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- Inaugural lectures by Professors from different Faculties of Lead City University
Recent Submissions
Risk Factors associated with Common Maternal Mental Disorders (CMMD) and its Influence on Physical Growth of Under-5 Children in Selected Hospitals in Ibadan
(Lead City University Ibadan, 2025-12) Saint-Perfect Oluwajomiloju, EMMA-JIMO
Maternal mental health is increasingly recognized as a critical determinant of child survival and development, yet it remains underexplored in Nigeria. This study assessed the prevalence of common maternal mental disorders (CMMD) and their association with child growth among mothers of children aged 0–59 months in Ibadan. A descriptive cross-sectional design was adopted, and 291 mother–child pairs were recruited from selected hospitals. Data were collected using the Self-Reporting Questionnaire (SRQ-20) and standard anthropometric measurements of children. Logistic regression analysis was employed to identify predictors of maternal mental health. The findings showed that 13.7% of mothers met the threshold for
CMMD, a prevalence aligning with most regional and global estimates, indicating that approximately one in seven mothers experienced common maternal mental disorders. Common symptoms included headaches (33.3%), nervousness (23.7%), and loss of interest in daily activities (24.1%), with 8.6% reporting suicidal ideation. Nutritional assessment revealed that 30.9% of children were stunted, 17.1% were underweight, 3.8% had severe acute malnutrition and 12% microcephaly while 5.8% were overweight, reflecting the emerging double burden of malnutrition. Maternal mental health was significantly associated with child acute malnutrition based on Mid-Upper Arm Circumference (p = 0.002) and head circumference (p = 0.048), though not with stunting or underweight. Independent predictors of CMMD included single motherhood (AOR= 9.75), low partner education level (AOR=7.69), high stress (AOR = 8.28), recent abuse (AOR = 8.71), and perceived unsafe neighborhoods (AOR = 45.08). The study concludes that maternal mental health is still under-recognized but has significant influence on child growth outcomes in Nigeria. It recommends integrating mental health screening into routine maternal and child health services, strengthening psychosocial support, and addressing socio-economic and community-level risk factors to improve both maternal well-being and child survival.
Keywords: Maternal Mental Health, Common Maternal Mental Disorders, Child Physical Growth, Depression, Anxiety, Somatic Symptoms
Wordcount: 286
In-vitro Phytochemical and Antibacterial Comparative Evaluation of Ocimum gratissimum and Moringa Oleifera leaves on some Enteric Bacteria
(Lead City University Ibadan, 2025-12) Oluseyi Oluyemisi ELATUROTI
The increasing resistance of some enteric bacteria to antibiotics has necessitated the need for non-conventional medicine against them. This study evaluated the phytochemical composition and antibacterial activity of Ocimum gratissimum (scent leaf) and Moringa oleifera leaf extracts against clinical isolates, comparing their efficacy with standard antibiotics. Phytochemical screening revealed the presence of phenols, alkaloids, glycosides, tannins, saponins, and terpenoids in varying concentrations, with methanol and aqueous extracts showing the highest yields. Quantitative analysis indicated that phenols and glycosides were predominant, particularly in methanol and aqueous extracts. The antibacterial activity of the extracts was tested on some enteric bacteria using different solvent fractions (Methanol, N- Hexane, Chloroform, And Aqeous) at concentrations ranging from 5– 25 µg/mL. Results showed that methanol and aqueous extracts exhibited the highest antibacterial activity, with inhibition zones comparable to or greater than some conventional antibiotics (Amoxicillin, Tetracycline, Ciprofloxacin, Ceftriaxone and Ceftazidime). Antimicrobial susceptibility testing was performed using agar well diffusion methods. Minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values were also determined. Phytochemical analysis revealed a higher concentration of phenols and glycosides in both plants, with methanol and water extracts showing the highest total bioactive content. Methanol extracts of M. oleifera exhibited the highest antimicrobial activity (overall mean: 11.92 ± 0.36 cfu/mL), compared to O. gratissimum (15.62 ± 0.59 cfu/mL). Additionally, n-hexane and chloroform extracts of M. oleifera demonstrated superior potency with mean inhibition values of 7.00 ± 0.32 cfu/mL and 10.85 ± 0.42 cfu/mL, respectively. In contrast, water extracts of O.gratissimum showed the highest activity (17.27 ± 0.80 cfu/mL), outperforming M. oleifera (14.56 ± 0.70 cfu/mL). MIC and MBC values supported these results. The overall susceptibility of bacterial isolates to plant extracts was 77.5 %, surpassing the 64.0 % susceptibility recorded for standard antibiotics. These suggest that O. gratissimum and M. oleifera may possess bioactive compounds with promising antibacterial properties.
Keywords: Moringa oleifera, Ocimum gratissimum, phytochemical, antibacterial effect, enteric bacteria
Word Count: 293
Microbial and Physicochemical Characterization of Effluent and Waste Water around a Detergent Manufacturing Complex in Ibadan, Nigeria
(Lead City University, Ibadan, 2025-12) Tayo Elijah EGBEDEYI
Industrialization has contributed immensely to economic growth, urban development, and improved standards of living across the world. The detergent manufacturing industry is one of the most polluting industrial sectors due to its high chemical input, complex production processes, and generation of large volumes of wastewater. The aim of this study is to characterize the microbial and molecular profile of effluent, workers wash water, and receiving surface water from a detergent manufacturing complex in Ibadan, Nigeria. Effluent and wastewater samples were collected from three distinct sites associated with the detergent manufacturing complex. Physicochemical parameters were measured and Microbial characterization involved both cultural and molecular techniques were carried out. The results of this study demonstrated that effluents and wastewater discharged from the detergent manufacturing complex in Ibadan are heavily polluted, with significant deviations from recommended physicochemical standards and a high microbial burden dominated by coliform bacteria. The predominance of Bacillus spp. (33 %), Staphylococcus sp. (15 %), Klebsiella spp. (3.0 %), and Escherichia coli. (6.1 %) among isolates is consistent with previous reports of the presence of microbial contamination in industrial effluents. Physicochemical analyses confirmed that the effluent was not compliant with World Health Organization (WHO) standards, particularly with respect to pH, BOD, COD, and nutrient load. Out of the three isolates tested, blaCTX-M, was detected in 2 (66.7 %) of the isolates, indicating a moderate prevalence of extended-spectrum β-lactamase (ESBL) producers in the sampled environment. The SUL1 gene, was identified in all isolates (100 %), suggesting widespread dissemination of sulfonamide resistance within effluent-associated bacteria. Overall, the study highlights the urgent need for stricter regulatory enforcement, improved effluent treatment technologies, and continuous monitoring of industrial wastewater.
Keywords: Effluents, Industrial discharge. Microorganisms, Ibadan, Pollutants, Contaminants
Word Count: 269
A Comparative Analysis of Machine Learning models in the Classification Techniques in the Prediction of Autism in Children
(Lead City University, Ibadan, 2025-12) Ebierimunu ABULE
Autism Spectrum Disorder (ASD) is a mild cognitive impairment known to affect a person’s language, communication, thought process and social behaviour. Some recent advances in machine learning made it possible to predict ASD using behavioural, demographic, and medical data, but the choice of optimal algorithms and feature selection techniques remains an open research question. This study used the ASD for children dataset obtained from the University of California, Irvine repository to investigate the performance of the selected machine learning models through a comparative analysis. Eight nonparametric models were compared in total, with four models SVM, KNN, GNB, and MLP being base learners, and the other four ensemble methods RF, XGBoost, Bootstrap Aggregating (Bagging), and Stacking. Hyperparameters of the models were autotuned, and GridSearchCV was employed to choose the model with the best combination of parameters. All base models performed brilliantly, with SVM being the best and most precise in this experiment with a result of 1, amongst all classifiers even before hyperparameter tuning. After hyperparameter tuning, SVM and MLP
ranked highest with all metrics returning scores of 100%. KNN returned 96.2% accuracy and recall and GNB trailed at 95.26% for both metrics for the ensemble methods, XGB and Bagging with SVM performed best at 100% for all metrics. RF produced an accuracy and recall of 98.1% while Bagging with KNN and GNB had both metrics at 95.2%. The classification performance of Bagging with MLP and the stacking classifier were equivalent across all metrics though, producing accuracy and recall of 99.5%. Based on the findings, it is recommended that thorough feature selection should be conducted to eliminate features that could lead to overfitting, such as "Q_Chat_10" in this study cross-validation technique was used to assess the generalizability of models to unseen data.
Keywords: Accuracy, Classifier, Defaults, Financial, Models, Predicting, Validation
Word Count: 300
Effects of Educational-Based Intervention on Knowledge and Skill of Breast Self- Examination among Nursing Mothers in Selected Secondary Health Facilities in Osun State
(Lead City University, Ibadan, 2025-12) Modupe Aduke AINA
Despite regular healthcare contact, there is limited empirical evidence on how structured educational interventions improve nursing mothers’ knowledge and skills of breast self- examination in Osun State. The study assessed the effect of an educational-based intervention on the knowledge and skills of breast self-examination (BSE) among nursing mothers in selected secondary health facilities in Osun State. A quasi-experimental design with control and experimental groups was adopted, involving 156 participants selected through multistage sampling procedure. Data collection employed a structured test paper and a teaching module. Instruments were validated through expert review and pilot testing. Data collection occurred in four stages: pre-intervention, planning, intervention, and post-intervention, with the intervention group receiving the teaching module and the control group routine health education. Data were analysed using SPSS version 27, applying descriptive statistics and independent t-tests at a 0.05 significance level. The study found that before the intervention, most nursing mothers in both groups had poor knowledge and skills of breast self-examination (BSE). After the intervention, 98.6% of the experimental group attained good knowledge, while the control group remained poor. Similarly, 82.4% of the experimental group gained good skills and 17.6% intermediate, while the control group showed no improvement. Independent t-test results showed no significant pre-intervention differences in knowledge (t(141)=0.379, p=0.705, d=-0.063) and skills (t(141)=0.553, p=0.581, d=-0.093). However, post-intervention results revealed significant improvements in knowledge (t(141)=40.889, p<0.001, d=6.843) and skills (t(141)=55.784, p<0.001, d=9.336) among the experimental group, indicating a strong intervention effect. The
study concluded that structured educational interventions significantly improved nursing mothers’ knowledge and skills in breast self-examination, unlike the control group. The study recommended integrating BSE training into maternal health services, regular follow-ups, staff training, and community outreach.
Keywords: Breast Self-Examination, Nursing Mothers, Nurse-led Intervention, Breast Cancer
Word Count: 276