Abstract Antimicrobial agents are widely used to treat bacterial infections; however, their misuse contributes to the growing problem of antibiotic resistance. This study assessed the prevalence of antibiotic resistance among bacterial isolates from patients in Gharyan City, Libya, and explored behavioral factors influencing resistance. Between January and June 2024, 66 clinical samples (urine, stool, vaginal swabs, blood, pus, and sputum) were collected from seven medical laboratories. A questionnaire survey was conducted among three groups: medical workers (n=50), non-medical individuals (n=50), and pharmacists (n=50). Laboratory identification employed selective media, Gram staining, and microscopy, while antibiotic susceptibility was tested using disc diffusion. Results revealed widespread inappropriate antibiotic use: 70% of pharmacists dispensed antibiotics without prescriptions, 40% provided incorrect dosages, and only 10% held formal pharmacy qualifications. Among medical workers, 40% used antibiotics without prescriptions and 56% failed to complete treatment courses. Infections were more common in females (77%) than males (23%), with the highest prevalence in younger age groups (<32 years, 39.4%). Staphylococcus spp (32%) and E. coli (27%) were the most frequent isolates. Overall resistance was high (75%), particularly in Pseudomonas spp (100%), E. coli (83%), and Staphylococcus spp (76%). Azithromycin (67%) and Doxycycline (49%) showed the highest resistance rates, while Meropenem (2%) and Nalidixic acid (3%) remained largely effective. These findings highlight alarming levels of antibiotic resistance in Gharyan, driven by misuse and poor prescribing practices. Strengthening pharmacist and physician training, enforcing prescription regulations, and raising public awareness are essential to mitigate resistance and preserve antibiotic efficacy. Keywords. Antibiotic Resistance, Bacterial Infection, Misuse of Antibiotics, Control and Surveillance.

Abstract

Periodontal disease is a multifactorial condition characterized by chronic inflammation and progressive destruction of supporting tissues, with significant implications for oral health and systemic well-being. This study investigated the correlation between inflammatory responses and regenerative outcomes in periodontal supporting tissues, emphasizing the role of epigenetic regulation. A mixed methods approach was employed, combining clinical assessments, patient-reported outcomes, and molecular analyses. Spearman correlation analysis revealed strong associations between clinical indicators of inflammation and perceived regenerative improvements, underscoring the interplay between destructive and reparative processes. Demographic and lifestyle factors, including smoking and systemic health conditions, were found to influence variability in responses. Regenerative therapies demonstrated favorable outcomes in terms of clinical attachment gain, alveolar bone support, and patient satisfaction. These findings highlight the importance of integrating epigenetic insights with clinical practice to develop personalized strategies for managing periodontal disease and enhancing tissue regeneration

ABSTRACT This study investigates the effects of self-assessment on students’ motivation and language proficiency among English Department students at the Faculty of Arts, AL-Assaba. A quantitative research design was adopted, using a questionnaire distributed to a sample of 60 students. The findings indicate that self-assessment plays a significant role in enhancing learners’ motivation, self-awareness, and perceived language proficiency. Most students reported positive attitudes toward self-assessment and felt it Improved their English skills. The study concludes that incorporating self-assessment into English language instruction can foster learner autonomy and Improve learning outcomes. It recommends Integrating self-assessment practices into curricula and providing guidance to students on effective self-assessment. Keywords: Self-assessment, Motivation, Language Proficiency, Learner Autonomy, English Department Students, Higher Education.  

تقدم هذه الورقة بحثاً حول إطار عمل يعتمد على التعلم العميق للتصنيف متعدد الملصقات لأمراض الصدر باستخدام صور الأشعة السينية وبنية EfficientNet. يعالج هذا الإطار تحدي تشخيص الحالات المتعددة التي تتشابه في المظهر وغالباً ما تحدث معاً. يستخدم إطار العمل تقنية التعلم بنقل الخبرة (Transfer Learning)، مع إجراء ضبط دقيق لعدة متغيرات من نموذج EfficientNet (من B0 إلى B4) باستخدام مجموعة بيانات NIH ChestX-ray14. كما يوظف البحث تقنيات Focal Loss و Stochastic Weight Averaging لمعالجة اختلال توازن البيانات وتحسين قدرة النموذج على التعميم. يعتمد هذا النهج على آليات الانتباه (Attention Mechanisms) وتقنية Grad-CAM لجعل النتائج أكثر قابلية للتفسير، مما يثبت أن التوقعات تستند إلى ميزات سريرية هامة. حقق النموذج درجات عالية في مقياس ROC-AUC للحالات الأكثر وضوحاً مثل الانصباب (0.8545)، وتضخم القلب (0.8003)، والاسترواح الصدري (0.7820). ومع ذلك، واجه النموذج صعوبة أكبر في تصنيف الحالات الأكثر دقة مثل الارتشاح، والعقيدات، والالتهاب الرئوي. يؤدي إطار العمل بشكل يضاهي أو يتفوق على العديد من نماذج الشبكات العصبية التلافيفية (CNN) القائمة من حيث الدقة والكفاءة. يوفر هذا البحث سير عمل قوياً وقابلاً للتكرار لتطبيقات الأشعة، مع إمكانية استخدامه في مهام تشخيصية أخرى. كما تشير الدراسة إلى بعض المعوقات، مثل اختلال توازن الفئات والحاجة إلى دعم أفضل لاتخاذ القرارات السريرية، وتقترح تحسينات مستقبلية مثل تقنيات أفضل لتعزيز البيانات ونمذجة عدم اليقين.