Journal of Medical and Health Studies (ISSN: 2710-1452) is a peer-reviewed International Journal published by Al-Kindi Center for Research and Development. The journal covers scholarly articles in the fields of Medicine and Public Health. All articles are freely available without charge to the users or their institutions; thus, ensuring high visibility and increasing citations for all research articles published. The journal aims to facilitate scholarly work on recent theoretical and practical aspects of health and medical sciences. Academics, policymakers, and researchers are welcome to submit their manuscripts at any time.
Al-Kindi Center for Research and Development
Admission Blood Glucose as a Predictor of Mortality and Severe Outcomes in Acute Organophosphorus Poisoning: A Systematic Review and Meta-Analysis
Acute organophosphorus poisoning remains a major toxicologic emergency and cause of morbidity and mortality, especially in agricultural and resource-limited settings. Early identification of patients at risk of deterioration is essential. Admission blood glucose has been proposed as a simple prognostic marker, but the evidence has not been synthesized in a focused review. This study aims to evaluate the association between admission blood glucose and mortality and severe clinical outcomes in acute organophosphorus poisoning. A systematic review and meta-analysis was conducted using PubMed and a supplementary Google Scholar search. PubMed yielded 157 records, and the first 200 Google Scholar records sorted by relevance were also screened. After title and abstract screening, full-text review, and exclusion of ineligible reports, 5 observational studies were included in the qualitative synthesis and 3 studies in the quantitative synthesis. The primary outcome was in-hospital mortality. Secondary outcomes included mechanical ventilation or respiratory failure and poisoning severity. Odds ratios (ORs) with 95% confidence intervals (CIs) were pooled using the Mantel-Haenszel random-effects model. Results from the included studies showed that elevated admission glucose was associated with significantly increased odds of mortality (OR 5.68, 95% CI 3.55-9.07; I² = 0%) and mechanical ventilation or respiratory failure (OR 4.46, 95% CI 3.02-6.59; I² = 0%). Narrative synthesis of the remaining studies also supported the association between higher admission glucose and greater poisoning severity. These findings point towards admission glucose being a simple and clinically useful early prognostic marker, although the available evidence is limited by observational design and variation in glycemic cut-offs.
Copyright (c) 2026 Journal of Medical and Health Studies
The Impact of Transformational Leadership on Nurse Burnout: A Systematic Literature Review
The impact of transformational leadership is a methodological literature review on nurse burnout in the healthcare environment at the global level, i.e., in the Gulf Cooperation Council (GCC) area. Nurse burnout, which is potentially characterised as emotional exhaustion, depersonalisation, and the absence of personal accomplishment, can become one of the gravest foretellers of the risks to safety, manpower shortages, and poor healthcare. Peer-reviewed articles published in 2020-25 in PubMed, CINAHL, Scopus, Web of Science, and Google Scholar were searched. The review of the evidence is what synthesises the results and suggests the correlation between transformational leadership and nurse burnout by including the mediating variables of job satisfaction, empowerment, and working environment and moderating variables of the healthcare setting, according to the PRISMA principles of systematic reviews and meta-analyses. The results show the existence of a stable negative association between transformational leaders and three dimensions of burnout as measured by the Maslach Burnout Inventory. Transformational leaders can alleviate the effects of emotional exhaustion with inspirational motivation and identifying the employee personally and depersonalisation with professional development, encouragement and shared decision-making (Boamah, 2022; Montenegro Mendez et al., 2025). The writers of this association found that job satisfaction, structural power, and favorable working environments (Alruwaili, 2025; Al Sabei et al., 2023). The suggested solutions to prevent nurse burnout presented by the review are the investment in transformational leadership development programs, an empowerment-based management strategy, and the solving of structural workload problems. Future studies ought to use the longitudinal design to investigate the existence of cultural moderators in the GCC healthcare systems.
Transformational leadership, nurse burnout, emotional exhaustion, depersonalization, systematic review, Maslach Burnout Inventory, job satisfaction, empowerment, work environment, healthcare leadership, GCC, nurse retention, PRISMA
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The Hidden Cost of Manual Equipment Management in Ambulatory Surgery Centers: A Quantitative Analysis of Financial Leakage and Operational Inefficiency
The growing financial and operational environments of Ambulatory Surgery Centers (ASCs) make efficiency and cost control of paramount importance to sustainability. Even though there has been a technological improvement in healthcare technology, most ASCs still use manual equipment management systems that feature paper-based tracking, disjointed documentation, and processes that are human operated. This paper explores this concealed cost of such hand-based practices by performing a quantitative investigation on financial leakage and inefficiency of operations. The analysis of the main performance indicators such as the frequency of downtimes, delays in maintenance, and misallocation of resources is conducted on the basis of data obtained based on the records on the utilization of equipment, maintenance records, and financial reports. The results indicate that the manual management of equipment is one of the contributing factors to the hidden financial losses in the form of long equipment downtime, ineffective inventory management, long lagging maintenance cycles, and inaccurate billing. Regression analysis shows that the relationship between the intensity of manual tracking and financial leakage is strong and positive and equipment utilization is negatively related to the operational costs. In addition, the paper pinpoints the systemic inefficiencies that interfere with the operations of the surgery, lower patient throughput, and burden administration. The findings highlight the necessity of moving to automated equipment management tools based on data with predictive maintenance, real-time tracking, and built-in financial monitoring. These systems can reduce the unseen losses, improve the functioning and general resource optimization in ASCs. The research paper is added to the existing literature on the topic of efficiency of healthcare operations, as it is based on real evidence of financial and operational threats of out-of-date manual systems and it outlines the practical directions of going digital.
Ambulatory Surgery Centers (ASCs); Equipment Management; Financial Leakage; Operational Inefficiency; Predictive Maintenance; Healthcare Analytics; Resource Utilization; Cost Optimization
A Structured Protocol for Sustainable Goal Achievement: Development and Application of the Open G.A.T.E.S Model
Goal achievement remains a central focus within coaching, counseling, and holistic health practices; however, many existing frameworks lack a structured approach that integrates feasibility, contextual awareness, and long-term sustainability. This paper introduces and expands upon the Open G.A.T.E.S Model, a structured protocol developed by Leslie Bereiter and Nicholas Bereiter, designed to guide individuals toward meaningful and sustainable goal attainment. The model is built upon five core components: Goals, Achievability, Timeframe, Environmental Impact, and Support and Sustainability. Each component provides a systematic lens through which clients can define their objectives, evaluate feasibility, establish realistic timelines, consider the broader impact of their goals, and ensure long-term maintenance through appropriate support systems. The protocol begins with clear goal identification and progresses through a sequential process that emphasizes practical achievability, structured time planning, and alignment with both personal and environmental contexts. A key feature of the model is its emphasis on environmental impact and sustainability, encouraging individuals to consider how their goals influence their immediate surroundings, including family, work, and community. The application of the Open G.A.T.E.S Model in professional practice was observed among a group of 12 practitioners, including life coaches, therapists, and counselors, who implemented the protocol with approximately 1,600 clients. Reported outcomes indicated a 90% success rate in goal achievement, suggesting strong practical effectiveness and adaptability across diverse client needs. The findings highlight the potential of the Open G.A.T.E.S Model as a comprehensive, structured approach to goal-setting that extends beyond traditional frameworks by incorporating holistic, sustainability-focused considerations. This protocol provides a practical, scalable tool for professionals seeking to enhance client outcomes in coaching and counseling settings.
Open G.A.T.E.S Model, Goal-Setting Framework, Sustainable Goal Achievement, Coaching and Counseling, Behavioral Change, Holistic Development, Personal Development Protocol
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Explainable and Trustworthy Deep Learning for MRI-Based Auxiliary Diagnosis of Alzheimer’s Disease
Alzheimer’s disease (AD) and its prodromal stage, mild cognitive impairment (MCI), are characterized by a long preclinical phase. Early identification is crucial for timely intervention and prognostic assessment. Structural MRI has become an important imaging modality for auxiliary diagnosis of AD due to its non-invasive nature, repeatability, and strong clinical accessibility. Deep learning has made significant progress in AD/MCI/CN classification, staging, differential diagnosis, and clinical risk prediction. However, the “black-box” nature of these models, insufficient cross-center generalization, and lack of trustworthy explanations severely limit their clinical translation. This review focuses on four main threads—“model pipeline—explanation methods—explanation evaluation—generalization and deployment”—to systematically summarize representative directions of MRI-based deep learning in AD-related tasks. It also outlines the primary pathways of explainable artificial intelligence (XAI) (saliency attribution, counterfactual explanation, and inherently interpretable architectures) and the key dimensions of trustworthiness assessment (fidelity, stability, anatomical/pathological plausibility, and human-factor usability). Methodological risks such as real-world data challenges, domain generalization, cross-scanner harmonization, and data leakage are discussed in depth. Key contributions include: (1) emphasizing that “explainability ≠ heatmap visualization” and proposing an explanation evaluation framework centered on fidelity and stability; (2) integrating risks such as shortcut learning, data leakage, and cross-scanner differences into a unified discussion of explainability and generalization; and (3) summarizing clinically translatable pathways from the perspectives of real-world application and privacy compliance. MRI-based deep learning is shifting from “pursuing accuracy” toward “trustworthy explanation + cross-center generalization + clinical usability.” Promising future directions include standardized explanation evaluation, domain generalization and harmonization techniques tailored to real-world distribution shifts, and human-interpretable explanations validated through multi-center real-world studies.
Alzheimer’s disease; structural MRI; deep learning; explainable artificial intelligence; trustworthiness assessment; generalization and real-world application
Incidence, Risk Factors, and Outcomes of VTE in Surgical and Obstetric Patients: A Systematic Review
Venous thromboembolism (VTE), comprising deep vein thrombosis (DVT) and pulmonary embolism (PE), is a leading cause of morbidity and mortality in hospitalized patients. Surgery and pregnancy are well-established high-risk conditions, but incidence, risk factors, and outcomes vary across populations. To systematically review the incidence, risk factors, and outcomes of VTE in surgical and obstetric patients. Following PRISMA guidelines, we searched Medline/PubMed, Embase, and Cochrane Library for studies (1990–2025) reporting VTE in surgical or obstetric populations. Cohort, case-control, and clinical studies providing data on incidence, risk factors, and outcomes were included. Data were abstracted on patient characteristics, VTE incidence, risk factors, and clinical outcomes. Postoperative VTE incidence ranges from 0.2–2% in general surgical populations and up to 4.5% in high-risk orthopedic patients without prophylaxis. Advanced age, active cancer, prolonged operative time, and major trauma are consistent surgical risk factors. Pregnancy-associated VTE is rare (~0.6–1.4 per 1000 pregnancies) but increases 3–10-fold antepartum and 12–35-fold postpartum, with cesarean delivery, thrombophilia, obesity, and infection as major contributors. VTE significantly impacts outcomes: 30-day mortality is 10–30% in surgical patients and PE accounts for 10–15% of maternal deaths. Post-thrombotic syndrome and recurrent VTE are common sequelae. VTE remains a clinically important complication in surgical and obstetric populations. Awareness of patient-specific and procedure-specific risk factors, along with adherence to prophylaxis guidelines, is critical to reduce morbidity and mortality.
nous thromboembolism, deep vein thrombosis, pulmonary embolism, surgery, pregnancy, postpartum, risk factors, incidence.
The Bloody Pericardium: Cardiac Tamponade Unmasking an Undiagnosed Malignancy: A Case Report
We report the case of a 58 year old male who presented to the emergency department with progressive shortness of breath, generalized weakness, and chest heaviness over one week, culminating in acute deterioration on the day of admission. He had no known history of malignancy but was a former smoker with a background of hypertension. On arrival, he appeared acutely unwell, with hypotension, tachycardia, and labored breathing. Physical examination revealed jugular venous distension, soft heart sounds, and reduced peripheral perfusion, raising concern for hemodynamic compromise. Initial electrocardiogram showed sinus tachycardia without ischemic changes, while chest radiography demonstrated marked enlargement of the cardiac silhouette suggestive of a large pericardial effusion. Urgent bedside echocardiography confirmed moderate to severe pericardial effusion with right sided chamber collapse, consistent with cardiac tamponade. The patient underwent immediate ultrasound guided pericardiocentesis, yielding approximately 400 milliliters of hemorrhagic fluid, with rapid clinical improvement in symptoms and hemodynamic parameters. A pericardial drain was left in situ for continued drainage. Laboratory analysis of the pericardial fluid revealed malignant cells. Subsequent imaging identified a suspicious lung lesion with mediastinal lymphadenopathy, suggestive of primary lung malignancy with pericardial involvement. The patient stabilized clinically following intervention and was discharged with plans for oncological evaluation and further diagnostic workup. This case highlights cardiac tamponade as a rare initial presentation of lung cancer and emphasizes the importance of early bedside echocardiography, prompt pericardial drainage, and consideration of malignancy in unexplained hemorrhagic pericardial effusion.
Malignant Pericardial Effusion, Malignancy, Pericardium, Tamponade, Cytology, Lung Cancer, Metastasis.
Vitamin D Status in North Batinah, Oman: Prevalence and Temporal Trends (2018-2022)
Background: Vitamin D plays a critical role in skeletal health and has important functions in immune regulation and chronic disease prevention. Despite abundant sunlight in the Middle East, vitamin D deficiency and insufficiency remain widely reported, with limited population-based data available from Oman. This study aimed to evaluate the prevalence and temporal trends of vitamin D status in North Batinah, Oman, and to identify demographic groups at increased risk of insufficiency and deficiency. Methods: A retrospective cross-sectional study was conducted using laboratory and clinical data from Suhar Hospital, the central referral laboratory in North Batinah. The study included 3,081 individuals of all ages who underwent serum 25-hydroxyvitamin D testing between January 2018 and November 2022. Vitamin D status was classified as deficient (<30 nmol/L), insufficient (30–50 nmol/L), or sufficient (>50 nmol/L). Demographic variables and vitamin D levels were analyzed, and temporal trends were assessed over the five-year period. Results: The overall mean vitamin D level was 82.5 ± 28.0 nmol/L. Vitamin D sufficiency was observed in 88.2% of adults and 89.0% of the pediatric population, while deficiency was rare (<1%). Insufficiency was more prevalent among females, particularly those aged 18–38 years (14.9%), and among adolescents aged 10–15 years (15.7%). Males had significantly higher vitamin D levels than females (p < 0.001). Temporal analysis demonstrated a significant decline in sufficiency from 93.1% in 2018 to 79.9% in 2021, followed by recovery to 91.0% in 2022 (p < 0.001), corresponding with reduced outdoor activity during the coronavirus pandemic period. Conclusion: Vitamin D sufficiency was high in this population, contrasting with commonly reported regional trends. However, insufficiency remains a clinically relevant concern, particularly among women and adolescents. These findings support a shift toward targeted, risk-based screening and highlight the importance of sustained public health strategies to maintain optimal vitamin D status.
AI-Enabled Machine Learning Framework for Depression Risk Prediction and Mental Health Trend Analysis in the United States
Mental health disorders such as anxiety and depression are rapidly increasing and represent a major challenge for modern healthcare systems. This study proposes a machine learning based framework for early detection and analysis of depression risk using behavioral and socio-demographic indicators. A dataset containing features such as age, sleep hours, stress level, income, and mental health days was used to train and evaluate multiple machine learning models, including Logistic Regression, Random Forest, and XGBoost. The results show that ensemble learning approaches outperform traditional models, with XGBoost achieving the highest predictive accuracy. Model evaluation using confusion matrix, ROC curve, and precision recall analysis demonstrates strong classification performance. Feature importance and explainable AI analysis using SHAP reveal that stress level and sleep hours are the most influential predictors of depression risk. Trend analysis across age groups and state-level risk visualization further highlights demographic and regional variations in mental health patterns. The findings demonstrate the potential of machine learning for large-scale mental health surveillance and data-driven public health decision-making.
Machine Learning, Depression Prediction, Mental Health Analytics, XGBoost, Explainable AI (XAI), SHAP, Public Health Surveillance, Predictive Healthcare Analytics
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Concentration-Dependent Antimicrobial Activity of Neem and Bitter Gourd Extracts: A Sustainable Approach to Mitigate Pollution in Dhaka
Air pollution in Dhaka city is increasingly severe, surpassing levels observed in many comparable urban areas. As lifestyle and dietary patterns shift, the selection of health-promoting vegetables and natural food components has become particularly important. Bitter gourd and neem extracts possess well documented antibacterial properties and are associated with improved liver and kidney function. Chronic exposure to polluted air heightens the risk of respiratory complications, a vulnerability further intensified by smoking. Therefore, avoiding tobacco use and incorporating natural protective foods into the daily diet may help strengthen immunity and mitigate the adverse health effects of rising air pollution.Aqueous leaf extracts of Azadirachta indica (neem) and Momordica charantia (bitter gourd) were tested against road dust particles collected from Dhaka city. Both extracts showed strong antimicrobial and antifungal activity and Gram staining confirmed the presence of mixed microbial species in Dhaka air pollution. A biochemical study involving 20 people from Rajshahi and 20 long term Dhaka residents indicted that the extract alone or combined, may help improve kidney, liver and lipid parameters. Chest X rays of six Dhaka residents also showed signs of respiratory effects linked to polluted air. These findings highlight the antimicrobial potential of the extracts and health risks associated with microbially contaminated urban air. The aim of this study is to evaluate the antibacterial and antifungal activities of aqueous extracts of Azadirachta indica (neem) and Momordica charantia (bitter gourd) and to assess their potential as dietary options for people in living Dhaka city to help counter pollution related health effects and support lung, liver and kidney function.
Bitter gourd, Neem, pollution protection, Natural remedy, Urban pollution, City health
Convulsions and Catastrophe: Status Epilepticus Complicated by Massive Pneumoperitoneum and Bowel Perforation: A Case Report
We report the case of a sixteen-year-old female with a known history of epilepsy who presented to the emergency department following prolonged generalized tonic-clonic seizures at home, consistent with status epilepticus, after recent noncompliance with her antiepileptic regimen. She had experienced multiple convulsive episodes over twenty minutes without full recovery between seizures, prompting urgent ambulance transport. On arrival, she remained confused, drowsy, and tachycardic, with stable oxygenation on supplemental oxygen. Initial examination revealed subtle postictal features including tongue biting, but no focal neurological deficits were detected. Unexpectedly, progressive abdominal distension was noted, with the abdomen tense and tympanic on percussion, raising concern for an acute intra-abdominal process. Urgent chest radiography demonstrated massive bilateral subdiaphragmatic free air, confirmed by contrast-enhanced CT of the abdomen, which revealed focal distal ileal perforation with surrounding inflammation and small amounts of free fluid, consistent with pneumoperitoneum secondary to bowel perforation. Laboratory studies demonstrated mild leukocytosis, elevated creatine kinase, and metabolic acidosis, reflecting physiological stress from prolonged seizures. The patient underwent multidisciplinary management, including intravenous anticonvulsants for seizure control, supportive care with fluids and oxygen, broad-spectrum antibiotics, and urgent surgical intervention with resection of the perforated ileal segment and primary anastomosis. Postoperatively, she was monitored in the intensive care unit, with gradual neurological recovery, resolution of abdominal distension, and restoration of bowel function. This case illustrates the rare but serious occurrence of seizure-induced gastrointestinal perforation, emphasizing the importance of vigilant clinical assessment, early imaging, and coordinated multidisciplinary care in adolescents with status epilepticus to identify and manage life-threatening complications promptly.
Epilepsy, Seizure, Status Epilepticus, Perforation, Surgical Abdomen, Air Under Diaphragm, Pneumoperitoneum, Benzodiazepines.
AI-Assisted Literature Mining Framework for miRNA–Target Mapping in Colorectal Cancer
Colorectal cancer is an important cause of mortality worldwide, and the complex molecular mechanisms that govern the development, progression, and spread of the disease are complex. Among these complex mechanisms, the role of miRNAs in the regulation of gene expression at the post-transcriptional level through the modulation of the expression of multiple target genes that are integral parts of oncogenic pathways is crucial. The discovery of the interactions between miRNAs and their targets is therefore essential in the understanding of colorectal cancer and the discovery of potential biomarkers and therapeutic targets. The exponential growth of scientific literature in the field of biomedicine has made the discovery of such interactions from the scientific literature an inefficient and difficult task. To overcome the inefficiencies associated with the traditional approaches for the discovery of miRNA–target interactions, the present study proposes a conceptual framework for AI-assisted literature mining for the discovery of miRNA–target interactions that are crucial in the understanding of colorectal cancer. The framework for the discovery of miRNA–target interactions is structured and aims at synthesizing the scientific evidence that is disseminated in the scientific community. The framework is therefore crucial in the development of the field of bioinformatics.
MicroRNA (miRNA), Colorectal Cancer, AI-assisted Literature Mining, miRNA–Target Interaction, Biomedical Text Mining
Explainable Transformer-Based Skin Lesion Classification from Clinical Images
Early and reliable detection of skin cancer is critical for reducing disease burden and improving patient outcomes, yet large-scale screening remains constrained by limited specialist availability and heterogeneous image acquisition conditions. This paper presents an efficient transformer-based framework for automated multiclass skin lesion classification, centered on an EfficientViT architecture designed to balance representational capacity and computational efficiency. The proposed approach is evaluated against lightweight transformer and CNN baselines, including DeiT-Tiny, Axial Attention Transformer, Swin Transformer-Tiny, and EfficientNetV2-S, using the PAD-UFES-20 dataset comprising 2,298 smartphone-acquired clinical images across six lesion categories. Experimental results show that EfficientViT achieves superior performance, reaching 99.40% accuracy and 99.78% PR-AUC, indicating robust discrimination under real-world acquisition variability. To enhance transparency and support clinical interpretability, Grad-CAM visual explanations are integrated to highlight lesion-relevant regions driving model predictions. Overall, the results demonstrate that EfficientViT provides an accurate and interpretable solution for practical skin lesion screening using consumer-grade images.
Skin cancer detection, skin lesion classification, deep learning, Vision Transformer, Grad-CAM, PR-AUC, Explainable Artificial Intelligences.
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Global–Local Attention Modeling for Reliable Multiclass Kidney Disease Classification from CT Images
Automated analysis of kidney abnormalities from computed tomography (CT) has gained increasing importance as imaging volumes grow and radiological workloads intensify. Despite recent progress, robust multiclass classification remains challenging due to overlapping visual characteristics, acquisition variability, and class imbalance across renal conditions. In this work, we present an attention-driven framework for multiclass kidney disease classification from CT images. The proposed approach is based on a Vision Transformer (ViT-B/16) architecture that explicitly models global anatomical context while preserving discriminative local renal features. A comprehensive evaluation is conducted against established convolutional and modern CNN-based models, including ResNet50, DenseNet121, EfficientNetV2-S, and ConvNeXt-Tiny, using a CT kidney dataset containing 12,446 images spanning normal, cyst, stone, and tumor classes. The proposed model achieves the best overall performance, with 98.90% accuracy and a PR-AUC of 99.23%, demonstrating strong class-wise discrimination under imbalance. To promote transparency, gradient- and attention-based explainability techniques are employed to visualize lesion-relevant regions influencing predictions. The results indicate that transformer-based modeling offers an effective and interpretable solution for reliable CT-based kidney disease screening.
Kidney CT analysis, multiclass classification, vision transformer, explainable artificial intelligence, Grad-CAM, health informatics
Swin Transformer–Driven Cervical Cell Classification with Explainable AI and Web-Based Screening
Accurate interpretation of cervical cytology images is essential for effective cervical cancer screening, yet manual assessment is time-consuming and subject to observer variability. This paper presents a transformer-based deep learning framework for automated cervical cell classification using Pap smear images. We conduct a systematic evaluation of modern attention-driven architectures, including MaxViT, Swin Transformer, EfficientFormer, and HorNet, under a unified preprocessing and training pipeline designed to handle staining variability and class imbalance. To enhance model transparency and clinical trust, explainable AI is integrated via Grad-CAM, enabling visual localization of cytomorphological regions that drive model decisions. Experiments on the Herlev and SIPaKMeD datasets demonstrate that the proposed Swin Transformer achieves superior and consistent performance, reaching 99.27% accuracy on Herlev and 98.82% accuracy on SIPaKMeD, with high MCC and PR-AUC values. In addition, a lightweight web-based application is developed to support dataset selection, real-time inference, confidence reporting, and visual explanation. The results confirm that hierarchical transformer architectures can deliver accurate, interpretable, and deployable solutions for computer-aided cervical cancer screening.
Cervical cancer screening, Pap smear images, vision transformers, deep learning, explainable AI, Grad-CAM, medical image analysis
CatBoost-Stacked Heterogeneous Deep Ensembles for Explainable Multi-Class Brain Tumor MRI Classification
Accurate brain tumor screening from MRI requires reliable four-class recognition (glioma, meningioma, pituitary, and no-tumor) under substantial multi-source variability, class imbalance, and the need for clinically interpretable decisions beyond aggregate accuracy. We propose a stacking ensemble that couples heterogeneous feature extractors (EfficientNetB0, MobileNetV2, GoogLeNet, a multi-level CapsuleNet, and a CNN) with a CatBoost meta-learner trained on concatenated class-probability vectors, enabling non-linear fusion of complementary error patterns across backbones. Experiments are conducted on M2, a merged dataset of 24,618 MR images assembled from four public sources, using an 80/5/15 split and stratified 10-fold cross-validation; to prevent evaluation leakage, imbalance mitigation (Borderline-SMOTE and label-preserving augmentation) is applied exclusively to the training folds. The proposed model attains 98.99±0.41% accuracy, 98.32±0.49% micro-F1, 99.33±0.27% PR-AUC, and 94.14±0.13% MCC on M2, consistently outperforming individual backbones, and Grad-CAM audits typically highlight tumor-relevant regions while residual failures concentrate in morphologically similar subtypes. This work contributes a compact, explainable stacking framework with a deployment-oriented inference workflow and visual auditing, validated at scale on heterogeneous MRI sources to support trustworthy brain tumor decision support in resource-constrained settings and real-world clinical practice.
Brain tumor, MRI, stacking ensemble, CatBoost, class imbalance, Grad-CAM, explainable AI
Unilateral Pleural Effusion as The Initial Manifestation of Occult Lung Carcinoma: A Case Report
Pleural effusion is a common clinical condition with a wide differential diagnosis, ranging from benign inflammatory processes to advanced malignancy. In some patients, malignant pleural effusion may represent the first manifestation of an underlying lung cancer. Early recognition and systematic evaluation are therefore essential for timely diagnosis and management. We report the case of a fifty eight year old Saudi male who presented with progressive shortness of breath and right sided chest discomfort of three weeks duration. The patient had a long history of cigarette smoking but no previous diagnosis of malignancy. Physical examination revealed reduced chest expansion and markedly diminished breath sounds over the right lower lung zone. Initial chest radiography demonstrated a moderate right sided pleural effusion without a clearly visible lung mass. Further diagnostic evaluation included contrast enhanced computed tomography of the chest, which confirmed a large right sided pleural effusion with partial lung collapse, mild pleural thickening, and a small irregular lesion in the right upper lobe. Diagnostic thoracentesis yielded exudative pleural fluid according to Light criteria. Microbiological studies were negative for bacterial infection and tuberculosis. Cytological examination of the pleural fluid revealed atypical epithelial cells suspicious for malignancy, and repeat analysis supported the diagnosis of malignant pleural effusion. Subsequent CT guided biopsy of the right upper lobe lesion demonstrated histopathological features consistent with pulmonary adenocarcinoma. Management initially focused on symptomatic relief through pleural fluid drainage and supportive care. Following confirmation of the diagnosis, the patient was referred for oncologic evaluation and further staging. This case highlights the importance of considering malignancy in patients presenting with unexplained pleural effusion, particularly in individuals with significant smoking history, and emphasizes the role of pleural fluid analysis and tissue biopsy in establishing the diagnosis.
Lung Cancer, Pleural Effusion, Malignant Pleural Effusion, Thoracocentesis, Cytology, Dyspnea, Chest Pain.
Drug Resistance to Anti-Angiogenic Therapy: Mechanisms, Clinical Implications, and Translational Strategies in Hepatocellular and Renal Cell Carcinoma
Angiogenesis remains a fundamental hallmark of solid tumor growth and metastatic progression, and blocking the vascular endothelial growth factor (VEGF) pathway has changed how systemic therapy works for hepatocellular carcinoma (HCC) and renal cell carcinoma (RCC). However, the clinical benefits of anti-angiogenic therapy are frequently transient. Resistance arises from synchronized adaptive mechanisms that include redundant pro-angiogenic signaling, hypoxia-induced transcriptional reprogramming, metabolic reconfiguration, immune microenvironment alteration, and structural vascular evasion. Although the combination of VEGF blockade with immune checkpoint inhibitors (ICIs) has enhanced survival outcomes in pivotal trials such as IMbrave150 and CheckMate 9ER, sustained responses remain infrequent [1,2]. This review combines mechanistic and clinical evidence to create a comprehensive framework for anti-angiogenic resistance. We analyze ligand redundancy (FGF, PDGF, Ang-2), hypoxia-inducible factor (HIF)-mediated metabolic adaptation focused on glycolysis and lactate export, endothelial glycolytic regulation through PFKFB3, myeloid-driven immunosuppression, and structural resistance mechanisms such as vessel co-option and vasculogenic mimicry. We integrate these biological domains with phase III clinical trial data from HCC and RCC and suggest a translational roadmap that prioritizes orthogonal metabolic targeting, microenvironmental reprogramming, structural interception, and biomarker-guided adaptive scheduling at the top of the list during the vascular normalization window. Reconceptualizing resistance as a systems-level adaptive network establishes a basis for more resilient and region-specific therapeutic approaches.
Anti-angiogenic resistance; VEGF blockade; Hepatocellular carcinoma; Renal cell carcinoma; Tumor microenvironment; Hypoxia-inducible factor; Endothelial metabolism; PFKFB3; Vessel co-option; Biomarker stratification
Mental Health Awareness and Comprehension of Substance Use Consequences: An Analysis of students in the Department of Medicine & Surgery at University of Abuja, Nigeria
The mental, neurological, and substance use disorders constitute a significant and increasing global health concern with especially dire consequences in the low- and middle-income countries. Stigma, superstitious beliefs, and religious interpretations are additional sociocultural variables that affect mental health outcomes in numerous African settings and usually lead to late diagnosis and poor access to care (Whiteford et al., 2015; Okpalauwaekwe et al., 2017). Drug abuse is one of the leading social and health problems of the youths, acting as a high-risk group due to its effects such as violence, unsafe sex, school dropouts and psychosis and mental disasters (Odejide, 2006). This paper focuses on understanding and awareness of mental health among medical students in the Department of Medicine and Surgery of the University of Abuja, Nigeria on substance use consequences. Using a structured questionnaire as a need assessment instrument, the research meets the levels of knowledge, perceptions, and socio-cultural limitations that affect help-seeking behaviors. Through the findings, evidence-based mental health education and early intervention strategies and campus-based substance use preventive programs are expected to be informed in Nigerian university settings.
Mental health awareness, substance use disorders, medical students, Youth mental health, Help-seeking behavior, Nigeria, University health
Perceived Stress and Psychological Morbidity Among Healthcare Workers: Post-Pandemic Study from India
Healthcare workers are often under tremendous stress, a situation made even more challenging by the COVID-19 pandemic. This stress puts them at higher risk for mental health issues and sleep problems. While there's a growing body of international research on this topic, there’s still limited data on healthcare workers experiences in tertiary care settings in India. The study aimed to understand how healthcare workers perceive stress, alongside symptoms of depression and anxiety, and to evaluate their sleep quality using well-established psychological assessment tools. The goal was also to explore how perceived stress relates to these mental health outcomes. We conducted a cross-sectional study in a tertiary care hospital in India, between October 2021 and June 2022. We gathered data from 225 healthcare workers, including doctors and nurses, through an anonymous online survey. We have recruited a total of 225 participants for the study including doctors and nurses. We applied various statistical methods, including descriptive statistics and regression analyses, to interpret the data. The participants mean age was approximately 26 years, with a majority (63.6%) being female. Most respondents were postgraduate doctors (54.22%). On average, participants have reported moderate levels of perceived stress, with a mean PSS-4 score of 7.86. More than half of the participants showed symptoms of depression and anxiety according to the DASS-21. Additionally, the mean PSQI score was 6.92, indicating poor sleep quality. We found a significant correlation between perceived stress and the severity of depressive (p < 0.001**) and anxiety symptoms (p = 0.002**). In a specific analysis of individuals with poor sleep (PSQI > 5), the overall model indicated significance (R = 0.348, p = 0.001**), but no individual predictors stood out. Our findings highlight that healthcare workers were experiencing significant levels of stress, mental health issues, and sleep disturbances in the aftermath of the pandemic. The clear link between perceived stress and the severity of depression and anxiety symptoms demonstrates an urgent need for focused mental health support systems for frontline healthcare workers.
Perceived stress, healthcare workers, depression, anxiety, sleep quality, India
Survey On Doctors of Neuroscience: Level of Knowledge, Attitude, Practice About Speech and Language Therapy Service in Dhaka City
Speech and Language Therapy (SLT) is a vital component in the rehabilitation of patients with neurological disorders. However, in Bangladesh, the level of knowledge, attitude, and practice (KAP) among doctors of neuroscience regarding SLT services remains largely unexplored. The study aimed to assess the level of knowledge, attitude, and practice about Speech and Language Therapy services among doctors of neuroscience in Dhaka city. A descriptive cross-sectional survey was conducted among 23 doctors of neuroscience working in selected hospitals of Dhaka city, including the National Institute of Neurosciences & Hospital, Enam Medical College & Hospital, and Anwar Khan Modern Medical College. A structured KAP questionnaire, adapted from previous studies and modified to suit the local context, was used for data collection. Data were collected through face-to-face interviews after obtaining ethical approval and written consent from participants. The inclusion criteria comprised practicing doctors of neuroscience (neurologists, neurosurgeons, pediatric neurologists/neurosurgeons, and neuroradiologists). Non-practicing doctors and other physicians were excluded. Descriptive statistical analysis was performed using SPSS version 20, with results presented in the form of tables, bar graphs, and pie charts. Associations between demographic variables and KAP scores were analyzed using chi-square tests. Among the 23 respondents, 78% were male and 22% female. The majority were neurologists (74%), with 52% having more than 10 years of job experience. While 60.9% had adequate knowledge of SLT services, 87% exhibited positive attitudes, and 87% demonstrated good practice levels. However, most associations between demographic characteristics and KAP levels were statistically non-significant. The findings revealed that although most doctors displayed a positive attitude toward SLT, knowledge and practice levels—especially in terms of referral and collaboration—were insufficient. The study highlights the need for increased awareness and collaboration between Speech and Language Therapists and doctors of neuroscience in Bangladesh. Strengthening interdisciplinary communication and implementing educational initiatives could improve referral practices and enhance patient rehabilitation outcomes.
Neuroscience doctors, Speech and Language Therapy (SLT), Knowledge, Attitude, Practice (KAP), Neurology, Neurosurgery, Neuroradiology, Interprofessional collaboration, Bangladesh.
Advances in Molecular Diagnostics and Biosensor Technologies for Potato Virus Y (PVY): Strategic Integration into U.S. Seed Certification and Precision Agriculture Systems
Potato virus Y (PVY) is a positive-sense single-stranded RNA virus in the Potyvirus genus (family Potyviridae). It is still one of the most important viral factors that limit potato production and is one of the main reasons why seed lots are downgraded or rejected in modern certification systems [1,2,3]. Over the last thirty years, the study of PVY epidemiology has changed from the traditional "ordinary" PVYo populations toward genetically complex recombinant lineages (e.g., PVYN-Wi, PVYNTNa, PVYN:O) that are often linked to tuber necrosis phenotypes and are harder to figure out using only old serological typing [1,2,3,4]. This change is happening because PVY has a strong ability to evolve. This is especially true in vegetatively propagated seed systems, where infection can build up over generations and recombinants can spread quickly once they are established [1,2,4]. Even though ELISA and RT-qPCR are still the most important tests for regulatory purposes, they are becoming harder to use because of the need for (i) higher analytical sensitivity in dormant tuber matrices, (ii) better differentiation of recombinant strain groups, and (iii) decentralized testing to speed up the time it takes to make a decision about seed lot certification. New nucleic acid platforms like RT-LAMP, RT-RPA, CRISPR-Cas assays, digital PCR, and high-throughput sequencing (HTS) are making it possible to achieve higher levels of sensitivity, portability, and sequence resolution [6,7,8,9,10,11,12]. At the same time, biosensor technologies like surface plasmon resonance, quartz crystal microbalance, electrochemical nano/graphene sensors, and aptamer-enabled devices are coming together with molecular recognition chemistry to make "sample-to-answer" formats that can be used in the field [13,14,15,16,17]. Adding these diagnostic outputs to precision agriculture tools like IoT dashboards, remote sensing, vector forecasting, and disease risk modeling can help contain problems sooner and make certification actions more defensible [18,19]. This review brings together information about PVY molecular evolution and recombination biology, focusing on what it means for seed certification in the U.S. It then looks at next-generation diagnostic methods through a translational lens, focusing on analytical performance, deployment feasibility, strain resolution, and standardization requirements. It also suggests an integrated surveillance framework that connects advanced detection to certification policy, agricultural biosecurity, and national food system resilience.
Potato virus Y; recombinant strains; seed potato certification; RT-qPCR; CRISPR-Cas diagnostics; isothermal amplification; biosensor platforms; high-throughput sequencing; precision agriculture.
Copyright (c) 2024 Journal of Medical and Health Studies
AI-Driven Glycemic Instability Risk Modeling for Proactive Intervention and Chronic Disease Management in U.S. Healthcare Systems
Hospitals with diabetic patients experience serious clinical problems because patients with diabetes develop glycemic instability which causes their blood glucose levels to fluctuate unpredictably. The process of identifying high-risk patients should start immediately because it requires accurate detection methods which help to protect patient safety. Healthcare experts face multiple difficulties when attempting to predict glycemic instability because clinical data exhibits extreme class imbalance and laboratory results plus medications and patient characteristics show complex interactions. The UCI 130-hospital diabetes dataset serves as the foundation for our complete machine learning and deep learning system which we developed to predict glycemic instability risk. The combination of Synthetic Minority Over-sampling Technique (SMOTE) with cost-sensitive learning provides us with a solution to tackle the challenges that arise from extreme class imbalance. The seven predictive models which include Logistic Regression, Random Forest, Gradient Boosting, XGBoost, Support Vector Machine, Multi-Layer Perceptron (MLP) and TabNet use a clinically informed decision threshold which helps them to detect medical conditions with high accuracy. The evaluation of model performance examines five metrics which include accuracy, precision, recall, F1-score and ROC-AUC. The experimental results show that machine learning through deep learning and ensemble methods achieves better results for detecting glycemic instability than traditional classifiers. The deep learning models TabNet and MLP exhibit high sensitivity for detecting unstable patients while Gradient Boosting and Random Forest demonstrate superior discriminative ability with ROC-AUC values close to 0.99. The analysis of feature importance shows that HbA1c levels, maximum glucose concentration, number of diagnoses, and insulin usage are the most influential predictors of instability. The research results demonstrate that models which use imbalance-aware machine learning together with explainable models can achieve accurate predictions.
Healthcare System Reform; Health Equity and Disparities; Value-Based Care; Health Information Interoperability; Public Health Infrastructure
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
Resilient Healthcare and Critical Urban Infrastructure Design Using AI-Driven Engineering and Project Management Systems
Ensuring resilient healthcare accessibility under infrastructure disruptions remains a critical challenge for rapidly urbanizing environments characterized by complex and interdependent systems. This study proposes and evaluates an AI-driven engineering and project management framework designed to enhance the resilience of healthcare delivery and critical urban infrastructure under diverse disruption scenarios. The framework integrates machine learning–based infrastructure criticality prediction, network-level resilience analysis, healthcare accessibility modeling, spatial impact assessment, and AI-enabled project management optimization within a unified decision-support system. Results show that ensemble-based machine learning models achieve consistently high predictive performance, with accuracy exceeding 98% and area-under-the-curve values approaching 0.99, enabling reliable identification of infrastructure components whose failure disproportionately affects healthcare accessibility. Network resilience analysis demonstrates that AI-guided strategies significantly outperform random and clustered failure scenarios by preserving connectivity and limiting efficiency loss. Connectivity degradation under clustered failures is four times higher than under AI-guided strategies, while AI-informed prioritization reduces connectivity loss by approximately 75%. Healthcare accessibility outcomes further confirm the effectiveness of the proposed framework. AI-guided strategies consistently limit travel-time increases and preserve population coverage within acceptable emergency thresholds, maintaining coverage above 99.7% even under severe disruptions. In contrast, clustered failures produce abrupt performance collapses and localized isolation. Spatial analyses reveal that accessibility degradation and failure impacts are highly heterogeneous, occurring in localized pockets rather than uniformly across the urban network. Importantly, AI-guided strategies distribute impacts more evenly, preventing cascading spatial failures and mitigating inequitable access outcomes. From a project management perspective, AI-driven risk prediction and reinforcement learning–based optimization significantly reduce schedule delays and cost overruns by aligning execution strategies with infrastructure and service criticality. Overall, the results demonstrate that integrating AI across infrastructure analysis, healthcare accessibility modeling, and project management enables robust, adaptive, and equitable resilience planning for urban healthcare systems.
Urban resilience; Healthcare accessibility; Critical infrastructure; Artificial intelligence; Digital twins; Project management; Smart cities
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
Impact of Early Emergency Department Management of Diabetic Ketoacidosis and Hyperosmolar Hyperglycemic State on ICU Admission and In-Hospital Mortality: A Retrospective Cohort Study
Diabetic ketoacidosis (DKA) and hyperosmolar hyperglycemic state (HHS) are life-threatening endocrine emergencies frequently encountered in emergency departments (EDs). Delayed recognition and treatment are associated with increased intensive care unit (ICU) admission and mortality [1,2]. To evaluate the impact of early recognition and ED-based management of DKA and HHS on ICU admission and in-hospital mortality. A retrospective cohort study was conducted among adult patients presenting to the ED with confirmed DKA or HHS. Data included time to diagnosis, time to initiation of insulin and intravenous fluids, electrolyte abnormalities, ICU admission, and in-hospital mortality. Early management was defined as diagnosis within ≤60 minutes and initiation of insulin and IV fluids within ≤90 minutes of ED arrival. Logistic regression analysis was used to identify independent predictors of ICU admission and mortality. Among 100 patients, early management was associated with significantly lower ICU admission (24.1% vs. 47.6%, p = 0.01) and mortality (5.2% vs. 14.3%, p = 0.04). Delayed management independently predicted ICU admission (OR 2.82, 95% CI 1.22–6.51) and in-hospital mortality (OR 3.12, 95% CI 1.01–9.65). Patients with HHS had higher ICU admission and mortality compared with those with DKA. Early recognition and timely ED management of DKA and HHS significantly improve clinical outcomes. Implementation of standardized ED protocols may reduce avoidable ICU admissions and in-hospital mortality, particularly in resource-limited settings.
Diabetic ketoacidosis; Hyperosmolar hyperglycemic state; Emergency department; ICU admission; Mortality
Early Recognition and Treatment of Sepsis and Its Impact on Mortality and ICU Outcomes: A Retrospective Cohort Study from Gaza, Palestine
Sepsis remains a leading cause of intensive care unit (ICU) admission and in-hospital mortality worldwide, with a disproportionately high burden in low-resource and conflict-affected settings. Early recognition and timely initiation of evidence-based management—particularly antimicrobial therapy and fluid resuscitation—are critical determinants of survival. However, data describing the epidemiology, management delays, and outcomes of sepsis in Gaza are scarce. To determine the prevalence of sepsis among adult internal medicine admissions at a tertiary hospital in Gaza, Palestine, and to examine the association between early recognition, treatment delays, and key clinical outcomes, including mortality, ICU transfer, and length of hospital stay.A retrospective cohort study was conducted including all adult patients admitted to the Internal Medicine Department at tertiary hospital between 3 August 2025 and 31 December 2025. Sepsis was defined as suspected or confirmed infection with evidence of acute organ dysfunction documented in the medical record. Primary outcomes were in-hospital mortality and ICU transfer; length of hospital stay was a secondary outcome. Multivariable logistic and linear regression analyses were performed using SPSS version 27.During the study period, 3,699 adult patients were admitted to the Internal Medicine Department at tertiary hospital of these, 2,145 patients (58.0%) fulfilled the clinical criteria for sepsis based on documented infection and evidence of acute organ dysfunction. Figure 1 illustrates the patient flow and classification according to sepsis status.Septic patients were significantly older than non-septic patients (mean age 57.8 ± 14.2 years vs. 53.5 ± 12.1 years, p < 0.001). A higher proportion of septic patients had two or more chronic comorbidities (60.0% vs. 25.0%, p < 0.001). Sex distribution was comparable between the two groups, with no statistically significant difference observed (Table 1).When stratified by age group, mortality increased progressively with advancing age among septic patients: 21.4% in patients aged 18–39 years, 32.7% in those aged 40–59 years, and 48.9% in patients aged ≥60 years (p < 0.001). The median time from documented sepsis recognition to initiation of antibiotic therapy was 2.6 hours (IQR 1.4–4.8). Septic patients who received antibiotics within 1 hour of recognition had significantly lower in-hospital mortality compared with those treated after more than 3 hours (24.3% vs. 44.8%, p < 0.001). A stepwise increase in mortality was observed with increasing delays in antibiotic administration (Figure 2). Septic patients experienced substantially worse outcomes than non-septic patients. In-hospital mortality among septic patients was 37.5% compared with 15.8% in non-septic patients (p < 0.001). ICU transfer occurred in 50.0% of septic patients versus 14.0% of non-septic patients (p < 0.001). The mean length of hospital stay was significantly longer in the sepsis group (14.8 ± 5.2 days) compared with the non-sepsis group (8.1 ± 4.0 days; p < 0.001) (Table 2). Among septic patients, those requiring ICU transfer had markedly higher mortality than those managed exclusively on the medical ward (49.6% vs. 25.1%, p < 0.001), reflecting more advanced disease severity at presentation or delayed escalation of care. Multivariable logistic regression analysis identified increasing age (OR 1.04 per year, 95% CI 1.02–1.07), presence of ≥2 comorbidities (OR 3.80, 95% CI 2.40–6.10), delayed antibiotic administration (OR 1.30 per hour, 95% CI 1.15–1.48), and ICU transfer (OR 2.90, 95% CI 1.95–4.20) as independent predictors of in-hospital mortality (Table 3). Linear regression analysis demonstrated that ICU transfer (+6.0 days), presence of multiple comorbidities (+2.8 days), and delayed antibiotic initiation (+1.2 days per hour) were independently associated with prolonged hospitalization (Table 4).
Sepsis; early recognition; antibiotic timing; fluid resuscitation; ICU transfer; mortality; length of stay; retrospective cohort.
AI Meta-Analysis of Gene-Expression Signatures That Predict Treatment Response
Predicting which patients will benefit from a therapy is one of the most practical promises of transcriptomics, but the literature is crowded with gene expression response signatures that do not generalize. A main reason is methodological: many signatures are trained on one cohort, one platform, and one response definition, so the reported genes partly reflect study specific noise, batch effects, and hidden confounding. This manuscript presents an AI assisted meta analysis workflow that aggregates evidence across independent cohorts to derive a consensus gene expression signature that is both biologically interpretable and prediction oriented. The approach combines standardized per gene effect sizes (Hedges g), random effects meta analysis, heterogeneity screening, and nested, cohort aware machine learning. We outline data acquisition from open repositories, harmonization, batch adjustment, and label mapping, then build a meta signature and train parsimonious classifiers using elastic net and gradient boosting. Model evaluation uses leave one study out validation to approximate real world deployment, with calibration and decision curve analysis to connect performance to clinical utility. The workflow is designed to be auditable: every inclusion decision is recorded, and all transformations are reversible and reproducible. By treating signatures as meta analytic objects rather than single study artifacts, the framework reduces overfitting, exposes context specific failure modes, and yields gene sets that map to known treatment response biology. We close with reporting guidance aligned to PRISMA, TRIPOD, and current best practice for transcriptomic predictors. A melanoma anti PD 1 case study is used to illustrate implementation and expected outputs.
AI; meta-analysis; gene expression; transcriptomics; treatment response; predictive biomarkers; multi-cohort integration; random-effects model; Hedges g; batch effect correction; cross-study validation; immunotherapy; anti–PD-1; melanoma; GEO; feature selection; elastic net; gradient boosting; external validation; calibration; decision curve analysis; reproducible research
A Transfer Learning–Based Deep Convolutional Neural Network Framework for Automated Multi-Class Eye Disease Classification in the USA Using Retinal Fundus Image
Eye diseases are among the leading causes of visual impairment and blindness worldwide, and delayed diagnosis frequently results in irreversible vision loss. Early and accurate detection remains challenging because many ocular conditions exhibit subtle visual features and current diagnosis relies heavily on manual clinical assessment, which is time-consuming and subject to inter-observer variability. This study proposes an automated deep learning–based framework for multi-class eye disease classification using retinal fundus images. A transfer learning strategy is employed by fine-tuning multiple pre-trained convolutional neural network architectures, including VGG-16, VGG-19, ResNet-50, ResNet-152, and DenseNet-121. The proposed system is evaluated on a publicly available benchmark dataset comprising eight ocular disease categories. Image preprocessing and model optimization techniques are applied to enhance classification performance. Experimental results show that the fine-tuned VGG-19 model achieves the best performance, reaching an overall accuracy of 95% with balanced precision, recall, and F1-score. These results demonstrate that transfer learning significantly improves diagnostic accuracy while reducing computational complexity. The proposed framework provides a reliable, scalable solution for automated eye disease diagnosis and has strong potential to support clinical decision-making in ophthalmic screening and telemedicine systems.
Eye Disease Classification; Deep Learning; Transfer Learning; Convolutional Neural Networks; Fundus Image Analysis; Multi-Class Classification; Medical Image Processing; Automated Diagnosis
When a Simple Rash Unmasks a Systemic Crisis: Persistent Inflammatory Skin Lesions Leading to the Diagnosis of Infantile Acute Leukemia
Skin manifestations can be an early but often overlooked sign of systemic malignancy in infancy. We report the case of an eight month old male infant who presented with persistent napkin dermatitis and progressive nodular skin lesions, ultimately leading to the diagnosis of acute leukemia with cutaneous involvement. The child initially developed a diaper area rash that was treated as napkin dermatitis with standard measures but failed to improve and progressively worsened. Weeks later, firm nodular lesions appeared over the hands and forearms, accompanied by pallor, low grade fever, reduced feeding, and increased sleepiness. Physical examination revealed severe diaper dermatitis, multiple non tender nodular skin lesions, pallor, hepatosplenomegaly, and faint bruising. Initial laboratory evaluation showed marked anemia, thrombocytopenia, and leukocytosis with circulating immature cells. Peripheral blood smear raised concern for acute leukemia. Bone marrow aspiration and biopsy confirmed acute leukemia with extensive blast infiltration. Skin biopsy from a nodular lesion demonstrated dermal infiltration by malignant cells, confirming leukemia cutis. Imaging revealed hepatosplenomegaly without focal lesions or evidence of infection. Infectious causes of the skin findings were excluded. Management focused on stabilization, supportive care, and early coordination with pediatric hematology. The infant received blood product support and empiric antibiotics while diagnostic evaluation was completed. Given the confirmed diagnosis and the need for specialized therapy, the patient was transferred to a tertiary pediatric oncology center for definitive management. This case highlights the importance of recognizing persistent or atypical skin findings in infants as potential indicators of serious systemic disease. Failure of common conditions such as diaper dermatitis to respond to standard therapy, especially when accompanied by systemic signs, should prompt thorough evaluation. Early recognition of leukemia cutis can shorten diagnostic delay and improve clinical outcomes in infantile acute leukemia.
Infantile Leukemia, Skin Rash, Dermatitis, Inflammatory lesions, Nodules, Papules, Leukocytosis.
Flash Pulmonary Edema After Unnecessary Normal Saline Infusion Unmasking Sickle Nephropathy: Highlighting the Controversy of Routine Fluid Use in Vaso-Occlusive Crisis.
Acute pulmonary edema following routine fluid administration is an uncommon but potentially life-threatening complication in adults with sickle cell disease (SCD). We report the case of a 42-year-old male with known SCD who presented with severe generalized body pain consistent with vaso-occlusive crisis (VOC). Initial management included intravenous normal saline for mild dehydration. Shortly after receiving approximately 500 mL, he developed acute shortness of breath, hypoxia, and fine basal crackles. Chest imaging demonstrated bilateral alveolar infiltrates consistent with pulmonary edema. Laboratory evaluation revealed baseline anemia, mild leukocytosis, elevated creatinine, and trace proteinuria, suggestive of subclinical sickle nephropathy. Echocardiography showed normal cardiac function, excluding primary cardiogenic causes. The patient received supplemental oxygen, cautious diuretics, and pain control while fluid administration was restricted. Transfusion support was provided to improve oxygen-carrying capacity and reduce sickling risk. Multidisciplinary management included hematology, nephrology, and respiratory care teams. The patient’s respiratory status gradually improved, and pulmonary edema resolved without further complications. This case highlights the risk of flash pulmonary edema triggered by standard intravenous fluids in patients with SCD and previously undiagnosed renal impairment. It underscores the importance of individualized fluid management, careful monitoring, and early recognition of subclinical sickle nephropathy. Clinicians should exercise caution with routine fluid boluses in VOC, even in patients without overt kidney disease, as modest fluid volumes may precipitate life-threatening pulmonary complications. Prompt identification and tailored therapy can prevent morbidity and guide safe management of VOC in adults with SCD.
Sickle Cell Disease, Normal Saline, Fluids, Vaso-occlusive Crisis, Sickle Nephropathy, Fluid Overload, Flash Pulmonary Edema, Mortality.