Avicenna - الأعداد السابقة

Introduction: Low back pain (LBP) is a prevalent problem that affects at least 80% of all individuals at some point in their lifetime. LBP was ranked sixth in terms of global disease burden and causes more disability globally than any other condition. Therefore, this study aims to assess the prevalence of LBP and associated risk factors among medical students in Egypt.

Methods: A previously validated questionnaire was adopted in our study. Quantitative data were summarized as mean and standard deviation, and the qualitative data were summarized as frequency and percentage. We used the chi-square test to detect an association between different variables and LBP at the point of data collection, 6 and 12 months.

Results: The questionnaire was filled out by 576 medical students. The total prevalence of participants who reported LBP at least once was (481/576, 83.5%). The point prevalence of LBP was 52.4%, while the 6- and 12-month prevalence was 70.1% and 71.5%, respectively. LBP prevalence was significantly associated with sex, with higher prevalence in females than males at point prevalence (57.6% vs. 47%, p = 0.011) and 6-month prevalence (75.8% vs. 64.2%, p = 0.002). Furthermore, body mass index was significantly associated with LBP during the last 6 months (p = 0.01). Also, LBP was significantly associated with physical activity frequency, chair characteristics, including sitting surface and back support, rest time, and type of daily activity.

Conclusion: There was a high prevalence of LBP among medical students in Egypt. The point, 6= and 12-month prevalence rates underscore the persistent nature of this condition. There is a need for targeted interventions focusing on ergonomics, physical activity, and lifestyle modifications to reduce the burden of LBP and improve overall well-being. Due to the limitations of study design, future longitudinal studies are still required.

This study investigates the concentrations of gross alpha and beta activities in tap water samples collected from Kumbotso local government, reporting values ranging from 0.04 ± 0.01 BqL⁻¹ to 0.9 ± 0.28 BqL⁻¹ for gross alpha activities, and 0.7 ± 0.8 BqL⁻¹ to 85.1 ± 11.7 BqL⁻¹ for gross beta activities. The findings reveal that gross beta activities exceed their alpha counterparts across all tap water samples, with both surpassing the World Health Organization (WHO)-recommended thresholds of 0.1 BqL⁻¹ for gross alpha and 1.0 BqL⁻¹ for gross beta activities. This indicates an annual effective dose exceeding WHO’s reference level of 0.1 mSvy⁻¹, highlighting a significant radiological risk to residents. The most vulnerable areas identified are Dorayi-1 and Gasau-2, where the annual effective dose rates are markedly higher than the recommended limits for adults and children. This work provides baseline radiometric data for tap and drinking water in the region, emphasizing the need for public awareness and immediate mitigation efforts to address these potential health hazards. Moreover, the findings underscore the importance of further research to confirm and expand upon these results, aiming to safeguard public health through informed policy and infrastructure improvements.

Introduction: The COVID-19 pandemic has posed significant challenges to healthcare systems, underscoring the need for effective triage tools to predict disease severity and guide clinical decision-making. Several prognostic scoring systems have been developed for this purpose, yet their relative effectiveness remains unclear. This study evaluates the performance of five commonly used scoring systems – CRB65, modified PRIEST (mPRIEST), NEWS2, Modified Early Warning Score (MEWS), and Quick COVID-19 Severity Index (qCSI) – in predicting in-hospital mortality and intensive care unit (ICU) admissions among COVID-19 patients.

Methods: A retrospective prognostic accuracy study was performed involving 7,674 adult patients diagnosed with COVID-19 at Hamad General Hospital, Qatar. Data were extracted from electronic medical records, and severity scores were calculated based on patients’ initial clinical presentation. The predictive accuracy of each score for mortality and ICU admission was assessed using the area under the receiver operating characteristic curve (ROC AUC), along with sensitivity, specificity, and Youden’s index.

Results: In the study cohort, the overall mortality rate was 1.1%, with an ICU admission rate of 5.2%. Among the various scoring systems evaluated, mPRIEST demonstrated the highest AUC for predicting mortality (0.899) and ICU admission (0.888), followed by qCSI (0.870 for mortality, 0.865 for ICU admission) and NEWS2 (0.858 and 0.866, respectively). In contrast, CRB65 and MEWS showed lower predictive accuracy, with AUC values of 0.776 and 0.672 for mortality and 0.692 and 0.670 for ICU admission, respectively.

Conclusion: The findings indicate that mPRIEST and qCSI are the most accurate prognostic scores among those evaluated for predicting mortality and ICU admission, thus serving as valuable tools for COVID-19 triage in emergency settings. NEWS2 also demonstrated strong predictive performance, whereas CRB65 and MEWS were comparatively less effective. These insights help optimize early risk stratification and resource allocation in clinical practice.

Artificial intelligence (AI) transforms trauma radiology by improving diagnostic speed, accuracy, and workflow efficiency. This study aims to review AI applications in trauma radiology, focusing on injury detection, emergency management planning, and disaster response systems. The results demonstrate that AI effectively identifies fractures, internal organ injuries, spinal trauma, and soft tissue damage through its high sensitivity and specificity. It also facilitates emergency care triage systems and supports clinical decision-making tools. In addition, AI enables mobile imaging, telemedicine capabilities and optimizes data management during disaster situations. AI shows substantial potential for improving outcomes in critical situations with limited resources despite facing obstacles related to diverse data sources, infrastructure issues, and ethical concerns. AI applications are changing how trauma radiology operates by providing better diagnostic capabilities. Yet, broader clinical implementation depends on resolving technical challenges and ethical and logistical issues while ensuring adequate validation.

Introduction: It has been determined that type A behavior patterns and coronary artery disease incidence are related. According to recent research, having type A personality traits raises the coronary disease. Although type D personality has not been thoroughly studied, some research suggests that it may also be a risk factor for those diseases. Our study aimed at examining the link between personality patterns and coronary disease.

Methods: At the Mohamed Tahar Maâmouri University Hospital in Nabeul, we conducted a cross-sectional study. One hundred individuals with coronary artery disease from the cardiology department were recruited. They were either hospitalized or outpatients. We also included 100 controls. Personality types A and D were screened using the Bortner questionnaire and the “DS-14” scale, respectively.

Results: Compared to 13% of controls, 45% of patients with coronary artery disease had a behavioral type D personality. The prevalence of type A was 35% in controls and 25% in patients with coronary artery disease. Type D personality was linked to coronary artery disease with an odds ratio = 4.8 (95% CI, 2.4–9.2) and a p = 0.002 after adjusting using binary logistic regression and accounting for confounding variables. We found that individuals with type D personalities were five times more likely to have coronary artery disease. There was no statistically significant correlation between coronary artery disease and type A personality (p = 0.123).

Conclusion: The study emphasizes the necessity of encouraging type D personality early detection to contribute to averting coronary artery disease.

Background: The concept of food addiction (FA) represents a significant shift in our understanding of the etiology of obesity and has the potential to optimize and streamline its management. The multifactorial etiology of FA requires a deeper investigation into various psychological correlates beyond genetic predispositions and the obesogenic environment.

Aim: The study aimed to identify psychological predictors of FA and investigate the relationships between FA and psychopathological symptoms in the context of adverse childhood experiences (ACEs).

Materials and Methods: A correlational, quantitative, non-experimental design was employed. A self-report assessment battery included the Yale Food Addiction Scale, the Adverse Childhood Experiences Questionnaire, and the Symptom Checklist-90. The research sample consisted of 188 participants.

Results: We identified psychoticism, depression, and ACEs as significant predictors of FA. Psychoticism and depression explained 24% of the variance of FA, while ACEs explained 10% of this variance. ACEs significantly mediated the relationship between psychopathological symptom prevalence and FA.

Conclusions: By investigating the psychological correlates of FA, this study aimed to gain a deeper understanding of how different risk factors may contribute to specific eating behaviors. These insights can inform the development of more effective personalized therapeutic interventions.

Background: Anatomy is a cornerstone of medical education, yet delivering effective instruction in resource-limited settings presents significant challenges. This study evaluates medical students’ perceptions of three prominent anatomy learning modalities, including educational videos, three-dimensional (3D) digital atlases, and plastic models, while examining differences in preference by gender and academic year. The study also explores key institutional barriers impacting anatomy education at Ibb University, Yemen.

Methods: A cross-sectional survey was conducted between April and December 2024 involving 171 undergraduate medical students from the first to third years at Ibb University. A rigorously validated 81-item questionnaire, distributed electronically via Google Forms, assessed perceived modality effectiveness, institutional challenges, and curricular preferences. Data analysis included descriptive statistics, chi-square tests for group comparisons, and multivariate logistic regression to identify predictors of learning modality preferences.

Results: Among respondents (58% female, 42% male; mean age, 21.2 ± 1.5 years), 3D digital atlases were most strongly endorsed for improving spatial visualization skills, with agreement decreasing from 88% in first-year year to 71% in third-year students (p = 0.01). Educational videos consistently facilitated conceptual understanding, with positive perceptions ranging from 79% to 72% across academic years (p = 0.21). Plastic models were viewed as less effective by senior students, particularly regarding exam recall (62% in early years vs. 48% in third-year; p = 0.02). Institutional challenges prominently included cadaver unavailability (89%) and insufficient teaching staff (78%), both highly significant (p < 0.001). Students favored blended learning formats (63%) and mixed assessment methods (70%). Logistic regression showed first-year students had over twice the odds of valuing 3D atlases (OR, 2.12 [95% CI, 1.45–3.10]) and were 24% less likely to endorse plastic models (OR, 0.76 [95% CI, 0.62–0.93]) compared to third-year counterparts. Female students had significantly higher odds of reporting laboratory overcrowding than males (OR, 1.37 [95% CI, 1.03–1.82]).

Conclusions: Medical students at Ibb University demonstrate clear preferences for multimodal anatomy learning approaches, shaped by gender and academic progression. Addressing institutional limitations such as resource scarcity and staffing deficiencies, alongside tailoring instructional methods to students’ educational stages, may significantly enhance anatomy education effectiveness in resource-constrained settings.

Diabetic ketoacidosis (DKA) is a life-threatening complication of diabetes that can lead to cerebral edema, organ failure, and even death. Traditional diagnosis depends on clinical assessment and laboratory tests, which may delay recognition and overlap with other conditions. Recent advances in artificial intelligence (AI), particularly machine learning (ML), present opportunities to improve early diagnosis, risk stratification, and management of DKA. Various ML models—such as Random Forest, K-Nearest Neighbors, XGBoost, and Long Short-Term Memory networks—have demonstrated high predictive accuracy for assessing DKA severity, prognosis, and mortality. However, many of these studies are limited by small, single-center cohorts and lack external validation. Although AI shows strong potential to transform DKA care, further standardization, validation across diverse populations, and integration into clinical workflows are required before routine adoption.