Much recent studies in trying to discover a more accurate and reliable method in age estimation have been ongoing. The study aimed at estimating age using the metrical parameters of the tibia bone. Long bones adjudged to be reliable also offer researchers an easy-to-use approach for its robustness and uniqueness. The cadaveric samples included bones of the right tibia of 78 males (53 profiled and 25 non-profiled for age) who are within the age range of 21 and 60 years. A convenience sampling technique was utilized for the bone collection. Two to three bone sections were collected from the mid-shaft of the right tibia using a hacksaw. The data was analyzed with SPSS 25. The findings showed that the mean of the marrow cavity diameter (MCD), marrow area (MA), radius, and area of cortex were 2.15 ± 0.07, 4.10 ± 0.27, 0.36 ± 0.01, and 0.45 ± 0.04, respectively. ANOVA test for variation shows a statistically significant (P=0) variation in the MCD and MA between the different age groups studied. The variation in the area of cortex was not statistically significant (P>0.05). The MCD, MA, and the radius of the cortex show weak correlation with age (r= 0.264, 0.363, and 0.031), and are hence poor assessors of age in males using the tibia.

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.

Cancer continues to be a leading cause of death globally, with almost 10 million people dying from the disease annually, presenting a significant global health challenge. While traditional therapies - surgery, radiotherapy, chemotherapy, targeted therapy and immunotherapy - have extended survival rates, they are often compromised due to systemic toxicity, inadequate pharmacokinetics, lack of selectivity, and drug resistance. Innovative drug delivery systems (DDS), especially nanotechnology-based DDS, have recently gained attention as potential methods to improve therapeutic outcomes and outcomes. This review critically examines the advances in drug delivery for cancer treatment, with particular emphasis on nanotechnology-based systems such as liposomes, polymeric nanoparticles, dendrimers, inorganic nanoparticles, exosomes, and antibody–drug conjugates. Various features such as passive and active targeting strategies, drug release in response to stimuli, internalization and intracellular trafficking, administration routes, and in vivo considerations are thoroughly reviewed. Further, the review outlines the current clinical translation, regulatory advances, and key challenges, such as biological barriers, protein corona, scalability and tumor heterogeneity. The review also outlines future perspectives - such as artificial intelligence-driven formulation development, multi-omics integration, organoid-based systems for drug validation and precision nanomedicine - are also discussed as key factors for next-generation cancer treatment. In general, advanced DDS are helping to transform non-specific, conventional chemotherapy into targeted, efficient and individualized cancer therapies.

Oral submucous fibrosis (OSMF) is a premalignant condition of insidious onset which affects the oral mucosa, pharynx, and oesophagus. Oral submucous fibrosis (OSMF) is a well-known precancerous oral lesion, characterized by scarring, tissue fibrosis, and premalignant lesions. The goal of clinical treatment is to reduce inflammation and improve patients' quality of life by enhancing mouth opening among others. The muscles of mastication are known to be affected resulting in limited mouth opening. Despite numerous therapeutic approaches, an ideal and universally accepted treatment modality remains elusive. Numerous treatment approaches for Oral Submucous Fibrosis exist, but there is limited robust evidence confirming their individual or collective effectiveness. While these treatments can alleviate the signs and symptoms of OSMF, a definitive cure remains elusive. This systematic review aims to assess and compare these various treatment modalities, focusing on their impact on clinical symptoms, functional outcomes, and disease progression. To achieve this, a comprehensive literature search was conducted across PUBMED, MEDLINE, EMBASE, and COCHRANE Library, limited to English-language publications. The search utilized incorporating the published literature till 2025 using the MeSH terms and keywords such as 'treatment modalities', 'Oral submucous fibrosis', ‘Mouth opening’, 'Diagnostic', and 'Therapeutic'. This review underscores the significance of habit control, physical therapy, intraoral appliances, as well as medicinal and surgical interventions in managing OSMF. Furthermore, it identifies areas where current knowledge is lacking, encouraging further research to develop more targeted therapies.

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.

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 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.