Bowen University

Stroke remains a leading cause of morbidity and mortality globally, with the greatest burden being borne by low- and middle-income countries such as Nigeria. Despite the high prevalence of modifiable risk factors, primary prevention strategies remain poorly implemented, and contextual barriers to prevention are underexplored. This study explored barriers to primary stroke prevention among high-risk adults in Nnewi, Anambra State, Southeastern Nigeria. A qualitative exploratory design was adopted, utilising in-depth interviews and focus group discussions among ten adults previously identified as at high risk of stroke in an earlier profiling study. Participants were recruited from Nnewi, a suburban industrial town in Anambra State, Nigeria. Data collection continued until thematic saturation was achieved. Transcripts were analysed using the General Inductive Approach to identify recurrent categories and themes. Participants aged between 38 and 67 years, and were predominantly male with varied educational and occupational backgrounds. Seven themes emerged: low risk perception and poor screening, misconceptions about stroke risk, faith and informal cultural prevention practices, poverty and access barriers, work and time constraints, difficulty sustaining lifestyle change, and family and gender role influences. Most participants reported that their present health status reduced the need for further screening and believed that their past normal body test createda sense of security regardingn their health. Our participants also reported not deeming the risk screening as necessary, and prioritized other household needs over health screening. Stress and familiar responsibilities were reported as unequally distributed, and yet they were unwilling to eliminate some stressors due to cultural expectations. Stroke prevention in Nigeria is hindered by an interplay of misconceptions, cultural practices, poor health attitudes, and financial constraints. This study points to the urgent need for context-sensitive interventions that integrate cultural and religious considerations, improve community awareness, and address affordability barriers. Policy makers should strengthen primary health care, expand insurance coverage, and invest in culturally-tailored health education.

Accurate prediction of effluent quality remains a significant challenge in wastewater treatment due to fluctuating influent loads, sensor noise, and non-linear system behaviour. This work evaluates three machine-learning models, Decision Tree, Random Forest, and XGBoost, using a five-year dataset (1825days) comprising daily influent and effluent measurements of pH, TDS, TSS, BOD, COD, and microbial indicators. A comprehensive preprocessing framework, including noise reduction and smoothing, reduced parameter variability by 28–51% and revealed clearer long-term patterns essential for modelling. Comparative analysis showed that Random Forest provided the most reliable generalisation, achieving test R² values of 0.414 (pH), 0.317 (TDS), 0.221 (BOD), 0.200 (COD), and 0.133 (TSS), outperforming XGBoost, which despite high training accuracy (e.g., R² = 0.93 for pH), suffered substantial overfitting with test R² dropping to as low as 0.046 for E. coli. Decision Tree performed the weakest overall, with consistently low predictive accuracy across all parameters. Effluent trend analysis indicated strong operational performance, including high removal efficiencies for BOD (92.7%), COD (89.1%), TSS (91.1%), and microbial indicators (~99%), while TDS removal remained limited (29.4%). All models struggled to predict microbial parameters due to their episodic variability and weak correlation with physicochemical inputs. Overall, Random Forest emerged as the most robust algorithm for effluent forecasting. At the same time, results highlight the need for advanced feature engineering, additional process-level data, and specialised modelling approaches to improve microbial prediction. These findings demonstrate the potential of machine-learning tools to enhance real-time monitoring, early anomaly detection, and decision support in wastewater treatment plants.

Abstract Ischemic stroke remains a leading cause of mortality and long-term disability worldwide, necessitating innovative therapeutic approaches beyond conventional treatments. RNA-based therapeutics have emerged as a promising strategy to modulate key pathological processes involved in stroke, including neuroinflammation, apoptosis, oxidative stress, and neurovascular dysfunction. Small interfering RNA (siRNA) and microRNA (miRNA)-based therapies enable post-transcriptional gene silencing to downregulate deleterious pathways, while messenger RNA (mRNA) and long non-coding RNA (lncRNA)-based strategies promote neuroprotection and neuronal recovery. Despite significant preclinical advancements, several challenges hinder clinical translation, including blood-brain barrier (BBB) permeability, RNA stability, immune responses, off-target effects, and efficient intracellular delivery. Recent breakthroughs in nanoparticle-based delivery systems, exosome-mediated transport, and CRISPR-based RNA editing offer novel solutions to these limitations, paving the way for precision medicine approaches in stroke treatment. Future research must focus on optimizing RNA formulations, refining delivery mechanisms, and conducting rigorous clinical trials to establish their safety and efficacy in stroke patients. If successfully translated into clinical practice, RNA-based therapeutics have the potential to revolutionize stroke management by providing targeted, disease-modifying interventions that enhance functional recovery and reduce long-term neurological deficits.

Intussusception is the commonest cause of emergency abdominal surgery in infants globally. It is the telescoping of bowel into another portion of bowel. Most intussusceptions are ileo-colic and idiopathic in origin, with exceptional causes being Meckel's diverticulum, intestinal polyps, and lymphomas. Affected infants often present with intermittent inconsolable crying, reflex vomiting, and passage of red currant jelly-like stool. Here, we present a rare case of colo-colic intussusception with a clump of roundworms serving as the pathological lead point in an older child in Sub-Saharan Africa. A laparotomy was performed, and a stoma was fashioned at the site of obstruction after milking out the clumps of worms. The case highlights that parasitic infection, which is faeco-orally transmitted, can be a cause of a surgical abdomen with its attending problems. This serves to raise awareness of this rare cause among health care providers and the importance of preventive health, with a focus on adequate hygiene, proper sanitation, and appropriate sewage disposal, as well as prompt and proper treatment of ascariasis in the Tropics.

Abstract Formaldehyde is a commonly used fixative in histological studies, with fixation outcomes significantly influenced by physical factors such as temperature. This study investigates the impact of varying fixation temperatures on tissue morphology and protein structural integrity using formaldehyde as the fixative. Ten (10) Wistar rats were sacrificed via cervical dislocation, and their liver, lungs, and kidneys were extracted. These organs were fixed in 10% Neutral Buffered Formalin at 25°C, 37°C, and 60°C for 24 hours. Standard tissue processing was followed, with 4 µm sections prepared for histological analysis using hematoxylin and eosin staining to assess tissue morphology. Additionally, 20 µm sections were analyzed using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy to examine protein spectral characteristics, employing the Agilent Cary 630 spectrometer in the 4000–600 cm⁻¹ IR range. Results revealed that tissues fixed at 25°C and 37°C showed superior preservation, while those at 60°C exhibited significant distortion for most of the tissues. Findings also showed that there was an elevation in beta-sheets as formalin temperature increases to 60°C compared with alpha-helixes on liver, lungs, and kidneys. However, performance test using receiver’s operating characteristics (ROC) curve showed potential of α – helix /β -sheet ratio to monitor effect of formalin-temperature increase on tissues and lung tissues appeared to be a perfect model for this study showing 100% accuracy, 100% sensitivity and 100% specificity to tell apart changes occuring to protein secondary structures between 25°C controlled formalin fixation and those at higher fixed at 37°C. These findings underscore the importance of optimizing fixation temperature to balance fixation speed and tissue integrity, drawing implications for antigen recovery in immunohistochemical studies.

This chapter provides a bibliometric review of research on Generative Artificial Intelligence (GAI) in Computer Science (CS) education over the past decade. A total of 582 documents been catalogued in Scopus for the period of 2014-2024 was used. The publications were analyzed using Biblioshiny for Bibliometrix and the results were used to identify gaps in the literature and guide future research directions on GAI's impact in education. The findings revealed the surge in publications and citations post 2020 can be attributed to large language models and AI coding assistants. Thematic and keyword analyses show that GAI has been applied primarily in programming education, intelligent tutoring, personalized learning, and assessment. These applications yield outcomes such as enhanced efficiency, student engagement, and scalability, yet also raise concerns about academic integrity, over-reliance, and algorithmic bias. GAI research is spearheaded by developed nations which poses fairness concerns, and simultaneously fosters advancement and moral quandaries in computer science pedagogy.

Diabetic retinopathy (DR) is a gradual and severe consequence of diabetes that increases the risk of visual impairment and blindness globally. Early and precise identification of DR is crucial for successful care, but availability to specialist diagnostic tools is restricted, especially in low-resource settings. This study aims to improve the automated identification of diabetic retinopathy using cutting-edge deep learning models—specifically ViT, CLIP, BLIP, ViLT, Florence-2, and ResNet50—which are applied to the APTOS Diabetic Retinopathy dataset. Diabetic Retinopathy (DR) diagnosis is often hindered by poor image quality and subtle differences across varying severity levels. The grading of DR is typically categorized into five stages: No DR, Mild, Moderate, Severe, and Proliferative, as defined by the International Clinical Diabetic Retinopathy Disease Severity Scale. To address artifacts such as uneven illumination, we employ Gaussian preprocessing to smooth noise and Wavelet transforms to enhance edge details. This study evaluates the impact of Gaussian and Wavelet preprocessing techniques on the performance of Vision-Language Models (e.g., CLIP, BLIP) and CNNs (e.g., RESNET50). Results indicate that Gaussian Filtering significantly enhances classification accuracy, yielding a 37% improvement for RESNET50 and 36% for BLIP compared to the baseline. This study demonstrates that appropriate preprocessing is critical for maximizing the potential of deep learning in medical imaging. Specifically, Gaussian Filtering proved superior to Wavelet techniques, offering substantial accuracy gains for models like BLIP and RESNET50. Future work will explore hybrid preprocessing pipelines to further distinguish between the subtle early stages of the disease.

Wireless communication system is crucial to telecommunications infrastructure and has played an essential role in national growth. However, the system's performance is hindered by multipath propagation, which has negatively impact in its performance. Amplify Quantized and Forward (AQF) cooperative relaying technique is ineffective because signal quality is degraded by amplification and blockages during transmission from the relay to the destination. Hence, an EMD-based AQF cooperative relaying for wireless communication system is proposed to enhance the existing AQF. The relays responsible for sending data to the second hub were determined by the multiple relay selection process. The selected relays processed the signal by passing it through EMD and amplifying it with the relay gain. Subsequently, the boosted signal was uniformly quantized at the relay nodes before its final send-off to the destination in the second transmission phase. The results showed that the proposed EMD-AQF technique outperformed the existing AQF, achieving a 74.5% reduction in bit error rate and a 65.8% increase in throughput.