Antimicrobial resistance (AMR) is a growing global health concern with profound implications for ophthalmology, where it compromises the management of ocular infections such as bacterial keratitis, conjunctivitis, endophthalmitis, and postoperative complications. Resistance in common ocular pathogens, including Staphylococcus aureus (S. aureus), Streptococcus pneumoniae (S. pneumoniae), Pseudomonas aeruginosa (P. aeruginosa), and coagulase-negative staphylococci (CoNS) emerge through genetic mutations, horizontal gene transfer, and biochemical mechanisms such as enzymatic degradation, target modification, efflux pumps, and reduced membrane permeability. Biofilm formation further complicates eradication on the ocular surface and interior. The key drivers of resistance include inappropriate or prolonged topical antibiotic use, routine prophylaxis in ocular surgery, subtherapeutic dosing, and cross-resistance with systemic antimicrobials. The rise in multidrug-resistant strains, particularly methicillin-resistant S. aureus, fluoroquinolone-resistant P. aeruginosa, and drug-resistant S. pneumoniae has been linked to delayed treatment response, increased healthcare costs, and sight-threatening outcomes. Recent advances in rapid diagnostics, molecular assays, and point-of-care testing support earlier and more precise detection of resistance, enabling timely therapeutic decisions. Promising strategies to address AMR in ophthalmology include antimicrobial stewardship, novel drug delivery platforms, and alternative approaches such as bacteriophage therapy and antimicrobial peptides. Emerging tools, including genomic surveillance, artificial intelligence (AI)-driven resistance prediction, and personalized antimicrobial regimens, further expand opportunities for innovation. Collectively, this review synthesizes current evidence on AMR in ocular disease, summarizing patterns of resistance, underlying mechanisms, and clinical consequences, while highlighting strategies for mitigation and underscoring the need for global awareness and collaboration among clinicians, researchers, and policymakers to safeguard vision.

Light plays a significant role in human physiology and behaviour, influencing sleep, mood, alertness and overall health. Yet light exposure remains a neglected determinant of health, with most evidence coming from high-income countries. This data note presents a dataset collected in Kumasi, Ghana, between October 2024 and February 2025, containing personal light exposure measured with wearable melanopic light loggers alongside behavioural and environmental factors obtained through self-reports. Fifteen participants (n=8 female, mean±SD age 22.6±1.2 years) wore three ActLumus light-logging devices continuously for seven days, recording light exposure every 10 seconds at the near-corneal plane, chest and wrist. Participants also completed daily questionnaires on sleep, mood and physical activity, as well as structured assessments of their sleep environment and light-related behaviours. This dataset provides the first high-resolution account of daily light exposure in sub-Saharan Africa. By enabling cross-context comparisons, it contributes to understanding the diversity of light environments globally and can inform interventions for sleep health, mental health and chronic disease prevention.

Artificial intelligence (AI) has transformed healthcare, and is becoming increasingly useful in eye care. We conducted a systematic review and meta-analysis of the use of AI in the diagnosis, detection, prediction, progression, and treatment of refractive errors (REs). The study adhered to the PRISMA checklist to ensure transparent reporting. The following databases were searched from inception to January 2025, with an English language restriction: PubMed, Web of Science, Embase, Scopus, Cochrane Library and Google Scholar. Two independent reviewers performed study screening, data extraction, and quality assessment, with a third author resolving discrepancies. All original studies on the use of AI techniques in RE were identified and the effectiveness of these techniques was compared. A critical appraisal was conducted using the QUADAS-2 risk-of-bias tool. A meta-analysis was performed using R software (version 4.5.0). Of 6,288 records retrieved, 45 met eligibility for systematic review, with 19 included in meta-analysis. Among these 45 studies, 55.5% (25/45) applied deep learning (DL) approaches, while 44.4% (20/45) employed machine learning (ML) techniques. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and summary of receiver operating characteristic (SROC) for detection and/or diagnosis studies were 0.94 (95%CI, 0.90-0.97), 0.96 (95%CI, 0.92-0.98), 382.56 (95% CI 111.91 -1307.77) and 0.98 (95%CI, 0.91-0.97), respectively. For prediction of REs, the pooled sensitivity, specificity, DOR, and SROC were 0.87 (95%CI, 0.73-0.94), 0.96 (95%CI, 0.90-0.980), 159.94 (95% CI, 40.17-636.85) and 0.96 (95%CI, 0.85-0.95), respectively. Among studies focused on progression, performance metrics ranged from AUC = 0.845-0.99, R² = 0.613-0.964, and MAE = 0.119D-0.49D. In treatment studies, performance varied more widely, with AUC values between 0.60–0.94 and MAE from 0.17D-0.54D. Collectively, AI technologies, particularly DL and ML, achieved high diagnostic and predictive accuracy in RE management. Future research should focus on developing generalizable models trained on diverse datasets to ensure broad clinical relevance.

Light plays a significant role in human physiology and behaviour, influencing sleep, mood, alertness and overall health. Yet light exposure remains a neglected determinant of health, with most evidence coming from high-income countries. This data note presents a dataset collected in Kumasi, Ghana, between October 2024 and February 2025, containing personal light exposure measured with wearable melanopic light loggers alongside behavioural and environmental factors obtained through self-reports. Fifteen participants (n=8 female, mean±SD age 22.6±1.2 years) wore three ActLumus light-logging devices continuously for seven days, recording light exposure every 10 seconds at the near-corneal plane, chest and wrist. Participants also completed daily questionnaires on sleep, mood and physical activity, as well as structured assessments of their sleep environment and light-related behaviours. This dataset provides the first high-resolution account of daily light exposure in sub-Saharan Africa. By enabling cross-context comparisons, it contributes to understanding the diversity of light environments globally and can inform interventions for sleep health, mental health and chronic disease prevention. Plain language summary Light affects how we sleep, feel and stay healthy. But most research on light exposure has been carried out in high-income countries, meaning we know very little about people’s real-life light environments in other parts of the world. This study provides the first detailed dataset of daily light exposure in sub-Saharan Africa. Fifteen young adults in Kumasi, Ghana, wore small devices that measured the light around them every few seconds for one week, while also answering short questions about their sleep, mood and daily routines. The dataset shows how people in Ghana experience light in everyday life and allows comparisons with data from other countries. By making this resource openly available, we hope to support future research on how light exposure influences sleep, mental health and the risk of long-term diseases — helping to shape healthier light environments worldwide.

Background: Ocular morbidity remains poorly characterized among prisoners; this systematic review and meta-analysis quantified its prevalence, spectrum and associated factors across sub-Saharan Africa (SSA). Methods: This review followed a published Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Protocols (PRISMA-P) and searched eight databases using prespecified terms, with study selection guided by PRISMA. Data were standardized and synthesized using random-effects meta-analysis to estimate pooled prevalence with 95% confidence intervals (CIs), with heterogeneity assessed using I ² and sensitivity and condition-specific analyses used to assess variation. Results: Six studies comprising 3,361 participants from four SSA countries, Nigeria, Uganda, Tanzania and Kenya, met the inclusion criteria. The pooled prevalence of ocular morbidity was 43.1% (95% CI: 12.4–77.1%; I ² = 99.2%). Conjunctivitis was the most prevalent condition (23.9%; 95% CI, 10.7–40.1%), followed by refractive error (19.5%; 95% CI, 12.5–27.5%) and presbyopia (18.6%; 95% CI, 5.2–37.7%). Cataract (7.4%), glaucoma (5.6%) and corneal scar or opacity (3.0%) were less prevalent. Age >50 years (OR = 11.96; 95% CI, 5.31–26.94) and a history of ocular trauma (OR = 5.21; 95% CI, 1.52–17.87) were associated with higher odds of ocular morbidity. Longer imprisonment was associated with xerophthalmia (OR = 20.1; 95% CI, 8.3–48.8%), whereas access to food from outside prison was associated with lower odds of ocular morbidity (OR = 0.45; 95% CI, 0.26–0.79). Conclusion: Ocular morbidity affects approximately four in ten prison inmates in SSA, with much of this burden preventable or treatable, despite considerable between-study heterogeneity and limited geographical evidence. Collectively, these findings support integrated prison eye care and standardized research to advance equitable interventions across SSA.

Mental health is a major public health concern, with disproportionate burdens in sub-Saharan Africa (SSA), where access to care is limited and stigma remains high. Social and digital platforms, including social media, mobile health (mHealth) applications, and SMS-based messaging, provide opportunities for information sharing, peer engagement, and tailored interventions that may enhance literacy and normalize help-seeking. Yet, they pose risks, including misinformation, exposure to harmful content, and reinforcement of stigma in diverse contexts. Despite this potential, evidence from SSA on the effects of social and digital media messaging on mental health knowledge, attitudes, behaviors, and help-seeking is scarce. This systematic review will assess the effect of social and digital media mental health messaging on help-seeking behaviors in SSA. Electronic databases, including PubMed, Psychological Information Database (PsycINFO), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Communication and Mass Media Complete, Scopus, Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science, Educational Resource Information Centre (ERIC), ProQuest Sociology, Google Scholar and Embase will be systematically queried using predefined keywords without language restrictions to ensure comprehensive evidence capture. Eligible studies will include interventions delivering mental health messaging through social media, mHealth applications, SMS, web-based platforms, or hybrid approaches, analyzing behavioral and psychological outcomes, and any kind of intervention studies. Methodological quality and risk of bias will be assessed using validated tools appropriate to each study design, including the Cochrane Risk of Bias 2 (RoB 2) tool, Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I), and Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist. Where appropriate, data will be synthesized with or without meta-analysis. This synthesis will clarify how social and digital media shape mental health outcomes, describe patterns across delivery channels, identify gaps, and inform culturally sensitive interventions to improve communication and promote mental health help-seeking in SSA.