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
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.
Copyright (c) 2026 Journal of Medical and Health Studies
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.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
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
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
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.
A Bleeding Conundrum: Intraventricular Hemorrhage and Gross Hematuria Secondary to Pneumosepsis
We report the case of a 67-year-old Saudi man with multiple comorbidities, including type 2 diabetes mellitus, hypertension, ischemic heart disease, and chronic kidney disease, who presented with progressive shortness of breath, altered mental status, and fever. His symptoms began five days prior with productive cough, pleuritic chest discomfort, and generalized body aches, initially managed at home. On presentation, he was hypotensive, tachycardic, hypoxemic, and disoriented, with signs of respiratory distress and new ecchymoses. Laboratory evaluation revealed leukocytosis, elevated inflammatory markers, acute kidney injury, and coagulopathy. Chest radiography demonstrated new bilateral multifocal pneumonic patches compared with prior baseline imaging. Blood cultures grew Streptococcus pneumoniae, confirming pneumococcal sepsis. During intensive care unit admission, he developed gross hematuria, progressive thrombocytopenia, prolonged coagulation times, and evidence of disseminated intravascular coagulation. Acute neurological deterioration prompted urgent CT imaging, which revealed extensive intraventricular hemorrhage with early hydrocephalus. Despite aggressive supportive care, including hemodynamic stabilization, transfusions, renal replacement therapy, and tailored antibiotics, the patient developed refractory shock and multiorgan failure. After multidisciplinary discussion and family consultation, care was transitioned to comfort measures, and he subsequently died. This case illustrates the fulminant and often fatal nature of pneumococcal sepsis complicated by disseminated intravascular coagulation and intracranial hemorrhage, emphasizing the need for early recognition, close monitoring, and multidisciplinary management in high-risk patients.
Disseminated Intravascular Coagulation, Pneumonia, Sepsis, Pneumosepsis, Bleeding, Brain Hemorrhage, Intraventricular Hemorrhage, Gross Hematuria, Platelets, Fibrinogen, PT, PTT, Coagulation profile.
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.
Deep Learning–Based Skin Cancer Diagnosis in the United States: Advances, Challenges, and Clinical Translation
Skin cancer represents one of the most common and potentially fatal malignancies in the United States. Where timely and accurate diagnosis is critical for reducing mortality and healthcare burden. Current diagnostic practices, including visual examination and dermoscopy. This rely heavily on clinician expertise and are subject to inter-observer variability, particularly in early-stage disease. In recent years, deep learning has emerged as a promising tool for automating skin cancer detection from dermoscopic images. With offering improved diagnostic accuracy and scalability. This article provides a comprehensive analysis of deep learning-based approaches for skin cancer classification, with a focus on convolutional neural network architectures, commonly used datasets, and performance evaluation metrics relevant to U.S. clinical settings. Key challenges, including dataset imbalance, limited representation of diverse skin tones, overfitting on small cohorts, and high computational demands, are critically examined. Additionally, the study discusses emerging trends toward lightweight and deployable models suited for real-time clinical workflows and mobile health applications within the United States healthcare system. The findings aim to support the development of robust, generalizable, and clinically translatable deep learning solutions for skin cancer diagnosis.
Skin cancer detection, Deep learning, Convolutional neural networks, Dermoscopic images, Medical image classification, Dataset bias, Lightweight models, Computer-aided diagnosis
Effectiveness of Hepatitis C Virus Screening Programs in Identifying New Cases and Preventing Further Transmission: A Health System Evaluation Study in Arar City, Northern Saudi Arabia
The Hepatitis C Virus (HCV) remains a significant public health concern, requiring screening initiatives for early detection and prevention of further transmission, despite the availability of effective antiviral treatments. This study assessed the effectiveness and impact of current HCV screening programs in identifying new infections in Arar City, Northern Saudi Arabia. A descriptive cross-sectional design was employed, utilizing routine HCV screening data obtained from Arar Hospital and the Central Blood Bank. The dataset included serological screening results and demographic characteristics of the individuals screened, analyzed using descriptive and inferential statistics. The findings indicate that hospital-based screening methods successfully detected HCV cases, particularly among individuals who engage with routine healthcare services. Screening activities were primarily limited to healthcare facilities, marked by insufficient community outreach, ineffective targeting of high-risk populations, and shortcomings in the continuity of treatment following positive screening results. These limitations may reduce the overall effectiveness of screening programs. In conclusion, while current HCV screening initiatives in Arar City promote early case detection, it is essential to expand community-based screening, implement systematic risk-based strategies, and reinforce linkage-to-care pathways to improve program effectiveness and further national and global HCV eradication goals.
Streamlining Clinical Endorsement: Electronic Sbar-Integrated Clinical Handover Management Framework
This study aimed to develop an electronic Situation–Background–Assessment– Recommendation (e-SBAR) system framework for nurses at South General Hospital, City of Naga. It employed a descriptive–comparative research design to assess nurses’ perceptions and challenges in implementing the manual SBAR tool and to identify key system features necessary for digital transition. The research was conducted at South General Hospital, a tertiary Level II facility under the CebuDoc Group. Using total sampling, the study involved fifty-five (55) nurse respondents across departments, namely Outpatient, Pediatrics, Emergency Room, Operating Room, Medical–Surgical Ward, Intensive Care Unit, and OB-Gyne Ward, along with selected information technology experts. Data were gathered through two researcher-developed instruments the SBAR Perceptions and Challenges Questionnaire and the SBAR System Features Checklist. Findings revealed that while SBAR was moderately practiced across departments, several operational challenges persisted, particularly time pressure, workload, and contextual limitations. The study concluded that digital standardization through an e-SBAR system could improve communication accuracy, patient safety, and workflow efficiency. It was strongly recommended that South General Hospital adopt and implement the proposed electronic SBAR system framework as a hospital-wide tool for clinical handover.
SBAR Communication Tool, Nurse Handover, Patient Safety, Communication Barriers
Clinicopathological Characteristics, Diagnosis, and Management of Ovarian Cancer: A Systematic Review
Ovarian cancer remains one of the leading causes of gynecological cancer-related mortality worldwide due to its insidious onset, late-stage diagnosis, and complex biological behavior. Despite advances in imaging, molecular diagnostics, and therapeutic strategies, overall survival rates remain suboptimal. This systematic review aims to synthesize current evidence on the clinicopathological characteristics, diagnostic approaches, and management strategies of ovarian cancer. A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, and Google Scholar for studies published between 2015 and 2024. A total of 20 sources were included, consisting of 17 peer-reviewed journal articles and 3 authoritative textbooks. Data were extracted on epidemiology, histopathological subtypes, molecular features, clinical presentation, diagnostic modalities, and treatment outcomes. The findings highlight that epithelial ovarian cancer, particularly high-grade serous carcinoma, represents the most prevalent and aggressive subtype. Late-stage presentation (FIGO stage III–IV) remains common, contributing significantly to poor prognosis. Multimodal management involving cytoreductive surgery and platinum-based chemotherapy continues to be the cornerstone of treatment, while targeted therapies and immunotherapy are emerging as promising adjuncts. This review underscores the importance of early detection strategies, standardized pathological classification, and personalized treatment approaches to improve survival outcomes in ovarian cancer patients.
Physiological and Molecular Characterization of “Hormonal Receptors and “HER-2/Neu” in Breast Cancer
The hormonal, reproductive and demographic factors all affect the clinical course of breast cancer, which is an important health problem that remains unresolved. CAans and MVG: / Comparison of primary versus metastatic breast cancer characteristics This study aimed to evaluate the characteristics of PBC + MBC. A descriptive analytical study was performed on the patients with primary and metastatic breast cancer to identify demographic, reproductive, clinical, histopathological, and hormonal factors. The average age of the patients was 42 years. The incidence of primary breast cancer occurred at 51–55 years, whereas for metastatic disease, it was at 36–40 years. In 22.5% of primary patients, a history of AML was present and 35% had metastatic disease. Precocious menarche at 12–14 years has been described in 57% patients. The majority of patients were multiparous (75%) and 72% experienced their primiparity at or ≤25 years. Breast feeding accounted for 55.2% of primary and 47.1% of metastatic patients. The majority of patients were post-menopausal women. Incidence of axillary lymph nodes was 85% in primary and 80% in mets. Stage II was the most common (60%) followed by stage IV (26%). IDC accounted for the most common type of tumor (81%). Primary–metastatic sites, hormone receptors ER 67.8% and 54.66%, PR 66.6% and 63%; HER2Neu was significantly more positive in metastasis cases (62.7%) than in primaries (53.5%). Breast cancer in the research population is early onset, and a high proportion of the cohort developed advanced disease. Reproductive factors and hormones, molecular tumor features have the tendency to shape the course of disease, emphasizing early diagnosis and personalized screening programs.
Breast cancer; Reproductive factors; Estrogen receptor (ER)”; Progesterone receptor (PR)”, “Immunohistochemistry”
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
The Impact of Socioeconomic Status as Determinants of Adherence to Chronic Treatments at Thi-Qar Governorate
Poor adherence to medication regimens highly linked to socioeconomic status. Non-adherence adversely impacts the efficacy, safety, and costs of treatments. Controlling socio-economics factors may improve patient’s adherence at all. This study conducted to understand the influence of socioeconomics factor as the main determinates of patient adherence to treatment. The participants were recruited from private pharmacies in Thi-Qar governorate. The survey used for data gathering included their sociodemographic characteristics, patient’s diseases and medications and questions about the distribution of patients according to their attitude toward health services and medications availability. 356 adult individuals (49.7% male and 50.3% female) were recruited to include in study. The majority of participants were stable in their economic situation in general (65%). Multivariable analysis showed that the monthly income and difficult paying for your medications due to insufficient monthly income? are highly linked or effect on the current economic situation of participants (p = 0.000), while age, gender, and occupation were not. Economic status strongly affects medication adherence. Despite prescription refill and adherence issues, most individuals are stable economically. Current research shows that many people lack health insurance, which affects medication adherence. To promote adherence, consider socioeconomic factors. Education and follow-up should be better tailored to low-income people.
Socioeconomic status, Adherence, Chronic treatments, Thi-Qar
Endometriosis and the Silent Heart Risk: A Systematic Review of Cardiovascular Disease in Affected Women
Endometriosis affects approximately 10% of reproductive-aged women worldwide and is increasingly recognized as a chronic systemic inflammatory condition that may contribute to cardiovascular disease (CVD). We conducted a PRISMA 2020–guided systematic review to evaluate the association between endometriosis and cardiovascular outcomes. We searched PubMed (Medline), Embase, Scopus, and Web of Science for observational studies and meta-analyses published between January 2000 and May 2025, with emphasis on evidence from 2019–2025. Eligible studies compared cardiovascular outcomes in women with clinically or surgically diagnosed endometriosis versus women without endometriosis. Outcomes included myocardial infarction, ischemic heart disease, stroke, hypertension, arrhythmias, heart failure, atherosclerotic markers, and venous thromboembolism. Two reviewers independently screened records, extracted data, and assessed study quality following PRISMA guidance. Large cohort studies and recent meta-analyses consistently demonstrate higher cardiovascular risk among women with endometriosis. Adjusted hazard ratios reported in primary studies ranged from 1.12 for new-onset hypertension to 1.52 for coronary artery disease. Meta-analyses including over 1.4 million women reported pooled hazard ratios of ~1.35 for ischemic heart disease and ~1.19 for cerebrovascular disease. Subclinical studies show impaired endothelial function and increased markers of early atherosclerosis in women with endometriosis. Endometriosis is associated with a modest but consistent increase in risk of several cardiovascular outcomes across diverse populations and study designs. These findings support systematic cardiovascular risk assessment and appropriate preventive strategies in women with endometriosis.
Impact of an Educational Intervention on Laboratory Technicians’ Knowledge of HIV Screening in the Northern Borders Region of Saudi Arabia
Ensuring the accuracy of HIV screening in blood banks is essential for maintaining transfusion safety and preventing transfusion-transmitted infections. This study evaluated the effectiveness of an educational intervention designed to improve HIV-related knowledge among laboratory technicians in the Central Blood Bank of the Northern Borders Region of Saudi Arabia. A quantitative descriptive design was employed, using a structured questionnaire administered before and after a targeted training program. Thirty laboratory technicians participated, completing assessments covering HIV transmission, diagnostic methods, quality assurance procedures, and biosafety practices. Paired t-test analysis demonstrated a statistically significant improvement in knowledge following the intervention, with mean scores increasing from 11.07 to 14.00 (p < 0.001). Effect size calculations indicated a large practical impact (Cohen’s d = 0.842), and internal consistency of the instrument improved from α = 0.762 pre-intervention to α = 0.794 post-intervention. Descriptive and frequency analyses revealed notable reductions in misconceptions related to the diagnostic window period, rapid test identification, test sensitivity, and WHO/UNAIDS testing strategies. The findings underscore the critical role of structured, continuous training in enhancing laboratory competency, standardizing testing practices, and reinforcing quality assurance within HIV screening services. Implementing routine, evidence-based educational programs can strengthen national blood safety systems and support alignment with international standards for HIV testing and transfusion safety.
Copyright (c) 2025 Journal of Medical and Health Studies
The Role of Parent–Child Attachment and Sleep Hygiene in the Development of ADHD in Children
Attention-Deficit/Hyperactivity Disorder (ADHD) is a multifactorial neurodevelopmental condition shaped by biological, environmental, and psychosocial influences. Beyond genetic vulnerability, emerging evidence highlights the developmental significance of contextual factors such as parent–child attachment, sleep hygiene, and emotional competence. This literature review synthesizes empirical findings examining how these three domains contribute to the development of ADHD in children. Understanding these mechanisms may help inform more comprehensive, family-centered approaches to ADHD prevention and management. Secure parent–child attachment was consistently associated with better emotional regulation, internalization of behavioral control, and reduced ADHD symptomatology, whereas maternal anxiety and low emotional availability were linked to greater symptom severity. Sleep hygiene also demonstrated a strong relationship with ADHD manifestations, with poor sleep routines, sleep disturbances, and inadequate sleep quality correlating with higher levels of inattention, hyperactivity, and emotional dysregulation. Interventions targeting sleep hygiene have been shown to improve both sleep and behavioral outcomes in ADHD populations. Furthermore, studies examining attachment and sleep jointly show that secure attachment promotes healthier sleep quality, and sleep quality partially mediates the relationship between attachment and well-being. In conclusion, the literature suggests that parent–child relational processes and sleep-related behaviors interact to shape self-regulatory development, which may influence ADHD symptoms.