Health Professions

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https://dspace.alquds.edu/handle/20.500.12213/10739

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    VITAGUARD AI: AN AI-DRIVEN FRAMEWORK FOR EARLY DISEASE DETECTION AND RISK PREDICTION IN CLINICAL LABORATORIES
    (Deanship of Scientific Research - Al-Quds University, 2026-05-12) Shams Owdetallah; Deema Zboun; Esra Al-Helale; Sara Alkhatib; Khalid Najjar; Amro Aboshareeha
    Background: The timely and accurate diagnosis of diseases is a cornerstone of modern healthcare, as it enables early intervention and preventative measures that significantly enhance patient outcomes. For chronic and serious conditions—including diabetes, cardiovascular issues, renal and liver diseases, and blood disorders like thalassemia—early detection is vital for avoiding complications, reducing medical costs, and easing the burden on the healthcare system. However, traditional diagnostic Objectives: The project aims to develop an integrated system powered by advanced machine learning algorithms to strengthen early disease detection and risk stratification. Methodology To address these challenges, we developed an innovative AI-driven framework based on advanced Machine Learning (ML) techniques to enhance disease classification and predict the risk of disease development in asymptomatic individuals. Our approach involves a multi-model selection process, in which multiple machine learning (ML) algorithms are evaluated using comprehensive clinical datasets and performance metrics, including accuracy, precision, recall, and F1-score. This systematic evaluation identifies the most efficient models for each disease category, ensuring an optimal predictive performance. Methods often fall short in terms of precision and efficiency, frequently leading to delayed diagnoses and missed opportunities for proactive clinical care.
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    THE PROGNOSTIC VALUE OF LACTATE AND CIRCADIAN HORMONE RHYTHMS IN ICU PATIENTS WITH SEVERE SEPSIS
    (Deanship of Scientific Research - Al-Quds University, 2026-05-12) Roaa AbuZaine; Hadil Askafi; Mohammed Abu Snania
    Background: Sepsis is a life-threatening dysregulated host response to infection. Since the 1960s, preclinical models have demonstrated that the timing of immune challenge significantly affects mortality, suggesting a critical link between circadian biology and inflammatory pathology. However, clinical translation of these findings remains limited. Objectives: This study evaluates the prognostic significance of diurnal variations in lactate and circadian hormones (cortisol and melatonin) in severe sepsis patients, assessing their correlation with disease severity scores, inflammatory markers, and mortality. Methods: This prospective observational study was conducted in a single ICU from January 2023 to October 2024. The cohort included healthy controls (n=5) and septic shock patients (n=10) defined by Sepsis-3 criteria (SOFA >2). Septic patients were stratified by survival status. Blood samples were collected at two circadian time points (early morning and nighttime) to assess diurnal variations. Plasma lactate, cortisol, and melatonin were measured and correlated with clinical severity scores (SOFA, SAPS II, APACHE II) and inflammatory markers (IL-6).
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    SMOKING PREVALENCE, KNOWLEDGE, AND PATTERNS AT PALESTINE POLYTECHNIC UNIVERSITY
    (Deanship of Scientific Research - Al-Quds University, 2026-05-12) Rushdi Zagharne; Shahd Shawamreh; Nora Taqatqa; Zaid Abu Mazen; Bahaa Zagharne; Abrar Al-Atrash; Nadia Omar
    Background: Tobacco use remains a leading cause of preventable morbidity and mortality worldwide. Smoking in its various forms continues to be highly prevalent among university students, yet institution-specific evidence in the Palestinian context remains limited. Objective: To determine the prevalence and patterns of tobacco use among students at Palestine Polytechnic University (PPU) and to examine associated sociodemographic, behavioral, and psychosocial factors. Methods: A cross-sectional study was conducted among undergraduate students (N = 781) using a structured questionnaire adapted from the WHO Global Adult Tobacco Survey (GATS). Descriptive statistics were used to summarize key variables. Chi-square tests assessed associations between categorical variables, and Pearson correlation analysis examined relationships between continuous variables. Multivariable logistic regression was performed to identify independent predictors of smoking. Statistical significance was set at p < .05.
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    OPTIMIZING MAGNESIUM QUANTIFICATION IN HUMAN SERUM: A COMPARATIVE EVALUATION OF XYLIDYL BLUE-BASED REAGENTS
    (Deanship of Scientific Research - Al-Quds University, 2026-05-12) Hiba Jafar Al-Halabieh; Jumana Khalid Odeh; Yumna Bassam Afaneh; Alaa Drabee
    Background: Magnesium (Mg2+) is a vital mineral essential for numerous physiological and biochemical processes. Accurate quantification of serum magnesium is clinicaly imperative for diagnosing electrolyte imbalances and managing chronic health conditions. While the Xylidyl Blue method remains a standard technique for magnesium measurement, its reliability is highly dependent on reagent composition and specific analytical conditions. This study aimed to optimize this methodology by evaluating various reagent formulations to enhance diagnostic accuracy. Methods: A systematic assessment was conducted on twelve distinct reagent formulations to determine their efficacy in measuring magnesium concentrations in human serum. Each formulation was rigorously evaluated for accuracy, precision, stability, and sensitivity using a standardized analytical protocol. Following initial screening, the optimal formulation, designated as Reagent R7, was selected for comprehensive validation. Its performance was benchmarked against a recognized commercial magnesium quantification kit (ELITech) using Bland-Altman analysis to assess clinical agreement.
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    KNOWLEDGE, ATTITUDE, AND PRACTICE (KAP) REGARDING THE USE OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE SETTINGS IN PALESTINE
    (Deanship of Scientific Research - Al-Quds University, 2026-05-12) Baraa Jobran; Bayan Abo-Laila; Israa Mohammad; Serin Abd-Elhameed; Ibrahim Ghannam
    Background: Artificial intelligence (AI) is rapidly transforming global healthcare systems, yet its successful integration relies heavily on the readiness and acceptance of the medical workforce. This study aims to assess the Knowledge, Attitudes, and Practices (KAP) of healthcare professionals in Palestine regarding AI applications, identifying factors influencing adoption and potential barriers to implementation. Methods: A cross-sectional study was conducted among 119 licensed healthcare providers across various departments in Palestine using a validated, self-administered online questionnaire. The instrument assessed demographic characteristics, factual knowledge, attitudes using a 5-point Likert scale, and practical engagement with AI. Statistical analysis, including Kruskal–Wallis and Chi-square tests, was performed to evaluate associations between sociodemographic variable s and KAP metrics.