University of Aleppo (Arabic: جَامِعَة حَلَب, romanized: Jāmiʿat Ḥalab, also called Aleppo University) is a public university located in Aleppo, Syria. It is the second largest university in Syria after the University of Damascus.During 2005-2006 the University had over 61,000 undergraduate students, over 1,500 post graduate students and approximately 2,400 faculty members. The university has 25 faculties and 10 intermediate colleges.
Abstract Aim of study Wildfires are one of the most significant natural hazards affecting ecosystems worldwide. They not only cause severe ecological damage but also pose substantial threats to human life and property. Therefore, modeling the spatial distribution of fire likelihood and potential fire behavior through Burn Probability (BP) and wildfire risk models is essential for effective planning, mitigation, and climate adaptation strategies. In recent decades, the Hyrcanian temperate forest region in northern Iran, particularly its protected areas, has been significantly affected by wildfires. This research aims to produce high-resolution and accurate BP maps, along with fire risk assessments for six protected areas in Golestan Province, northeastern Iran. Methodology The Minimum Travel Time (MTT) fire growth algorithm was implemented in FlamMap to estimate spatial BPs, utilizing customized fuel models based on the study area and considering climatic, physiographic, vegetation, and anthropogenic variables. Support Vector Regression (SVR), employing the ε-SVM approach, was used to predict fire risk rates. The accuracy of the classification maps was evaluated using historical fire ignition data. The fire risk prediction maps were categorized into four risk classes. The study analyzed the impact of various factors on BP rates across the different sites. Results and disscussion Results indicated significant variations in BP under different conditions and across different protected areas, ranging from near zero in high-elevation zones at all sites to a maximum of 0.096 in the Golestan National Park (GNP). The highest BPs were associated with lower elevations (< 500 m) and fine fuel models (FM1, FM3, and FM4). Notably, regions such as GNP, Loveh, and both Upper-Zav and Lower-Zav showed a higher concentration of fire occurrences in high and extreme-risk zones. The models achieved high overall accuracies, ranging from 88–95%, with areas classified as high and very-high risk accounting for 54–72% of the observed fire ignitions across the study regions. The findings suggest that support vector regression (SVR) methods, when combined with region-specific calibration and spatial analysis, offer a powerful and transferable approach for predicting wildfire risk.
Bronchopulmonary dysplasia (BPD) – also termed chronic lung disease of prematurity – remains a principal cause of death and long-term morbidity in very preterm infants. Azithromycin is active against Ureaplasma and has both antibacterial and anti-inflammatory properties, making it a plausible preventive therapy. Evidence, however, is inconclusive. We evaluated whether azithromycin improves survival free of moderate or severe, physiologically defined chronic lung disease in this high-risk population. Following the Cochrane Handbook, we conducted a systematic review and meta-analysis and reported according to PRISMA (PROSPERO CRD42024589280). We searched PubMed, SCOPUS, Web of Science, EMBASE, and CENTRAL from inception to April 2024. Dichotomous outcomes were pooled as risk ratios (RRs), and continuous outcomes were pooled as mean differences (MDs) using RevMan 5.4; P < 0.05 denoted statistical significance. Seven double-blind randomized controlled trials involving 1,481 infants (azithromycin, n = 740; placebo, n = 741) met the inclusion criteria. Azithromycin did not significantly reduce any primary outcome: BPD (RR, 0.97; 95
NiTi archwires are distinguished by their ability to deliver gentle, continuous forces over a wide activation range, over time, their crystalline structure has evolved: beginning with conventional NiTi, progressing to super-elastic variants, and then advancing to heat-activated. In this study, we examined three 0.016″ × 0.022″ NiTi rectangular wires: (1) super-elastic NT3 SE®, (2) heat-activated Thermal Ti-D® (active at 25 °C), and (3) heat-activated Thermal Ti-Lite® ( 35 °C)—by measuring their force release at molar, premolar, and incisor positions to determine whether unloading forces differ by location. A randomized, in-vitro trial was conducted at Aleppo University. Each group included six 30 mm wire sections for each wire type and position (total n = 18 per position). A three-point bending test at 37 °C (Deflection: 3.1 mm at 1 mm/min over a 10 mm span) measured unloading forces at 0.5, 1-, 2-, and 3-mm. Plateau length (3 to 0.5 mm), average force, and slope were recorded. Data were analyzed using one-way ANOVA with post-hoc Sidak’s test; reliability was confirmed via Pearson correlation (r = 0.877). NT3 SE® and Thermal Ti-D® wires showed similar unloading forces across positions (molar: 2.65 ± 0.11, 1.60 ± 0.18, 1.40 ± 0.18, 1.09 ± 0.19 N; incisor: 2.55 ± 0.12, 1.50 ± 0.18, 1.30 ± 0.18, 1.00 ± 0.15 N at 3, 2, 1, and 0.5 mm, respectively), with molar values approximately 10
Sleep quality significantly impacts cognitive function and mental health, yet medical students globally report high rates of sleep disturbances. This cross-sectional study investigates the prevalence of poor sleep quality and its associations with mental health and academic performance among medical students in three Syrian universities. An online survey was administered to 722 medical students from Kalamoon University, Al Sham Private University, and Syrian Private University. Validated tools, including `assessed sleep quality and psychological distress. Academic performance was measured via self-reported GPA. After exclusions, 682 participants were analyzed using SPSS v27, with chi-square tests and regression models (α = 0.05). Poor sleep quality (PSQI ≥ 5) was prevalent in 83.3
OBJECTIVES:The aim of this systematic review and meta-analysis is to evaluate the diagnostic accuracy of artificial intelligence (AI) based algorithms in detecting pneumoperitoneum on medical imaging. METHODS:Online databases were searched until June 2024. Statistical analyses were conducted using Open Meta-Analyst software and STATA 17.0. The analysis included overall sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Meta-regression and subgroup analyses were conducted to identify sources of heterogeneity among the included studies. RESULTS:Among the 14 AI-based radiograph models analysed, AI demonstrated high diagnostic accuracy for pneumoperitoneum, with a sensitivity of 83.6% (95% CI: 80.2%-86.4%), specificity of 92.9% (95% CI: 88.3%-95.8%), negative likelihood ratio of 0.18, and positive likelihood ratio of 11.76 (all P < .001). Deep learning models showed higher sensitivity (83.7%) but slightly lower specificity (91.2%) compared to machine learning models (sensitivity 77%, specificity 98%). The AUC was 0.93, with a DOR of 76. Meta-regression revealed larger sample sizes significantly improved specificity. Deeks' funnel plot showed no publication bias. CONCLUSIONS:AI models are effective in diagnosing pneumoperitoneum. The high accuracy of these models enhances the potential for rapid and precise detection, thereby improving patient management. Future prospective multicentre studies with larger sample sizes and comparisons of various models are highly anticipated. ADVANCES IN KNOWLEDGE:This is the first meta-analysis to evaluate AI's diagnostic accuracy for pneumoperitoneum, revealing high sensitivity and specificity, comparing deep learning and machine learning performance, and highlighting AI's potential to enhance early diagnosis and prioritization in clinical workflows.
