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
Predictive Business Analytics For Reducing Healthcare Costs And Enhancing Patient Outcomes Across U.S. Public Health Systems
The increasing healthcare expenses, especially in U.S. government-funded health systems, are a significant burden to policymakers and medical practitioners. Although the issue of predictive analytics as an intervention in enhancing patient outcomes and cost reduction has gained traction as a concept, there is a knowledge gap in the area of how such technologies can be systematized in large-scale public health environments. The aim of the research was to assess the role of predictive business analytics in terms of healthcare costs and patient outcomes in the U.S. public health systems. The research employed a mixed-methods design, which involved both quantitative analyses of the data on costs and patient outcomes from 50 public health facilities, as well as qualitative analyses of the effectiveness of predictive models. The data were gathered in the electronic health records (EHRs) and examined with the help of regression models, t-tests, and correlation. The results showed that the healthcare costs reduced significantly (mean reduction of 8.5% p < 0.05) and that patient outcomes also improved, with a 12% decline in hospital readmission rates and a 15-point increase in patient satisfaction scores (p < 0.01). These findings highlight the possible opportunities of predictive analytics in streamlining health care delivery and minimizing expenses. The study multiplies the existing literature in the field of application of data-driven decision-making to the field of healthcare, providing empirical evidence of its effectiveness in the public health systems. This study opens the possibilities of wider implementation of predictive analytics tools to improve cost-efficiency and patient care in the field of public health.
healthcare costs, predictive analytics, patient outcomes, public health systems, U.S. healthcare
Copyright (c) 2023 https://creativecommons.org/licenses/by-nc/4.0
AI-Driven Prediction of Mental Disorders: Enhancing Early Diagnosis and Intervention in the USA
Early detection of mental disorders remains one of the most pressing challenges in U.S. public health, as socioeconomic and behavioral indicators often precede clinical diagnosis but are rarely integrated into predictive frameworks. This study develops an AI-driven diagnostic pipeline that fuses demographic, behavioral, and social determinants of health to predict risk for major mental disorders, including anxiety, depression, and post-traumatic stress disorder (PTSD). Using a population-scale dataset of over 10,000 anonymized health records combining age, sex, BMI, income, education, and lifestyle behaviors, we benchmark five machine learning models, Logistic Regression, Random Forest, XGBoost, Support Vector Machine (SVM), and a Multi-Layer Perceptron (MLP), across both unbalanced and SMOTE-balanced conditions. The evaluation integrates multiple dimensions: discrimination (ROC-AUC, PR-AUC), calibration (Brier score, reliability curves), and fairness across demographic subgroups (sex, rural–urban classification). SHAP-based explainability is employed to interpret model behavior and to identify dominant risk predictors and interaction effects, while robustness checks probe performance under covariate shifts and synthetic missingness. Results show that ensemble and deep models outperform classical baselines, with XGBoost achieving an average ROC-AUC of 0.90 and strong calibration stability. Income level, alcohol consumption, and BMI category emerge as top predictors, reflecting known epidemiological associations. Subgroup analysis demonstrates consistent performance across demographic segments, underscoring model fairness and generalizability. Collectively, the findings illustrate how interpretable AI can enhance early detection and risk stratification for mental health conditions, providing a data-driven foundation for preventive interventions, policy guidance, and equitable digital mental health systems in the United States.
Copyright (c) 2025 Journal of Medical and Health Studies
Applications of single-cell sequencing in schizophrenia research
Schizophrenia (SCZ) is a highly heritable, severe neuropsychiatric disorder characterized by complex genetic architecture and heterogeneous clinical manifestations. Despite decades of research, the precise molecular and cellular mechanisms underlying its pathogenesis remain incompletely elucidated, which significantly hinders the development of effective preventive strategies and complicates treatment approaches. This comprehensive review systematically examines the emerging application of cutting-edge single-cell sequencing technologies in SCZ research. Single-cell sequencing serves as an unprecedented high-resolution molecular microscope, providing researchers with the remarkable ability to distinguish and characterize the minute individual cellular and molecular components that constitute the extraordinarily complex SCZ "puzzle". The systematic integration of these diverse molecular fragments into a unified, coherent pathophysiological framework holds tremendous promise for yielding transformative research advancements that could fundamentally alter our approach to SCZ diagnosis, treatment, and prevention.
Single-cell sequencing; Schizophrenia (SCZ)
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
Application of Single-Cell RNA Sequencing Technology: Advancing Targeted Fatty Acids for Antitumor Therapy
The occurrence and progression of tumors are closely linked to the abnormal metabolic mechanism of fatty acids. In tumor cells, fatty acids are highly expressed and provide molecular structural substances for cells, thereby sustaining tumor growth. In previous experiments, researchers cultured cells in a fatty acid-deficient environment and analyzed the growth status of the cells in batches. These studies confirmed that fatty acids play crucial roles in tumorigenesis and have spurred research on targeted regulation for tumor therapy. However, traditional cellular and molecular experiments have certain limitations, making it difficult to resolve the key issue of metabolic heterogeneity among different subpopulations of tumor cells. This limitation has contributed to the failure of targeted therapies to achieve long-lasting therapeutic effects in the treatment of some solid tumors. In contrast, single-cell RNA sequencing (scRNA-seq) technology can analyze gene expression profiles at the single-cell level, overcoming the constraints of traditional methods, and has thus been widely applied in research on tumor metabolism. This technology can accurately identify tumor cell subpopulations with specific phenotypes (such as cancer cell subpopulations with high expression of fatty acid synthase and radiotherapy-resistant glioblastoma cell subpopulations dependent on fatty acid oxidation), clarify the remodeling effects of metabolic heterogeneity on the tumor microenvironment, and provide a detailed molecular mechanism basis for research on targeted tumor therapy. Consequently, it can effectively address the problem of metabolic heterogeneity in tumor cells. In the future, we can integrate scRNA-seq with technologies such as lipidomics and spatial transcriptomics to promote its clinical translation, guide personalized targeted therapy, and provide a precise and efficient new strategy for metabolic targeted therapy in tumors.
Single-cell RNA sequencing technology; Fatty acids; Metabolic heterogeneity; Cancer; Targeted therapy
Data-Driven Predictive Analytics Framework for Combating Antibiotic Resistance through AI-Enabled Drug Discovery and Surveillance
Antibiotic resistance (AR) has become a pressing concern to the world at large. This has been the case as a proactive strategy can now only be achieved through the use of intelligent solutions toward curbing AR. Research has shown that the development of antibiotics has been hampered by the long drug development process. Moreover, the increase in resistance among microbes has been faster compared to the development of effective antibiotics. This researcher introduces a Data-Driven Predictive Analytics Framework. This has been based on Manik et al. (2018-2023), as the researcher has used AI solutions for the development of medications. This has progressed from drug development to the usage of analytics for chronic and neurological disorders. This has finally led to the development of the Data-Driven Predictive Analysis Framework. This will consist of four aspects: (1) the development of data from multiple sources including genomics and phenomics. This will also involve the usage of the environment. (2) This study will apply analytics solutions to predict the development of AR. (3) AI solutions will also incorporate solutions to develop medications. This has been the final aspect as the researcher concludes the drug development framework. (4) This will involve the development of solutions across the world. This has been the case as the world has been suffering due to the lack of effective antibiotic development. This has been based on the CDC as well as WHO guidelines.
Antibiotic resistance, predictive analytics, AI-enabled drug discovery, big data, multi-omics, surveillance, machine learning.
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
Spontaneous Coronary Artery Dissection in a Young Female: A Rare Cardiac Catastrophe Without Traditional Risk Factors
Spontaneous coronary artery dissection (SCAD) is an uncommon cause of acute coronary syndrome, predominantly affecting young women without traditional cardiovascular risk factors. We report a 32-year-old female who presented with sudden-onset, severe retrosternal chest pain radiating to the left arm, associated with diaphoresis and mild dyspnea. She had no history of hypertension, diabetes, dyslipidemia, smoking, or family history of premature coronary artery disease. Initial evaluation revealed subtle ST-segment changes on electrocardiogram and modestly elevated high-sensitivity troponin I levels. Coronary angiography demonstrated a type 2 SCAD of the mid-left anterior descending artery, confirmed by intracoronary imaging showing an intramural hematoma with preserved distal flow. Laboratory workup, echocardiography, and computed tomography angiography for extracoronary vascular assessment were unremarkable. Given hemodynamic stability and maintained coronary perfusion, the patient was managed conservatively with dual antiplatelet therapy, beta-blockers, and close monitoring, with plans for follow-up imaging to assess vascular healing. This case underscores the importance of considering SCAD in young women presenting with acute chest pain, highlights the critical role of coronary imaging in diagnosis, and demonstrates that tailored conservative management can lead to favorable outcomes while avoiding unnecessary interventions.
Spontaneous coronary artery dissection, SCAD, acute coronary syndrome, young female, intramural hematoma, conservative management, coronary angiography, non-atherosclerotic coronary disease, cardiac emergency, chest pain.
Unmasking Connective Tissue Disease Through Pulmonary Hypertension: A Diagnostic Challenge
Pulmonary arterial hypertension (PAH) is a severe and potentially life-threatening manifestation of connective tissue diseases, often presenting insidiously and complicating timely diagnosis. We report the case of a 48-year-old Saudi woman with no prior chronic illness who presented with progressive exertional dyspnea and fatigue over six weeks. Examination revealed mild central cyanosis, elevated jugular venous pressure, and a prominent right ventricular heave. Laboratory evaluation showed elevated NT-proBNP and mild hypoxemia, while echocardiography demonstrated right ventricular dilation and an estimated pulmonary artery systolic pressure of 70 mmHg. Right heart catheterization confirmed pre-capillary pulmonary hypertension. Serologic workup revealed high-titer anti-U1 RNP antibodies, ANA positivity, and features consistent with mixed connective tissue disease (MCTD), with no evidence of left heart disease, thromboembolic disease, or significant interstitial lung involvement. Management included combination PAH-targeted therapy with an endothelin receptor antagonist and a phosphodiesterase-5 inhibitor, alongside low-dose corticosteroid immunosuppression. Supportive care comprised oxygen supplementation and close hemodynamic monitoring. Over three months, the patient’s functional status improved from WHO functional class III to II, with reduction in NT-proBNP and increased six-minute walk distance. This case underscores the diagnostic challenge of MCTD presenting primarily with PAH, emphasizing that subtle systemic features may precede cardiovascular compromise. Early recognition, prompt hemodynamic assessment, and a multidisciplinary management strategy are crucial to improve prognosis and prevent irreversible right ventricular dysfunction. Clinicians should maintain a high index of suspicion for PAH in patients with connective tissue disease, even in the absence of overt systemic manifestations.
Pulmonary hypertension, connective tissue disease, autoimmune disease, interstitial lung disease, systemic sclerosis, vasculopathy, diagnostic challenge, and pulmonary vascular disease.
Recurring Abdominal Pain: The Hidden Clue of Antiphospholipid Syndrome
Recurrent abdominal pain is a common clinical presentation, yet when episodes are severe, episodic, and unexplained by routine gastrointestinal investigations, vascular etiologies must be considered. We present a 52-year-old previously healthy man with a six-week history of intermittent, severe central abdominal pain, culminating in an acute exacerbation. Prior evaluations, including ultrasound, non-contrast CT, and endoscopy, were unrevealing. On acute presentation, laboratory studies demonstrated elevated D-dimer and mild lactate elevation, prompting contrast-enhanced CT angiography, which revealed a proximal superior mesenteric artery thrombus with preserved bowel viability. Further workup confirmed triple-positive antiphospholipid syndrome (APS), establishing the underlying prothrombotic etiology. The patient underwent urgent systemic anticoagulation with unfractionated heparin followed by successful endovascular thrombectomy and catheter-directed thrombolysis. He was subsequently transitioned to long-term warfarin therapy with multidisciplinary follow-up. This case highlights the protean manifestations of APS, demonstrating that recurrent, transient mesenteric ischemia may precede catastrophic arterial thrombosis. Early recognition, prompt vascular imaging, and tailored anticoagulation—combined with minimally invasive revascularization when feasible—are pivotal in preventing bowel infarction and optimizing outcomes. Clinicians should maintain high suspicion for APS in atypical thrombotic presentations beyond the classic venous or cerebrovascular events.
Abdominal Pain, Antiphospholipid Syndrome, Thrombophilia, Mesenteric Ischemia, Arterial Thrombosis, Lupus Anticoagulant, Anticardiolipin, AntiB2-Glycoprotein I, Anticoagulation, Thrombosis, Small Bowel Infarction.
Gut Warning: Acute Mesenteric Ischemia as The First Sign of Hidden Atrial Fibrillation
Some surgical abdomens may present with severe pain that appears disproportionate to physical examination findings, a red flag that clinicians must not overlook, as early mucosal ischemia can progress to transmural necrosis and eventual signs of peritonitis. Atrial fibrillation may remain clinically silent until it manifests through a first embolic event, as illustrated in this case. This report highlights the life-saving value of maintaining a low threshold for abdominal CT angiography in patients with atrial fibrillation. We present a 64-year-old male who arrived with acute, severe abdominal pain of sudden onset, persisting for approximately five hours prior to hospital admission. Laboratory findings of leukocytosis and lactic acidosis indicated acute intestinal injury and early tissue hypoxia, prompting a high index of suspicion for acute mesenteric ischemia which was later confirmed by CT angiography of the abdomen. Initial management included intravenous fluids, broad-spectrum antibiotics, rate control, analgesia, and gastrointestinal decompression, followed by definitive treatment via endovascular embolectomy. The patient demonstrated an excellent recovery, with postoperative angiography confirming successful reperfusion, and was discharged after an uncomplicated ICU course on long-term anticoagulation therapy.
Advancing U.S. Healthcare with LLM–Diffusion Hybrid Models for Synthetic Skin Image Generation and Dermatological AI
The integration of large language models (LLMs) with diffusion-based generative architectures has redefined the boundaries of medical image synthesis, particularly in dermatological diagnostics. This study presents a novel hybrid model for synthetic skin image generation, leveraging the textual understanding capabilities of LLMs and the generative precision of diffusion models. The dataset was derived from the UCI Skin Segmentation Dataset, consisting of high-resolution dermal samples categorized into skin and non-skin classes. Following extensive preprocessing and feature extraction, semantic conditioning through LLMs was applied to guide the diffusion process, resulting in highly realistic and clinically relevant synthetic skin images. Experimental results demonstrate superior performance compared to traditional GANs and autoencoder-based models, achieving a Structural Similarity Index (SSIM) of 0.982, PSNR of 38.7 dB, and FID score of 5.43, indicating exceptional image fidelity and diversity. The proposed model also facilitates data augmentation for machine learning models in dermatology, enhancing classification accuracy by 7.5% on average. Beyond academic relevance, the implementation of this hybrid architecture holds immense potential for U.S. healthcare applications, enabling scalable skin disease datasets, supporting dermatological AI training, and improving diagnostic precision in rural and underserved communities.
Synthetic skin image generation, diffusion model, large language model (LLM), medical image synthesis, dermatology AI, generative models, healthcare innovation
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
Complications of the Patient with Hemophilia Associated with Transfusion of Blood Components or Derivatives: 35 Cases
Hemophilia is a congenital bleeding disorder characterized by deficient or absent clotting factors, most commonly Factor VIII (Hemophilia A) or Factor IX (Hemophilia B). Regular transfusions of blood products, such as clotting factor concentrates and plasma derivatives, are essential in managing bleeding episodes in hemophilic patients. However, transfusion therapy is not without risks. This study aims to explore the complications associated with blood component transfusion in 35 hemophilic patients, analyzing a range of adverse events that arose following transfusion therapy. The study retrospectively reviewed the medical records of 35 hemophilic patients who received blood component transfusions over a 2-years period. Data were collected on the type of transfused products, the number of transfusions, and the complications encountered. These complications included allergic reactions, the development of inhibitors (antibodies against transfused clotting factors), viral infections (such as hepatitis and HIV), iron overload, and thrombotic events. A total of 35 hemophilic patients (27 male, 8 female) participated in this study. The mean age was 34.2 years, with a range spanning from 8 to 68 years. Hemophilia A (n = 22, 62.9%) was more common than Hemophilia B (n = 13, 37.1%). The severity of hemophilia was categorized as severe (n = 15, 42.9%), moderate (n = 12, 34.3%), and mild (n = 8, 22.8%). The mean number of transfusions per patient was 10.3, with clotting factor concentrates being the most frequently used transfusion product (n = 29, 82.9%). The study revealed a 34% complication rate (n = 12). The most common complications were allergic reactions (14.3%), inhibitor development (20%), and iron overload (11.4%). Thrombotic events were less frequent (5.7%), while one case of hepatitis B (2.9%) was noted, which was attributed to older plasma-derived factor products. Statistical analysis showed a significant correlation between severe hemophilia and inhibitor development (71% of severe cases developed inhibitors, p < 0.05). No significant association was observed between transfusion product type and the occurrence of complications. The study highlights the need for careful monitoring and management of hemophilic patients receiving transfusions. Strategies to minimize risks, such as the use of recombinant clotting factors, individualized transfusion protocols, and regular screening for inhibitors, are critical in improving patient outcomes and reducing complications.
Hemophilia, Blood Transfusion, Clotting Factor Concentrates, Plasma Derivatives, Transfusion Complications, Inhibitor Development
AI-Driven Biomedical Innovation: Integrating Big Data Analytics, Wearable Intelligence, and Predictive Modeling for Global Health and Sustainability
Artificial intelligence (AI) and big data analytics have transformed biomedical innovation by facilitating predictive, personalized, and precision-oriented methodologies in healthcare. This research integrates the methodological and empirical contributions of key studies in drug development, wearable health analytics, multi-omics modeling, and AI-driven predictions of chronic and oncological diseases. This study employs a qualitative-quantitative meta-synthesis to amalgamate evidence from Manik et al. (2020), Miah et al. (2019), and related interdisciplinary research to develop an AI Bio-Innovation Framework (AIBF) that integrates generative AI, deep learning, and multi-modal data across the healthcare continuum. Research indicates that AI-driven predictive analytics enhance disease detection accuracy by 20–30%, decrease diagnostic latency by 35–40%, and facilitate 25% quicker therapy modeling relative to traditional methods. Furthermore, the integration of wearable technology and multi-omics data facilitates real-time, population-wide monitoring of cardiovascular, neurological, and metabolic diseases. The AIBF model integrates biomedical informatics with sustainable innovation by enhancing computing resource efficiency and reducing experimental redundancy. The research asserts that data-driven biomedicine, enhanced by explainable AI, federated learning, and scalable cloud infrastructure, can expedite discovery processes while adhering to global health and environmental goals. This work synthesizes deep learning applications in cardiovascular and cervical cancer detection, antibiotic resistance modeling, and multi-omics integration, establishing a next-generation paradigm for AI-driven, precision-guided healthcare systems that enhance both human and environmental resilience.
Artificial Intelligence, Big Data Analytics, Wearable Health, Multi-Omics, Precision Medicine, Predictive Modeling, Biomedical Innovation, Sustainability
Reconstruction of soft tissue defects of the distal foot and ankle remains challenging due to poor vascularization and limited local tissue availability. This report describes two clinical cases managed with a retrograde internal medial plantar flap for coverage of distal plantar defects. The first patient presented with soft tissue loss of the forefoot following trauma, and the second had a medial plantar defect after tumor excision. In both cases, the medial plantar artery and its venae comitantes were preserved as the vascular pedicle. The flap was elevated and rotated in a retrograde manner to cover the defect, while the donor site was grafted with split-thickness skin. Both flaps survived completely without venous congestion or necrosis. Postoperative recovery was uneventful, and full weight-bearing was achieved within four weeks. The reconstructed areas showed durable, stable, and sensate coverage with minimal donor site morbidity. The retrograde internal medial plantar flap is a reliable and technically straightforward option for reconstruction of distal plantar and ankle defects. It offers well-vascularized and sensitive coverage, providing a functional and aesthetic alternative to free flaps, especially in settings with limited microsurgical resources.
Medial plantar flap, retrograde flow, plantar reconstruction, soft tissue defect, case report, trauma, tumor excision, flap viability, plastic surgery
From Gall Bladder Pain to Dilutional Emergency: Cholecystitis-Induced SIADH
In the setting of severe infections, hyponatremia should not automatically be attributed to dehydration, as intense inflammatory responses can act as powerful neuroendocrine stimuli, triggering a cytokine surge that enhances central ADH release and leads to SIADH (Syndrome of Inappropriate Antidiuretic Hormone Secretion). If not recognized early, this can result in rapid clinical deterioration. This case illustrates that exact scenario, emphasizing the importance of proper volume assessment and timely measurement of urinary osmolality and sodium. A 65-year-old male presented to our hospital with profound loss of consciousness and a generalized tonic-clonic seizure, secondary to severe hyponatremia in association with clinical and ultrasonographic findings consistent with acute cholecystitis. Through conservative management, including medical stabilization, close monitoring of sodium levels, and initiation of antibiotic therapy, a delayed cholecystectomy was safely performed. The patient remained seizure-free with preserved cognitive function and was discharged in stable condition.
Evaluating the Impact of Artificial Intelligence on Risk Assessment in Digital Banking
The introduction of AI has revolutionized how risk assessments are carried out in digital banking, through providing advanced means for fraud detection, credit scoring and real-time monitoring. This study considers the impact of artificial intelligence technologies on credit scoring models in digital banking. By using mixed techniques of qualitative and quantitative analysis alongside case studies and examination of the latest developments, the research explores to what extent the accuracy and efficiency of risk management is enhanced through machine learning, natural language processing and predictive analytics. The findings demonstrate that AI-based approaches can improve efficiency and scalability of RAs, yet also raise new issues as ensuring interpretability and as of regulation and ethics testing. Recommendations for Financial Institutions Conclusion The final section of the paper presents a set of practical recommendations on how to leverage AI solutions responsibly by financial institutions that need to reconcile being innovative with being compliant in the ever-changing landscape of digital finance.
Artificial Intelligence, Risk Assessment, Digital Banking, Machine Learning, Fraud Detection
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
Enhancing Medical Imaging Diagnostics Using Deep Learning Techniques
Recent breakthroughs in medical imaging and artificial intelligence (AI) have facilitated the transformation of illness diagnosis and treatment planning through deep learning models. Multi-modal medical imaging fusion, which amalgamates complementing data from diverse imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound, augments diagnostic precision in the identification of intricate diseases. Deep learning methodologies, especially convolutional neural networks (CNNs) and transformer-based architectures, have exhibited exceptional efficacy in extracting and integrating multi-source imaging information, hence enabling more accurate and thorough clinical evaluations. This paper examines recent improvements in deep learning applications for medical imaging diagnostics, contrasts their performance with traditional methods, and gives a case study demonstrating the enhanced diagnostic accuracy attained by deep neural networks. We present a thorough technique for incorporating deep learning models into clinical procedures, substantiated by experimental findings. The results demonstrate that deep learning not only improves diagnosis accuracy but also has the potential to better patient outcomes and optimize healthcare delivery.
Deep Learning, Image Segmentation, Imaging modalities, Disease Detection, Precision Medicine
Integrating Deep Learning and Interpretable Regression Models for Transparent Decision Support in Healthcare Diagnostics
Deep learning models have demonstrated exceptional predictive capabilities in healthcare diagnostics, yet their black-box nature limits clinical adoption due to a lack of interpretability and trust. This study addresses this limitation by developing a hybrid decision-support framework that integrates deep representation learning with interpretable regression modeling. Using the MIMIC-IV dataset, sourced from U.S. intensive care units, and comprising patient demographics, vital signs, and laboratory data, we train a deep neural network to learn 128-dimensional patient embeddings that capture underlying physiological patterns. These embeddings are then used as inputs to interpretable regression models, Logistic Regression, and Generalized Additive Models, to predict hospital mortality while maintaining transparency. SHAP-based interpretability analysis is employed to quantify and visualize the contribution of each embedding dimension and clinical feature to model predictions. Experimental results show that the hybrid model achieves competitive performance relative to standalone deep models, while providing clear feature-level explanations through regression coefficients and SHAP importance rankings. The findings demonstrate that deep–deep-interpretable hybrid architectures can bridge the performance–explainability divide, offering a viable pathway for deploying transparent, trustworthy AI systems in clinical diagnostics. This integration not only enhances predictive reliability but also strengthens clinician confidence through evidence-based, interpretable decision support.
Inflammatory Biomarkers Predicting Osteoporosis in Autoimmune and Chronic Inflammatory Diseases: A Systematic Review
Chronic autoimmune and inflammatory diseases (e.g. rheumatoid arthritis [RA], systemic lupus erythematosus [SLE], inflammatory bowel disease [IBD], ankylosing spondylitis [AS], psoriasis) are frequently accompanied by systemic bone loss and osteoporosis [1,5,6,7,8,10,11]. Pro-inflammatory cytokines (IL-6, TNF-α, etc.) can drive RANKL-mediated osteoclastogenesis[2]. The utility of circulating inflammatory biomarkers (e.g. CRP, IL-6, TNF-α) as predictors of low bone mineral density (BMD) in these diseases is unclear. We performed a systematic review to identify primary human studies linking inflammatory marker levels to osteoporosis or low BMD in patients with autoimmune/inflammatory conditions. We followed PRISMA 2020 guidelines[3] and searched PubMed, Scopus, Web of Science, and other databases through 2025. Inclusion criteria encompassed observational human studies (cohort, case-control, cross-sectional) assessing associations between inflammatory biomarkers (CRP, IL-6, TNF-α, etc.) and BMD or osteoporosis in autoimmune/chronic inflammatory disorders. We excluded reviews, editorials, case reports, non-human studies, and studies without relevant bone outcomes. Two reviewers independently screened records, extracted data (population details, disease, biomarkers, BMD results), and assessed study quality (using JBI checklists[4]). Data were synthesized qualitatively. The search identified ~900 records; after deduplication and screening, nine studies met inclusion (representing RA, SLE, AS, IBD, and psoriasis/PsA). These included studies with sample sizes ranging ~40–141 patients. In RA (three studies), higher IL-6 levels were inversely correlated with lumbar spine and femoral neck BMD[5]. In SLE (one study), elevated CRP and lupus nephritis were linked to lower BMD[6]. In AS (one study), higher CRP and disease activity were associated with greater BMD loss[7]. In Crohn’s disease (one longitudinal cohort), persistently elevated CRP predicted lack of improvement in BMD over time[8]. Overall, inflammatory markers – especially IL-6 and CRP – tended to be higher in patients with lower BMD. Multiple studies suggest that systemic inflammatory activity (reflected by IL-6, CRP, etc.) accompanies bone loss in autoimmune diseases. While heterogeneity and limited sample sizes preclude definitive conclusions, these findings support the concept that inflammatory biomarker levels may help identify patients at risk for osteoporosis. Larger, longitudinal studies are needed to confirm these associations and guide clinical use of biomarkers for bone health monitoring.
Inferior Bodylift in Moroccan Patients: Experience at the Mohamed V Military Training Hospital
Post-weight loss sequelae, whether following natural weight reduction or bariatric surgery, can significantly impact patients’ quality of life on physical, aesthetic, and psychological levels. Lower bodylift surgery, first introduced by Gonzalez-Ulloa in 1961, has since become a key procedure for addressing these issues by comprehensively reshaping body contours. This is a retrospective descriptive study conducted between January 2020 and January 2025, including 20 female patients who underwent lower bodylift surgery following the technique described by Pascal and Le Louarn. All procedures were performed by the same surgeon in the Department of Plastic, Reconstructive and Burn Surgery at the Mohammed V Military Teaching Hospital in Rabat. Data collection included clinical, anthropometric, surgical, and postoperative outcome parameters. The mean age of the patients was 42 years. All had post-bariatric excess skin, with stable weight for at least six months prior to surgery. The mean BMI was 26.7 kg/m². A diffuse adipocutaneous excess was present in 80% of cases. Surgery was performed under general anesthesia in two stages, including systematic liposuction, rectus diastasis repair, and oval umbilical reconstruction. Average operative time was 7 hours and 20 minutes. Aesthetic outcomes were rated as satisfactory to very satisfactory in all cases, particularly regarding overall contour, scar placement, and umbilical appearance. No major complications were reported. In the context of rising global obesity rates and growing aesthetic demands, the lower bodylift has emerged as a safe and effective surgical solution. When performed under optimal conditions and tailored to individual patient characteristics, it provides durable body contour restoration and significantly enhances patients’ quality of life.
Lower bodylift, weight loss, surgery, quality of life
Teorhinoplasty: A New Era in Aesthetic and Functional Rhinoplasty
Rhinoplasty, a complex procedure at the crossroads of aesthetic and functional demands, aims to restore nasal harmony while preserving a natural appearance. In this study, we present our experience with Teorhinoplasty, a standardized technique, to assess its aesthetic and functional outcomes, reproducibility, as well as its indications and limitations. Six patients (5 women and 1 man), with a mean age of 30.3 years, were included. All underwent Teorhinoplasty performed by the same surgeon between January 2022 and January 2025. Postoperative outcomes were deemed satisfactory by both patients and the surgeon. Teorhinoplasty remains a standardized preservation technique. It offers a simplified alternative to conventional methods, with reproducible and predictable results. However, careful patient selection is essential due to its specific limitations.
Teorhinoplasty, New approach, Surgery, Aesthetic
OncoViz USA: ML-Driven Insights into Cancer Incidence, Mortality, and ScreeningDisparities
Cancer incidence and mortality in the United States have shown overall improvement in recent decades, yet not all populations and regions have benefited equally. We present OncoViz USA, a comprehensive analysis integrating public datasets from 2010 to 2020 to uncover factors underlying the rising cancer burden and lagging screening rates. Using national cancer registries (CDC USCS) alongside data on screening (BRFSS), social determinants (ACS), and healthcare access (HRSA), we applied machine learning models (gradient boosting and random forests) to identify key predictors of high cancer incidence/mortality and low screening uptake. Model explainability tools (SHAP values) highlighted the contributions of demographics, socioeconomic status, health behaviors, and healthcare access to geographic disparities. We further conducted time-series forecasting (Prophet/ARIMA) to project short-term cancer trends and spatial analyses (Moran’s I and cluster detection) to identify high-risk “hotspots.” Our results indicate that socioeconomic and healthcare-access variables, including poverty, educational attainment, insurance coverage, and provider availability, are the strongest drivers of persistent cancer disparities, alongside behavioral factors such as smoking. Areas with increasing cancer death rates or poor screening uptake clustered in economically disadvantaged and rural regions. The findings provide data-driven insight into where targeted screening and prevention efforts are most urgently needed. The OncoViz USA approach demonstrates the power of machine learning and multi-source data integration to inform public health strategies aimed at achieving equitable cancer outcomes.
Copyright (c) 2021 Journal of Medical and Health Studies
From Pancreas to Heart: Takotsubo Cardiomyopathy Following Acute Pancreatitis
In cases of severe acute pancreatitis, clinicians must maintain a vigilant approach toward potential cardiac complications, particularly when confronted with unexplained electrocardiographic changes or modest elevations in cardiac biomarkers such as troponin. This report presents a compelling example, detailing the clinical course of a 62-year-old woman who arrived at the emergency department with acute-onset, intense epigastric pain radiating to the back, accompanied by worsening nausea and multiple episodes of non-bilious, non-bloody vomiting. Though her cardiac symptoms were limited to subtle palpitations and mild dyspnea, further evaluation revealed mildly elevated troponin levels, a known systemic inflammatory trigger, and hallmark echocardiographic findings of apical ballooning with basal hyperkinesis—collectively fulfilling the diagnostic criteria for Takotsubo cardiomyopathy (TCM). This diagnosis demanded a nuanced management strategy, balancing the need for intravenous fluid resuscitation to support pancreatic perfusion against the risk of precipitating or exacerbating left ventricular dysfunction. Continuous ECG surveillance and echocardiographic assessment of ventricular filling were essential to guide volume status and avoid complications such as iatrogenic pulmonary edema or potentially life-threatening arrhythmias, including QT prolongation—particularly in the context of antiemetic therapy with agents such as ondansetron. The patient was managed conservatively for pancreatitis, while cardiac dysfunction was addressed with a short course of beta-blockers and angiotensin-converting enzyme inhibitors, titrated carefully in accordance with hemodynamic parameters. This case underscores the increasingly recognized association between systemic inflammatory states and stress-induced cardiomyopathy, revealing a notable gap in current clinical guidelines and emphasizing the critical need for increased diagnostic vigilance and interdisciplinary coordination in managing such complex presentations.
From Headache to PRES: Early Recognition of Preeclampsia in a Pregnant Patient
Early neuroimaging should be strongly considered in pregnant patients presenting with any neurological symptoms, even when symptoms appear minor, such as headache, as this may enable timely detection of serious underlying conditions. Delaying intervention until the onset of seizures risks missing early indicators of life-threatening complications such as Posterior Reversible Encephalopathy Syndrome (PRES), which can serve as a premonitory sign of advancing eclampsia, particularly in the setting of preeclampsia. This case report illustrates that clinical principle through the presentation of a 29-year-old Saudi woman, gravida 2 para 1, at 33+4 weeks of gestation, who arrived at obstetric triage with a three-day history of progressively worsening fronto-occipital headache. Her presentation was complicated by neurological findings including confusion and bilateral ankle clonus—hallmarks of neurological irritability—later confirmed as PRES via classic MRI features. Management involved urgent blood pressure control, administration of magnesium sulfate for seizure prophylaxis, and expedited delivery due to both high-risk maternal status and signs of severe fetal compromise on Doppler and non-stress testing. This case is unique in demonstrating how subtle early neurological symptoms can unmask severe pathology, and it underscores the critical importance of multidisciplinary vigilance in preventing maternal and fetal morbidity; it offers valuable clinical insights into the timely recognition and management of PRES in the context of hypertensive disorders of pregnancy.
A Multimodal Big Data and Explainable AI Framework for Personalized Cancer Care: Extending Methods for Clinical Translation
Recent advancements in big data analytics and artificial intelligence (AI) have revolutionized precision oncology, enabling the prediction of therapy responses, patient stratification, and the personalization of treatment courses. This study expands upon the methodologies and results presented in "Leveraging Big Data Analytics for Personalized Cancer Treatment: An Overview of Current Approaches and Future Directions" (Journal of Engineering, 2025) by formulating and implementing an innovative framework for multimodal, data-driven cancer care. The system amalgamates genetic, transcriptomic, imaging, clinical, and patient-reported data streams with machine learning models, causal inference methodologies, and explainable AI to produce personalized treatment-effect predictions. The suggested approach, named OncoSage, exhibits enhanced stability, predictive accuracy, and interpretability when evaluated against benchmark datasets such as TCGA, METABRIC, and TCIA, surpassing traditional models. Significant contributions encompass schema-first data governance, uncertainty quantification using conformal prediction, target-trial emulation for treatment impact estimation, and fairness-aware monitoring in federated environments. The findings underscore the clinical relevance of explainable big-data pipelines in oncology, providing clear and ethically sound decision assistance that connects computational capabilities with practical implementation in clinical settings. This study enhances the expanding field of translational cancer informatics by offering a replicable, therapeutically pertinent, and governance-oriented framework for future customized oncology systems.
Precision oncology, Big data analytics, Explainable AI, Causal inference, Personalized cancer treatment, Multimodal learning, Federated learning, Clinical decision support
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
Bias Mitigation in Federated Healthcare Cloud Models
Federated learning (FL) provides a privacy sensitive model training approach that can be applied to train predictive models in more than one hospital without having to share raw patient information. Nevertheless, heterogeneity in hospital data, which is created by differences in demographics, diagnostic practices, and treatment procedures, may create bias in algorithms and produce unfair or unfair predictive results. The proposed study is aimed at identifying and preventing bias in federated healthcare cloud models to provide equal decision-making opportunities to various groups of people. Fairness measures like demographic parity and equalized odds are used in bias detection, which is accompanied by model performance audits across institutions. Mitigation techniques consist of reweighting data, training with fairness constraints and subsequent calibration of post-processing to achieve a tradeoff of predictive accuracy against fairness goals. Secure aggregation methods are also examined in order to preserve privacy and make collaborative fairness auditing possible in clouds. The experimental evidence shows that the inclusion of bias mitigation measures leads to a large boost in enhancing fairness without reducing the overall model utility, which makes federated learning a more trustworthy choice in practical healthcare settings.
Federated Learning, Healthcare Cloud Models, Algorithmic Bias, Fairness, Bias Mitigation, Secure Aggregation, Predictive Modeling, Equitable AI
The Effect of Psychological Stress on the Level of Hematological Parameters and Cortisol Hormone among Students in the University
Stress is a mechanism used by individuals to deal with problems and challenges that might arise from the inside or outside. The brain and the architecture of the body are directly impacted by stress. Among other things, psychological issues, health issues, sadness, anxiety, discomfort, and exhaustion can result from an inability to adjust to a stressful circumstance. This research was conducted on students of University to find out the effect of psychological stress on some immunological and hematological parameters. The research was conducted on twenty specific college students and blood samples were drawn from them in three stages. The first stage is the stage before exposure to psychological stress and during the rest period. The second stage is drawing blood from students while they are exposed to psychological stress (during exam times). The third stage is drawing blood from students after the psychological stress factor has passed and during the period of complete rest. The results showed a significant decrease in the value of the total white cell count and lymphocyte percentage and a significant increase in the percentage of neutrophil during the period of exposure to the factor of psychological stress as compared to the period before exposure to stress and then re-rise after the vanishing of stress-causing factor. On the other hand, the results showed a significant increase in the number of red blood cells and hemoglobin concentration during the period of exposure to psychological stress, in comparison with the two periods before and after the stress factor (at resting period) was removed. the results of the hormonal analysis showed a significant and clear increase in the concentration of cortisone during the period of exposure to psychological factor and as compared with the rest period before and after exposure to the psychological stress factor. For the first time in Karbala province, it has been proved that the research results showed clear influence of psychological factors on the immune status related to human health.
Stress, University students, Cortisol, WBC, RBC
Impact of Oral Contraceptives on Body Measurements and Associated Side Effects Among Women in Karbala: A Cross-Sectional Study
The study found that using OCP was strongly linked to a higher incidence of negative effects, namely headaches and weight gain.The purpose of this study is to determine the frequent adverse effects experienced by women in Karbala Province and to evaluate the influence of oral contraceptives on anthropometric measurements.Women in Karbala who were of reproductive age participated in a cross-sectional study. Both structured interviews and physical measurements were used to gather data. Participants were divided into groups according to whether or not they used oral contraceptives. To assess variations in body weight, BMI, and reported side effects, statistical analysis was employed.According to the study, using OCP was significant. The most commonly reported adverse effects were irregular menstruation, mood swings, headache, and nausea.The use of oral contraceptives is linked to a number of adverse effects and may affect body measurements. The significance of personalized counseling and monitoring of women on OCPs in Karbala is underscored by these findings.
Oral contraceptives, Body measurements , Side effects ,Women’s health ,Cross-sectional study
Translation of Arabic Folk Medical Terms with Om and Abu by AI: A Comparison of Microsoft Copilot and DeepSeek
Although "أم om" and " أبو abu" in Arabic literally mean "mother" and "father", they have several meanings, usages, and are used in numerous contexts. They are used in some surnames, technical expressions, metonyms, general collocations, and idioms. They are used in names of people, cities, monuments, birds, insects, fish, animals, plants, brands, names of medicines (أبو نمر Tiger balm), body organs (أم التلافيف many plies), and disease names and medical conditions (أبو دغيم mumps) where they are used as a prefix and do not mean “mother”. Due to their extensive use in folk medical terms, this study investigates the translation of Arabic folk medical terms containing Om and Abu by Microsoft Copilot (MC) and DeepSeek (DS) in terms of accuracy, the translation strategies they use, the causes of translation errors and whether translation students can depend on AI in translating such terms. Analysis of a sample of 205 Arabic folk medical terms containing Om- and Abu by MC and DS showed that 46% of the terms were correctly translated by MC and 66% by DS (أُمُّ الدَّمِ الأَوَّلِيَّة primary aneurysm,أُمُّ الدَّمِ البَطْنِيَّة abdominal aneurysm). MC rendered more literal word-for word translations than DS (16% and 11% respectively). Here أم Om and أبو Abu were literally translated as “mother” and “father” not as a prefix. أبو الرّكَب was translated as “father of the knees” instead of “dengue”, أم التلافيف “mother of folds” instead of omasum psalterium. MC and DS rendered lexical variants (synonyms) as cerebral aneurysm for أُمُّ الدَّمِ الدِّماغِيَّة instead of brain aneurysm. Both MC and DS rendered equivalents with a different word order from the dictionary definition (cavernous carotid aneurysm for أُمُّ الدَّمِ السُّباتِيَّةُ الكَهْفِيَّة instead of caroticocavernous aneurysm). AI translated folk medical terms containing أم Om & أبو Abu with lower accuracy than modern medical terms but higher accuracy rate than expressions of impossibility, Gaza-Israel war terminology, grammatical terms used metaphorically, and zero expressions which are current and commonly used by Arabic speakers. Comparisons with human translation of Om and Abu expressions, strategies used, causes of mistranslations and why DS outperformed MC in translating were made. Users need to understand how to prompt and guide AI models by specifying the context, intent, and audience and should interpret AI outputs critically.
Folk medical terms, Om & Abu terms, AI translation, Copilot, DeepSeek, Human translation, literal translation, word-for word translation
The Great Imitator: Pediatric Omental Torsion Presenting as Acute Appendicitis, A Case Report
We describe a case of a 10 years-old boy, who presented with signs and symptoms of acute appendicitis. Intraoperatively, he was diagnosed to have omental torsion with reactive appendicitis. We present this case to highlight the diagnostic dilemma.
omental torsion, acute abdomen , pediatrics, diagnostic dilemma
Healthcare Supply-Chain Optimization: Strategies for Efficiency and Resilience
Effective supply-chain management (SCM) is crucial to the healthcare sector in order to guarantee the prompt delivery of necessary medications, medical equipment, and personal protective equipment (PPE). The COVID-19 pandemic revealed weaknesses in international healthcare supply chains, such as inefficiencies, delays, and shortages that jeopardised operational resilience and patient safety (Govindan et al., 2020). By reviewing the literature and suggesting methodological strategies that use advanced analytics, digital technology, and sustainability issues, this paper explores healthcare supply-chain optimisation. The study identifies key issues such demand uncertainty, inventory mismanagement, procurement delays, and lack of visibility across multi-tier networks through a thorough literature assessment of 30 peer-reviewed papers published up to 2022 (Kumar et al., 2021). The approach uses a mixed-methods design that blends qualitative case-based evaluations with quantitative optimisation models. Digital enablers like blockchain, the Internet of Things (IoT), and artificial intelligence (AI) are assessed alongside optimisation methods like mixed-integer programming, simulation modelling, and data-driven decision-support systems. Research results demonstrate the advantages of blockchain for traceability, predictive analytics for demand forecasting, and green logistics for minimising environmental impacts (Marques & de Carvalho, 2020). With a focus on sustainability, efficiency, and resilience, the debate integrates theoretical and practical insights into a multifaceted framework for healthcare supply-chain optimisation. The study comes to the conclusion that creating strong supply-chain systems requires a combination of collaborative governance, sophisticated technology adoption, and ongoing risk assessment. The lack of real-time pandemic-era data and longitudinal case evidence are among the limitations. Future directions call for a closer examination of circular economy models, global-local supply chain integration, and AI-driven platforms.
Copyright (c) 2022 Journal of Medical and Health Studies