Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 29 appraisals
Artificial intelligence in medicine
Green artificial intelligence in health applications
As the healthcare sector increasingly integrates Artificial Intelligence (AI) technologies to improve operational effectiveness, diagnosis, and therapy, the environmental footprint of these innovations has become a growing concern. High energy consumption, electronic waste, and carbon emissions associated with the deployment and training of AI models pose sustainability challenges that must be addressed. This paper investigates the concept and application of Green AI in healthcare, aiming to balance technological advancement with environmental responsibility. This systematic review explores key themes, including green computing practices, the adoption of energy-efficient AI models, and the use of renewable energy sources within healthcare settings. It identifies a range of healthcare applications employing Green AI, highlights emerging trends, and emphasizes the growing importance of environmental awareness in AI development. Furthermore, the study examines enabling tools and techniques, outlines barriers to adoption, and highlights how Green AI can help streamline processes, reduce resource waste, and promote environmentally friendly medical procedures like telemedicine. The review also discusses the significance of policy frameworks, international initiatives, and cross-sector collaboration in promoting environmentally responsible AI deployment. Finally, the paper presents practical implications and outlines future research directions to guide the sustainable evolution of AI in healthcare.
1 Aug 2026
Read appraisal →European journal of nutrition
The effect of system-level intervention in early childhood education and care on children's dietary intake and environmental impact of the diet
PURPOSE: Food systems have a large impact on human health and environmental sustainability. Early childhood education and care is an ideal setting for systemic change. We performed a cluster-randomised trial aiming at climate-friendly and healthy sustainable diet at 23 daycare centres in four Finnish municipalities in 2022 and evaluated system-level intervention effects on food consumption and nutrient intake, and climate and biodiversity impacts of the diet. METHODS: The intervention was initially designed by the research group and refined through co-creation workshops with food service and daycare professionals, and municipal decision makers. The 10-month intervention comprised a modified menu towards more plant-based food supply, and food education. Children's (n = 111) diet was assessed at baseline, mid-term, and/or at the end of the intervention by food frequency questionnaires. The climate and biodiversity impacts per megajoule were assessed using previously specified coefficients for the foods. Multilevel linear mixed models were used in the analysis. RESULTS: During the intervention, children's consumption of legumes and legume products and fish from nearby areas increased, while that of red meat, sausages, and other meat products decreased (Bonferroni-corrected p < 0.001). The changes in food consumption were substantial, with effect sizes ranging from 0.9 to 2.69. Nutrient intake was not affected by the intervention. Intervention decreased climate and biodiversity impacts of food provided at daycare (Bonferroni-corrected p < 0.001, p = 0.003, respectively). CONCLUSION: This study demonstrated that well-planned systemic change in meal provision and food education at daycare resulted in healthier and more climate-friendly diets among children.
20 July 2026
Read appraisal →Nano letters
Wearable Self-Powered Biomedical Smart Sensors Deriving from E-Waste
Power requirements represent a critical challenge for wearable sensors. Self-powered sensing systems enabled by miniaturized energy-storage devices (MESDs) offer a promising solution. However, the proliferation of MESDs inevitably generates electronic waste (e-waste), which causes environmental concerns. Transient electronics that degrade into eco-friendly residuals provide opportunities for the development of green power sources. Herein, flexible e-waste-friendly power sources based on degradable MXene films were developed for the integration of wearable, self-powered biomedical sensors. The proposed transient MXene film-based supercapacitors (TMFSCs) possess good energy storage capability and mechanical flexibility and can be completely degraded into eco-friendly residuals within minutes. Furthermore, a wearable self-powered biomedical smart sensor was designed for real-time monitoring of pulse signals in real-life scenarios, and the obtained pulse rate is conducive to early evaluation of human health. Collectively, TMFSCs are considerably competitive for future eco-friendly flexible MESDs toward next-generation sustainable wearable and portable sensing electronics.
3 July 2026
Read appraisal →La Medicina del lavoro
Cadmium Exposure and Risk of Pancreatic Cancer: A Systematic Review and Meta-Analysis
INTRODUCTION: Pancreatic cancer is a highly lethal malignancy with poor prognosis and limited treatment options. Environmental exposures, particularly to heavy metals such as cadmium, may contribute to its etiology. This systematic review and meta-analysis evaluated the association between cadmium exposure from different sources and pancreatic cancer incidence or mortality. METHODS: Following PRISMA guidelines, we searched PubMed, EMBASE, and Scopus. Eighteen studies comprising 20 risk estimates were included. Random-effects meta-analyses were conducted overall and stratified by exposure source, gender, region, study design, and outcome. Dose-response relationships were assessed using meta-regression of cadmium exposure measures. Publication bias was evaluated using funnel plots and Egger's test. RESULTS: Overall, cadmium exposure was associated with an increased risk of pancreatic cancer [relative risk (RR) = 1.69, 95% confidence interval (CI): 1.28-2.22]. Occupational exposure showed the most consistent association (RR = 1.38, 95% CI: 1.19-1.61), followed by urinary and blood/serum biomarkers. Risk was higher in men than in women, and in case-control than in cohort studies. Dose-response analysis did not reveal a linear trend. There was limited evidence of publication bias overall, though some asymmetry was observed for urinary cadmium studies (P = 0.045). CONCLUSION: Cadmium exposure was associated with pancreatic cancer risk, particularly in occupational and biomarker-based studies. While findings support a potential causal link, heterogeneity, residual confounding, and limited dose-response data necessitate cautious interpretation.
26 June 2026
Read appraisal →Reviews on environmental health
Air quality and public health co-benefits of national transport policies
INTRODUCTION: Transport is a major contributor to urban air pollution and premature mortality worldwide. This systematic review evaluates the air-quality and public health co-benefits of national and regional transport policies aimed at reducing population exposure to air pollution. CONTENT: Following PRISMA 2020 guidelines, this review synthesized evidence published between 2011 and 2024 on the health and environmental impacts of national or regional transport policies. Ten eligible studies were reviewed, covering diesel vehicle phase-outs, inspection and maintenance programs, electric vehicle incentives, fuel taxation, and promotion of public or active transport. SUMMARY: Across multiple settings, these policies were associated with reductions in PM2.5 concentrations and with avoided premature mortality, respiratory and cardiovascular hospitalizations, and healthcare costs. The largest and most consistent health benefits were observed for integrated policy packages, particularly those combining regulatory and technological measures such as fuel quality improvements, vehicle electrification and diesel phase-out, complemented by behavioral or fiscal instruments. Most available evidence originates from high-income countries, while studies from low- and middle-income regions remain scarce. The limited number of eligible studies and their concentration in specific regions and modeling approaches constrain generalizability, especially to settings with different transport systems, resource availability, and air-quality profiles. OUTLOOK: Embedding health-impact assessment within transport planning and expanding long-term evaluations in low- and middle-income countries are essential to ensure equitable and sustained air-quality and health improvements. Future policy frameworks should prioritize source-oriented regulatory and technological measures such as vehicle electrification, fuel-quality regulation, and diesel phase-out implemented within integrated policy packages to maximize public-health co-benefits.
25 June 2026
Read appraisal →Environmental monitoring and assessment
YOLO-geochemical integration for real-time aeolian dust monitoring: a critical review of signatures, health risks, and policy implications
Aeolian dust is one of the most pervasive natural air pollutants, strongly influencing atmospheric chemistry, visibility, climate feedback, and human health. This review critically evaluates the integration of geochemical fingerprinting techniques and YOLO-based deep learning frameworks for real-time aeolian dust monitoring, with the overarching objective of advancing source attribution, health risk assessment, and evidence-based policy formulation in dust-prone regions. The Middle East, particularly the Qatar Peninsula, experiences recurrent dust storms that transport mineral particles and trace metals, contributing to degraded air quality, reduced solar energy efficiency, and elevated risks of respiratory and cardiovascular diseases. Traditional monitoring approaches including satellite observations, ground-based measurements, and geochemical analyses such as inductively coupled plasma mass spectrometry (ICP-MS), scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS), and X-ray diffraction (XRD) have improved source apportionment and compositional understanding, yet they often lack real-time detection and high-resolution spatiotemporal coverage. Emerging artificial intelligence (AI) tools, particularly the You Only Look Once (YOLO) deep learning framework, offer transformative opportunities for atmospheric dust monitoring by enabling rapid, automated plume detection across multispectral and hyperspectral datasets. This review demonstrates that ICP-MS, SEM-EDS, and XRD, when applied in combination, provide robust multi-dimensional source fingerprinting: ICP-MS isotopic ratios discriminate crystal from anthropogenic contributions, SEM-EDS resolves particle-level morphology and elemental composition, and XRD quantifies mineralogical assemblages that vary systematically with source region and transport pathway. Concurrently, successive YOLO generations (YOLOv5 through YOLOv10) have achieved real-time dust plume detection with mAP values exceeding 88% on satellite and UAV imagery. The proposed multimodal YOLO-geochemical integration framework, in which YOLO-guided plume detection triggers targeted field sampling and geochemical labels iteratively retrain detection models, enables compositionally resolved, real-time exposure mapping that directly supports inhalation risk assessment against WHO PM2.5 and PM10 thresholds and metal-specific reference concentrations. Collectively, these findings establish a scalable, evidence-based monitoring architecture that strengthens early-warning systems, informs air quality regulation, and advances global strategies to mitigate the environmental and public health burdens of dust storms.
7 June 2026
Read appraisal →Medicine
Machine learning identifies traffic-related pollutant mixtures as potential factors suggestive of association with osteoporosis in a cross-sectional analysis of NHANES
The combined effect of traffic-related pollutant mixtures on osteoporosis (OP) remains unclear. This study aimed to evaluate such associations using machine learning and mixture modeling approaches. A cross-sectional analysis was conducted among 3053 participants from the National Health and Nutrition Examination Survey 2015 to 2018. Eighteen exposures were assessed, including heavy metals, polycyclic aromatic hydrocarbons, phthalates, per- and polyfluoroalkyl substances, volatile organic compounds, diesel exhaust, and behavioral factors. OP was defined by femoral neck bone mineral density T-score ≤-2.5. Weighted quantile sum regression and 5 machine learning models were applied, accounting for complex survey design. OP prevalence was 12.97%. Weighted quantile sum regression showed a positive but nonsignificant mixture odds ratio (odds ratio = 1.21; 95% confidence interval: 0.77-2.63; P = .412). The largest positive weights were daily outdoor work duration (0.240) and occupational diesel exhaust (0.136). Random forest identified female sex, age, vitamin D, blood lead, and body mass index as top predictors. Least Absolute Shrinkage and Selection Operator achieved the highest test area under the receiver operating characteristic curve (0.81) with the lowest cross-validation standard deviation (0.0167). Its coefficients highlighted occupational diesel exhaust (β = 0.40) as a notable environmental factor alongside female sex (β = 0.95) and age (β = 0.70). Traffic-related pollutant mixtures showed a cross-sectional association with OP in a nationally representative US population, with occupational diesel exhaust and prolonged outdoor work emerging as key contributors in mixture models. However, the nonsignificant overall mixture effect and potential for reverse causation warrant caution in interpretation. These findings support considering environmental and occupational factors in OP prevention, although the overall mixture effect did not reach statistical significance.
31 May 2026
Read appraisal →Journal of breath research
Investigating short-term lung inflammation using exhaled breath VOCs from exposure to candles: a randomized controlled crossover study among young mild asthmatics
Lung inflammation is associated with a response to pollution or irritants and is reported to release volatile organic compounds (VOCs) in breath, that can be monitored non-invasively. An approach to studying short-term lung inflammation is exposure to environmental emissions. This study used brief exposure to PM₂.₅ emissions from two types of burning candles to evaluate potential differences in airway inflammation among mildly asthmatic individuals. The aim was to explore VOCs linked to lung inflammation by exposure to candle emissions; emissions from candle A, a newly developed candle with a smaller emission to the surroundings and candle B, a well-characterised commercially available candle. Seventeen non-smoking asthmatics (11 female) with a mean age of 21.9 years participated in a randomised controlled double-blind crossover study including two exposure sessions: i) air with candle A emissions (mean PM2.554.1µg m-3), and ii) air with candle B emissions (mean PM2.598.4µg m-3). Participants underwent two five-hour, double-blind exposure sessions in a controlled climate chamber, each involving emissions from one of two candle types. Exhaled breath was collected at baseline (0 h), immediately post-exposure (5 h), and the following morning (24 h). VOCs were analysed using both a targeted panel of 23 predefined compounds and an untargeted workflow, which yielded 21 breath-derived VOCs after ambient-blank filtering. Statistical comparisons used Wilcoxon signed-rank tests with Benjamini-Hochberg correction. Across both exposure sessions, ten exhaled VOCs showed significant changes between baseline and immediate post-exposure (5 h), including increases in several aldehydes and decreases in selected sulfur-containing compounds. These changes were not present at 24 h, where samples resembled baseline profiles. The ten VOCs were identified from a combined dataset of 23 targeted compounds and 21 untargeted breath-derived VOCs; only the untargeted analysis yielded significant findings after correction. PCA demonstrated clear separation between baseline and 5 h samples, particularly for candle B, for which 0 h, 5 h and 24 h samples formed distinct clusters. This candle-specific response produced a multi-VOC signature capable of distinguishing individuals immediately after exposure, suggesting that exhaled VOCs can reflect short-term airway inflammatory responses. The findings suggest that an emission-specific, exhaled breath, multi-VOC profile can detect individuals exposed to inflammation triggers, such as candles. This indicates that VOCs could serve as biomarkers for detecting short-term airway inflammation, as evidenced by the systematic difference in VOCs observed 5 h after exposure to candle B. This research contributes to the advancing field of VOC-based health monitoring and might affect public health implications.
30 May 2026
Read appraisal →Cancer causes & control : CCC
Built environment (BE) and cancer: a systematic review of the BE's impact during the treatment journey and patient outcomes
The role of the built environment (BE) in cancer research has been extensively explored in prevention, focusing on its influence as a determinant of exposure to carcinogens and a lifestyle shaper, particularly through geographic accessibility to resources like transportation, healthy food, and diagnostic centers. However, the BE's role and influence during the treatment period and its connection with patient outcomes remain comparatively understudied. This systematic review employed the CDC definition of BE, following PRISMA guidelines and AI-driven software (ASReview) for a thorough and efficient literature review. A total of 31 articles were identified (27 database, 4 snowballing) that examined the BE's role during the treatment phase. These studies investigated diverse BE characteristics including urban features (e.g., transportation, green environment, food environment) and architectural features (e.g., design, aesthetics) during the cancer patient treatment journey. Among the included studies, the most analyzed outcomes were Physical Activity (PA) (11 articles), followed by survival and mortality (9 articles). Findings revealed positive associations between specific BE characteristics-such as green spaces, accessibility, transportation options, and aesthetics-and cancer patients' physical activity levels, quality of life, and mental health status; however, findings are inconsistent. These results underscore the potential of integrating urban and architectural BE analysis to enhance the psychosocial dimensions of cancer treatment, improve healthcare delivery, and open new pathways for research strategies.
28 May 2026
Read appraisal →mSphere
Novel machine learning-based approach to identify viral biomarkers of human respiratory emissions from oral and nasal metagenomes
Humans spend approximately 90% of their lives in built environments, making virus transmission indoors a key determinant of health. Environmental sampling of respiratory viral pathogens is often challenging because of frequent non-detect measurements. Non-detect measurements do not differentiate between samples containing low or no pathogens from samples that simply lack respiratory expulsions altogether. This ambiguity can be resolved by scanning samples for a biomarker of human respiratory emissions. To do so, reliable biomarkers for environmental monitoring need to be identified. Ideal biomarkers are prevalent across individuals, abundant, and unique to the human respiratory tract. Here, we present a new machine learning-based approach to query for suitable biomarker candidates from publicly available metagenomes and apply it to identify viral biomarkers of healthy oral and nasal microbiomes. Twelve viral biomarker candidates were selected from 1,232 curated viral operational taxonomic units. The viral biomarker candidates had as much as 63% prevalence across respiratory metagenomes, and prevalence was further increased to 77%-81% by combining two or three biomarkers. Real-time PCR confirmed that these viral biomarkers were prevalent and abundant in nasal swabs and saliva samples. Notably, top candidate biomarkers remained stable and detectable through multiple lab purification steps, increasing confidence in their viral origins and demonstrating their suitability for environmental monitoring. These findings demonstrate that existing metagenomes can be used to identify effective biomarker candidates for environmental sampling.IMPORTANCEDeveloping non-pharmaceutical interventions to reduce virus transmission indoors relies on robust environmental monitoring methods. Monitoring viral pathogens is challenging because of frequent non-detect measurements that introduce uncertainty. For instance, a non-detect measurement could indicate either the absence of the pathogen or simply the lack of human respiratory activity and, thus, exposure. To aid in distinguishing these scenarios, this study identifies viruses from the human respiratory tract using publicly available sequencing data that can be incorporated into environmental monitoring as biomarkers of human respiratory activity. These viral biomarkers will improve indoor monitoring to help enact interventions to mitigate virus transmission. Furthermore, our approach to identify biomarkers from existing metagenomes can be adapted for future biomarker identification in any system.
28 May 2026
Read appraisal →Environmental monitoring and assessment
XGBoost-based analysis of short- and long-term COVID-19 air quality impacts in Tehran, Iran.
This study examines the air quality trends associated with COVID-19 lockdowns and the subsequent recovery on air quality in Tehran, employing an established XGBoost framework for de-weathering with daily data from 22 monitoring stations from 2019 to 2025. A comprehensive analysis was conducted on six principal pollutants (PM₂.₅, PM₁₀, NO₂, O₃, SO₂, and CO) across four distinct temporal phases: Pre-pandemic, Pandemic, Post-COVID, and After Termination. While the model effectively reduced meteorological bias, exhibiting strong efficacy for particulate matter (R = 0.78-0.88; IOA = 0.68-0.88; FAC2 > 0.95), we explicitly acknowledge less consistent outcomes for reactive gases such as O₃ (R < 0.75; NMGE > 0.30). The imposition of lockdown measures coincided with significant reductions in pollutants associated with vehicular traffic: NO₂ exhibited a decline of - 30 to - 36%, PM₂.₅ decreased by - 18 to - 22%, and CO diminished by - 20%. In contrast, O₃ experienced an increase of + 20-30%, indicative of diminished NO titration. Principal Component Analysis (PCA) corroborated these trends, with PC1 (NO₂, PM₂.₅, CO) accounting for approximately 60% of the variance throughout the pandemic. In subsequent phases, partial recoveries were noted: PM₂.₅ escalated by + 15-20%, NO₂ stabilized at around -10%, SO₂ consistently decreased (-30% by 2025), whereas O₃ surged by + 20% by 2025, clustering with PM₁₀ in PC2, thereby highlighting the impacts of dust and secondary formation processes. Lockdowns yielded transient enhancements in air quality, with restricted long-term advantages. Sustainable progress necessitates the adoption of comprehensive policies targeting traffic, industrial emissions, VOCs, and dust management.
25 May 2026
Read appraisal →PloS one
Optimizing bike-sharing station locations: A machine learning and artificial neural networks approach using geospatial and demographic data
In the modern world, public transportation amenities are noticeably on the rise, with urban bike-sharing systems becoming well-established in many major cities. However, not all cities have these systems, and planning optimal locations for bike-sharing stations is a complex task that requires consideration of many factors. To address this, the authors of this research paper developed a model to predict suitable locations for bike-sharing stations, utilizing machine learning techniques and artificial neural networks. These techniques utilized land cover and demographic data to train the model, achieving a high accuracy of 0.977. The predicted bike-sharing stations not only align with existing networks but also support their expansion, as many suggested locations are near major intersections and public transportation stops, confirming their suitability for the urban bike network. Additionally, the model was applied to Rzeszów, a city without a current bike-sharing system, where it successfully identified optimal locations for new stations. This demonstrates the methodology's practical applicability and its valuable support for planning bike-sharing infrastructure in urban areas.
21 May 2026
Read appraisal →International journal of biometeorology
Machine learning and dose response effect models: an integrated approach to analyze the association between environmental variables and Cuneo Emergency Department admissions for Acute Otitis Media (2007-2015)
Acute otitis media (AOM) is a leading cause of pediatric Emergency Department visits, particularly among children under five years of age. Although its seasonal pattern is well established, the role of air pollution and meteorological factors remains unclear. This study aims to investigate their impact on daily AOM visits by integrating machine learning and epidemiological approaches. We conducted a retrospective analysis of pediatric AOM diagnoses (2007-2015) at S. Croce and Carle Hospital (Cuneo, Italy). Predictors included PM10, NO₂, O₃, and eleven meteorological variables. Ensemble machine learning models (Random Forest, XGBoost, and AdaBoost) were trained and validated using 10-fold cross-validation. Model interpretability was assessed through SHAP values. Distributed Lag Nonlinear Models (DLNM) were applied to estimate delayed exposure-response relationships over lag periods of 0-1, 0-3, 0-5, and 0-10 days, with results expressed as Relative Risks (RRs) and 95% Confidence Intervals (CIs). AdaBoost showed the best performance (R² = 0.974; MAE = 0.019 cases/day; cross-validated R² = 0.987). SHAP analysis identified mean temperature as the most influential predictor (44%), while PM10, NO₂, and O₃ each contributed approximately 10%. DLNM analysis confirmed a strong and consistent effect of temperature across all lag periods (RR > 1.20, CI > 1). Moderate associations were observed for NO₂ and PM10 (RR: 1.02-1.04). O₃ exhibited smaller but significant effects at shorter lags (RR = 1.01 at 0-1 days; RR = 1.02 at 0-3 days; CI > 1). Environmental factors, particularly temperature, play a significant role in pediatric AOM incidence. The integration of machine learning and DLNM enhances predictive accuracy and improves the understanding of exposure timing. These findings support the development of early warning systems and targeted preventive strategies under adverse environmental conditions. Further validation in larger urban settings is needed.
20 May 2026
Read appraisal →PloS one
The carbon footprint of surgical operations: 2023-2025 systematic review update
Surgical care significantly contributes to greenhouse gas emissions, with operating rooms consuming three to six times more energy than other departments relying heavily on single-use materials and anaesthetic gases. We conducted a systematic review of literature from 2023 to 2025 following PRISMA guidelines. The PubMed, Scopus and Healthcare LCA databases were searched, and eligibility criteria were applied. A total of 26 studies met inclusion criteria. Data were extracted on each study's scope and system boundaries, reported emission values, identified carbon hotspots, and any mitigation strategies evaluated. A structured quality assessment was conducted to evaluate the reliability of the included studies and to identify potential sources of bias. The included studies demonstrated wide variability in reported carbon footprints of surgery, ranging from under 5 kgCO₂e for minor procedures to over over 400 kgCO₂e for complex single-stage operations and approximately 1,000 kgCO₂e for whole multistage patient pathways. This systematic review underscores that surgical operations could have a significant carbon footprint, with emissions hotspots concentrated in consumable materials and energy consumption. It also reveals substantial variability and methodological heterogeneity in how surgical carbon footprints are calculated, pointing to the urgent need for standardized life-cycle based frameworks in surgical settings. Establishing common standards will enable more reliable benchmarking of surgical emissions and better comparisons across studies.
19 May 2026
Read appraisal →International journal of food microbiology
Machine learning-based modeling for monitoring and predicting the detection rate and severity of pathogenic microorganisms in seafood
Pathogenic microbial contamination in seafood presents persistent risks to food safety and public health. Conventional monitoring methods frequently lack adequate sensitivity and specificity, particularly under dynamic environmental conditions. To address these limitations, this study developed a machine learning (ML) framework utilizing microbial testing data from seafood samples collected in Yantai, China (2014-2024), with 2025 data reserved for external validation. The framework simultaneously predicts pathogen prevalence and severity levels. And, exploratory data analysis assessed variable distributions and statistical associations with detection rates. Time series analysis identified significant long-term trends and seasonal patterns, while spatial analysis revealed heterogeneity in contamination risk across administrative regions, particularly in coastal economic zones and major ports. Applying a dynamic threshold optimization strategy, detection rates were predicted using six ML algorithms: ROSE-LASSO, LightGBM, XGBoost, k-NN, CART, and SVM. LightGBM demonstrated optimal performance with 97.6% specificity and 99.9% positive predictive value (PPV) at an optimized threshold of 0.157. External validation confirmed its robustness, yielding 91.2% sensitivity, a 92.4% F1 score, and 93.6% PPV. For three-class severity classification, XGBoost achieved 93.7% overall accuracy and high sensitivity (80.0%) for high-risk samples, with an AUC of 0.989. By integrating spatiotemporal environmental features with adaptive thresholding, this framework provides a scalable approach for pathogen risk identification within coastal seafood supply chains, supporting early warning systems and evidence-based resource allocation.
17 May 2026
Read appraisal →Journal of medical Internet research
Plain Language Summarization of Environmental Health Research Using Generative AI: Community-Engaged Qualitative Study
BACKGROUND: Plain language summaries (PLSs) are increasingly required in environmental health publications to improve public accessibility. Generative artificial intelligence (AI) systems such as large language models can automatically produce such summaries; however, automated summaries often overlook local context and cultural relevance-limitations that are critical in environmental health research, where affected communities face disproportionate exposure and health risks. OBJECTIVE: This study aimed to develop and refine community-informed prompts for generating PLSs of environmental health research using generative AI. METHODS: We conducted a community-engaged qualitative study in Louisville, Kentucky, involving workshops with 97 participants from 4 stakeholder groups: summer interns at a local social justice nonprofit organization, participants in a youth development program, participants in a faith-based community organization, and members of a community advisory board focused on environmental justice. Participants reviewed PLSs generated using 3 different prompt styles in GPT-4o (ChatGPT, OpenAI). Feedback was collected through structured discussions and facilitator notes. Data were analyzed using thematic analysis informed by knowledge translation frameworks to identify preferred structural, linguistic, and contextual features of the summaries. RESULTS: Participants consistently preferred summaries between 300 and 400 words, written at a sixth- to eighth-grade reading level. Key priorities included presenting definitions before findings, using headings and bullet points to improve readability, and clearly explaining real-world and environmental justice implications. Narrative summaries without structure were viewed as overly long and difficult to interpret, while purely bullet-based formats were considered too simplified. Feedback from youth participants emphasized clarity and practical relevance, while faith-based participants highlighted the importance of trust and contextual framing. These insights informed the development of a community-refined prompt that included definitions, key findings, an introduction, and a concluding section on community implications. CONCLUSIONS: Community-engaged prompt development may improve the relevance and interpretability of AI-generated PLSs of environmental health research. Incorporating stakeholder perspectives into prompt design offers a replicable strategy for improving research translation and ensuring AI-generated summaries reflect the informational needs of affected communities.
15 May 2026
Read appraisal →International journal of biometeorology
Recent trajectories of biometeorology in the Global South: a systematic review of biometeorology research in the International Journal of Biometeorology
Literature reviews have become increasingly prominent in scientific discourse; however, few have critically examined the geographic distribution of research, particularly in the field of biometeorology, where studies remain disproportionately concentrated in the Global North. Building on previous regional reviews and special issues, this paper critically examines geographic disparities in publication trends, authorship, and methodological approaches to aid in identifying challenges and opportunities for equitable and justified future research. Using a PRISMA-style framework, 591 empirical studies conducted across 147 Global South countries were identified from a total of 3,046 publications in the International Journal of Biometeorology (IJBM) between 2000 and 2024. The review reveals a steady increase in contributions from the Global South over time, but highlights persistent gaps in representation, especially in author affiliation and research leadership. Thematic and methodological analyses highlight the diversity and innovation present in Global South contexts, while also revealing structural barriers to equitable participation in the field. This work advocates for more inclusive research practices and enhanced institutional support to bridge the divide between the Global North and South, fostering a more representative and collaborative future for biometeorology.
12 May 2026
Read appraisal →JMIR formative research
Feasibility of Integrating Wearable Devices and Ecological Momentary Assessment for Real-Time Environmental Exposure Estimation: Proof-of-Concept Study
BACKGROUND: Environmental exposures such as heat and air pollution are critical determinants of health, yet traditional assessment methods rely on stationary monitors or residential address proxies that fail to capture the exposures that individuals experience throughout the day. OBJECTIVE: This pilot study aimed to assess the feasibility of integrating ecological momentary assessment (EMA), wearable devices, and continuous GPS tracking to capture real-time environmental exposures and to explore associations with concurrent health outcomes. METHODS: In total, 7 young adults (aged approximately 16 to 24 years; 5/7, 71% female) wore Fitbit Charge 6 watches from July 2025 to August 2025 (mean 28.1, SD 1.1 days), recording sleep quality and duration, resting heart rate, breathing rate, heart rate variability, and physical activity. GPS location measured at up to 5-minute intervals (mean 19.7, SD 25.8 measurements per day) was linked to ambient temperature, humidity, and air pollution data (particulate matter <2.5 um or <10 um in diameter, nitrogen dioxide, sulfur dioxide, ozone, and carbon monoxide) derived from monitoring stations, satellites, and climate models using data-integration algorithms accessed via an application programming interface. EMA surveys administered 3 times per day captured participants' emotional states and location (inside or outside). Feasibility targets were ≥3 GPS measurements per day, ≥1 survey completed per day, and complete sleep data on ≥50% of days. We examined exploratory bivariate correlations between environmental exposures, physiological measures, and self-reported mood, adjusting for multiple comparisons using false discovery rate correction. RESULTS: Of the 7 participants, 5 (71%) met predefined feasibility targets. Mean compliance included 565 (SD 457) GPS coordinates per participant, 1.4 (SD 0.2) EMA surveys per day, and complete Fitbit sleep data on 64% (SD 27%) of days. Surveys identified barriers to compliance, including perceived complexity of the system and forgetting to put the Fitbit watch back on after removing it. Exploratory correlations (6/7, 86% of participants with complete Fitbit data) revealed associations between nitrogen dioxide and heat exposure and reduced heart rate variability (a marker of parasympathetic tone), and between air pollutants (sulfur dioxide) and increased negative emotions. Heat exposure showed a paradoxical pattern of lower self-reported sadness but reduced heart rate variability with higher levels of heat exposure. Given the small sample size, these correlations should be considered preliminary and hypothesis generating rather than definitive findings. CONCLUSIONS: This study demonstrates that the multimodal integration of wearable devices, GPS tracking, and EMA is feasible for capturing real-time environmental exposures and concurrent health outcomes in young adults. This approach addresses critical exposure misclassification issues in environmental health research that relies on residential addresses as proxies. Preliminary patterns suggest complex relationships between environmental exposures and both physiological and emotional outcomes, warranting further investigation in larger, more diverse samples. This approach could inform future personalized environmental health interventions.
11 May 2026
Read appraisal →PloS one
Research on interpretable machine learning modeling and spatial prediction of cross-landform landslide disaster mechanism: A case study of Shaanxi Province
The differential understanding of the landslidetriggering mechanisms across various geomorphic units is vital for enhancing regional disaster prevention. This study investigates the Loess Plateau area in northern Shaanxi and the Qinba Mountain Area in southern Shaanxi as the study areas, and constructs a 15dimensional evaluation factor system covering topography, geology, vegetation, and human activities. After eliminating collinearity factors via double tests of the Pearson correlation coefficient and variance inflation factor, Bayesian optimization is used to optimize hyperparameters for CatBoost, Random Forest, LightGBM, and XGBoost, and the SHAP framework is combined to perform global attribution, single-factor dependency analysis, and interaction-effect quantification. The results demonstrate that CatBoost performs best across both geomorphic areas, achieving AUCs of 0.8307 and 0.8252 in the test sets. In northern Shaanxi, a discrete patch pattern driven by "human-water" coupling is observed, with population density and the topographic moisture index contributing 37.2%. In contrast, Southern Shaanxi exhibits a continuous, band-shaped distribution controlled by "structure-topography" is observed, and elevation and lithology dominate the model's decision-making. The SHAP-dependent map identified cross-geomorphic differences in the population density threshold of 99.83 people/km2, the slope threshold of 2.21° in northern Shaanxi, and the elevation threshold of 1194.73m in southern Shaanxi. Furthermore, the interaction network revealed an antagonistic effect of DEM and POP in northern Shaanxi and of DEM and LULC in southern Shaanxi. The framework proposed a quantitative, explainable basis for differentiated disaster prevention strategies in this research.
11 May 2026
Read appraisal →Journal of diabetes science and technology
Diabetes-Technology and Waste: The Future is Now.
The downside of the success story of diabetes technology is the amount of waste generated by manufacturing and using such medical products. Quantitative surveys performed in the United States and Germany document how many pounds of plastic waste, batteries, and electronic parts are piling up per patient with diabetes each year; however, the actual environmental burden of diabetes care has not been assessed yet. Given the highly probable further increase in usage of diabetes technology devices, what are the options to change the situation? Ideally, one would avoid generating waste by optimizing the design of the products (Eco-Design); however, this approach faces a number of complex regulatory and economic hurdles. The issue of waste management is becoming increasingly important as the use of wearable devices increases. Stewardship of the environment will require all stakeholders to address waste management.
4 May 2026
Read appraisal →PloS one
Optimizing LSTM networks and feature selection algorithms using GEE data
Feature selection uncertainty and suboptimal model configuration in flood susceptibility mapping (FSM) are critical challenges for disaster risk reduction. This study introduces a novel integrated framework that couples feature selection strategy with metaheuristic-optimized deep learning for high-precision FSM in the flood-prone Khuzestan Province, Iran. An initial set of 19 factors was sourced from Google Earth Engine (GEE). The most influential variables were identified using an ensemble of nine feature selection methods, including Boruta, Boruta-SHAP, Elastic-Net, Mutual Information, Permutation Importance, Recursive Feature Elimination (RFE), Sequential Forward Selection (SFS), Stability Selection, and Deep Feature Importance. For model development, 1,000 sample points were used, consisting of 500 randomly selected non-flood points (value 0) and 500 flood points (value 1), with the trained model subsequently generalized to the entire study area. In this process, a frequency-based consensus rule was applied, whereby variables were retained only if selected by a majority of methods. This process established the Normalized Difference Vegetation Index (NDVI) and Daily Minimum Temperature (TMMN) as the most critical predictors. A Long Short-Term Memory (LSTM) was developed using this optimal feature set and subsequently enhanced through hyperparameter optimization with five advanced metaheuristic algorithms, including WOA, GWO, OOA, CSA, and HOA. The model validation demonstrated that optimization significantly boosted performance, with the LSTM-WOA model emerging as superior, achieving the highest F1-Score (0.88) and Cohen's Kappa (0.75). The final FSM identified the northwestern and central regions as the highest susceptibility. The study innovation lies in its formalized consensus feature selection and comparative metaheuristic optimization, providing a reliable tool for FSM in arid and semi-arid regions.
3 May 2026
Read appraisal →PloS one
Mukara: A deep learning alternative to the four-step travel demand model with a case study on interurban highway traffic prediction in the UK.
Accurate traffic volume prediction is essential for managing congestion, improving road safety, mitigating environmental impacts, and supporting long-term transportation planning. The traditional four-step travel demand model (FSM) is a well-established framework, but it relies on static survey data, substantial calibration effort, and simplified behavioural assumptions that may not adequately capture complex travel patterns. In contrast, data-driven models are capable of learning nonlinear relationships from large datasets, yet they are often designed for short-term forecasting and typically do not target the long-term, segment-level volume estimation tasks required for strategic planning. This study proposes Mukara, a deep learning framework that directly approximates the mapping from external socioeconomic and network features to observed traffic volumes on highway trunk road segments. The model is trained on eight years of data from England and Wales and incorporates population, employment, land use, road network characteristics, and points of interest as inputs. Mukara achieves a mean GEH of 50.74, a mean absolute error of 8,989 vehicles per day, and an R2 of 0.583 under random cross-validation, outperforming baseline models and existing studies under comparable settings. Under a more stringent region-based spatial cross-validation scheme, performance remains robust, demonstrating strong spatial transferability. Ablation experiments further demonstrate the robustness of the proposed architecture and reveal the relative importance of different input feature groups for prediction.
17 Apr 2026
Read appraisal →Water research
Representation of urban and rural contexts in the application of wastewater surveillance for antimicrobial resistance: A systematic review.
Wastewater surveillance (WWS) can reveal community health trends through targeted monitoring of sewage. Given concerns regarding the global dissemination of drug-resistant infections, there is growing integration of antimicrobial resistance (AMR) in WWS infrastructure. However, most WWS has focused on highly urbanized sewersheds in resource-rich areas, which may exacerbate disparities in disease burdens and clinical datasets. This work presents both a systematic and scoping review of current published WWS efforts targeting antimicrobial resistance (AMR), with the aim to quantify and compare applied monitoring efforts capturing rural vs. urban areas and lower-income vs. higher-income countries. Of the 2,873 deduplicated manuscripts identified through a systematic search of four databases, 454 examined untreated domestic sewage and were characterized via a brief scoping review. Of these, 48 articles included an explicit focus on monitoring for population health surveillance, and were subject to methodological data extraction and risk of bias assessment. Sampling trends observed in the scoping and systematic review were similar. Only 13-19 % of scoping review sampling sites were classified as rural, and 44 % were located within lower- or middle-income countries (LMICs). Few articles examined AMR within unsewered communities. While analytical methods were similar across income brackets, rural monitoring efforts were more reliant on culture-based techniques than urban efforts. Monitoring in rural sites and LMICs were typically part of multi-site campaigns and featured less longitudinal sampling. Overall, methodological inconsistencies rendered a meta-analysis infeasible. The combined lack of research in rural and low-income areas indicates that key at-risk communities for circulation of AMR are an oversight.
17 Apr 2026
Read appraisal →Global health action
Impact of climate change on pediatric health outcomes
Climate change has become one of the most critical health issues globally in the twenty-first century with children bearing the disproportionate burden of the burden since they are more vulnerable than adults because of their physiological, behavioral, and developmental capacities. It is a systematic review that rates the evidence of the relationship between climatic exposures such as heat, air-pollution, and extreme weather events and pediatric health outcomes. The number of peer-reviewed studies involved was 23 published in 2000-2025, which represented different geographic areas and study designs and assessed acute and chronic health outcomes. The Newcastle-Ottawa Scale and the ROBINS-I tool were used to evaluate the methodological quality, and the majority of the studies had low to moderate risks of bias. The narrative synthesis shows that there are always links between air pollutants especially PM2.5, NO2 and O3 and respiratory morbidity, prevalence of asthma and hospitalization of children. Amplified temperatures as well as heat waves were associated with increased cases of heat illness, dehydration, and febrile state in infants and young children. There were elevated cases of diarrheal and vector-related infections, especially in low-resource settings, which were linked to extreme weather events especially floods. Although the overall results were similar, significant differences in the regions and methods were found, and low-income countries show little evidence. In addition, exposures as analyzed in most studies were usually considered individually, which may have underestimated the cumulative or compound climate risks. Main findings: This review shows that rising temperatures, air pollutants, and extreme weather events are consistently linked with higher rates of respiratory illness, heat-related illness, and infectious diseases among children.Added knowledge: The synthesis brings together two decades of evidence showing that children across multiple continents experience similar health vulnerabilities when exposed to climate-related environmental conditions.Global health impact for policy and action: The findings highlight an urgent need for stronger climate-responsive child health policies and early warning systems to protect children from worsening environmental threats worldwide.
16 Apr 2026
Read appraisal →Tobacco control
Nitrosamine and nicotine exposure after switching from filtered to unfiltered cigarette smoking: a cross-over clinical trial
BACKGROUND: The cellulose acetate filter is a plastic attachment on nearly all commercial cigarettes sold worldwide. It is the main component of discarded cigarette butts, and the most prevalent waste item collected during urban and beach cleanups. This waste leaches toxic chemicals, including nicotine, metals and tobacco-specific nitrosamines and contributes to environmental microplastic pollution. There is growing international interest in reducing plastic waste from single-use, non-essential products such as cellulose acetate cigarette filters. Public health and environmental advocates recommend a ban on the sale of filtered cigarettes to reduce environmental pollution caused by discarded cigarette butts and to discourage cigarette smoking. RESEARCH QUESTION: What potential health and behavioural implications might arise among people who smoke if filtered cigarettes are removed from the market. METHODS: We conducted a cross-over, randomised clinical trial of 29 people who smoke to evaluate changes in biomarkers for nicotine and tobacco-specific nitrosamine exposure when switching between filtered and unfiltered cigarette smoking. RESULTS: Although unfiltered smoking showed a higher trend in the geometric means of 4-[(methylnitrosamino])-1-[3-pyridyl]-1-butanol (NNAL) compared with filtered cigarettes, regression models for urinary cotinine and NNAL showed no significant differences when switching between filtered and unfiltered cigarettes. CONCLUSION: This proof-of-principle study suggests there is no increased risk to people who smoke when switching to unfiltered cigarette smoking. Although larger studies might provide more evidence regarding unfiltered cigarette smoking, banning the sale of filtered cigarettes may be an important policy intervention to both reduce hazardous tobacco waste and discourage smoking.
15 Apr 2026
Read appraisal →BMJ open respiratory research
Occupational exposure to toxic particles and risk of pulmonary sarcoidosis: a systematic review and meta-analysis
INTRODUCTION: The onset of sarcoidosis is likely the result of a complex genetic-environment-immunological interaction. This systematic review and meta-analysis aimed to identify occupational toxic particles associated with an increased risk of developing pulmonary sarcoidosis. METHODS: Publications in English, published from January 2000 to January 2025, were searched in PubMed, Scopus and JSTOR. The risk of bias was assessed for all included studies. Sensitivity analyses stratified by study quality were conducted to evaluate the robustness of the findings and potential bias. To assess publication bias, a funnel plot was used when more than six studies were included in the analysis. RESULTS: Five categories of toxic particles were identified to be associated with increased risk of the onset of sarcoidosis: chemicals, inorganic dusts, metals, mixed dusts and fumes and organic dusts. The quantitative analysis includes data from 13 studies. The results suggested that occupational silica, pesticides, mould/mildew and World Trade Center ((WTC) dust exposures were associated with increased odds of pulmonary sarcoidosis. Sensitivity analysis confirmed the robustness of the association for WTC dust and mould, whereas the risk associated with silica appeared attenuated in high-quality studies. However, gold exposure was identified as a protective factor. CONCLUSION: Pulmonary sarcoidosis is associated with occupational silica, pesticides, WTC dust and mould. Future research should prioritise gene-environment interactions and granuloma mineralogy to refine preventive strategies and disease management.
12 Apr 2026
Read appraisal →Analytical chemistry
Multicomponent Simultaneous Identification Network (MSINet): An Advanced Deep Learning Model for Boosting Multiplex SERS Detection in Untreated Real Samples
Surface-enhanced Raman spectroscopy (SERS) represents unique advantages for on-site analysis due to its high sensitivity, high specificity, and applicability to gaseous, liquid, and solid samples. However, the complex and unknown components in matrices, as well as competitive adsorptions between targets, will cause signal fluctuation and spectral overlap, which make it challenging to detect multiple targets in untreated real samples. Herein, we designed a deep learning model named the multicomponent simultaneous identification network (MSINet) for analyzing the SERS spectra of untreated samples and output qualitative and content evaluation results of multiple inside targets. The hierarchical feature extraction and multiple feature integration made MSINet robust to signal fluctuations and spectral overlaps while requiring only a small amount of training to quantify target contents in any samples due to its threshold judgment rule. Using the MSINet-assisted SERS detection scheme, the presence and content levels of multiple UV absorbers in seawater, food additives in ready-to-drink cocktails, and biomarkers in human serum could all be determined with ≥0.90 accuracy when facing testing databases. But in contrast, the accuracy in evaluating the target contents in the above untreated samples by the standard curve method was as low as 0.05. The powerful ability to process spectral data, an easy-to-use graphical user interface, and "zero" extra cost make MSINet a universal tool to assist arbitrary SERS sensors in performing high-throughput detection of real samples, leading to great potential in on-site analysis.
8 Apr 2026
Read appraisal →Environmental geochemistry and health
Deciphering stone mining-induced hazardous heavy metal contamination in agricultural soils using source attribution, health-dietary risk analysis, and machine learning-driven insights
Mining activities significantly contribute to the release of hazardous heavy metals (HHMs) into agricultural soils, especially where stone extraction is intensive. This study assessed metal content, source identification, and possible health risks associated with HHMs (Cd, Pb, Cr, Cu, Ni) in stone mines affected agricultural soils of India. The results revealed that Zone 1 exhibited higher concentrations of Cr (308.95 ± 23.03 mg/kg), Ni (117.11 ± 16.21 mg/kg), and Cd (4.21 ± 1.26 mg/kg), surpasses the threshold limit set by WHO/FAO for agricultural soils. In contrast, Zone 2 had considerably lower HHMs concentrations for Cr (60.24 ± 10.68 mg/kg), Ni (31.61 ± 7.52 mg/kg) and Cd (0.66 ± 0.13 mg/kg). Geospatial distribution and self-organizing map (SOM) indicated spatial clustering of HHMs, with elevated concentrations centered in the southern part of the study area. Positive matrix factorization showed that geogenic sources and mining activities affect significantly to HHMs pollution. Monte Carlo simulation revealed that children are more prone to exposure to HHMs than adults, with ingestion identified as the primary exposure route. Hazard quotient values calculated from free ion activity model exceeded the threshold limit for Pb (1.59E+00) and Cd (6.32E-01). Severity adjustment margin of exposure highlighted severe human health risk for Cr and Ni. Random forest analysis indicated that bioavailable HHMs significantly influence the accumulation of metals in rice tissues which elevating dietary risk. The integration of robust statistical tools offers insights into the dynamics of HHMs contamination and provide policymakers and environmental scientists with useful information to mitigate the potential risks caused by mining on nearby agricultural lands.
5 Apr 2026
Read appraisal →Journal of hazardous materials
Structuring large language models for chemical health risk reasoning in environmental and occupational exposure
Large language models (LLMs) have revolutionized numerous scientific fields. However, their application in environmental health remains limited, as they are prone to hallucinations and struggle with reasoning over complex, real-world exposure data. Chemical risks, especially from rare or poorly studied compounds, span diverse occupational and everyday scenarios, where structured knowledge is often incomplete and poorly integrated. To address these challenges, we present a health risk assistant that integrates deep learning-based compound property prediction, retrieval-augmented generation (RAG), and prompt engineering into a unified reasoning framework. Central to our design are two synergistic strategies: semantic structuring, which reorganizes heterogeneous knowledge into context-consistent and semantically rich profiles, and domain-aware prompting, which guides large language models through structured and role-specific reasoning aligned with toxicological goals. The framework enables the system to map physicochemical attributes to exposure pathways and disease outcomes with improved relevance and fidelity. Evaluated on a 100-question benchmark spanning chronic toxicity, occupational hazards, and exposure-specific risks, our LLM-RAG system consistently outperforms baseline models in correctness, faithfulness, and domain-specific helpfulness. Beyond answering queries, our approach demonstrates how integrated knowledge curation and tailored prompting can enable trustworthy and scalable AI reasoning for public and environmental health protection.
4 Apr 2026
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