The consumption of carbohydrates significantly influences long-term health outcomes, emphasizing the importance of quality over quantity. The Glycemic Index serves as a useful metric by assessing the rate at which foods elevate blood glucose levels, with pure glucose designated as the baseline score of 100. Reliance on high-GI foods, scoring 70 or above, substantially increases the risk of metabolic conditions such as cardiovascular disease and diabetes. Conversely, incorporating whole, unprocessed foods such as fresh produce and grains introduces natural fibre that moderates the metabolic process. This fibre effectively prevents rapid sugar combustion, thus avoiding energy surges and subsequent drops. By adopting such dietary choices, individuals provide their bodies with more consistent and dependable fuel, supporting sustained energy levels throughout the day. This approach mitigates rapid sugar metabolism, thereby reducing the likelihood of mid-afternoon energy declines and fatigue. Focusing on nutrient-dense foods not only satisfies satiation but also supplies the necessary stable fuel to maintain overall balance and health over time.

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Background: Mosquito-borne diseases (MBDs) pose a significant public health risk in Liberia. While malaria remains endemic, emerging arboviruses offer an increasing danger. This study examined community-level knowledge, attitudes, practices (KAP), and perceptions of environmental risk regarding MBD in Montserrado County, Liberia. Method: A community-based crosssectional survey of 384 adults from seven distinct socioeconomic categories was conducted in Montserrado County. Data were collected using a validated and pretested questionnaire and analysed in R (version 4.3.0). The independent predictors of health-promoting activities were identified using descriptive statistics, Pearson’s chi-square tests, one-way ANOVA for group comparisons, and multivariable logistic regression. Results: The data found that malaria awareness was high (98.2%), but dengue (24.7%) and chikungunya (12.0%) had significant knowledge gaps. The mean knowledge score was 12.66 ± 3.12 (maximum 20), whereas the mean practice score was 6.53 ± 1.60. Higher education levels were significantly associated with higher knowledge scores (F = 168.93, p < 0.001) but not with yellow fever vaccination status (χ² = 4.57, p = 0.335). There was a substantial positive correlation between knowledge and practice scores. Despite high awareness of environmental risks (36.05 ± 5.56), implementing consistent preventive measures was challenging due to structural constraints. Conclusion: The findings indicate a significant “knowledge-practice gap,” with a special focus on non-malarial arboviruses. Malaria-centric paradigms should be replaced with Integrated Vector Management (IVM). Policymakers in West Africa must address the structural and environmental risk factors of MBD to enable long-term community-based prevention.

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Background: Diarrhoeal infections remain a serious public health problem in West Africa’s urban slums, where Water, Sanitation, and Hygiene (WASH) practices play an important role in disease transmission. This study investigates the sociodemographic factors that determine WASH practices and their impact on diarrhoeal disease in West Point, Liberia’s largest slum neighbourhood. Methods: A community-based cross-sectional study was conducted in West Point, Montserrado County, Liberia, between December 2024 and January 2025. Using systematic random selection, 250 households were selected from five geographic zones. WASH practices, socio-demographic variables, and the frequency of diarrhoeal disease were collected using standardised questionnaires based on WHO/UNICEF JMP standards. Statistical analysis included descriptive statistics, chisquare tests, odds ratio calculations, and multivariate logistic regression. Results: According to the survey, 27.2% of households had diarrhoea in the last three months, much higher than the national average of 22%. Households with no formal education had a 2.84-fold increased incidence of diarrhoea (AOR = 2.84). Access to improved water was 52.0%, sanitation was 25.6%, and hygiene was 11.2%, with just 1.6% of families having access to all three components. There were significant regional inequalities, with the South Zone and Coastal Zone having the highest diarrhoea rates. Inadequate formal education, low water availability, and open defecation behaviours all significantly predicted diarrhoea, according to a logistic regression analysis. Conclusions: West Point’s WASH practices are poor and are significantly associated with diarrhoea. Insufficient combined WASH access and high loads in certain areas underscore the need for tailored interventions. The findings suggest comprehensive strategies that prioritise improvements in water quality, sanitation infrastructure, and hygiene education, particularly in highburden areas and among people with low educational backgrounds.

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Objectives: The deep learning model’s capacity to recognise brain cancers in MRI images. The model is intended to automatically analyse MRI scans and determine whether a tumour is present, producing reliable, accurate classification results. Methods: A previously trained CNN was used to classify MRI images. The MRI image in the dataset was first reduced and normalised during preprocessing to ensure accurate input to the model. After processing, the model produced a probability value indicating the likelihood that the image contained a tumour. Findings: The available MRI cases were correctly classified as tumours, and no non-tumour cases were identified. The prediction’s tumour probability of 99.74% indicates how confident the model was in its classification result. Novelty: This work demonstrates a CNN-based approach to identifying brain tumours from MRI data. Even with a small input sample, the system generated accurate and reliable predictions. The proposed method demonstrates how deep learning models could aid in cancer detection and potentially serve as a useful adjunct to clinical decision-making.

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