Minia University

This study investigated lacosamide (LCM), a third-generation antiepileptic drug, as a potential gonadoprotective agent against cyclophosphamide (CP)-induced testicular injury. The research addressed the critical need for safer antiepileptic alternatives in cancer patients requiring concurrent chemotherapy and seizure control. Male Wistar rats underwent comprehensive biochemical and histopathological analyses including Western blot, quantitative reverse transcription polymerase chain reaction (qRT-PCR), immunohistochemistry, steroidogenic enzyme activity assessments, sexual hormone measurements, sperm quality evaluations, and molecular docking studies to evaluate LCM's protective mechanisms against CP-induced testicular damage. CP administration significantly elevated oxidative stress markers, pro-inflammatory mediators (Nuclear factor kappa B [NF-κB], Tumor necrosis factor-α [TNF-α], Interleukin-6 [IL-6], Interleukin-1β [IL-1β]), and apoptotic signaling (Bcl-2-associated x protein [Bax], caspase-3), while activating regulatory proteins c-jun N-terminal kinase 1 (JNK1) and Signal transducer and activator of transcription 3 (STAT-3) (identified as novel markers in CP-induced testicular injury). CP suppressed antioxidant defenses, steroidogenic factors (steroidogenic acute regulatory protein [StAR], 3β-hydroxysteroid dehydrogenase [3β-HSD], 17β-hydroxysteroid dehydrogenase [17β-HSD]), Heme oxygenase 1 (HO-1) expression, and androgen receptor (AR) functionality, resulting in decreased testosterone, gonadotropin levels, and compromised spermatogenesis. LCM treatment effectively mitigated these deleterious effects through multi-target protective mechanisms including c-jun N-terminal kinase 1(JNK1)-mediated apoptosis inhibition, HO-1 upregulation, IL-6/ signal transducer and activator of transcription 3 (STAT-3) signaling suppression, prevention of p-JNK1/STAT-3 crosstalk, and AR preservation. This study establishes LCM's multi-target protective efficacy, supporting its potential as a safer antiepileptic alternative providing dual benefits of seizure control and reproductive preservation during chemotherapy.

Forensic anthropology plays a key role in the identification process, especially in the estimation of important biological parameters like sex and stature, which are cornerstones in criminal investigations and disaster victim identification. While numerous studies have explored these parameters using lower and upper limb bones in various populations, data from Jordanians remain scarce. This study evaluates the predictive value of upper limb measurements for sex estimation and stature estimation in a Jordanian population, with the goal of establishing the first population-specific reference standards to strengthen forensic anthropological practice in Jordan. A sample of 200 right-handed Jordanian participants was included. Several upper limb measurements, including total arm length, upper arm length, forearm length, ulnar length, breadth of wrist, and length of hand, were recorded alongside height at the Faculty of Medicine, Mutah University. Correlation and regression analyses were applied to derive sex-specific stature estimation models, while discriminant function analysis was used to assess the potential of these variables for sex estimation. Participant ages ranged from 20 to 25 years old. All upper limb dimensions were significantly larger in males (p < 0.05). In males, ulnar length (r = 0.55) and upper arm length (r = 0.52) correlated most strongly with height, while in females, hand length (r = 0.68) and total arm length (r = 0.65) were the best predictors. The lowest standard error of estimate (SEE) for single-measurement prediction was obtained using ulnar length in males (5.65 cm) and hand length in females (4.49 cm). Multiple regression further improved prediction accuracy, reducing the SEE to 3.97 cm in females when using hand length, total arm length, and forearm length, and to 5.14 cm in males when using ulnar length, upper arm length, and total arm length. Ulnar length and wrist breadth were the best indicators for sex classification, with an accuracy of 84–85%. This cross-sectional study provides the first reference dataset for sex and stature estimation from upper limb measurements in a Jordanian population. Findings suggest the need for sex-specific, population-specific regression models, particularly in contexts where only upper limb remains are available. Future research should involve larger, more heterogeneous samples and strengthen the forensic applicability of the results by validating these equations on skeletal remains.

Autonomous Aerial Vehicles (AAVs) - known as drones - employment in delivery services is one of the promising transformative technologies. The AAV industry has taken significant steps to develop drones to fulfill the needs of delivery services. However, AAVs have limitations related to the flight range and payload capacity. Therefore, drone route planning is crucial to reducing the effectiveness of these challenges. The recent emergence of Large Language Models (LLMs) has opened new possibilities for solving combinatorial problems using in-context learning (ICL). Unlike traditional machine learning models, LLMs can generate solutions without requiring task-specific fine-tuning by leveraging solved examples within their input prompts. In this study, we explore the application of LLMs to the Drone Routing Problem (DRP), leveraging various ICL strategies to generate optimized delivery routes. Our solution ensures that drone routes are planned to reduce the traveling distance for the full route. Notably, it ensures that drones don't mess any delivery points and fast delivery routes. Through extensive experimentation, we evaluate the effectiveness of different prompt engineering techniques in guiding LLMs to produce high-quality, non-hallucinated route plans. We compared our model results to heuristic-based generated routes to demonstrate the variation between our technique and other techniques. The results demonstrate that LLMs, when properly prompted, can reliably generate valid routing solutions, highlighting their potential as a flexible and adaptive tool for drone logistics planning. Project link: https://github.com/ahmed-abdulhuy/Solve-TSP-using-GPT3.5.git.

Medical students frequently experience health problems but often resort to self-medication instead of consulting a doctor. This practice may be influenced by various factors and is associated with serious health risks. This study aimed to assess the prevalence, predictors, and contributing factors for self-medication during illness among medical students in Egypt. A cross-sectional study was conducted among 1,128 medical students from seven public universities across three regions of Egypt. Data on self-medication practices, self-reported reasons, and associated factors were collected using an online survey distributed through university-affiliated channels. Binary logistic regression analysis was used to identify factors associated with self-medication for illness. Self-medication for illness during the past year was reported by 66.6% of participants. In univariate analysis, significant predictors included older age (Crude Odds Ratio (COR) = 1.55, 95% CI: 1.21, 1.99), advancing academic year (COR range: 1.69–2.60 vs. first-year students), having a part-time job (COR = 1.68, 95% CI: 1.04, 2.73), and having a chronic illness (COR = 1.75, 95% CI: 1.11, 2.76). Self-medication was also associated with confidence in self-diagnosis (COR = 1.66, 95% CI: 1.28, 2.16), barriers to healthcare access (COR = 1.55, 95% CI: 1.21, 2.00), discrimination in healthcare settings (COR = 1.40, 95% CI: 1.01, 1.95), and delaying healthcare-seeking (COR = 1.77, 95% CI: 1.32, 2.38). In multivariable analysis, significant predictors included advancing academic year (Adjusted Odds Ratio (AOR) = 1.71, 2.26, 95% CI: 1.14, 4.03), confidence in self-diagnosis (AOR = 1.63, 95% CI: 1.25, 2.13), delaying healthcare-seeking (AOR = 1.54, 95% CI: 1.13, 2.10), and experiencing barriers to healthcare access (AOR = 1.31, 95% CI: 1.00, 1.72). The most common self-reported reasons for self-medication was lack of time (43.4%) and convenience (42.5%). A substantial proportion of Egyptian medical students rely on self-medication during illness, influenced by academic progression, confidence in self-diagnosis, and healthcare access barriers. Addressing these factors is crucial to promoting safer healthcare-seeking behaviors among future physicians. Not applicable.

A simple, sensitive, precise, and cost-effective high-performance thin-layer chromatographic method has been developed and validated for the simultaneous determination of Amlodipine and some Angiotensin II Receptor Antagonist drugs, including Olmesartan, Telmisartan, Candesartan, Losartan, and Irbesartan, in spiked human plasma. The HPTLC analysis utilized a mobile phase consisting of toluene: ethyl acetate: methanol: acetone: acetic acid (6:1.5:1:0.5:1, v/v/v/v/v) on an aluminum-backed layer of silica gel 60 F254. Amlodipine demonstrated a linear relationship within the range of 60-600 ng/band, while the AIIRA drugs showed linearity within the range of 90-900 ng/band. The proposed method exhibited good linearity, with correlation coefficients (r) ranging from 0.9939 to 0.9998 for all five studied mixtures. The method was validated according to International Council for Harmonization guidelines for linearity, accuracy, precision, robustness, and detection and quantitation limits. Amlodipine and the investigated Angiotensin II Receptor Antagonist drugs were successfully detected and quantified in both bulk drug and plasma samples, yielding high recovery percentages and low standard deviation values. The greenness of the proposed HPTLC method has been evaluated using the standards of greenness profile (AGREE and GAPI) and Eco-Scale.