Artificial intelligence (AI), coupled with machine learning (ML) has been rapidly incorporated into pharmaceutical discovery and development. We reviewed 53 publications from 2018-2026 to summarize current applications of AI/ML in drug discovery. AI and ML have potential to impact every step of the drug development pipeline and have already shown to drastically reduce time frames for developing therapeutics. Specific deep learning models such as graph neural networks and transformers have shown promise in de novo molecular generation, molecular property prediction, and target recognition. Accurate protein structure prediction using AlphaFold allows for exploration of drug-target binding. De novo drug design with reinforcement learning allows for targeted design of molecules with desired properties. Machine learning models for QSAR provide more accurate toxicity predictions and ADMET profiling to avoid potential failures during drug development. However, current limitations include lack of interpretability, data limitations, and lack of regulatory approval. According to a review of recent literature, AI has the potential to decrease the time required for drug discovery from years to months and lower the cost of drug development. This review discusses recent advances, successful clinical examples, and opportunities for artificial intelligence/machine learning in drug discovery.

Foreign body ingestion of the upper gastrointestinal (GI) tract is a frequent cause of emergency endoscopy. Although most ingested foreign bodies pass spontaneously, some require urgent intervention to prevent serious complications. We conducted a retrospective monocentric study over three years (May 2022–July 2025) including all patients admitted for emergency upper GI endoscopy for foreign body ingestion at CHU Ibn Sina Rabat. Among 506 emergency endoscopies, 42 cases (8.3%) were performed for foreign body extraction. The mean age was 39 years, with a male predominance. Food impaction was the most common cause. The esophagus was the most frequent location. Endoscopic extraction was successful in most cases, while a minority required surgical intervention. Early endoscopic management adapted to the type and location of the foreign body ensures high success rates and reduces complications.

With the acceleration of the “Going Global” strategy in Hebei Province, this study aimed to examine the foreign-related communication competence of non-English major undergraduates and its alignment with local enterprise demands. A total of 308 undergraduates from a local application-oriented university in Hebei Province were investigated using questionnaires and semi-structured interviews, with data analyzed via SPSS 26.0. Results showed that participants’ overall competence was moderately low, with workplace English reading and writing as the core shortcoming, and structural mismatch with job requirements in equipment manufacturing, infrastructure and new energy industries. Self-rated English proficiency, gender and CET performance significantly affected the competence, while exam-oriented learning contributed little to intercultural literacy. Students had strong improvement motivation but cognitive misperception of job demands and heavy reliance on translation tools. This study provides empirical evidence for optimizing college English teaching to cultivate talents for Hebei’s internationalized enterprises.

The purpose of study was to find out the effect of concurrent complex training and aerobic training on anaerobic power of college handball players. To achieve this purpose of the study, forty five collegiate men handball players from affiliated colleges of Alagappa University, Karaikudi, were randomly selected as subjects. The age of the subjects ranged between 18 and 25 years. They were divided into three equal groups. The experimental group-1 (n=15, COM.T (a) AT, underwent complex training after aerobic training, the experimental group-2 (n=15, COM.T (b) AT) underwent complex training before aerobic training, and group 3 served as control group (n=10, CG) did not undergo any specific training. The anaerobic power was selected as criterion variable and the measurement was recorded in watts. The selected two treatments were performed 3 days in a week for the period of twelve weeks, as per the stipulated training program. The collected pre and post data was critically analysed with apt statistical tool of one-way analysis of co-variance, for observed the significant adjusted post-test mean difference of three groups. The Scheffe’s post hoc test was used to find out pair-wise comparisons between groups with. To test the hypothesis 0.05 level of significant was fixed in this study.

The rapid advancement of artificial intelligence (AI) technologies has significantly transformed digital communication while simultaneously enabling sophisticated cyber threats, particularly AI-powered scams and deepfake-based deception. Deepfake technologies, which involve the generation of highly realistic synthetic audio-visual content, are increasingly exploited for impersonation, fraud, and misinformation, thereby posing serious risks to digital trust and cybersecurity. In Nigeria, the widespread adoption of digital platforms among tertiary institution students has heightened their exposure to such threats. This study examined the roles of cybersecurity awareness, digital literacy, and media literacy in shaping students’ preparedness to detect AI-powered scams and deepfakes in tertiary institutions in Enugu State, Nigeria. A descriptive survey design was adopted, involving 469 students selected through a multistage sampling technique from universities, polytechnics, and colleges of education. Data were collected using a structured Google Forms questionnaire and analysed using mean, standard deviation, and independent samples t-test at a 0.05 level of significance. The findings revealed that students possessed cybersecurity awareness, digital literacy, and media literacy to a great extent (Grand Mean = 3.34), and demonstrated preparedness against AI-powered scams and deepfakes to a great extent (Grand Mean = 3.21). However, their ability to detect manipulated media remained relatively weak. No significant difference was found between male and female students in both awareness and preparedness. The study concludes that while students demonstrate reasonable awareness, targeted educational interventions are required to improve their ability to detect sophisticated AI-driven threats. It recommends the integration of deepfake awareness and AI fraud detection strategies into tertiary institution curricula.

The overall objective of this study was to assess the hygienic quality of food sold on the streets of Kisangani and to identify alternative solutions to combat the pathogenic bacteria contaminating these foods. Bacteriological analyses of the samples (isolation and enumeration) revealed high bacterial loads of FMAT, Enterobacteria, and Salmonella, indicating significant food contamination. Thus, the average bacterial counts ranged from 13,209.09 to 648,272.42 CFU/g for FMAT; 30 to 809.09 CFU/g for Enterobacteria; and 0 to 1,663.64 CFU/g for Salmonella. Furthermore, Staphylococci were not detected at any of the sites. High levels of FMAT, Enterobacteria, and Salmonella were observed in the municipalities of Makiso, Kisangani, and Kabondo, respectively.

The purpose of study was to find out the effect of varied neuro mucular training on muscular endurance of school athletes. To achieve this purpose of the study, forty five school boys athletes from St.Marys school Nagerkiol, were randomly selected as subjects. The age of the subjects ranged between 12 and 13 years. They were divided into three equal groups. The experimental group-1, underwent jump rope training the experimental group-2 underwent ladder training and group 3 served as control group and did not do any specific training. The muscular endurance was selected as criterion variable and the measurement was recorded in counts. The selected two treatments were performed 3 days in a week for the period of twelve weeks, as per the stipulated training program. The collected pre and post data was critically analysed with apt statistical tool of one-way analysis of co-variance, for observed the significant adjusted post-test mean difference of three groups. The Scheffe’s post hoc test was used to find out pair-wise comparisons between groups with. To test the hypothesis 0.05 level of significant was fixed in this study.

The increasing volume of Automatic Identification System (AIS) data generated by maritime vessels poses significant challenges in data storage, transmission, and real-time processing, particularly in bandwidth-constrained environments. Traditional trajectory compression methods often fail to preserve safety-critical information, which is essential for collision avoidance and maritime situational awareness. This study proposes a Risk-Aware Deep Learning method that integrates sequence-to-sequence Long Short-Term Memory (LSTM) models with attention mechanisms and a domain-informed risk assessment framework to compress AIS trajectories efficiently. By assigning dynamic risk scores based on proximity to other vessels, traffic density, navigational hazards, and vessel manoeuvres, the model prioritises the preservation of high-risk trajectory segments. Experimental results demonstrate that the proposed method outperforms traditional geometric, spatiotemporal, and autoencoder-based approaches in terms of compression ratio, reconstruction fidelity, and safety feature retention. With a risk preservation score of 95% and a compression ratio of 7.5, this model provides an effective solution for maritime data management and supports real-time monitoring, predictive analytics, and autonomous navigation. Future work will explore real-time deployment, federated learning, and the integration of multi-modal maritime data sources.

Medical students are future healthcare leaders whose personal health behaviors influence their clinical advocacy. This study assessed oral hygiene knowledge, practices, and clinical status among medical students at the University of Dongola, Sudan, and explored barriers to maintaining optimal oral health. A cross-sectional survey was conducted between July and December 2024 among 208 students in Years 2 and 6, using a validated questionnaire and clinical examinations. Data included demographics, brushing habits, awareness of systemic links, and perceived barriers. Statistical analysis employed Pearson’s Chi-square test (P < 0.05). Most participants were female (66.0%) and aged 20–24 years. A significant knowledge–practice gap was observed: although 78.0% acknowledged oral health importance, only 41.0% brushed twice daily. Caries prevalence was 54.0%, and 67.3% were categorized as having poor oral hygiene. No significant associations were found between hygiene status and gender (P = 0.556) or academic level (P = 0.995). Academic workload was the most cited barrier (70.2%). Awareness of oral health links with diabetes (44.2%) and heart disease (30.8%) was limited. Findings reveal poor oral hygiene practices and high caries prevalence among medical students, highlighting the need for curriculum integration of oral health modules and preventive screening programs to foster healthier behaviors and effective health advocacy.

الحمد لله الذي علم بالقلم، علم الإنسان ما لم يعلم، والصلاة والسلام على أفصح العرب لساناً، وأطهرهم جناناً، نبينا محمد وعلى آله وصحبه ومن تبعهم بإحسان إلى يوم الدين. تعد اللغة العربية الركيزة الأساسية للهوية الإسلامية، والوعاء الحضاري الذي استوعب علوم الأمة وتراثها عبر القرون. وفي ظل الثورة المعلوماتية التي يشهدها القرن الحادي والعشرون، لم يعد التعليم التقليدي القائم على التلقين كافياً لمواكبة متطلبات العصر، مما استوجب دمج التقنيات الحديثة في العملية التعليمية لضمان جودة المخرجات، خاصة في مرحلة التعليم الثانوي التي تمثل حجر الزاوية في بناء شخصية الطالب العلمية واللغوية. وتكتسب ولاية "كِبّي" في نيجيريا أهمية خاصة في هذا السياق؛ نظراً لتاريخها العريق في التعليم العربي والإسلامي، إلا أن هذا النظام التعليمي يواجه اليوم تحديات مفصلية تتأرجح بين الحفاظ على الأصالة وبين ضرورة الانفتاح على الوسائل السمعية والبصرية الحديثة. إن استخدام أجهزة العرض الرقمية، والحواسيب، والمنصات التفاعلية لم يعد ترفاً، بل

Bread is a food commonly sold and consumed in Kisangani. Throughout its production and distribution chain, it undergoes multiple handling processes. To mitigate the potential risks associated with its consumption, a study was conducted to assess its microbiological quality. The serial dilution technique enabled the isolation and enumeration of bacteria, while the assessment of hygienic quality was carried out in accordance with Codex Alimentarius standards. The results obtained showed that the high bacterial load was found in retailers 11, 12, 21, 31, and 51, with 11.71 × 10² CFU (salmonella), 41.23 × 10⁵ CFU (FMAT), 6.36 × 10² CFU (enterobacteria), 43.46 × 10⁵ CFU (yeasts and molds), and 27.55 × 10² CFU (staphylococci). The bread sold and consumed in four of the five bakeries sampled and that sold by retailers does not meet food hygiene standards in Kisangani.

The increasing integration of artificial intelligence (AI) into organizational environments has created new opportunities to improve information technology (IT) management processes. AI tools support managerial decision-making, automate routine and complex operational tasks, and enhance system monitoring, diagnosis, and performance optimization, enabling organizations to manage large volumes of data more efficiently and respond to operational needs more quickly and accurately. However, integrating AI into existing IT management systems presents several technical and managerial challenges, including system complexity, legacy infrastructure limitations, data governance requirements, security risks, compliance obligations, and the need for effective managerial oversight. Without a structured implementation approach, AI adoption may introduce operational risks and reduce transparency in IT processes. This paper proposes a conceptual framework for embedding AI tools into IT management systems by addressing both technical architecture and management requirements. The framework identifies key components such as data integration layers, AI analytics modules, management control interfaces, and governance mechanisms. It also highlights how predictive analytics and intelligent automation can enhance operational efficiency, risk management, and strategic planning while maintaining transparency and accountability. The study provides a structured approach to help organizations design AI-enabled IT management systems aligned with organizational objectives and effective managerial control.

Green synthesis of silver nanoparticles (AgNPs) has emerged as a sustainable, cost-effective alternative to traditional chemical and physical methods (Sati et al.,2025). This study explores the plant-mediated synthesis of AgNPs using bioactive extracts, which serve as both reducing and stabilizing agents. The resulting nanoparticles exhibit unique tunable optical properties, primarily characterized by strong Surface Plasmon Resonance (SPR). We demonstrate that these biogenic AgNPs significantly enhance the detection sensitivity of SPR-based biosensors compared to conventional substrates. Characterization through UV-Vi’s spectroscopy, SEM, and EDX confirms the formation of spherical AgNPs with high elemental purity. These findings provide a roadmap for the development of eco-friendly, high-performance diagnostic tools for environmental and biomedical monitoring.

Hepato-Adrenal Syndrome is an underdiagnosed condition that is still incompletely understood but appear multifactorial. His prevalence can reach 72% in series of hospitalized patients for cirrhosis. change in protein homeostasis imposed by hypoalbuminemia induced by cirrhosis and the decrease in all globulin binding outside of SHBG tends to cause diagnostic doubts. We report the case of a patient with a viral cirrhosis at the stage of decompensation in whom we diagnosed a hepato-adrenal syndrome suggested by evocative clinical symptoms and associated to a refractory hyponatremia. The spectacular evolution under hydrocortisone led us to reiterate our diagnosis even before performing the adrenal stimulation test. We propose a review of literature that highlights the epidemiological, pathophysiological and diagnostic aspects of this pathological entity.

This study takes the Appraisal system as a theoretical tool, focuses on the rural tourism news discourses of China Daily, and deeply analyzes the discourse logic of constructing the Chinese-style harmonious concept through three types of resources: Attitude, Engagement, and Graduation. The results show that Attitude resources account for 38.83%, among which Judgment resources reach 61.30%, focusing on ecological and employment effects; Engagement resources account for 32.71%, forming a dialogic structure through expert opinions and villager feedback; Graduation resources account for 28.46%, strengthening the evaluative intensity with quantitative data. The three types of resources work together to transform the concept of “harmony” into a concrete narrative: realizing the value unity of protection and development in the ecological dimension, promoting the integration of traditional craftsmanship and modern tourism at the cultural level, and achieving a positive cycle of industrial development and people's livelihood improvement in the economic and social fields. The study not only enriches the practice of Appraisal Theory in analyzing rural tourism news discourses but also helps expand the research perspective of discourse analysis. The analysis of such rural tourism news discourses is still worthy of further in-depth exploration in the future.