Journal of Medical and Health Studies
type: channel
description: 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.
publisher: Al-Kindi Center for Research and Development
language: en
publication name: Journal of Medical and Health Studies
issn: 2710-1452
items: Assessing the Nexus between Digital Maturity and Institutional Accountability in Bangladesh’s Public Health System: A 2022 Cross-Sectional Analysis
items: Comparative analysis of explainable machine learning models for cancer classification using cytological features
items: Diagnostic Value of Ultrasound-Guided Core Needle Biopsy in the Diagnosis of Pulmonary Masses at Ho Chi Minh City Oncology Hospital
items: Therapeutic Effects of “Phu Thao Khang – PTP” Solution in the Treatment of Vaginitis and Cervicitis Caused by Mixed Bacterial Infection
items: Ultrasound and Mammographic Features of Ductal Carcinoma in Situ: A Retrospective Review of 70 Ultrasound-Guided Core Needle Biopsy Cases at Ho Chi Minh City Oncology Hospital
items: Admission Blood Glucose as a Predictor of Mortality and Severe Outcomes in Acute Organophosphorus Poisoning: A Systematic Review and Meta-Analysis
items: The Impact of Transformational Leadership on Nurse Burnout: A Systematic Literature Review
items: The Hidden Cost of Manual Equipment Management in Ambulatory Surgery Centers: A Quantitative Analysis of Financial Leakage and Operational Inefficiency
items: A Structured Protocol for Sustainable Goal Achievement: Development and Application of the Open G.A.T.E.S Model
items: Explainable and Trustworthy Deep Learning for MRI-Based Auxiliary Diagnosis of Alzheimer’s Disease
items: Incidence, Risk Factors, and Outcomes of VTE in Surgical and Obstetric Patients: A Systematic Review
items: The Bloody Pericardium: Cardiac Tamponade Unmasking an Undiagnosed Malignancy: A Case Report
items: Vitamin D Status in North Batinah, Oman: Prevalence and Temporal Trends (2018-2022)
items: AI-Enabled Machine Learning Framework for Depression Risk Prediction and Mental Health Trend Analysis in the United States
items: Concentration-Dependent Antimicrobial Activity of Neem and Bitter Gourd Extracts: A Sustainable Approach to Mitigate Pollution in Dhaka
items: Convulsions and Catastrophe: Status Epilepticus Complicated by Massive Pneumoperitoneum and Bowel Perforation: A Case Report
items: AI-Assisted Literature Mining Framework for miRNA–Target Mapping in Colorectal Cancer
items: Explainable Transformer-Based Skin Lesion Classification from Clinical Images
items: Global–Local Attention Modeling for Reliable Multiclass Kidney Disease Classification from CT Images
items: Swin Transformer–Driven Cervical Cell Classification with Explainable AI and Web-Based Screening
items: CatBoost-Stacked Heterogeneous Deep Ensembles for Explainable Multi-Class Brain Tumor MRI Classification
items: Unilateral Pleural Effusion as The Initial Manifestation of Occult Lung Carcinoma: A Case Report
items: Drug Resistance to Anti-Angiogenic Therapy: Mechanisms, Clinical Implications, and Translational Strategies in Hepatocellular and Renal Cell Carcinoma
items: Mental Health Awareness and Comprehension of Substance Use Consequences: An Analysis of students in the Department of Medicine & Surgery at University of Abuja, Nigeria
items: Perceived Stress and Psychological Morbidity Among Healthcare Workers: Post-Pandemic Study from India
items: Survey On Doctors of Neuroscience: Level of Knowledge, Attitude, Practice About Speech and Language Therapy Service in Dhaka City
items: Advances in Molecular Diagnostics and Biosensor Technologies for Potato Virus Y (PVY): Strategic Integration into U.S. Seed Certification and Precision Agriculture Systems
items: AI-Driven Glycemic Instability Risk Modeling for Proactive Intervention and Chronic Disease Management in U.S. Healthcare Systems
items: Resilient Healthcare and Critical Urban Infrastructure Design Using AI-Driven Engineering and Project Management Systems
items: Impact of Early Emergency Department Management of Diabetic Ketoacidosis and Hyperosmolar Hyperglycemic State on ICU Admission and In-Hospital Mortality: A Retrospective Cohort Study

Assessing the Nexus between Digital Maturity and Institutional Accountability in Bangladesh’s Public Health System: A 2022 Cross-Sectional Analysis
type: item
description: In the 2022 post-pandemic landscape, the digitalization of health services in the Global South has transitioned toward increased institutionalization. This research investigates the statistical association between perceived facility-level digital governance maturity and institutional accountability across a stratified sample of 450 public healthcare facilities in Bangladesh. Employing a cross-sectional quantitative design, the study sampled health administrators across eight divisions to evaluate digital literacy, infrastructure maturity, and policy knowledge as observed correlates of accountability performance. Data were analyzed using Ordinal Logistic Regression (OLR). Results indicate that higher levels of digital literacy (OR=3.64, 95% CI [2.89, 4.60]) and infrastructure maturity (OR=2.81, 95% CI [2.21, 3.58]) are positively associated with achievement of higher accountability tiers. These association patterns suggest a "policy dissemination gap," in which technical adoption has outpaced administrative understanding of data governance standards. While these findings suggest that digital maturity correlates with transparency, the study is limited by its cross-sectional design, reliance on self-reported administrative perceptions, and lack of external validation against objective system audit logs. The associations provide an empirical basis for refining the 2023-2027 Digital Health Strategy by prioritizing human capital and policy clarity alongside hardware expansion.
subject: Research Article
  topic: section
subject: Artificial Intelligence, Big Data Analytics, Wearable Health, Multi-Omics, Precision Medicine, Predictive Modeling, Biomedical Innovation, Sustainability
  topic: keywords
creator: Md Ikram Hossain Bhuiyan
creator: Tahmina Bhuiyan Mumu
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/
date: 2022-10-30
publication date: 2022-10-30
starting page: 192
ending page: 200
doi: 10.32996/jmhs.2022.3.4.28

Comparative analysis of explainable machine learning models for cancer classification using cytological features
type: item
description: Breast cancer is among the causes of cancer related deaths globally with the greatest impact being in the low resource and high volume health care facilities where timely and accurate screening is paramount. This research report is a explainable machine learning model of breast cancer diagnosis based on quantitative features of fine needle aspirate images of breast masses. The data set contains 569 samples and 30 real-valued predictors of cell nuclei morphology, and it does not contain missing values, and the class distribution is moderate. It uses a structured preprocessing pipeline, such as the division of data into training and held-out test sets, feature normalization, and the careful management of class imbalance.Several classification models are compared, among them, Random Forest and Gaussian Naive Bayes, to compare the predictive accuracy and reliability of the model. Experimental outcomes have shown that the Random Forest model obtains the best performance with an accuracy of 0.96 on the held out test set, and balanced precision and recall on the benign and malignant classes. The confusion matrix shows that there is low misclassification rate and only false positive and false negative are three and three respectively. Contrastingly, Gaussian Naive Bayes has a higher accuracy of 0.93, and is less sensitive to malignant cases because of its independence assumptions which are not completely met in the dataset as verified by correlation analysis. These results are also supported by receiver operating characteristic analysis, whose area under the curve value is 1.00 in random forest and 0.99 in Gaussian naive bayes.The findings emphasize the role of model selection in clinical decision support systems, especially in cases where false negatives have to be minimized. The suggested structure focuses on interpretability and high practical use thus appropriate in deployment-oriented screening processes in resource-constrained settings. This paper shows that explainab…
subject: Research Article
  topic: section
subject: Breast cancer classification, explainable machine learning, fine needle aspirate cytology, cytological feature analysis, Random Forest, Gaussian Naive Bayes
  topic: keywords
creator: Md Ismail Hossain Siddiqui
creator: Md. Soebur Rahman
creator: Abdul Aziz Kabir
creator: Farhad Uddin Mahmud
creator: Saeed Ur Rashid
creator: Ramisa Samin Shammah
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2023-10-29
publication date: 2023-10-29
starting page: 110
ending page: 150
doi: 10.32996/jmhs.2023.4.5.14

Diagnostic Value of Ultrasound-Guided Core Needle Biopsy in the Diagnosis of Pulmonary Masses at Ho Chi Minh City Oncology Hospital
type: item
description: Histopathological confirmation is essential for the diagnosis of pulmonary masses. Although computed tomography (CT)-guided transthoracic biopsy is widely used, it remains associated with a relatively high complication rate, especially pneumothorax. Ultrasound-guided core needle biopsy is a practical alternative for peripheral lung lesions abutting the pleura. This study aims to evaluate the diagnostic performance and safety of ultrasound-guided core needle biopsy for pulmonary masses at Ho Chi Minh City Oncology Hospital. A cross-sectional descriptive study was conducted in 136 consecutive patients with peripheral pulmonary masses visible on ultrasound who underwent ultrasound-guided core needle biopsy from July 2024 to February 2025. Demographic characteristics, lesion size, depth, pleural contact length (PCL), procedure time, number of tissue cores, pathological results, and complications were analyzed. The mean age was 62.3 ± 11.1 years, and 72.8% of patients were male. Definitive histopathological diagnosis was achieved in 128/136 cases (94.1%). Malignancy was identified in 107 cases (78.7%), including 90 primary lung malignancies and 17 metastatic lesions. Mean procedure time was 15.6 minutes, and a mean of 4.9 ± 1.17 cores was obtained per patient. Pneumothorax occurred in 10 patients (7.4%), all mild and self-limited, and mild hemoptysis occurred in 2 patients (1.5%). No severe complications or procedure-related deaths were observed. Ultrasound-guided core needle biopsy is a safe, efficient, and accurate technique for diagnosing peripheral pulmonary masses. It provides a high diagnostic yield with a low complication rate and is a valuable first-line biopsy approach for appropriately selected pleural-based lung lesions.
subject: Research Article
  topic: section
subject: ultrasound-guided biopsy; core needle biopsy; lung mass; peripheral pulmonary lesion; diagnostic yield; pneumothorax
  topic: keywords
creator: Huynh Thi Do Quyen
creator: Pham The Hung
creator: Nguyen Vinh Thinh
creator: Huynh Khanh Phu
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-04-22
publication date: 2026-04-22
starting page: 74
ending page: 80
doi: 10.32996/jmhs.2026.7.6.8

Therapeutic Effects of “Phu Thao Khang – PTP” Solution in the Treatment of Vaginitis and Cervicitis Caused by Mixed Bacterial Infection
type: item
description: This study evaluated the adjunctive effect of Phu Thao Khang-PTP solution on selected paraclinical outcomes in women with vaginitis and cervicitis caused by mixed bacterial infection. An open-label controlled clinical intervention was conducted in 60 sexually active women aged >18 years who were diagnosed with vaginitis and/or cervicitis at Tue Tinh Hospital from April 2025 to March 2026. Participants were allocated into a study group receiving daily genital cleansing with Phu Thao Khang-PTP plus one Polygynax vaginal capsule daily for 10 days, or a control group receiving Polygynax alone for 10 days. Outcomes were assessed at baseline (D0) and day 10 (D10) using cervical examination, vaginal discharge characteristics, Candida testing, and Gram staining. After treatment, the study group achieved higher rates of normalization of cervical findings (86.7% vs. 56.7%), Candida clearance (100% vs. 86.7%), Gram-positive bacterial clearance (96.7% vs. 83.3%), Gram-negative bacterial clearance (93.3% vs. 76.7%), and overall grade A response (83.3% vs. 56.7%). No adverse effects were observed. Phu Thao Khang-PTP combined with Polygynax may be an effective and safe adjunctive treatment for mixed vaginitis and cervicitis.
subject: Research Article
  topic: section
subject: Vaginitis; Cervicitis; Mixed bacterial infection; Phu Thao Khang-PTP; Polygynax
  topic: keywords
creator: Thuy Phuong Pham
creator: Hoang Anh Bui
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-04-22
publication date: 2026-04-22
starting page: 66
ending page: 73
doi: 10.32996/jmhs.2026.7.6.7

Ultrasound and Mammographic Features of Ductal Carcinoma in Situ: A Retrospective Review of 70 Ultrasound-Guided Core Needle Biopsy Cases at Ho Chi Minh City Oncology Hospital
type: item
description: Background: Ductal carcinoma in situ (DCIS) is a heterogeneous breast malignancy with variable mammographic and sonographic appearances. Although mammography remains the primary modality for detecting DCIS, especially calcified lesions, ultrasound is particularly useful in dense breasts, non-calcified lesions, and biopsy guidance. Upgrade from DCIS diagnosed on core needle biopsy (CNB) to invasive carcinoma on surgical pathology remains clinically important because it may alter surgical planning. Methods: We conducted a retrospective review of ultrasound-guided 14-gauge CNB cases diagnosed as DCIS at the Department of Endoscopy and Ultrasound, Ho Chi Minh City Oncology Hospital, from June 2024 to December 2024. Among 153 CNB-proven DCIS cases, 70 had complete ultrasound, mammography, CNB pathology, and surgical pathology data and were included in the final analysis. Results: Of the 70 included cases, 24 were upgraded to invasive carcinoma on surgical pathology, yielding an upgrade rate of 34.0%. On mammography, the most common presentation was a mass with calcifications (50.0%), followed by non-calcified mass lesions (21.4%) and calcifications alone (14.3%). On ultrasound, 55 lesions (78.6%) appeared as masses and 15 (21.4%) as non-mass lesions. Among upgraded cases, 21 of 24 (87.5%) were mass-forming lesions. Large lesion size (≥20 mm) and multifocality were the most notable features associated with upgrade. Conclusion: DCIS shows diverse mammographic and ultrasound appearances in ultrasound-guided biopsy practice, with a predominance of mass-forming lesions. Lesion size of at least 20 mm and multifocality appear to be useful clues for predicting upgrade from DCIS to invasive carcinoma.
subject: Research Article
  topic: section
subject: Ductal carcinoma in situ, DCIS, mammography, ultrasound, core needle biopsy, upgrade, breast cancer
  topic: keywords
creator: Huynh Thi Do Quyen
creator: Bui Thi Huong Giang
creator: Mai Yen Ngan
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-04-20
publication date: 2026-04-20
starting page: 60
ending page: 65
doi: 10.32996/jmhs.2026.7.6.6

Admission Blood Glucose as a Predictor of Mortality and Severe Outcomes in Acute Organophosphorus Poisoning: A Systematic Review and Meta-Analysis
type: item
description: 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.
subject: Research Article
  topic: section
subject: acute organophosphorus poisoning
  topic: keywords
subject: hyperglycemia
  topic: keywords
subject: blood glucose
  topic: keywords
subject: glycemic status
  topic: keywords
subject: mortality
  topic: keywords
subject: mechanical ventilation
  topic: keywords
subject: systematic review
  topic: keywords
subject: meta-analysis
  topic: keywords
creator: Saeed Alzahrani
creator: Hesham Ajaj
creator: Duaa Abu Salim
creator: Abdulrahman Toonisi
creator: Yasir Alhusayni
creator: Fatma Toonisi
rights: Copyright (c) 2026 Journal of Medical and Health Studies
date: 2026-04-09
publication date: 2026-04-09
starting page: 51
ending page: 59
doi: 10.32996/jmhs.2026.7.6.5

The Impact of Transformational Leadership on Nurse Burnout: A Systematic Literature Review
type: item
description: 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.
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: Transformational leadership, nurse burnout, emotional exhaustion, depersonalization, systematic review, Maslach Burnout Inventory, job satisfaction, empowerment, work environment, healthcare leadership, GCC, nurse retention, PRISMA
creator: Sheikha Mohammed Amuri AL Mushaikh
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-04-07
publication date: 2026-04-07
starting page: 24
ending page: 42
doi: 10.32996/jmhs.2026.7.6.3

The Hidden Cost of Manual Equipment Management in Ambulatory Surgery Centers: A Quantitative Analysis of Financial Leakage and Operational Inefficiency
type: item
description: 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 p…
subject: Research Article
  topic: section
subject: Ambulatory Surgery Centers (ASCs); Equipment Management; Financial Leakage; Operational Inefficiency; Predictive Maintenance; Healthcare Analytics; Resource Utilization; Cost Optimization
  topic: keywords
creator: Syed Khundmir Azmi
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-04-06
publication date: 2026-04-06
starting page: 11
ending page: 23
doi: 10.32996/jmhs.2026.7.6.2

A Structured Protocol for Sustainable Goal Achievement: Development and Application of the Open G.A.T.E.S Model
type: item
description: 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 enhanc…
subject: Research Article
  topic: section
subject: Open G.A.T.E.S Model, Goal-Setting Framework, Sustainable Goal Achievement, Coaching and Counseling, Behavioral Change, Holistic Development, Personal Development Protocol
  topic: keywords
creator: Leslie Bereiter
creator: Nicholas Bereiter
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2026-04-03
publication date: 2026-04-03
starting page: 01
ending page: 10
doi: 10.32996/jmhs.2026.7.6.1

Explainable and Trustworthy Deep Learning for MRI-Based Auxiliary Diagnosis of Alzheimer’s Disease
type: item
description: 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.” Promis…
subject: Research Article
  topic: section
subject: Alzheimer’s disease; structural MRI; deep learning; explainable artificial intelligence; trustworthiness assessment; generalization and real-world application
  topic: keywords
creator: Mingxuan Zhang
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 100
ending page: 104
doi: 10.32996/jmhs.2026.7.5.13

Incidence, Risk Factors, and Outcomes of VTE in Surgical and Obstetric Patients: A Systematic Review
type: item
description: 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.
subject: Research Article
  topic: section
subject: nous thromboembolism, deep vein thrombosis, pulmonary embolism, surgery, pregnancy, postpartum, risk factors, incidence.
  topic: keywords
creator: Jad Mohamed El Hage Ali
creator: Basimathul Ain Mohamed Saleem
creator: Abdulla Al-Ani
creator: Husain Ebrahim Alkhatam
creator: Sara Ebrahim Alabood
creator: Shahnaz Ebrahim Rayan
creator: Abeer Ebrahim Mohamed
creator: Fatema J. Alasheeri
creator: Fatema Yusuf Hasan
creator: Omar Mohamed Elbana
creator: Ebrahim Mahfoodh Zaid
creator: Fatema Mustafa Marhoon
creator: Marwan Mohamed Elbana
creator: Kawthar Mahmood Sultan
creator: Mohamed Hasan Alsaffaf
rights: Copyright (c) 2026 Journal of Medical and Health Studies
date: 2026-03-31
publication date: 2026-03-31
starting page: 94
ending page: 99
doi: 10.32996/jmhs.2026.7.5.12

The Bloody Pericardium: Cardiac Tamponade Unmasking an Undiagnosed Malignancy: A Case Report
type: item
description: 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.
subject: Research Article
  topic: section
subject: Malignant Pericardial Effusion, Malignancy, Pericardium, Tamponade, Cytology, Lung Cancer, Metastasis.
  topic: keywords
creator: Zainab Kamil Aljammali
creator: Mohamed Naazim Vadhood
creator: Adeeb Kandeel
creator: Roaya Abdullah H. Yusuf
creator: Zainab Husain Alsagheer
creator: Zahra Sayed Mohamed Jaafar
creator: Janna M. Al-Jalili
creator: Hanin Sami Ebrahim
creator: Joshua Charly
creator: Fathma Nafeesa
rights: Copyright (c) 2026 Journal of Medical and Health Studies
date: 2026-03-29
publication date: 2026-03-29
starting page: 85
ending page: 93
doi: 10.32996/jmhs.2026.7.5.11

Vitamin D Status in North Batinah, Oman: Prevalence and Temporal Trends (2018-2022)
type: item
description: 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 impor…
subject: Research Article
  topic: section
subject: Vitamin D status
  topic: keywords
subject: 25-hydroxyvitamin D
  topic: keywords
subject: Temporal trends
  topic: keywords
subject: Vitamin D insufficiency
  topic: keywords
subject: Oman
  topic: keywords
creator: Salima Al Maqbali
creator: Tasnim Alswaii
creator: Hoor Al Maharbi
creator: Shatha Al Handhali
creator: Thuriya Al Hashimi
creator: Sanam Anwar
rights: Copyright (c) 2026 Journal of Medical and Health Studies
date: 2026-04-08
publication date: 2026-04-08
starting page: 43
ending page: 50
doi: 10.32996/jmhs.2026.7.6.4

AI-Enabled Machine Learning Framework for Depression Risk Prediction and Mental Health Trend Analysis in the United States
type: item
description: 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.
subject: Research Article
  topic: section
subject: Machine Learning, Depression Prediction, Mental Health Analytics, XGBoost, Explainable AI (XAI), SHAP, Public Health Surveillance, Predictive Healthcare Analytics
  topic: keywords
creator: Mustafizur Rahaman
creator: Farmina Sharmin
creator: Sadia Afrin Dipa
creator: Nurtaz Begum Asha
creator: Mousumi Akter
creator: Mostafizur Rahman Shakil
creator: Sajib Barman
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2024-10-15
publication date: 2024-10-15
starting page: 257
ending page: 272
doi: 10.32996/

Concentration-Dependent Antimicrobial Activity of Neem and Bitter Gourd Extracts: A Sustainable Approach to Mitigate Pollution in Dhaka
type: item
description: 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.
subject: Research Article
  topic: section
subject: Bitter gourd, Neem, pollution protection, Natural remedy, Urban pollution, City health
  topic: keywords
creator: Tamanna Rashid
creator: Dipongkar Ray Sobuj
creator: Md Mahfuz Miah
creator: Saiful Islam Arif
creator: Md. Maniruzzaman
creator: Sabiha Akter
creator: Md. Karim Hossain
creator: Aklima Akter Shima
creator: Md Tarikul Islam Tarek
creator: Md Azharul Islam Ifti
creator: Mst. Tasnova
creator: Kaisar Ahmad Chowdhury
creator: Md. Rakib Rased Rana
creator: Md. Sadikuj Jaman
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2026-03-18
publication date: 2026-03-18
starting page: 72
ending page: 84
doi: 10.32996/jmhs.2026.7.5.10

Convulsions and Catastrophe: Status Epilepticus Complicated by Massive Pneumoperitoneum and Bowel Perforation: A Case Report
type: item
description: 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 epilepti…
subject: Research Article
  topic: section
subject: Epilepsy, Seizure, Status Epilepticus, Perforation, Surgical Abdomen, Air Under Diaphragm, Pneumoperitoneum, Benzodiazepines.
  topic: keywords
creator: Nimrah Ali
creator: Abdullah Ammar Omira
creator: Faezah Ahmed Khaliqi
creator: Sarah Younis
creator: Khulood Hashem Neama
creator: Hawra Ramzi Alshowaikh
creator: Nebras Zakareya Naser
creator: Hussain Ali Alhawaj
creator: Ghadeer Abdullah Yaqoob
creator: Fatema J. Alasheeri
creator: Naeema Radhi Rabeea
creator: Nada Abdelmaksoud
creator: Shahed Belal Saad
rights: Copyright (c) 2026 Journal of Medical and Health Studies
date: 2026-03-17
publication date: 2026-03-17
starting page: 56
ending page: 64
doi: 10.32996/jmhs.2026.7.5.8

AI-Assisted Literature Mining Framework for miRNA–Target Mapping in Colorectal Cancer
type: item
description: 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.
subject: Research Article
  topic: section
subject: MicroRNA (miRNA), Colorectal Cancer, AI-assisted Literature Mining, miRNA–Target Interaction, Biomedical Text Mining
  topic: keywords
creator: Swati Kulshrestha Swati Kulshrestha
rights: Copyright (c) 2026 Journal of Medical and Health Studies
date: 2026-03-18
publication date: 2026-03-18
starting page: 65
ending page: 71
doi: 10.32996/jmhs.2026.7.5.9

Explainable Transformer-Based Skin Lesion Classification from Clinical Images
type: item
description: 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.
subject: Research Article
  topic: section
subject: Skin cancer detection, skin lesion classification, deep learning, Vision Transformer, Grad-CAM, PR-AUC, Explainable Artificial Intelligences.
  topic: keywords
creator: Ahmed Ali Linkon
creator: Mostafizur Rahman Shakil
creator: Shahriar Ahmed
creator: Md Rashel Miah
creator: Asif Hassan Malik
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-08
publication date: 2026-03-08
starting page: 46
ending page: 55
doi: 10.32996/jmhs.2026.7.5.7

Global–Local Attention Modeling for Reliable Multiclass Kidney Disease Classification from CT Images
type: item
description: 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.
subject: Research Article
  topic: section
subject: Kidney CT analysis, multiclass classification, vision transformer, explainable artificial intelligence, Grad-CAM, health informatics
  topic: keywords
creator: Shahriar Ahmed
creator: Md Rashel Miah
creator: Mostafizur Rahman Shakil
creator: Ahmed Ali Linkon
creator: Md Ismail Hossain Siddiqui
creator: Asif Hassan Malik
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-03-08
publication date: 2026-03-08
starting page: 36
ending page: 45
doi: 10.32996/jmhs.2026.7.5.6

Swin Transformer–Driven Cervical Cell Classification with Explainable AI and Web-Based Screening
type: item
description: 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.
subject: Research Article
  topic: section
subject: Cervical cancer screening, Pap smear images, vision transformers, deep learning, explainable AI, Grad-CAM, medical image analysis
  topic: keywords
creator: Mostafizur Rahman Shakil
creator: Asif Hassan Malik
creator: Md Ismail Hossain Siddiqui
creator: Shahriar Ahmed
creator: Md Rashel Miah
creator: Ahmed Ali Linkon
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-03-08
publication date: 2026-03-08
starting page: 25
ending page: 35
doi: 10.32996/jmhs.2026.7.5.5

CatBoost-Stacked Heterogeneous Deep Ensembles for Explainable Multi-Class Brain Tumor MRI Classification
type: item
description: 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.
subject: Research Article
  topic: section
subject: Brain tumor, MRI, stacking ensemble, CatBoost, class imbalance, Grad-CAM, explainable AI
  topic: keywords
creator: Partha Chakraborty
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-08
publication date: 2026-03-08
starting page: 18
ending page: 24
doi: 10.32996/jmhs.2026.7.5.4

Unilateral Pleural Effusion as The Initial Manifestation of Occult Lung Carcinoma: A Case Report
type: item
description: 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 histo…
subject: Research Article
  topic: section
subject: Lung Cancer, Pleural Effusion, Malignant Pleural Effusion, Thoracocentesis, Cytology, Dyspnea, Chest Pain.
  topic: keywords
creator: Athira Krishnan
creator: Dhaneesh Pavithran
creator: Neeraj Rajesh Sharma
creator: Hanin Hasan Alhusaini
creator: Saleel Majeed Ismaeel Ali Abdulaal
creator: Esraa Ashraf Elgendy
creator: Yusuf Ahmed Almalki
creator: Dani Delihasan
creator: Ahmed Mohamed Ghoniem
creator: Mohamed Naazim Vadhood
creator: Basimathul Ain Mohamed Saleem
creator: Nada Abdelmaksoud Mohamed Abdelmaksoud
creator: Shahed Belal Saad
rights: Copyright (c) 2026 Journal of Medical and Health Studies
date: 2026-03-05
publication date: 2026-03-05
starting page: 09
ending page: 17
doi: 10.32996/jmhs.2026.7.5.3

Drug Resistance to Anti-Angiogenic Therapy: Mechanisms, Clinical Implications, and Translational Strategies in Hepatocellular and Renal Cell Carcinoma
type: item
description: 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.
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: Anti-angiogenic resistance; VEGF blockade; Hepatocellular carcinoma; Renal cell carcinoma; Tumor microenvironment; Hypoxia-inducible factor; Endothelial metabolism; PFKFB3; Vessel co-option; Biomarker stratification
creator: Mohammad Mahmudul Hasan Bhuyain
creator: Fariya Chowdhury
rights: Copyright (c) 2026 Journal of Medical and Health Studies
date: 2026-03-03
publication date: 2026-03-03
starting page: 01
ending page: 08
doi: 10.32996/jmhs.2026.7.5.2

Mental Health Awareness and Comprehension of Substance Use Consequences: An Analysis of students in the Department of Medicine & Surgery at University of Abuja, Nigeria
type: item
description: 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.
subject: Research Article
  topic: section
subject: Mental health awareness, substance use disorders, medical students, Youth mental health, Help-seeking behavior, Nigeria, University health
  topic: keywords
creator: Kaliyat Gamba
creator: Angela Ogar
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-02-25
publication date: 2026-02-25
starting page: 15
ending page: 35
doi: 10.32996/jmhs.2026.7.4.2

Perceived Stress and Psychological Morbidity Among Healthcare Workers: Post-Pandemic Study from India
type: item
description: 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 afterma…
subject: Research Article
  topic: section
subject: Perceived stress, healthcare workers, depression, anxiety, sleep quality, India
  topic: keywords
creator: Prahasith Kuchanapally
creator: Akhila Ganta
creator: Vijay Patlolla
rights: Copyright (c) 2026 Journal of Medical and Health Studies
date: 2026-02-26
publication date: 2026-02-26
starting page: 36
ending page: 42
doi: 10.32996/jmhs.2026.7.4.3

Survey On Doctors of Neuroscience: Level of Knowledge, Attitude, Practice About Speech and Language Therapy Service in Dhaka City
type: item
description: 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 …
subject: Research Article
  topic: section
subject: Neuroscience doctors, Speech and Language Therapy (SLT), Knowledge, Attitude, Practice (KAP), Neurology, Neurosurgery, Neuroradiology, Interprofessional collaboration, Bangladesh.
  topic: keywords
creator: Jabia Islam Parika
creator: Md. Sazzad Hossain
creator: Nahid Parvez
creator: Israt Afrin Mimma
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-24
publication date: 2026-02-24
starting page: 41
ending page: 47
doi: 10.32996/jmhs.2026.7.3.5

Advances in Molecular Diagnostics and Biosensor Technologies for Potato Virus Y (PVY): Strategic Integration into U.S. Seed Certification and Precision Agriculture Systems
type: item
description: 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 certif…
subject: Research Article
  topic: section
subject: Potato virus Y; recombinant strains; seed potato certification; RT-qPCR; CRISPR-Cas diagnostics; isothermal amplification; biosensor platforms; high-throughput sequencing; precision agriculture.
  topic: keywords
creator: Mohammad Mahmudul Hasan Bhuyain
creator: Fariya Chowdhury
rights: Copyright (c) 2024 Journal of Medical and Health Studies
date: 2024-12-28
publication date: 2024-12-28
starting page: 247
ending page: 256
doi: 10.32996/jmhs.2024.5.4.27

AI-Driven Glycemic Instability Risk Modeling for Proactive Intervention and Chronic Disease Management in U.S. Healthcare Systems
type: item
description: 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 mode…
subject: Research Article
  topic: section
subject: Healthcare System Reform; Health Equity and Disparities; Value-Based Care; Health Information Interoperability; Public Health Infrastructure
  topic: keywords
creator: Rabi Sankar Mondal
creator: Tamjida Nasreen Purba
creator: Nafisa Nusrat Purba
creator: Md Moshiur Rahman
creator: Tawfiqur Rahman Sikder
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2026-01-09
publication date: 2026-01-09
starting page: 29
ending page: 43
doi: 10.32996/jmhs.2023.4.6.21

Resilient Healthcare and Critical Urban Infrastructure Design Using AI-Driven Engineering and Project Management Systems
type: item
description: 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…
subject: Research Article
  topic: section
subject: Urban resilience; Healthcare accessibility; Critical infrastructure; Artificial intelligence; Digital twins; Project management; Smart cities
  topic: keywords
creator: Md Imrul Hasan
creator: Chapal Barua
creator: Md Saidur Rahman
creator: Kazi Rezwana Alam
creator: Jesmin Ul Zannat Kabir
creator: Kazi Rakib Hasan Saurav
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2023-12-25
publication date: 2023-12-25
starting page: 161
ending page: 172
doi: 10.32996/jmhs.2023.4.6.20

Impact of Early Emergency Department Management of Diabetic Ketoacidosis and Hyperosmolar Hyperglycemic State on ICU Admission and In-Hospital Mortality: A Retrospective Cohort Study
type: item
description: 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.
subject: Research Article
  topic: section
subject: Diabetic ketoacidosis; Hyperosmolar hyperglycemic state; Emergency department; ICU admission; Mortality
  topic: keywords
creator: Madleen Abu Aser
creator: Sharif Jamal Salha
creator: Ghassan Ashour Abed Ashour
rights: Copyright (c) 2026 Journal of Medical and Health Studies
date: 2026-02-14
publication date: 2026-02-14
starting page: 27
ending page: 40
doi: 10.32996/jmhs.2026.7.3.4