations, such as delivery of packages, construction work, hurricane hunting, 3D mapping, preserving wildlife, agricultural operations, search and rescue, etc. Since these devices are unmanned vehicles, the effectiveness of their navigation control system determines how well they operate. It is however difficult to design robust controller for such systems. This is due to the fact that quadcopters are highly sensitive to control inputs. The focus of this paper is to design body frame and simulation of a quadcopter for planetary expedition. It uses a linear proportional-derivative integral (PID) controller to control the quadcopter’s altitude, attitude, direction, and location in space. The created control system uses six degrees of freedom (3 linear and 3 angular motions) to control both rectangular x, y, and z positions as well as orientation. The body components of the quadcopter are designed using AutoCAD and imported to Simulink/Matlab for assembly and performance evaluation considering the external environment with varying wind speed on earth and mars. The results revealed that the designed quadcopter was reliable and capable of adapting for exclusive wind disturbances.
An Automated Deep Learning Based Clinical Decision Support System for Early Detection of Diabetic Retinopathy from Fundus Images
Diabetic Retinopathy (DR) is one of the leading causes of preventable blindness among diabetic patients, making early detection and timely diagnosis essential for effective treatment and vision preservation. Manual examination of retinal fundus images is often time-consuming and depends on the expertise of ophthalmologists, creating the need for reliable automated screening systems. This study proposes an intelligent deep learning-based framework for the early detection and severity grading of Diabetic Retinopathy using retinal fundus images. The proposed approach employs a hybrid deep learning architecture that combines a U-Net encoder for accurate lesion segmentation with a ResNet50-based classifier for automated classification of retinal images into five clinically recognized DR severity levels. The segmentation module effectively identifies retinal abnormalities such as microaneurysms, hemorrhages, and exudates, while the classification module accurately predicts the stage of the disease. The proposed framework improves diagnostic performance by achieving over 7% higher classification accuracy compared with conventional CNN-based models and demonstrates robust performance across diverse retinal image datasets. By providing accurate, efficient, and automated screening, the proposed system assists ophthalmologists in early diagnosis, reduces diagnostic workload, and supports timely clinical intervention. Overall, the framework offers a scalable and reliable computer-aided diagnostic solution for Diabetic Retinopathy screening and contributes to improved eye healthcare through intelligent deep learning techniques.
Surgical Management of Pediatric Odontogenic Tumors: Clinical, Radiographic, and Histopathological Outcomes
Background: Pediatric odontogenic tumors are uncommon lesions that may present with variable clinical and radiographic features, making accurate diagnosis and selection of an appropriate surgical approach important for achieving favorable outcomes while minimizing morbidity. Aim: The present study aimed to evaluate the clinical, radiographic, histopathological, and postoperative outcomes of surgically managed pediatric odontogenic tumors and to assess the relationship between surgical approach, radiographic characteristics, and tumor recurrence. Materials and Methods: A total of 30 pediatric patients with histopathologically diagnosed odontogenic tumors were evaluated. Demographic characteristics, anatomical site, clinical presentation, radiographic features, histopathological diagnosis, surgical procedure, early postoperative complications, treatment response, and recurrence were assessed. Descriptive statistics were expressed as mean ± standard deviation or frequency and percentage. Associations between categorical variables were evaluated using the chi-square test or Fisher’s exact test, with statistical significance established at p < 0.05. Results: The study population had a mean age of 12.7 ± 3.1 years, with males comprising 60.0% of the participants. The mandible was involved in 76.7% of cases, with the posterior mandible being the most frequently affected site (53.3%). Painless swelling was the predominant clinical presentation (66.7%). Radiographically, unilocular radiolucency was most common (60.0%), while 83.3% of lesions had well-defined borders and 63.3% demonstrated cortical expansion. Unicystic ameloblastoma was the most frequent histopathological diagnosis (26.7%), followed by adenomatoid odontogenic tumor (20.0%) and odontoma (16.7%). Enucleation with curettage was the most commonly performed surgical procedure (43.3%), followed by enucleation alone (23.3%). Primary bone healing without complications occurred in 83.3% of cases. Overall, complete clinical and radiographic resolution was achieved in 90.0% of patients, while recurrence occurred in 3.3% of cases. Recurrence was not significantly associated with conservative versus radical/segmental surgery (p = 0.540). However, cortical expansion (p = 0.001) and root resorption/displacement (p = 0.036) were significantly associated with recurrence. Conclusion: Pediatric odontogenic tumors in this study showed a predominance of mandibular involvement and commonly presented as painless swelling with well-defined, predominantly unilocular radiolucent lesions. Conservative surgical management was frequently used and was associated with favorable healing and treatment outcomes. Although overall recurrence was low, selected radiographic features, particularly cortical expansion and root resorption or displacement, were associated with recurrence and may be useful in postoperative risk assessment and follow-up planning.
Evaluation & Comparison of Gingival Depigmentation Using Conventional Scalpel Technique and Diode Laser – A Clinical Study
Background: Gingival melanin pigmentation is a common physiologic finding that may present as dark discoloration of the gingiva. Although it does not represent a pathological condition, it can be an esthetic concern for individuals, particularly those with a high smile line or excessive gingival display. Various techniques have been proposed for the management of gingival hyperpigmentation, among which the conventional scalpel technique and diode laser therapy are frequently used. The scalpel technique has long been considered the gold standard because of its simplicity and cost-effectiveness, whereas diode lasers have gained popularity due to advantages such as improved haemostasis, precision, and patient comfort. In view of these considerations, a comparative evaluation of these two treatment modalities becomes clinically relevant. Objective: The present study was undertaken to evaluate and compare gingival depigmentation performed using the conventional scalpel technique and diode laser with respect to pigmentation reduction, wound healing, and patient-related outcomes. Materials and Methodology: The study was conducted on 20 patients aged between 18 and 35 years who presented with gingival melanin pigmentation and reported to the Department of Periodontology and Implantology, Sri Siddhartha Dental College and Hospital, Tumkur. A randomized split-mouth design was employed in which each patient received both treatment modalities. One site was treated using the conventional scalpel technique (Group A), while the contralateral site was managed using a diode laser (Group B). Gingival pigmentation was evaluated using the Dummett–Gupta Oral Pigmentation Index (DOPI). Wound healing was assessed using the Wound Healing Index (WHI), and patient response was recorded using the Visual Analog Scale (VAS) for pain along with a questionnaire evaluating discomfort and esthetic satisfaction. Clinical assessments were carried out preoperatively and postoperatively at 1 week, 1 month, 3 months, and 6 months. Statistical analysis was performed using the Wilcoxon signed-rank test for intergroup comparisons and the Friedman test for intragroup comparisons. Results: Both treatment modalities resulted in a noticeable reduction in gingival pigmentation. Baseline pigmentation scores were comparable between the two groups. At all postoperative evaluation periods, the diode laser group demonstrated lower pigmentation scores compared with the scalpel group, and the difference was statistically significant. Wound healing progressed satisfactorily in both groups, with no significant difference between the techniques. Patients treated with the diode laser generally reported less postoperative pain and discomfort. The overall esthetic outcome was favourable in both groups, with most patients expressing complete satisfaction with the treatment results. Conclusion: Within the limitations of the present study, both the conventional scalpel technique and diode laser were found to be effective methods for gingival depigmentation. The diode laser showed advantages in terms of reduced postoperative discomfort and improved patient experience. However, the scalpel technique remains a dependable and economical approach for the management of gingival hyperpigmentation.
An Explainable Artificial Intelligence Framework for Predicting Anti-Diabetic Phytochemicals from Indian Medicinal Plants
The systematic identification of bioactive phytochemicals from medicinal plants is hindered by the chemical variation among natural compounds and the low interpretability of traditional computational screening methods. Hence, in this study, we proposed a transparent, explainable artificial intelligence (XAI) framework to predict and rank potential antidiabetic compounds from Indian ethnomedicine. Using physicochemical and structural molecular descriptors, a curated dataset of 386 phytochemicals was created and analysed through classifications with Logistic Regression, Support Vector Machine, Random Forest, and Extreme Gradient Boosting (XGBoost). Model performance was evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and MCC for classification tasks, while Shapley Additive exPlanations (SHAP) were used to explain molecular-level results. The best predictive performance was achieved by XGBoost, with a ROC-AUC of 0.947, showing better generalisation than other classifiers. In conclusion, the SHAP analysis revealed that topological polar surface area, lipophilicity (LogP), potential hydrogen-bonding groups, molecular weight and structural features played key roles as determinants influencing antidiabetic efficacy and alluded to nonlinear structure–activity relationships in medicinal plant phytochemical studies. Moreover, the XAI-driven approach also identified non-dietary pharmacologically relevant phytochemicals quercetin, berberine, kaempferol, gymnemic acid and curcumin as further prospective computational candidates. This framework combines prediction accuracy with structural interpretability to determine candidate structures and can be used as a final step to elevate the phytochemical exploration space by helping choose structure-based natural-product candidates for subsequent experimentally validated target-specific probes.
Structural and Orthorhombic Phase Formation of Magnetic Fe3O4 Nanoparticles for Degradation of Methyl Orange Dye
A thriving economy and the overall growth of civilization depend on having access to pure, healthy water. Magnetite nanomaterials possess a significant surface-to-volume ratio, a high sensitivity and reactivity, substantial adsorption capacity, and facile fictionalization, rendering them appropriate for wastewater treatment applications. This study details the manufacture and characterization of well-crystallized magnetite iron oxide nanomaterials, synthesized at around 80°C. Magnetite Fe3O4 NPs were produced using the co-precipitation technique with Fe2+ and Fe3+ salts of ferric chloride and ferrous sulphate in 2:1 ratio, and characterized employing the techniques of XRD, FTIR, and ultraviolet irradiation XRD reveals presence of magnetite Fe3O4 nanoparticles with orthorhombic crystal structure possessing a standard particle dimensions of about 19nm calculated by scherrer’s equation. The synthesized magnetite Fe3O4 nanoparticles will be employed as an adsorptive removal of Methyl orange dye. MO is toxic, non- biodegradable and can cause serious environmental damage if released into water. It may result diarrhea, nausea, skin irritation and exposure to high doses of methyl orange dye can be fatal. The current research revealed that magnetite Fe3O4 NPs are good adsorbent material for the elimination of organic dye (MO) from wastewater.
Cloud-Based Integration of Vitis Vinifera Phenolic and Cohort Data Questions Beverage-Type Polyphenol Proxies
Background. Research on plant-derived bioactives routinely substitutes the food or beverage that carries a compound for the compound itself, and the heterogeneous data needed to test that substitution are rarely brought into a single queryable structure. Methods. Two openly available sources were integrated in a containerised, cloud-executed pipeline: a phenolic analysis of 178 wine samples from three Vitis vinifera cultivars, and the NHANES I Epidemiologic Follow-up Study cohort of 1,629 adults. A six-table star schema was loaded into a row store and a columnar store, and query latency, data-volume scaling and parallel execution were benchmarked. Composition was compared by analysis of variance and principal component analysis; 10-year all-cause mortality was modelled by sequentially adjusted logistic regression, four classifiers, feature ablation and E-value analysis. Results. The columnar store was 34 times faster than the row store at 1.63 million fact rows, and the single-threaded analytical workload reached 79% parallel efficiency on two cores. Cultivar explained 24–73% of variance in phenolic constituents, with a 3.8-fold difference in flavanoid content. Wine drinkers had lower crude mortality than beer drinkers (9.8% versus 18.3%; odds ratio 0.43, 95% CI 0.24–0.77), but adjustment removed 58% of the association (0.70, 0.35–1.39; E-value 1.68) and removing beverage type changed discrimination by under 0.001 AUC. Conclusion. Beverage type is a poor surrogate for polyphenol dose. Natural-product epidemiology should quantify the bioactive, and the integration infrastructure to do so is inexpensive.
Smart Web-Based Representation of Human Health Data: Publicly Available Datasets, Contemporary Methodologies and a Comparative Analysis of Models
The web is now the surface on which most human health information is aggregated, interpreted and acted upon. Hospital portals, national biobank workbenches, consumer wearable dashboards and clinician-facing decision support tools are all, architecturally, web systems that must turn heterogeneous biomedical evidence into something a person can reason about in seconds. This paper surveys that problem as it stands in mid-2026. We propose a six-layer reference architecture that separates acquisition, interoperability, representation learning, inference, presentation and governance, and we argue that the representation and presentation layers are the ones the literature has left under-specified. We then catalogue the publicly available human-health datasets that can legitimately support such systems, reporting verified scale figures and access conditions for critical-care records (MIMIC-IV v3.1: 364,627 individuals, 546,028 hospitalisations, 94,458 ICU stays), radiographic corpora (MIMIC-CXR: 377,110 images; CheXpert: 224,316 radiographs), population genomic resources (All of Us: more than 535,000 whole genome sequences; UK Biobank: whole-genome sequencing of 490,640 participants) and few-shot benchmark suites such as EHRSHOT. Nine model families are reviewed, from gradient-boosted trees through EHR transformers, clinical and general-purpose large language models, imaging foundation models, multimodal fusion, graph neural networks, retrieval-augmented generation and federated learning. A quantitative synthesis then pairs published benchmark with the properties that decide whether a model can be served inside a web representation layer: latency class, memory footprint, interpretability, data-governance posture and regulatory exposure. Three findings stand out. Leaderboard accuracy has decoupled from deployability: reported MedQA accuracy climbed from 67.6% to 96.0% between late 2022 and late 2024, yet a 2026 Nature Medicine evaluation found specialised clinical AI tools performing no better than a general web search summary on real physician queries, with frontier general-purpose models ahead of both. Second, four of the eight quantified public resources with a defined coverage window end nine or more years before 2026, so learned representations encode historical rather than current practice. Third, of the 60 works cited here, only five address the presentation layer, against 15 each for acquisition, inference and governance. The paper closes with a four-tier evaluation protocol and eleven open challenges, calibrated to the compliance timeline the EU Artificial Intelligence Act imposes through 2027.
Women's Attention to the Natural Environment in the Novel Particles by Dewi (Dee) Lestari: An Ecofeminist Study
Literary works are one of the vehicles to convey solutions to natural environmental problems. Through literary works, authors as part of society can voice their hearts about the phenomena they see and/or experience. For this reason, it is important to conduct research that examines literary works. This research is in line with UNNES' position as a Conservation University, especially related to the first pillar of 'Biodiversity Conservation' which aims to protect, preserve, utilize, and develop the environment, flora, and fauna wisely and sustainably. In addition, this research correlates with the SDGs goal 15 "Terrestrial ecosystems", which is to manage forests sustainably and stop land degradation. This research uses a theoretical approach to ecofeminism, while methodologically it uses a qualitative descriptive approach. The results of this study are expected to provide a clear picture of the form of women's attention and the way women respond to natural environmental problems.
MEFS: Multistage Ensemble Feature Selection Framework for Diabetes Risk Prediction Using Relevance, Separability, and Feature Diversity
Diabetes mellitus is among the most common metabolic diseases and requires early and accurate diagnosis in order to avoid any additional deterioration and complications. The performance of machine learning algorithms is promising in diabetes prediction, but their efficiency is usually hindered by redundant features, class imbalance, and poor interpretability of the developed models. Common feature selection methods usually employ only a relevance measure and neglect to incorporate nonlinear dependency, separability, and diversity, leading to poor predictive efficiency and high computational cost. To overcome these problems, we propose Multistage Ensemble Feature Selection (MEFS) framework which combines Nonlinear Dynamic Conditional Relevance Feature Selection (NDCRFS), Hellinger Distance (HD), and Feature Hamming Diversity (FHD) to select an optimal set of informative features for diabetes predictions. Firstly, the data is preprocessed using Feature-Specific Mean Variance Normalization (FSMVN) and Adaptive Synthetic Sampling (ADASYN). Then, the proposed feature selection method utilizes nonlinear analysis, distribution comparison, and redundancy elimination using an ensemble selection process. The proposed feature selection framework subsequently combines nonlinear relevance analysis, distribution-based discrimination, and redundancy reduction through an ensemble ranking strategy. Performance evaluation of the feature subset is conducted using SVM, LR, RF, XGBoost, and MLP classifiers with ten-fold cross-validation on the Pima Indians Diabetes dataset. From the experimental analysis, it can be seen that the MSFS helps in reducing the size of the original feature space by 37.5%, utilizes lesser memory with an approximation of 37% memory savings, and helps in saving computational training time without affecting prediction accuracy to any significant extent. The statistical verification process conducted through the Friedman test and Wilcoxon signed rank test proves that the suggested methodology performs as well as existing methods of feature selection without deteriorating performance.
Proximate, Nutritional, and Phytochemical Content of Several Macrofungi Samples Utilized by the Bunggu Tribe, West Sulawesi, Indonesia
Wild edible macrofungi contain higher protein than vegetables, high fiber, vitamins and minerals, and low fat content. These mushrooms are also rich in various nutrients and bioactive compounds that can be developed into dietary supplements for health. In this study, fourteen species of wild macrofungi collected from various locations in Pasangkayu Regency were analyzed for their proximate composition, nutrients, and phytochemical compounds. The results showed that the highest water content was found in Schizophyllum commune, while the highest protein content was found in the species Volvariella volvaceae, Pleurotus ostreatus, and Picnoporus sanguineus. Furthermore, the highest fat and ash content was also found in the species Volvariella volvaceae, while the highest carbohydrate content was found in the species Termitomyces striatus. The highest P content was found in the species Volvariella volvaceae and Pleurotus ostreatus, while the highest Fe content was found in the species Ganoderma sp, Picnoporus sanguineus, Auricularia auricula-judae, Volvariella volvaceae, and Auricularia polytrica. All macrofungi samples tested contained alkaloids, flavonoids, and phenols, while tannins were found in Cookeina speciosa, Lentinus squarrosulus, Picnoporus sanguineus, and Trametes versicolor. Saponins were only detected in Lentinus squarrosulus, Lentinus sp. and Mycena sp., while carbohydrates were only detected in Schizophyllum commune, Picnoporus sanguineus and Ganoderma sp. However, none of the macrofungi samples tested contained steroids, coumarins or carotenoids.
AI-Powered Wearable IoT Devices for Mental Health Assessment, Stress Prediction, and Personalized Healthcare Recommendation Systems in Machine Learning and Cloud Computing
AI, wearable Internet of Things (IoT), machine learning and cloud computing are coming together in a manner that enables continual and individual mental health care. This paper introduces an AI-driven Wearable IoT system to evaluate mental health, predict stress, and provide personalized healthcare suggestions. This proposed method combines physiological and behavioural data like heart rate, heart-rate variability, electrodermal activity, skin temperature, motion data, sleep patterns, and physical activity via wearable devices. These diverse signals can be converted to digital biomarkers via machine learning and deep learning algorithms for the early detection of stress states and forecast for future changes in mental health status. Cloud computing offers scalable data storage, model training, longitudinal analysis and secure healthcare-data-management, while intelligent recommendation layer creates personalized interventions according to the physiological, behavioral pattern of each individual. Recent research shows that wearable-based stress detection and multimodal sensing, digital phenotyping, and mood prediction are feasible, but there are issues of generalizability, data quality, privacy, interpretability, and validation in clinical settings. The suggested framework, therefore, promotes multimodal intelligence, personalized prediction, secure integration with the cloud and prevention support for healthcare, instead of the clinical diagnostic autonomy.
Aphrodisiac Potential of Hibiscus sabdariffa: Phosphodiesterse-5 Inhibition and Sexual Behavior Study Using in Silico and in Vivo Models
Background: Driven by in vitro, in silico, and in vivo studies, the current work investigates the therapeutic potential of Hibiscus sabdariffa L. flower extract in modulating male sexual function through phosphodiesterase-5 (PDE-5) inhibition. Sexual dysfunction cause is also a s metabolic disorder. The bioactive components including gossypitrin, luteolin, hibiscetin-3-glucoside, and delphinidin-3-sambubioside, were found using molecular docking studies to have beneficial binding affinities for PDE-5 that were either higher than or equivalent to those of the reference with sildenafil. The stability and potent intermolecular interactions of these compounds within the PDE-5 active site have been further demonstrated by molecular dynamics (MD) simulation, indicating their potential pharmacological significance. Methods: To assess the extract's impact on male genitals, in vivo tests were carried out on rat’s models. at doses of 100, 200, and 400 mg/kg, the animals were split into five groups: Measurements of parameters like mount frequency, mount latency, and intromission delay showed that sexual performance improved in a dose-dependent manner. Results: The behavioral results were supported by biochemical analyses that showed a substantial rise in serum testosterone levels and nitric oxide concentration. Furthermore, the extract's PDE-5 inhibitory efficacy validated its mechanism of action in improving erectile function. Conclusions: When combined, the results show that Hibiscus sabdariffa flower extract is a potential natural substitute for enhancing male sexual health via a variety of multimodal mechanisms, such as nitric oxide modulation, testosterone regulation, and PDE-5 inhibition. Its therapeutic potential is highlighted by this integrated method that combines computational predictions with experimental confirmation, opening the door for the eventual development of phytotherapeutics that target erectile dysfunction.
Good and Evil: A Psychologycal Transformations in Maleficent (2014)
Good and evil play a prominent role in this universe. People adhere to certain rules that guide them toward the good side as well as rules that lead to the evil side. Actions that do not harm anyone are considered good deeds, while those that cause harm to others are classified as evil deeds. Some individuals may be driven toward evil due to their past experiences or other reasons. Conversely, many people can change from being evil to good, depending on their circumstances and psychological state. Furthermore, elders are responsible for guiding their children from evil to good, as children learn by observing society without fully understanding the underlying issues. This study examines the concepts of good and evil through the lens of two characters from the movie Maleficent. It also delves into their backgrounds to understand the reasons for their transformations. Ultimately, this research offers a new perspective for the upcoming generation, helping them to develop a balanced understanding of good and evil.
The Silent Struggles of the Unconscious: Severus Snape Through a Freudian Lens
This paper offers a thorough psychoanalytic interpretation of Severus Snape, one of the most intricate antagonists in J.K. Rowling’s Harry Potter series, through the lens of Sigmund Freud's theories. Snape's character reflects unresolved childhood trauma, emotional repression, and inner conflicts stemming from unconscious desires. His unrequited love for Lily Potter, coupled with feelings of rejection and humiliation, greatly influences his personality and moral decisions. By employing Freud’s concepts of the id, ego, and superego, along with mechanisms of repression and projection, this paper examines how Snape’s exterior coldness and cruelty mask deep psychological wounds. Rather than simply categorizing Snape as a villain or a hero, this study illustrates him as a symbol of the silent struggles within the unconscious mind. This analysis highlights the intersection of literature and psychology, showing how Rowling creates complex antagonists whose internal battles reflect broader human experiences.