The individuals, belonging to all age groups, genders, occupations, communities, and socio-economic backgrounds, are required to pay attention to health and well-being. This needs to be from physical and psychological perspectives. Throughout individuals’ lives, they need to augment their knowledge of factors that promote good health and well-being. All types of factors should be implemented appropriately to generate the desired outcomes. Family members carry out this task during childhood, whereas during adulthood, individuals need to focus on it themselves. As a consequence of promoting good health and well-being from physical and psychological perspectives, individuals will contribute to the upgradation of various skills and abilities. Furthermore, they will perform well in a wide range of job duties and responsibilities in personal life, in educational programs at institutions of all levels, and across a wide range of employment settings. In addition, they will contribute to the upgradation of overall personality traits and standards of living. Hence, individuals need to put in their best efforts to carry out this task in an adequate and well-organised manner. Therefore, it is well-understood that health and well-being are indispensable to the lives of all individuals. The main concepts addressed in this research paper are: understanding the meaning and significance of promoting health and well-being, measures to implement to achieve this, and the advantages of promoting health and wellbeing.

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Background: Although HIV and Hepatitis B virus (HBV) have similar transmission patterns in West Africa, little is known abouthowthe population perceives HIV/HBV co-infection. This study examines people’s attitudes, knowledge, and healthseeking habits in three Liberian communities about HIV/HBV coinfection. Methods: From December 2025 to January 2026, we conducted a community-based survey in Todee, St. PaulRiver, and Greater Monrovia. A standardized questionnaire, guided by the Health Belief Model and the Theory of Planned Behavior, was given to 40 individuals who were purposefully chosen. The analysis used descriptive statistics, chi-square testing, and logistic regression. Results: Only 65.0% of participants knew about Hepatitis B, and only 35.0% identified HIV/HBV co-infection, despite 95.0% being aware of HIV/AIDS. Educational level strongly influenced testing behaviours and knowledge outcomes (β = 0.42, p < 0.01). The major obstacle to healthcare access was reported as cost (40.0%), followed by fear ofstigma (22.5%). Trust in healthcare practitioners was modest (mean 2.60/5), while felt stigma was high (3.52/5). The logistic model showed that HIV testing was substantially predicted by education (OR=1.8, 95% CI: 1.2-2.7) and healthcare trust (OR=1.5, 95% CI: 1.1-2.1). Conclusions: The Liberian population hassignificant information deficits about HIV/HBV co-infection. The findings highlight the need to remove financial barriers, eliminate stigma, train healthcare workers, and provide comprehensive health information. The cultural context of Liberia’s post-conflict situation should inform these efforts.

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Objectives: Using machine-learning approaches, the researchers aimed to identify and prioritise drivers of incomplete measles vaccination among children aged 12-23 months in Grand Bassa County, Liberia. Design: Cross-sectional research conducted in a community setting. Setting: Five randomly chosen districts in Grand Bassa County, Liberia, containing both urban and rural communities. Participants: 374 caregivers of infants aged 12-23 months, recruited using multistage sampling between October 2024 and February 2025. The response rate was 87.0%. Primary and secondary outcome measures: The primary outcome was MCV2 completion. MCV1 coverage and dropout rates were considered secondary outcomes. The important determinants were found using ensemble machine learning (Random Forest, XGBoost, and LightGBM) with weighted voting. Results: MCV1 coverage was 62.8% (95% CI: 59.8 to 65.8), and MCV2 coverage was 43.6% (95% CI: 40.6 to 46.6), resulting in a 30.6% dropout rate. The ensemble model attained an accuracy of 60.0% (AUC = 0.585, 95% CI: 0.545 to 0.625). The greatest predictors discovered by feature importance analysis were caregiver education (importance=0.156), distance to health facility (importance=0.142), trust in health workers (importance=0.138), and measles knowledge (importance=0.131). Conclusions: Caregiver education, geographic access, provider trust, and knowledge all substantially impact measles vaccination completion. Targeted interventions that address these characteristics might significantly increase vaccination coverage in Liberia and other low-resource countries.

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Mind Care AI is an AI-driven conversational agent designed to bridge the gap between unmet demand for mental health services and limited therapeutic resources. The platform combines advanced natural language processing (NLP), finetuned transformer models (Llama 3.2), cognitive-behavioural therapy (CBT) modules, and a reinforcement-learning personalisation layer to deliver real-time, evidence-based mental health support. In a four-week pilot study with 100 participants, the emotion detection module achieved an accuracy of 88% and an F-score of 0.88. At the same time, user engagement metrics showed high repeat-usage rates and positive satisfaction scores. MindCare AI operates 24/7, addresses stigma through a discreet self-care coach interface, and follows a blended model of care that escalates users to professional counsellors when necessary. Privacy is ensured through AES-256 encryption and compliance with GDPR and HIPAA. Future work includes multimodal input, multilingual support, wearable integration, and deeper integration with clinical workflows. These results validate MindCare AI as a promising, scalable solution for democratising access to mental health support.

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Coronary artery disease (CAD) continues at the forefront of mortality sources across the globe. Its early detection using heart sound signals seems promising for integration into Wearable Body Area Networks (WBANs). On the other hand, WBAN-based CAD detection systems face challenges such as noise, motion artefacts, and poor signal quality, which in turn reduce diagnostic performance. Literature surveys indicate that most current models struggle due to insufficient feature extraction, fragile classification, and poor generalisation, leading to the outlined dilemma. We propose a robust classification algorithm that combines MFCC feature extraction with Random Forests to achieve high detection accuracy, addressing these problems and filling the research gap. For our research, we used the Heartbeat Sounds datasets from Kaggle, which encompass recordings from both clinical and non-clinical environments (Sets A and B). We derived 13 MFCC features per recording and employed an 80-20 stratified train-test split to balance the evaluation. The Random Forest classifier, powered by 100 decision trees, has achieved astonishing effectiveness, with 95% overall accuracy, 0.97 F1 Score for healthy cases, and 0.86 F1 Score for pathological cases. Our results exceed those of five recent baseline papers by a wide margin in precision, recall, and overall classification accuracy. Thus, they support the validity of the method we proposed for CAD detection using real heart sound data.

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