Lagos State Polytechnic

Digital health and artificial intelligence technologies such as telemedicine, mobile health applications, clinical decision support systems, and predictive analytics are rapidly transforming healthcare delivery across diverse global settings. Although these innovations offer substantial potential to improve efficiency, expand coverage, and enhance clinical outcomes, growing concern remains that they may unintentionally reinforce or worsen existing health inequities affecting underserved populations. Because digital and artificial intelligence health research spans multiple disciplines, this narrative review synthesizes evidence from global health, health equity frameworks, artificial intelligence ethics, and sociotechnical systems to examine how digital transformation shapes equitable health outcomes. The findings show that digital and artificial intelligence tools can improve access to healthcare services, reduce geographic and logistical barriers, and enhance diagnostic accuracy. However, these benefits are not evenly distributed. Persistent disparities related to digital connectivity, affordability, digital literacy, data representation, and algorithmic performance may result in exclusion or harm among marginalized groups. To systematically explain these patterns, this review introduces the Equity Pathways Model, which identifies three interconnected mechanisms through which digital health and artificial intelligence influence equity outcomes. The Access pathway explains how digital tools can reduce traditional barriers to care while simultaneously creating new forms of exclusion linked to technology access and digital skills. The Quality of Care pathway highlights how artificial intelligence systems may enhance clinical decision making but also produce biased or unreliable outcomes for underrepresented populations. The System Structural pathway emphasizes how governance arrangements, political economy, institutional capacity, and sociocultural context shape whether digital innovations mitigate or reinforce structural inequities. Together, these pathways provide a framework for distinguishing digital health and artificial intelligence interventions that enhance equity, those that have neutral effects, and those that exacerbate disparities. The review offers an international perspective and proposes equity by design principles to guide researchers, policymakers, and technology developers in ensuring that digital health and artificial intelligence innovations contribute to the advancement of global health equity, particularly in low resource and underserved settings.

<b>Objectives: </b>This systematic review aimed to evaluate the role of secretory immunoglobulin A (sIgA) and related mucosal immune markers in modulating susceptibility to colonization or infection by antimicrobial-resistant (AMR) bacteria in human and animal models.<br /> <b>Methods: </b>Studies were included if they assessed mucosal sIgA levels about AMR outcomes, reported original data, and were published in English between 2015 and June 2025. Exclusion criteria included a lack of mucosal immune data or AMR-related endpoints. Databases searched included PubMed, ScienceDirect, and EBSCO (Knowledge and Library Hub), with the last search performed on June 10, 2025. The quality of the included studies was assessed using the checklist for quantitative studies, and data were extracted and synthesized narratively, stratified by immune markers, study design, and mucosal compartment.<br /> <b>Results: </b>After the systematic search of the databases considered for this review and review of available literature indexed in them, eleven studies that met the inclusion criteria of this study were included in this review.  The mucosal sites investigated in most of these studies were gastrointestinal, respiratory, oral, and genitourinary mucosa. Of the eleven (11) studies, four carried out investigations in humans while the remaining seven utilized animal models. All the included studies reported secretory immunoglobulin A (sIgA) level, whereas five of the studies reported other indicators, such as cytokines and IgG, alongside sIgA levels. There was consistency in their results as increased sIgA levels were linked to decreased infection or colonization by AMR pathogens. Moreover, vaccination-based interventional studies showed an increase in sIgA post-vaccination or probiotic treatment.<br /> <b>Conclusion: </b>Although in this review we included studies with varying methodological approaches as well as studies featuring humans and animal models, the evidence from this review indicates that mucosal sIgA offers protection against antimicrobial-resistant (AMR) bacteria colonization. Therefore, findings from this study suggest that in the quest for addressing AMR that approaches targeting mucosal immunity preventative measures should be explored through standardized trials.

Ultra-processed food (UPF) consumption has become a prominent dietary pattern in modern populations, raising public health concerns due to its potential role in obesity and mental health disorders. This study examined the association between UPF intake, obesity, and depression among U.S. adults using nationally representative data. Data were drawn from the National Health and Nutrition Examination Survey (NHANES) cycles 2005-2018, yielding a complete-case, weighted sample of 97,983,914 U.S. adults (n = 16,976). Survey-weighted means and proportions were estimated, and subgroup analyses were conducted by sociodemographic and health characteristics. Multivariable logistic and linear regression models assessed the relationships between UPF intake (as a percentage of energy intake) and obesity (BMI ≥ 30 kg/m²) and depression (Patient Health Questionnaire-9 (PHQ-9) score ≥ 10). All analyses incorporated NHANES's complex sampling design using STATA version 18 (StataCorp., College Station, TX, USA). The mean UPF intake was 22.36% (95% CI: 21.72-23.01). Higher UPF consumption was significantly associated with increased odds of depression (OR = 1.01; 95% CI: 1.00-1.01; p < 0.001), even after adjustment for age, weight, gender, physical activity, and race/ethnicity. However, no significant association was found between UPF intake and obesity (OR = 1.00; 95% CI: 0.99-1.00; p = 0.32). Linear regression results confirmed a positive association between UPF and PHQ-9 scores (β = 0.010; p < 0.001), but only a minimal relationship with BMI (β = 0.002; p < 0.05). Subgroup differences in UPF intake were observed by gender, physical activity, and mental health status. Higher UPF intake is associated with increased depressive symptoms but not with obesity after controlling for confounding variables. These findings emphasize the potential mental health implications of ultra-processed dietary patterns and support public health strategies aimed at reducing UPF consumption to promote psychological well-being.

Abstract: This study investigates the emission levels of Volatile Organic Compounds (VOCs) across different tillage operations and tractor speeds in agricultural practices. A field experiment was conducted on a 14.4-hectare farmland using four tractor models (MF new, MF old, NH new, NH old) at three different speeds (15 km/h, 20 km/h, and 24 km/h). The tillage operations included 1st plough, 2nd plough, harrowing, and ridging, with a total of 432 experimental runs. VOC emissions were monitored using a Multireal Pro Handheld Gas Analyzer. The results indicate that the highest VOC emissions occurred at 20 km/h, while at 24 km/h, emissions remained relatively stable across all tillage operations. Among the tillage operations, ridging and 1st ploughing exhibited the highest VOC emissions, while harrowing consistently produced the lowest. These findings emphasize the need for speed regulation and optimized tillage operations to mitigate environmental impact. Future research should explore sustainable tillage practices and emission control technologies to promote environmentally friendly farming.

This study endeavors to construct a comprehensive conceptual framework elucidating the moderating influence of income on factors influencing the adoption of agricultural technology among cassava farmers in Nigeria. Agricultural technology, herein, refers to the innovative utilization of emerging Information and Communication Technologies tailored for agricultural applications within remote environments. The agricultural sector holds paramount significance within the Nigerian economy, engaging nearly 70% of the labor force and contributing over 40% to the gross domestic product. The adoption of technology in agriculture signifies potential enhancements in income, poverty alleviation, improved national nutrition and health, reduced food prices, and employment opportunities. However, the income of cassava farmers plays a pivotal role in ensuring their sustainability. Regrettably, their income streams are often unstable due to fluctuations in market prices, adversely impacting their ability to finance the adoption of technology to bolster productivity. Therefore, this research aims to develop a robust conceptual framework delineating the moderating role of income on the determinants of agricultural technology adoption behavior, particularly pertinent within the Nigerian context. It is imperative to tailor conceptual frameworks specifically for Nigeria, given that frameworks derived from other regions may not be directly applicable to the unique challenges and dynamics of the Nigerian agricultural landscape.