Antimicrobial resistance (AMR) is a growing global threat, undermining the efficacy of antibiotics and contributing to poor clinical outcomes. Escherichia coli (E. Coli), though typically a harmless gut commensal, plays a pivotal role in the carriage and spread of resistance genes through plasmid-mediated mechanisms. This review investigates three key AMR genes, QnrA, ErmB, and AAC(6’)-Ib-cr, in fecal E. Coli isolates. These genes confer resistance to fluoroquinolones, macrolides, and aminoglycosides, respectively, and are often embedded within mobile genetic elements, increasing their capacity for horizontal transfer across species and environments. Using molecular detection techniques, including conventional PCR, multiplex PCR, and qPCR, the study explores gene prevalence and public health implications in under-monitored regions, particularly Nigeria. Findings highlight the silent burden of AMR in asymptomatic carriers and emphasize the importance of fecal surveillance, integration of genomic tools, and One Health strategies. The study calls for routine gene-based monitoring and stricter regulation of antibiotic use to curb the spread of resistance determinants in both clinical and community settings.

Microplastics (MPs) are widespread pollutants in freshwater aquaculture systems, entering through the degradation of pond linings and nets, contaminated aquafeeds, agricultural runoff and atmospheric deposition. Nile tilapia (Oreochromis niloticus) an omnivorous and intensively farmed fish, is heavily exposed to MPs however, the interaction between MPs, gut microbiota and mucosal immunity in this species has not been comprehensively assessed. In this review, we summarize recent evidence on the sources, uptake and accumulation of MPs and on how MP exposure causes gut dysbiosis, characterized by decreased alpha diversity, increased abundance of Proteobacteria and reduced levels of beneficial Firmicutes and Fusobacteria, which may affect short chain fatty acid biosynthesis. We then examine how dysbiosis and barrier dysfunction are linked to activation of the TLR4/MyD88/NF-κB, MAPK, JAK/STAT, Nrf2/Keap1 and NLRP3 inflammasome pathways and to oxidative and histopathological damage in the intestine and liver. Special attention is given to age dependent susceptibility, as juvenile fish show dysregulated inflammatory genes under conditions in which no effects are observed in adults. Candidate molecular, biochemical, hematological and non-invasive mucus-based biomarkers are discussed alongside the feasibility of multi omics approaches (metagenomics, transcriptomics, proteomics and metabolomics) for resolving host microbiota environment interactions. Mitigation strategies, including multi species probiotics, algal supplements, engineered biochar, feed management and biodegradable materials, are also evaluated. Key knowledge gaps remain in chronic low dose exposure, mixture effects, nanoplastics, reversibility of dysbiosis and methodological standardization. Addressing these gaps will be essential for developing resilient and sustainable tilapia aquaculture.

This study investigated the effect of engine load on the performance and emission characteristics of a compression ignition engine fuelled with watermelon seed oil methyl ester (WOME) modified with metal and oxygenated additives. Watermelon seed oil was extracted and converted to biodiesel through acid esterification followed by alkaline transesterification. The oil yield was 48.7828%, while the biodiesel yield was 85.28 vol.%. Acid esterification reduced the free fatty acid content from 5.4464 to 2.347 mg/g, facilitating effective alkaline transesterification. The physicochemical properties of the produced WOME included a density of 0.881 g/cm³, kinematic viscosity of 3.146 mm²/s, moisture content of 0.0389%, cetane number of 57.34, flash point of 94°C, and cloud point of −6°C, indicating favourable fuel characteristics for compression ignition engine applications. The WOME was blended with petroleum diesel and modified with copper (II) oxide (CuO), copper (II) sulphate (CuSO₄), and acetoacetic ester (Ace), and the resulting fuel blends were tested under varying engine loads. The results showed that engine load significantly influenced fuel consumption, brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), exhaust gas temperature (EGT), and gaseous emissions. At a 30 kg load, the lowest BSFC recorded was 0.2438 kg/kW-hr, while the highest BTE was 38.06%. Increasing engine load generally increased fuel consumption, H₂S, NO₂, CO, and EGT, while CH₄ emissions decreased. The optimized WOME blend demonstrated the best overall engine performance and the lowest overall emissions, with the minimum CH₄ emission of 52.7 ppm. The study concludes that watermelon seed oil is a promising renewable feedstock for biodiesel production and that modification of WOME with CuO, CuSO₄, and acetoacetic ester can improve engine performance and emission characteristics under varying load conditions. The findings demonstrate the potential of additive-modified WOME-diesel blends as a cleaner and more efficient alternative to conventional petroleum diesel in compression ignition engines.

Expectant and lactating mothers resort to medicinal plants for it claimed nutritional values, such practices effects on offspring neurodevelopment and cognitive functions are unclear. Hence, this study assessed cognitive function and hippocampus in juvenile rat exposed to aqueous leaf extract of Jatropha tanjorensis (ALEJT) in-utero and postnatal life. Thirty litters were randomly assigned 3 groups (n=10) following dams intake. Control: 20ml/kg of distilled water; In-utero and In-utero+postnatal days 15-21 ALEJT 500mg/kg oral exposure respectively. Cognitive assay after weaning: Novel object recognition task (NORT); Morris water maze (MWM) and T maze; Histological studies: hematoxylin and eosin staining. NORT short term memory; exposed groups index of habituation increased against control. Index of discrimination differed not. Long term memory: Group 3 index of habituation increased (p<0.05) against control, and was positive against other Groups. Exposed groups index of discrimination was negative against control. Group 2 (p<0.05) reduced against control. During MWM acquisition training, day 3 swim latency (SL) was shorter in Group 3 (p< 0.01) against control. During reversal training, day 1 SL was shorter in Group 3 against control (p<0.05) and Group 2 (p<0.01); shorter on day 2 (p<0.05) against Group 2 and on day 3 (p<0.01) against control. During probe trial Group 3 spent time in Northwest quadrant against the retention quadrant (Southwest). Group 3 SL during visible platform was shorter (P<0.01) against control. Percentage alternation differed not. Exposed rats hippocampus showed altered pyramidal cells, with reactive astrocytes present. ALEJT impairs cognitive function with potential injuries to hippocampus of exposed rats.

This study investigates the relationship between strategic leadership, employee performance, and the mediating role of employee engagement at Enugu State University of Science and Technology (ESUT) in Enugu State, Nigeria. Utilizing a mixed-methods approach, data was collected through surveys and interviews with 200 employees across various departments. Results indicate a significant positive correlation between strategic leadership and employee performance, mediated by high levels of employee engagement. The findings revealed that engaged employees demonstrate a 30% increase in performance metrics compared to their disengaged counterparts. Furthermore, strategic leadership practices, such as clear communication and supportive management, were found to enhance engagement levels among employees. The study underscores the importance of strategic leadership in fostering an environment conducive to employee engagement, ultimately leading to improved organizational performance.

Assessment of therapeutic outcomes in hospitalized patients is important for ensuring effective treatment and supporting clinical decision-making. Main objective of the study was to evaluate therapeutic outcomes in hospitalized patients using laboratory and vital parameters, assess drug effectiveness, and identify factors influencing clinical improvement in a tertiary care teaching hospital. A prospective observational study was conducted over six months in the General Medicine department, including 100 patients. Case files data on demographics, diagnosis, prescribed medications, vital signs, and laboratory parameters were collected using a structured form and analyzed using descriptive statistics. Among the patients, 54% were female and 46% were male, with a mean age of 44.3 ± 13.21 years. The most common conditions were renal disorders (17%), acute febrile illness (10%), and diabetes mellitus (9%). Most patients had a hospital stay of 4 days (37%). A total of 452 drugs were prescribed, with antibiotics (17.52%) and proton pump inhibitors (16.5%) being the most frequently used. Laboratory findings showed improvement in haemoglobin, sodium, and potassium levels, while reductions were observed in WBC count and random blood sugar. Vital parameters improved notably in blood pressure and body temperature. The observed improvements in laboratory and vital parameters indicate effective therapeutic management and highlight the importance of continuous monitoring in hospitalized patients. Pharmacist-led monitoring plays a significant role in optimizing therapy, promoting rational drug use, and enhancing patient safety, thereby improving therapeutic outcomes in hospital settings.

In order to address the challenge brought by artificial intelligence in traditional English teaching, we analyze the major challenges and problems faced by English teaching in the artificial intelligence environment. Based on practical teaching conditions, we put forward coping strategies from the perspectives of teachers, students, classroom teaching, categorized training, curriculum culture and usage specifications. By adopting these strategies in English teaching, the data reveal that the integration of AI has exerted positive impacts on teacher development, student growth, teaching modes, teaching resources and teaching effectiveness. The study shows that AI instructional design developed based on practical conditions and the integration of AI technologies and tools into teaching activities can exert positive effects.