Journal of Medicine and Health Research

Utility of Plasma Level of Macromolecules to Predict Dysmetabolism and Energy Imbalance among Individuals Living with HIV at Nnewi, Nigeria

Uchenna Modestus Ezugwu, Onyebuchi Desmond Chikezie — Thu, 28 May 2026 00:00:00 +0000

Introduction: HIV infection is well-documented to disrupt normal metabolic processes and elevate energy demands, with these effects becoming more pronounced as the disease advances. The present investigation measured circulating concentrations of Nicotinamide Adenine Dinucleotide (NADH), Flavin Adenine Dinucleotide (FAD), Acetyl-CoA (ACA), and Adenosine Diphosphate (ADP) in a cohort of HIV-positive individuals.

Methods: A longitudinal, prospective case-controlled design was adopted, enrolling 77 HAART-naive, HIV-seropositive adults (aged 18–60 years) attending the antiretroviral treatment clinic at Nnamdi Azikiwe University Teaching Hospital (NAUTH), Nnewi, Nigeria, alongside 36 HIV-seronegative healthy volunteers serving as controls. NADH, FAD, ACA, and ADP concentrations were quantified using enzyme-linked immunosorbent assay (ELISA), and overall energy balance was estimated through a validated mathematical formula. All data were analyzed with SPSS version 23.0.

Results: ACA and NADH concentrations were significantly reduced (P<0.05) in HIV-infected individuals (both HAART and Naïve groups) regardless of treatment status relative to controls. ADP and FAD levels were additionally depressed (P<0.05) in patients who had completed 12 months of HAART compared with both the pre-treatment HIV cohort and healthy controls. Calculated energy balance scores were markedly lower (P<0.05) across all HIV groups versus controls, with post-treatment patients recording the greatest significantly energy deficit compared to their treatment-naive counterparts.

Conclusion: Statistically significant alterations in key metabolic macromolecules among HIV subjects—both prior to and during antiretroviral therapy suggests metabolic disruption. These findings indicate that circulating levels of NADH, ADP, ACA, and FAD hold promise as accessible biomarkers for detecting early energy deficits and guiding nutritional or therapeutic interventions in HIV-infected populations.

Predictive Modeling of Stature from Arm Length, Shoulder Breadth, and Foot Length among the Yoruba Ethnic Group in Nigeria

Peace Chigeru, Michael Okon, Raymond Nwachukwu Olisa — Tue, 02 Jun 2026 00:00:00 +0000

Background: In the fields of bioarchaeology, forensic science, and physical anthropology, stature estimation is essential. It entails using quantifiable body factors to estimate or anticipate a person's height.

Aim: This study aims to investigate whether arm length, shoulder breadth, and foot length can be used to predict stature among the indigenous Yoruba people in Ibadan, Oyo State, Nigeria.

Method: The study employed a descriptive cross-sectional design; including 300 subjects (150 males and 150 females) aged 18 to 35 years. The anthropometric characteristics were measured using a mega-size calliper. The data were analysed using an IBM version of the Statistical Package for the Social Sciences (SPSS, version 23). Linear regression was employed to estimate gender and stature. A probability less than 0.05 (p<0.05) was considered statistically significant, with a 95% confidence level.

Result: A strong positive correlation existed in all subjects between stature, arm length, shoulder breadth, and foot length (R = 0.78, SEE = 4.968). The stature predictive power of males (R = 0.65) and females (R =0.73). Significant sexual dimorphism was noted in all parameters.

Conclusion: This shows a strong positive correlation between the subjects. These findings will be useful in forensic sciences, anthropology, medical sciences, and bioarchaeology.

An Integrated Multi-modal Wearable Sensor System for Real-time Monitoring of Athlete Stress, Hydration, and Tendon Stiffness Analysis

S. RanjithKumar, G. Nallavan — Wed, 10 Jun 2026 00:00:00 +0000

Continuous monitoring of physiological and musculoskeletal conditions is essential for improving athlete performance and preventing sports-related injuries. Advancements in wearable sensor technology enable non-invasive, real-time tracking of stress, hydration, and tendon health through integrated multi-modal systems. This study presents the development of an integrated multi-modal wearable sensor system for real-time monitoring of athlete stress, hydration, and tendon stiffness. The system leverages an Arduino Uno microcontroller interfaced with multiple physiological and biomechanical sensors, including heart rate and heart rate variability sensors (MAX30102/05), galvanic skin response (GSR) modules, bioimpedance circuits, skin temperature sensors, high-resolution inertial measurement units (MPU6050/ICM-20948), and strain sensors (Flexi Force). By collecting and pre-processing analog and digital signals, the system provides continuous assessment of athlete physiological states and musculoskeletal load without the need for EMG. Data are transmitted wirelessly to a mobile or PC interface for further analysis, while real-time alerts are delivered through vibration motors, LEDs, or buzzers to prevent stress-related issues, dehydration, and tendon overuse injuries. Validation demonstrates the feasibility of integrating multiple sensing modalities in a compact wearable platform, enabling proactive athlete health management and performance optimization.

Knowledge, Prevalence and Factors Associated with Self-medication among University Students in Kano, Nigeria: A Cross-sectional Study

Chinenye Charity Ike, Yolo Bakumor Smith — Wed, 10 Jun 2026 00:00:00 +0000

Introduction: Self-medication is common in university settings and carries well-documented risks including antimicrobial resistance, adverse drug reactions, and delayed care. Students are particularly exposed, given their autonomy and easy access to medicines without prescriptions. This study aimed to assess the knowledge, prevalence, and factors associated with self-medication among students at Bayero University, Kano, Nigeria.

Methods: A descriptive cross-sectional study was conducted among 354 students selected by multi-stage sampling, combining proportionate stratified allocation across academic levels with consecutive recruitment within strata. Data were collected using a semi-structured, self-administered questionnaire. Knowledge of self-medication was assessed with a validated ten-item scale and categorised as good (score ≥7), fair (5–6), or poor (≤4). Data were analysed in IBM SPSS version 27 using descriptive statistics, chi-square tests, and binary logistic regression (P ≤ .05).

Results: All 354 questionnaires were completed (response rate 100%). Self-medication was reported by 235 respondents (66.4%, 95% CI: 61.5%–71.3%). Most respondents (232, 65.5%) demonstrated good knowledge of self-medication, assessed using a ten-item scale. Across 1,408 total responses, CNS stimulants (13.4%), antimalarials (12.7%), and antibiotics (12.6%) were the most frequently reported drug categories. On chi-square analysis, ethnicity (χ² = 7.94, P = .047) and income source (χ² = 9.42, P = .024) were significantly associated with self-medication behaviour; knowledge level was not (χ² = 1.41, P = .494). On binary logistic regression, Yoruba students were significantly less likely to self-medicate than Hausa students (aOR = 0.475, 95% CI: 0.238–0.948, P = .035), and students earning through personal work were significantly less likely to self-medicate than those supported by parents or guardians (aOR = 0.500, 95% CI: 0.278–0.900, P = .021).

Conclusion: Self-medication was highly prevalent among students at Bayero University, Kano, despite most demonstrating good knowledge of associated risks. The absence of a significant knowledge-practice association, alongside the significance of ethnicity and income source, suggests that structural and socio-cultural factors are stronger drivers of the practice than awareness alone. Interventions should prioritise improving affordable healthcare access, regulating drug sales at the point of purchase, and addressing the specific socio-cultural contexts that shape health-seeking behaviour among university students in Northern Nigeria.

Prevalence and Determinants of Spirometric Abnormalities among Individuals Undergoing Pulmonary Function Testing in South Gujarat, India

Komal B. Zinzala, Manisha N. Shah, Samir P. Gami — Thu, 25 Jun 2026 00:00:00 +0000

Introduction: Pulmonary function tests (PFTs) are widely used to detect ventilatory abnormalities and assess respiratory health. Spirometry plays an important role in the diagnosis, classification and monitoring of respiratory diseases. However, data on spirometric patterns and their associated determinants in the Indian population remain limited. This study aimed to determine the prevalence and distribution of spirometric abnormalities and to evaluate their association with demographic and lifestyle factors among individuals undergoing pulmonary function testing.

Methods: A cross-sectional study was conducted from January to June 2024 among 875 participants undergoing spirometry at tertiary care hospitals and community-based health check-up camps in South Gujarat, India. Spirometry was performed in accordance with American Thoracic Society (ATS) and European Respiratory Society (ERS) guidelines using a Spirolab portable spirometer. Pulmonary function patterns were classified as normal, obstructive, restrictive or mixed ventilatory defects. The severity of impairment was graded as mild, moderate or severe according to standard interpretative criteria. Associations between spirometric patterns and demographic or lifestyle variables were tested using chi-square tests.

Results: Of the 875 participants, 50.4% had normal spirometry and 49.6% had abnormal pulmonary function findings. The most common abnormality was a restrictive spirometric pattern (44.7%), followed by obstructive (3.2%) and mixed ventilatory defects (1.7%). Mild impairment was observed in 25.26% of participants, moderate impairment in 18.63% and severe impairment in 4.0%. Significant associations were observed between spirometric abnormalities and age group (p < 0.0001), sex (p = 0.0081) and smoking status (p < 0.0001). No significant association was found between body mass index (BMI) and spirometric patterns (p = 0.6155).

Conclusion: Abnormal spirometric findings were common among participants in the study population. The association of age, sex and smoking status with pulmonary function abnormalities highlights the importance of spirometry in respiratory screening and smoking-related health interventions.

Energy Poverty and Gendered Inequities in African Communities: A Review of Health Impacts and Policy Gaps

Sat, 23 May 2026 00:00:00 +0000

Energy poverty remains one of the most widespread structural barriers to human development across sub-Saharan and North Africa. Its impact is not experienced equally across society, but rather disproportionately borne by women and girls, influenced by deeply rooted gender inequalities and societal division of energy-related domestic tasks into women's domain in most African societies. This review focuses on the African continent and draws on peer-reviewed studies, multilateral agency reports, and policy documents, primarily from 2015 to 2026.

The aim is to critically review, from a gender perspective, the nature of energy poverty in Africa. Specific aims are to: (1) describe the burden and distribution of energy poverty by region in Africa; (2) capture health outcomes of gendered exposure to energy; (3) explore structural and governance drivers of the gender-energy nexus; (4) critically question policy actions and highlight persistent gaps in design and (5) identify gaps in evidence base.

A narrative review design was used. The literature was collected from four databases (PubMed, Scopus, Web of Science, and Google Scholar), as well as grey literature from the WHO, IEA, World Bank, and the Clean Cooking Alliance. The main publication period was 2015-2026, and studies conducted prior to that were retained only if their conclusions had not been replicated. Sources that specifically mention Africa were included. Sources that were not peer-reviewed, opinion pieces without empirical support, and studies relevant only to contexts outside Africa were excluded.

Women living in energy-poor households are exposed to long-term indoor air pollution from burning biomass fuels such as firewood, charcoal, or crop residues. This exposure increases their risk of respiratory illness, cardiovascular disease, and other long-term health problems. Gender-sensitivity in policy responses has been lacking, focusing on how to disseminate technology rather than the social and economic factors that affect women's access to and ability to sustain clean energy transitions.

In conclusion, energy poverty as a gender justice problem needs policy frameworks that recognize women as producers, not consumers. Integrating gender-responsive design into electrification and clean cooking programmes, and closing persistent gaps in sex-disaggregated data, are prerequisites for meaningful progress.

Predictive Modeling and Machine Learning Approaches in Agrochemical Exposure and Health Risk Assessment

Eric Oppong — Mon, 25 May 2026 00:00:00 +0000

The increasing global reliance on agrochemicals, including pesticides, herbicides, and fertilizers, has substantially enhanced agricultural productivity while intensifying concerns regarding environmental contamination and human health risks. Human exposure occurs through occupational, environmental, and dietary pathways and is associated with acute toxicity as well as chronic conditions such as cancer, neurodegenerative disorders, and endocrine disruption. This narrative review systematically synthesized recent peer-reviewed literature on predictive modeling and machine learning (ML) applications in agrochemical exposure and health risk assessment, drawing from studies focused on environmental monitoring, biomonitoring, geospatial analysis, and health outcome prediction. Evidence was selected based on relevance to ML-driven risk assessment frameworks, model applicability, and comparative analytical value. Findings indicate that ML approaches, including Random Forest, Support Vector Machines, Artificial Neural Networks, and Gradient Boosting, consistently outperform many conventional deterministic and probabilistic models in handling nonlinear interactions, integrating high-dimensional datasets, and improving predictive accuracy across spatial–temporal and biomonitoring contexts. Ensemble and deep learning models demonstrated particularly strong performance for exposure estimation and disease risk stratification, although challenges remain regarding data quality, interpretability, overfitting, and ethical governance. The review highlights emerging opportunities in explainable artificial intelligence, wearable sensor integration, and real-time surveillance systems to enhance model transparency and public health applicability. Overall, ML-driven predictive frameworks represent a transformative advancement in agrochemical risk assessment and offer significant potential to strengthen evidence-based regulatory policies, targeted interventions, and sustainable environmental health decision-making.

Medicinal Plant Diversity, Traditional Therapeutics and Conservation Perspectives in Odisha, India

Himansu Bhusana Nayak — Fri, 05 Jun 2026 00:00:00 +0000

Medicinal plants constitute an essential component of traditional healthcare systems and continue to play a significant role in primary healthcare, pharmaceutical research, and biodiversity conservation. Odisha, located in eastern India, possesses rich floristic diversity owing to its varied ecological regions, including coastal ecosystems, central plateaus, forested landscapes, and the Eastern Ghats. The state is inhabited by numerous tribal communities that have preserved extensive ethnomedicinal knowledge through generations. This review synthesizes available information on medicinal plant diversity, traditional therapeutic applications, phytochemical constituents, ethnopharmacological significance, and conservation perspectives in Odisha. Information was compiled from published scientific literature, ethnobotanical studies, books, and government reports. Several medicinal plant species, including Tinospora cordifolia, Andrographis paniculata, Rauvolfia serpentina, Ocimum sanctum, Curcuma longa, and Azadirachta indica, are widely utilized for the treatment of infectious diseases, metabolic disorders, respiratory ailments, inflammatory conditions, and general health maintenance. The review further highlights the importance of bioactive phytochemicals such as alkaloids, flavonoids, phenolic compounds, terpenoids, and glycosides that contribute to the pharmacological properties of medicinal plants. Traditional medicinal knowledge preserved by indigenous communities remains an important resource for future drug discovery and sustainable healthcare development. However, habitat degradation, overexploitation, deforestation, and loss of traditional knowledge threaten medicinal plant resources in the region. Strengthening conservation strategies, scientific validation, sustainable utilization practices, and community participation is essential for safeguarding Odisha's medicinal plant heritage and promoting its contribution to healthcare and biodiversity conservation.

Mechanisms, Models, and Future Directions for Predicting Biochemical Pathways Underlying Antibiotic Resistance in Microbial Communities Using Artificial Intelligence

Wed, 10 Jun 2026 00:00:00 +0000

Antimicrobial resistance (AMR) has emerged as one of the most pressing global public health challenges, reducing the effectiveness of existing antimicrobial treatments and increasing the burden of infectious diseases worldwide. The development of resistance is driven by complex ecological interactions, genetic transfer, and adaptive biochemical processes within microbial communities. However, current gene-based analyses alone are insufficient to fully explain these mechanisms, necessitating a broader investigation of biochemical pathways and molecular networks involved in resistance regulation. This study explores the potential of Artificial Intelligence (AI) in advancing the understanding of AMR through the analysis of large-scale microbiome and multi-omics datasets. Machine learning, deep learning, and network-based modeling approaches are considered for their ability to detect hidden biological patterns, predict resistance-associated pathways, and model microbial ecosystem behavior.AI-based approaches demonstrate significant potential in transforming AMR research from traditional descriptive analysis to predictive and interpretive modeling. These methods enhance the ability to identify resistance mechanisms, forecast evolutionary trends, and improve the understanding of microbial interactions at a systems level. Despite these advantages, challenges such as data quality limitations, lack of model interpretability, inadequate standardization, and unequal access to computational resources remain significant barriers. Ethical concerns related to data governance and clinical implementation must also be addressed. Future advancements in explainable AI, integrated multi-omics analysis, and robust computational frameworks are expected to improve predictive accuracy and support better diagnostic and treatment strategies. These developments hold strong potential for strengthening global efforts against antimicrobial resistance.

The Rise of Smart Hospitals: Biomedical Engineering Innovations in Automation, Monitoring, and Digital Healthcare

Fri, 03 Jul 2026 00:00:00 +0000

Background: Smart hospitals are technologically integrated healthcare environments that use biomedical engineering, intelligent automation, advanced monitoring systems and digital healthcare infrastructures to support clinical care and operational efficiency.

Aim: This narrative review aimed to critically analyse biomedical engineering innovations that contribute to smart hospitals, with emphasis on healthcare automation, advanced patient monitoring and digital healthcare ecosystems.

Method: Peer-reviewed articles and relevant scientific publications were reviewed from major databases, including PubMed, Scopus, ScienceDirect and Google Scholar. The literature was organised thematically to examine healthcare automation, artificial intelligence-assisted diagnostics, Internet of Medical Things systems, robotic healthcare technologies, telemedicine, predictive monitoring, digital infrastructures and cybersecurity challenges.

Findings: The reviewed literature indicates that biomedical engineering has contributed to smart hospital development through automation of clinical workflows, wearable and implantable biosensors, robotic-assisted systems, predictive analytics, interoperable communication networks and digitally connected healthcare platforms. Artificial intelligence and digital monitoring tools may support real-time clinical observation, diagnostic assistance and personalised care management. The manuscript also shows that robotics, smart operating rooms, tele-ICU platforms, cloud systems, edge computing and interoperability standards are closely linked to the transition from conventional hospitals to intelligent healthcare environments. However, implementation remains constrained by interoperability limitations, cybersecurity risks, infrastructure costs, clinician adoption, algorithmic bias, privacy concerns and scalability challenges.

Conclusion: Smart hospitals depend on the careful integration of secure, explainable, interoperable, affordable and human-centred biomedical engineering systems. Sustainable innovation, ethical governance, workforce readiness and equitable deployment remain essential for strengthening intelligent healthcare ecosystems while maintaining patient safety, clinical accountability and overall practical feasibility.

Data Governance Challenges in Artificial Intelligence-Enabled Healthcare in Low- and Middle-Income Countries

Mon, 13 Jul 2026 00:00:00 +0000

Background: Artificial intelligence (AI) has the potential to transform healthcare delivery in low- and middle-income countries (LMICs), where the disease burden from preventable causes is high and healthcare resources are severely limited. Although major technological breakthroughs have occurred, systemic, regulatory, and infrastructural barriers continue to hinder the implementation of AI-powered health solutions in these settings.

Objectives: This review aims to examine and synthesise key data governance (DG) challenges and concerns in the context of health systems in LMICs, develop an analytical framework for assessing readiness to address data governance issues in AI-enabled health systems, and identify key areas for data governance policy interventions and future research.

Methods: A structured narrative review of peer-reviewed literature from PubMed, Scopus, and Web of Science, together with relevant grey literature sources from January 2015 to December 2023, was conducted. AI governance, digital health infrastructure, digital health data regulation, and algorithmic accountability in LMICs were considered. Findings were analysed thematically to synthesise governance concerns across key governance dimensions.

Results: Seven major governance challenge domains were identified: (1) weak and fragmented regulatory frameworks; (2) poor data quality and limited interoperability; (3) inadequate patient data protection mechanisms; (4) algorithmic bias due to undersampling of local populations; (5) inadequate digital health infrastructure; (6) limited digital literacy among healthcare professionals; and (7) ethical tensions related to consent, privacy, and community trust. Cross-cutting themes of power asymmetry in global AI development and donor dependency in digital health financing were also identified.

Conclusions: The promise of AI for LMIC health systems needs to be supported by robust, place-sensitive governance structures that extend beyond technology alone. Before equitable AI adoption can be pursued, investments in regulatory capacity, data systems, algorithmic accountability mechanisms, and stakeholder engagement are critical preconditions. A Governance Readiness Framework is proposed to support policymakers, health ministries, and development partners.

Medical Nomenclature and Patient Understanding: Lessons from the Polycystic Ovary Syndrome to Polyendocrine Metabolic Ovarian Syndrome Transition

Wed, 22 Jul 2026 00:00:00 +0000

Background: Medical nomenclature can influence patients, clinicians, diagnostic accuracy, and health outcomes. On 12 May 2026, after a multi-year global consensus process, polycystic ovary syndrome (PCOS) was formally renamed polyendocrine metabolic ovarian syndrome (PMOS). The change reflected the recognition that the older term was scientifically imprecise and could contribute to delayed diagnosis, fragmented care, stigma, and limited patient understanding.

Objectives: Using the PCOS-to-PMOS transition as a case study, this narrative review examines how an imprecise name may contribute to diagnostic delay, stigma, clinician confusion, and poorer outcomes. It synthesises evidence across five linked questions: how disease names shape patient understanding and health literacy; how they contribute to diagnostic delay and misdiagnosis; how they relate to stigma and psychosocial burden; how they influence clinician decision-making and care coordination; and what these effects imply for healthcare quality and patient safety.

Methods: A narrative review of peer-reviewed literature, professional society publications, and grey literature was conducted using PubMed, Scopus, Web of Science, Google Scholar, and professional society websites. Search terms related to the phenomenon of interest, including “disease nomenclature,” “disease naming,” and “medical terminology,” were combined with terms describing its proposed effects, including “patient understanding,” “health literacy,” “diagnostic delay,” “stigma,” and “medical cognition,” and with condition-specific terms, including “PCOS,” “polycystic ovary syndrome,” and “PMOS.” Priority was given to studies addressing the effects of disease names on diagnosis, clinical outcomes, and health literacy.

Findings: The evidence indicates that an imprecise name can weaken diagnostic accuracy through identifiable cognitive pathways. Cognitive load theory, conceptual anchoring, and semantic priming together explain how a name shapes patients’ and clinicians’ understanding. Historical reforms, from “hysteria” to specific diagnoses and from “Mongolism” to Down syndrome, show that more accurate naming has accompanied improved recognition and care. PCOS affects approximately one in eight women, or more than 170 million worldwide, yet up to 70% may remain undiagnosed or face diagnostic delays of several years. The PMOS revision illustrates how evidence-based terminology can be aligned with current scientific understanding.

Conclusions: Medical terminology is not merely semantic; it shapes clinical reasoning, patient comprehension, and diagnostic and therapeutic pathways, with potential consequences for outcomes. The PCOS-to-PMOS transition shows that systematic, evidence-based nomenclature change is feasible and that disease names should reflect scientific understanding while supporting optimal care. Healthcare systems should treat nomenclature accuracy as a component of quality improvement and patient safety.

Public Trust in Artificial Intelligence-Generated Health Information: A Critical Narrative Review of Health Literacy and Source Credibility

Mon, 17 Aug 2026 00:00:00 +0000

Conversational systems built on large language models have become an ordinary route through which members of the public obtain health information, answering questions directly rather than returning ranked links to identifiable publishers. Whether such information is acted upon depends less on its accuracy than on whether it is trusted, and two explanatory traditions have been recruited to explain that trust: a receiver-centred tradition rooted in health literacy, and a message-centred tradition rooted in source credibility. This critical narrative review examines how well these traditions account for public trust in artificial intelligence-generated health information, where they conflict, and what the resulting evidence base can and cannot support. Literature was identified through Europe PMC, Crossref Metadata Search and OpenAlex, supplemented by citation searching and an authoritative institutional source, with a final search date of 9 June 2026. The synthesis is organised around conceptual foundations, observed patterns and determinants of trust, source-credibility cues and the attribution problem, health literacy as a contested moderator, the quality of the object of trust, and consequences for behaviour and equity. Three findings emerge with reasonable confidence. First, the widely assumed penalty attached to machine authorship is neither consistent nor robust: experimental and meta-analytic evidence points to small, conditional and sometimes absent effects, and blinded quality evaluations are routinely conflated with labelled trust experiments. Second, health literacy and its digital derivatives perform poorly as predictors of trust in representative surveys, whereas artificial intelligence literacy and perceived usefulness perform better, which suggests that the receiver-side account has been specified at the wrong level. Third, the stylistic properties that raise perceived credibility, particularly fluency and warmth, are partly antagonistic to accuracy, creating a structural rather than incidental risk. The evidence base remains dominated by cross-sectional designs, self-reported constructs and single-country convenience samples, and almost no work measures whether trust is calibrated to actual accuracy. Research priorities should shift from explaining trust levels to measuring trust calibration, and from self-reported literacy to performance-based appraisal.

Artificial Intelligence in the Optimisation of Glucagon-Like Peptide-1 Receptor agonist Therapy for Obesity and Type 2 Diabetes: A Critical Narrative Review of Treatment Monitoring and Clinical Decision Support

Mon, 17 Aug 2026 00:00:00 +0000

Glucagon-like peptide-1 receptor agonists have changed what can be achieved pharmacologically in obesity and type 2 diabetes, yet the average effects reported in registration trials conceal wide interindividual variation in weight and glycaemic response, high rates of early discontinuation, incomplete dose escalation in routine care, and pronounced inequity in who receives treatment. These features have prompted the claim that artificial intelligence, and machine learning in particular, can improve candidate selection, treatment monitoring and prescribing decisions. This critical narrative review evaluates whether the accessible evidence supports that claim. Literature was identified through structured searching of a biomedical bibliographic index and a scholarly metadata registry, supplemented by backward citation searching and verification against publisher records. Five interrelated domains were examined: prediction of response and identification of candidates; monitoring through continuous physiological, behavioural and imaging data streams; decision support ranging from registry-derived rule induction to large language models; automated safety signal detection; and the methodological, regulatory and equity conditions under which such systems would have to operate. The evidence base is expanding quickly but remains immature. Most predictive models are developed retrospectively in single data environments, evaluated by discrimination alone, and reported without calibration, decision-curve analysis or external validation. Several report performance figures that appear strong but depend on outcome definitions dominated by baseline values, so the apparent accuracy reflects arithmetic rather than clinical insight. Monitoring applications and digital engagement platforms show consistent associations with better outcomes, but the observational designs cannot separate the effect of the technology from the characteristics of people who engage with it. No randomised evaluation has yet demonstrated that an artificial intelligence system improves patient-relevant outcomes specifically in glucagon-like peptide-1 receptor agonist therapy. Prospective, calibration-aware, externally validated and equity-audited evaluation is the principal requirement before deployment can be justified.

Artificial Intelligence in Mental Healthcare: A Critical Narrative Review of Diagnosis, Treatment Personalisation and Patient Monitoring

Mon, 17 Aug 2026 00:00:00 +0000

Artificial intelligence has been proposed as a corrective to three persistent problems in mental healthcare: diagnostic imprecision, the trial-and-error character of treatment selection, and the episodic nature of clinical monitoring. The volume of primary research has expanded rapidly, yet few tools have altered routine practice. This critical narrative review examines evidence across the three domains in which artificial intelligence has been most extensively applied to mental health, namely diagnostic classification and risk detection, treatment personalisation, and continuous patient monitoring, and asks why demonstrated technical performance has so rarely converted into demonstrated clinical benefit. Literature was identified through a bibliographic metadata registry, a biomedical citation index, targeted searching of scholarly and institutional sources, and backward and forward citation tracking, covering January 2015 to 11 June 2026, with earlier work retained where conceptually necessary. Evidence was appraised for design adequacy, validation strategy, sample representativeness, outcome definition and reporting transparency, then synthesised thematically rather than study by study. Three findings recur. Apparent accuracy is systematically inflated by internal validation, small and selected samples, and reference standards of limited reliability; where external validation has been attempted, discrimination frequently falls towards chance. The three domains differ markedly in evidential maturity, since monitoring and conversational intervention now rest on randomised evidence and pooled effect estimates, whereas diagnostic classification and treatment-response prediction remain largely at the model-development stage. The binding constraints on translation are infrastructural and epistemic rather than algorithmic, encompassing narrow training populations, unreliable outcome labels, absent prospective evaluation and immature governance. Unresolved questions include whether any model confers benefit over routine care in prospective use, how algorithmic outputs should enter clinical judgement, and how safety should be established for generative systems operating outside professional supervision. Progress will depend less on model refinement than on representative longitudinal datasets, standardised outcome definitions, prospective impact evaluation and governance capable of distinguishing wellness products from clinical instruments.

Chest Pain in the Emergency Department: A Critical Narrative Review of Diagnostic Strategies, Risk Stratification and Unresolved Evidence Gaps

Domina Petric — Tue, 18 Aug 2026 00:00:00 +0000

Chest pain remains one of the most frequent reasons for attendance at emergency departments worldwide, and the diagnostic task it presents is unusually asymmetric: a small proportion of patients harbour an immediately life-threatening condition, while the majority have benign or self-limiting causes that nonetheless consume substantial diagnostic resources. The past decade has transformed the evidence base. High-sensitivity cardiac troponin assays, rapid serial-sampling algorithms, structured clinical decision pathways, early coronary computed tomography angiography and, more recently, machine-learning decision support have all been evaluated in large cohorts and randomised trials. This critical narrative review examines how far that transformation has improved patient-relevant outcomes rather than diagnostic process measures alone. Literature was identified through structured searching of a biomedical bibliographic index and a scholarly metadata registry, with citation-based supplementation, and appraised for design adequacy, spectrum representativeness, outcome definition and reproducibility. The synthesis identifies a consistent pattern: analytical and diagnostic performance has improved markedly, yet randomised evidence repeatedly shows neutral effects on death and recurrent infarction, and a persistent gap separates rule-out efficiency from therapeutic benefit. Performance of validated algorithms degrades in renal impairment, older age, very early presentation and among women assessed against undifferentiated thresholds. Structured pathways demonstrate favourable safety in observational validation but heterogeneous adherence in practice, and the definition of an acceptable missed-event threshold remains a value judgement rather than an empirical finding. Conditions outside the acute coronary syndrome framework, including acute aortic syndromes, pulmonary embolism, myocardial infarction with non-obstructive coronary arteries and non-cardiac causes, remain comparatively neglected in pathway design and in follow-up provision. Equity gaps in triage, testing and treatment persist across sex, ethnicity, language and health-system context. Priority research needs include outcome-anchored trials of rule-out strategies, prospective evaluation of computational decision support in routine practice, and evidence generation in settings where troponin testing and advanced imaging are not universally available.

One Health, One Genome: A Critical Appraisal of Artificial Intelligence–enabled Genomics for Global Infectious Disease Surveillance

Wed, 19 Aug 2026 00:00:00 +0000

Pathogen genomics has become a routine instrument of infectious disease surveillance, and machine learning is increasingly proposed as the means by which sequence data generated across human, animal and environmental sectors can be converted into anticipatory public health intelligence. The premise that a single integrated genomic evidence base can serve all three sectors, and that artificial intelligence can extract predictive signal from it, has attracted substantial investment, yet the supporting evidence remains uneven and has not been appraised critically as a whole. This review evaluates the strength, consistency and limitations of the literature on artificial intelligence–enabled genomics for One Health infectious disease surveillance, covering lineage assignment and phylogenetic automation, sequence-based fitness and antigenic escape prediction, protein and genome language models, cross-species host and spillover inference, genotype-to-phenotype prediction of antimicrobial resistance, and wastewater and environmental metagenomics. Literature was identified through Europe PMC, Crossref and the Directory of Open Access Journals, supplemented by institutional sources, with all bibliographic records and digital object identifiers verified against registration metadata. The evidence is strongest where algorithms perform structured classification against well-curated reference data, notably clade and lineage assignment and resistance determinant detection in taxa with dense phenotype-linked genome collections. Confidence weakens progressively for retrospective fitness inference, and is weakest for prospective cross-species risk prediction, where reported discrimination is difficult to separate from sampling bias in the underlying host–virus association records. Independent reanalyses indicate that apparent predictive skill often reflects research effort and taxonomic structure rather than transferable biological signal. Recurrent methodological problems include data leakage across phylogenetically related sequences, absent external validation, severe geographical concentration of both genomes and metadata, and an almost complete lack of evaluation against public health decision outcomes rather than classification metrics. Progress depends less on model architecture than on representative sampling across sectors, interoperable contextual metadata, prospective evaluation designs, and governance arrangements that address equity and dual-use risk simultaneously.

Electronic Waste Exposure and Public Health Risks among Informal Recycling Workers: A Critical Narrative Review of Occupational Mixtures, Health Effects and Prevention

Hameedat Titilade Sanusi, Ogunyemi Joel, George-Oloja Nkechinyere — Sat, 22 Aug 2026 00:00:00 +0000

Electronic waste recycling sustains livelihoods and recovers valuable materials, yet informal processing can transfer a substantial fraction of the environmental and occupational costs of the digital economy to workers with limited access to engineering controls, occupational health services and social protection. This critical narrative review evaluates the evidence linking informal electronic waste work with hazardous exposures and adverse health outcomes, with emphasis on worker-specific studies rather than community exposure alone. Literature published from 1 January 2000 to 13 June 2026 was identified through accessible scholarly indexes and institutional sources, supplemented by citation searching and verification of bibliographic metadata and Digital Object Identifiers. The evidence consistently demonstrates task-related exposure to complex mixtures of metals, particulate matter, polycyclic aromatic hydrocarbons, polychlorinated biphenyls and other combustion- or process-derived contaminants. Biomonitoring studies, particularly from Ghana, show elevated internal doses for several toxicants, while studies from Bangladesh, Nigeria, Thailand, Chile and Hong Kong broaden the geographic evidence. The most defensible health conclusions concern frequent injuries, musculoskeletal symptoms, eye and skin complaints, and measurable occupational exposure. Evidence for respiratory impairment is increasingly suggestive, supported by recent worker studies, but remains sensitive to study design and comparator selection. Cardiovascular autonomic changes, renal and hepatic alterations, endocrine disruption, neurobehavioural symptoms and epigenetic biomarkers are biologically plausible and sometimes associated with exposure, yet causal inference remains limited by cross-sectional designs, mixed tasks, background contamination, exposure misclassification and residual confounding. Formalisation and personal protective equipment alone cannot be assumed to eliminate risk. Prevention requires source control, safer processing, task redesign, hygiene, appropriate respiratory protection, exposure and health surveillance, and governance that does not remove livelihoods without viable alternatives. Future research should prioritise longitudinal, task-resolved, mixture-aware studies and intervention evaluations capable of distinguishing exposure reduction from displacement of risk.