International Journal of Science for Global Sustainability
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A 9 Year Retrospective Analysis of Veterinary Surgical Cases in Selected Cities of North-Western Nigeria
Get PDFVeterinary medical records are essential for evaluating surgical procedures and predicting outcomes. This study analyzed veterinary surgical records over a nine-year period (2015–2024) from Sokoto, Birnin-Kebbi, and Gusau in North-Western Nigeria. A total of 6,088 surgical case records were reviewed from government-owned and registered veterinary hospitals and clinics. Surgical cases were categorized into cosmetic, gastrointestinal, musculoskeletal, reproductive, and ear, nose, and throat (ENT) procedures. Data were analyzed by year, quarter, month, species, sex, and age. Surgical burden peaked in Sokoto in 2017 and in Birnin-Kebbi and Gusau in 2023. The first quarter consistently recorded the highest caseload, particularly in January and February, while the second quarter especially May had the lowest. Musculoskeletal surgeries were the most common (67.08%), while ENT surgeries were the least (1.12%). Ovine species predominated, whereas no surgical cases were recorded in porcine species. More females (46.16%) than males underwent surgery, with 12% of records had no gender data. Adult animals accounted for 55.29% of surgical patients. Pre-anaesthetic assessments were absent in 98.28% of cases, and anaesthetic protocols were unrecorded in 93.63%. post-operative care was only documented in 93.18% of cases. This study provides reference data on the prevalence of profiled surgical conditions and also highlights the urgent need to employ electronic medical records keeping system, standardized protocols, and improved documentation practices through staff training and targeted intervention strategies in veterinary healthcare in the region
Application of machine learning models to classify Parkinson disease patients using accelerometer
Get PDFParkinson’s disease (PD) greatly affects mobility, emphasizing the critical need for early detection to optimize treatment outcomes. This study explores the application of advanced machine learning techniques to analyse data collected from specialized motion tracking sensors known as accelerometers. These sensors monitor individual’s movements and symptoms associated with Parkinson’s disease, with a primary focus on comprehending the unique movement patterns and related symptoms prevalent in those affected by this condition. This research leverages the capabilities of machine learning, employing diverse algorithms such as support vector machines, neural networks, and random forest. Through an integrated approach, the study aims to construct a robust ensemble model capable of synthesizing insights from these techniques. The dataset utilized originates from the University of Zaragoza Hospital, encompassing a diverse range of participants, including individuals both diagnosed and undiagnosed with Parkinson’s disease. This diversity ensures a comprehensive exploration of various movement patterns and symptoms among heterogeneous individuals. The primary objective revolves around precise differentiation between individuals affected by Parkinson’s disease and those who are not. To achieve this, the study adopts an ensemble model strategically designed to reconcile conflicting predictions from individual classifiers. This methodological approach seeks consensus by aggregating multiple classifier opinions, thereby minimizing uncertainties arising from divergent predictions. The outcomes of this study are particularly significant, with the ensemble model demonstrating superior performance over traditional machine learning models. The best-performing ensemble model, as identified through comparative analysis, achieved an impressive accuracy of 65.69%, specificity of 73.5%, and precision of 71.17%. These metrics underscore the potential of ensemble classifiers in enhancing diagnostic precision, thereby contributing to the early detection and continuous monitoring of parkinson disease
Microbial Assessment of Retention Samples and Impact in Monitoring Microbial Quality of Liquid Preparations in Pharmaceutical Industries
Get PDFThe impact of retention samples in monitoring microbial quality of liquid preparations in pharmaceutical industries cannot be over-emphasized. The challenge of microbial contamination and monitoring microbial quality of liquid preparations call for the need to keep retention samples of liquid preparations in pharmaceutical industries. Seven (7) retention samples of registered liquid preparations (Mist Magnesium Trisilicate Mixture, Paracetamol Syrup, Cough Syrup, Vitamin C Syrup, Gentian Violet, Calamine Lotion and Hydrogen Peroxide) that were not less than two years inside the retention room were collected from two selected pharmaceutical industries and microbiology purity tests were carried out using standard methods. The results revealed absence of pathogenic bacteria in all the retention samples. The highest values of 46.67 cfu/ml and 30.00 cfu/ml obtained for total viable aerobic mesophilic bacteria plate count and fungi from Cough Syrup and Vitamin C Syrup respectively might possibly be due to the use of sugar and other excipients that support microbial growth. The results show that the microbial quality of the liquid preparations represented by the retention samples were satisfactory as they conformed to the British Pharmacopeia (BP) specifications for liquid specifications. This suggests that the liquid preparations produced followed current good manufacturing practice (cGMP) as shown in the results obtained from the retention samples. Therefore, retention samples play vital role in the monitoring of microbial quality of liquid preparations and subsequently contribute to drug development and annual product quality review in pharmaceutical industries
Modelling Dynamic Panel Data Using Hierarchical Bayesian Approach
Get PDFThis study developed a hierarchical Bayesian framework for dynamic panel data models to address the challenges of unequal variances regularly encountered in empirical research. Dynamic panel data models with temporal dynamics were estimated using OLS and GMM. It was discovered that estimated parameters were biased, inconsistent and unreliable. The developed estimator proved to be robust in addressing the shortfall and outperformed other estimators under various panel data configurations. Estimating a robust hierarchical Bayesian model for dynamic panel and assessing its performance under various panel structure (N<T, N=T, N>T) of cross-sectional units (N) and time dimensions (T). The framework provides great flexibility in handling heteroscedasticity and diverse panel systems by using several Markov Chain Monte Carlo (MCMC) experiments were carried out to asses the performances of the estimators are gauge the suitability of posterior estimators. It was discovered that the developed estimator exhibited numerical standard errors and posterior values which closely match fixed parameter values. Sensitivity analyses also demonstrate the significance of prior specification, showing that notably informative priors improve estimation accuracy. This work addresses key limitations of classical and Bayesian methods, providing practical framework for modelling dynamic panel data. Its application amplifies to various empirical contexts, making it a treasured tool for researchers managing datasets with unequal error variances
Impact of Doping Concentration and Temperature on Carrier Mobilities in 4H-SiC Based Semiconductor Devices: A Sentaurus TCAD Simulation Study
Get PDFThis study examines the influence of doping concentration and temperature on carrier mobilities in semiconductor device development. The introduction of small quantities of dopants during crystal growth or device fabrication significantly affects the device's electrical properties. Using the Sentaurus Technology Computer Aided Design(TCAD) modeling tool, the work simulates and evaluates device characteristics, focusing on parameters like electron and hole mobilities. Doping concentration and temperature emerge as crucial factors shaping device behavior in both the epitaxial and bulk regions. Findings show that higher doping concentrations lead to reduced mobility, with electron mobility consistently exceeding hole mobility, underscoring electrons' role as the majority carriers in n-type devices. Schottky and ohmic contacts in different regions further illustrate the impact of doping on electrical properties. Additionally, the study reveals a linear relationship between temperature increases and reduced electron and hole mobilities, attributed to mechanisms such as impurity-induced defects and lattice vibrations. The stability of the detector's performance is emphasized at constant temperatures. Ultimately, this research offers valuable insights for optimizing semiconductor devices and advancing electronic technology.
 
Evaluation Of Pesticides Practices and In Silico Toxicity Profilling Of Pesticides Used In Tsafe Zamfara State, Nigeria
Get PDFsome of the major difficulties such as time and cost in evaluating pesticides through traditional techniques (in vitro and In vivo). This study aims to identify the type of pesticides being used and to access attitudes towards pesticide use and safe handling and analyze using computational methods the toxicity and pharmacokinetic properties of commonly used pesticides in Tsafe. Data was gathered through interview of 206 respondents. Chemical library of chemicals from Tsafe was created and then screened using for ADMET, drug likeness and toxicity prediction was done using insilico methods (SwissADME). Using PyRx and PyMol, molecular docking was performed on the predicted target proteins acetylcholinesterase and MAP kinase for two of the pesticides in Tsafe. The results indicated that the pesticides used in Tsafe were chlorpyrifos, cypermethrin, deltamethrin, dichlorvos, paraquat, and glyphosate. According to the prediction analysis's results, all selected pesticides exhibited good human intestinal absorption. Dichlorvos and paraquat were able to cross the blood-brain barrier, and all of the pesticides were found to be highly toxic. The docking studies indicate that chlorpyrifos has a lower binding energy to acetylcholinesterase compared to donepezil, a strong cholinesterase inhibitor drug. Cypermethrin exhibits a higher binding energy for MAP kinase compared to methotrexate, a well-established anticancer agent. Six highly toxic pesticides were identified, with some capable of crossing the blood-brain barrier. Docking analysis showed strong protein interactions, indicating potential health risks
Heavy Metal Pollution and Soil Health around Local Aluminum Pot Production Sites in Sokoto
Get PDFThis study analyzed soil samples from three locations around a local aluminum pot production center in Sokoto, Nigeria, in April 2025. The samples were tested for various physicochemical properties and heavy metal content. Key findings include pH levels ranged from 6.5 to 6.7, organic matter content was between 0.280% and 0.384%, nutrient levels varied as nitrogen found to be 0.07% to 0.081%, phosphorus 3.13 to 3.36 mg/kg and potassium 1.38 to 1.59 cmol/kg. The sodium, magnesium and calcium were found to be within the ranged of 0.65 – 0.74, 0.40 – 0.70 and 0.45 – 0.60 respectively. The heavy metal concentrations exceeded FAO/ISRIC standards as lead found to be 0.085 to 0.165 ppm, chromium 0.4268 to 0.5087 ppm, cadmium 0.0544 to 0.1045 ppm, copper 0.0544 to 0.2932 ppm and zinc 21.4670 to 26.9675 ppm. The study suggests that the soil around the aluminum pot production center poses environmental and health risks due to elevated heavy metal levels
Prognosticating Risk Factors of Undernutrition in Under Five Years Children in Nigeria Using Ensemble Technique
Get PDFInsufficient nutritional intake and nutrient imbalances is referred to as malnutrition, which signifies a universal health matter that has a substantial consequence on children, leading to short-term and long-term consequences for their growth, development and general comfort. This research utilized the potentials of stack ensemble techniques in enhancing the prediction of Child undernutrition in Nigeria. The study exploits three key indicators such as: underweight, stunting and wasting from Nigerian Multiple Indicator Cluster Survey (MICS) 2021 dataset. Four different machine learning classifiers were employed namely: decision tree (DT), random forest (RF), k-nearest neighbors (KNN) and the stack ensemble technique meta classifier that is concatenated with logistic regression (LR) to form stack ensemble technique in other to improve the prognostic risk factors of children under the age of five, considering their nutritional status. Based on their ability to predict outcomes, these classifiers were assessed and contrasted using standard machine learning evaluation metrics like. Accuracy, precision, recall and F1-score respectively. The effectiveness of machine learning techniques, specifically the Stack Ensemble technique, in prognosticating and comprehending the factors that donate undernutrition in Nigerian children is demonstrated by this study. The results offer important information for developing policies and focused actions
Unveiling The Safety Disparity: Knowledge VS Practice in Waste Scavenging in Yola North Urban Area, Nigeria
Get PDFWaste scavenging is an important activity in developing countries aiding resource recovery and poverty reduction. Despite knowing the dangers posed by the activity, the waste scavengers’ occupational safety is to some extent compromised due to poor utilization of Personal Protective Equipment (PPE). This study investigated the disparity between knowledge and practice of waste scavengers regarding occupational risks and safety measures in Yola urban area, through surveys and interviews with 405 participants across 55 dealers' depots. The study findings discovered 375 waste scavengers recognized the potential hazard associated with scavenging job, out of which only 41 were actually using PPEs regularly. While 30 claimed ignorant of the potential risks of the activity. This therefore categorized waste scavengers into three distinct groups in Yola urban area viz: Safe Scavengers" who possess the knowledge of dangers of scavenging and use one or more PPE regularly 41 (10.12%); “Knowledgeable non-PPEs users" who have the knowledge but do not use PPE 334 (82.47%); and "Unaware non-PPEs users" lacking both knowledge and PPE usage 30 (7.41%) mostly children. Null hypotheses were formulated where significant difference observed between Knowledgeable non-users and safety practice (p=0.016). On the other hand, no significant difference found between Safe Scavengers and safety practice (p=0.053); as well as Unaware non-users and safety practice (p=0.060). Pie chart clearly shows the knowledge/practice gap and three distinct group of the scavengers. Recommendation proposed involvement of scavengers’ middlemen and waste management authorities in developing comprehensive strategies that promote a culture of safety, provide training on safety practices, and ensure the availability of appropriate protective gear to narrow down the gap
Antioxidant Activity and Bioactive Compounds of Vernonia Amygdalina L. Leaves in Methanolic and Aqueous Extracts
Get PDFIn recognition of its numerous medicinal benefits, bitter leaf (Vernonia amygdalina L.) is widely used in traditional medicine. Despite substantial research, a comprehensive comparative analysis of the antioxidants and phytochemical properties of its methanolic and aqueous extracts remains undocumented. This study addresses this gap by examining the differences in antioxidant values and phytochemical constituents between the two extracts using advanced analytical techniques. NMR and FTIR spectroscopy were employed to investigate probable phytochemicals, while HPLC and GC-MS determined the differences in biological components. Both DPPH and ABTS radical-scavenging tests evaluated variations in antioxidant activity. NMR Results indicated the presence of aromatic protons, phenolic hydroxyl groups, methyl groups, and methoxylated substances by the singlet signal at 7.1–7.5 ppm, singlet at 4.0–4.5 ppm, doublet at 1.0–1.5 ppm, and singlet at 2.0–2.5 ppm, respectively. FTIR results showed a C=O stretching at 1700 cm-1, an O-H stretching at about 3250 cm-1, and a C=C stretching between 1500 and 1600 cm-1. Methanolic extracts had higher phytochemical values according to GC-MS and HPLC data, except for rutin, delta-cadinene, and alpha-cadinol, which were higher in the aqueous extract. The methanolic extract exhibited superior antioxidant activity in DPPH and ABTS assays, with maximal scavenging activities of 90.5% and 96.8%, respectively. At 200 µg/mL, the aqueous extract demonstrated scavenging activities of 85.4% and 92.7%. These findings underscore the enhanced potential of methanolic extract for therapeutic applications