Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 27 appraisals
Environmental science & technology
Bridging the Gap between External and Internal Exposure: Challenges and a Machine Learning Approach Validated Using Benzene Ring-Containing Pollutants
Establishing quantitative linkages between external and internal exposures is essential for accurate exposure and health risk assessment, yet this goal remains hindered by multiple challenges. This Perspective systematically reviews major challenges limiting the quantitative linkage across three dimensions: (i) External exposure: complex multimedia and multipathway exposure, environmental factor variability, and substantial data gaps; (ii) Internal exposure: difficulties in biological sample collection, large-scale biomonitoring, and appropriate biomarker selection; and (iii) External-internal exposure linkage: interindividual variability, scarce toxicokinetic parameters, and methodological limitations. In this context, the Perspective proposes ML-EIExpLink (machine learning-based external-internal exposure linkage), a framework integrating multisource, multidimensional variables, including pollutant physicochemical properties, ADME parameters, exposure factors, environmental conditions, and socioeconomic indicators, to model complex external-internal exposure relationships. An exploratory case study on benzene ring-containing pollutants demonstrates the framework's feasibility and reliability. Using gradient boosting decision trees as the core predictive engine, the model achieved robust predictive performance, with an external validation Qext2 of 0.784. Most key variables, including external air concentration, molecular planarity, and temperature, exhibited nonlinear and threshold effects on predicted blood concentrations. This Perspective introduces a novel ML-based framework for establishing robust, interpretable external-internal exposure linkages, thereby providing a useful basis for improved exposure assessment and health risk evaluation.
22 July 2026
Read appraisal →PloS one
Air quality index prediction using machine learning regression models: A comparative analysis
Air plays a vital role in human life, and poor air quality can lead to respiratory infections. Given the significant impact of air quality on people's health, monitoring and assessing air quality is crucial. With advancements in machine learning (ML) and artificial intelligence (AI), we now have extensive tools to measure the Air Quality Index (AQI). Air quality is influenced by various pollutants, including carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2), which are prevalent in highly polluted areas and contribute to a wide range of illnesses. Particulate matter, such as PM2.5 (particles with an aerodynamic diameter of less than 2.5 µm) and PM10, poses additional health risks. To address these concerns, this study focuses on predicting AQI values for major cities in Pakistan, specifically Karachi and Peshawar, using four prominent ML algorithms: Random Forest (RF), Gradient Boosting (GB), Linear Regression (LR), and Ridge Regression (RR). The results indicate that the models effectively predicted AQI using evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R2). This research's novelty lies in using the latest AQI datasets for Karachi and Peshawar and applying standard scaling for AQI normalization. Additionally, the study compares evaluation metric results across different cities, highlighting the importance of using standard scalers to achieve optimal model performance. This research underscores the value of advanced ML techniques for accurate AQI prediction and analysis.
10 July 2026
Read appraisal →BMJ open
Impact of climatic variables on postoperative outcomes in various surgical disciplines: a systematic review.
OBJECTIVES: The impact of climate change on health is well recognised, yet its influence on postoperative complications remains underexplored. This systematic review examines how climatic factors-particularly temperature, humidity and seasonal variations-affect surgical outcomes. DESIGN: Systematic review with narrative synthesis following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines. DATA SOURCES: A comprehensive search of PubMed, Web of Science and the Cochrane Library from June 2015 to June 2025 was conducted. An additional supplementary PubMed search was performed to identify further relevant studies. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Included were studies published in English or German that examined associations between climatic factors, including temperature, humidity, precipitation or seasonality and postoperative outcomes in surgical patients. Studies without a surgical or climatic focus, animal studies, reviews and commentaries were excluded. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently screened studies and extracted data using predefined criteria. Risk of bias was assessed using the Newcastle-Ottawa Scale. Due to methodological heterogeneity, findings were synthesised narratively. RESULTS: Fifteen studies were included. High ambient temperatures were frequently associated with increased risks of surgical site infections (SSIs). Some large studies reported that even a one-degree rise significantly elevated infection rates. Humidity was also linked to higher infection rates, with evidence suggesting an independent effect in some cases. Seasonal changes influenced complication rates across regions and surgical disciplines, with infection and pneumonia peaks in summer or winter months. A few studies reported increased risks during heavy rainfall or institutional transitions such as the 'July effect'. CONCLUSION: Overall, elevated temperature and humidity appear to be associated with increased postoperative risks, especially SSIs. Seasonal patterns may further influence outcomes depending on geography and procedure type. While the available evidence supports a link between climate and surgical complications, methodological differences and limited adjustment for confounders highlight the need for more rigorous studies.
5 July 2026
Read appraisal →Journal of hazardous materials
Biomarker-anchored screening of dietary microplastics via a gut microbiota-informed machine learning model
Microplastics (MPs) are increasingly recognized as emerging contaminants in the human diet, yet the absence of unified biomarker-anchored screening thresholds hampers quantitative risk assessment. This study establishes a gut microbiota-anchored machine learning framework that links Firmicutes/Bacteroidetes (F/B) ratio thresholds with real-world dietary MP exposure data, enabling a biomarker-anchored screening of dysbiosis-related signals associated with disease risk. Across the compiled disease cohorts, obesity provided the largest and most data-sufficient contrast in this dataset and was therefore selected as the anchor condition for threshold derivation. Receiver operating characteristic (ROC) analysis determined a model-derived screening cutoff for the F/B ratio (cutoff = 1.08), which was further used to define a three-tier risk classification system. A dataset comprising 558 dietary MP records from 129 studies across 25 countries was incorporated to predict food- and region-specific risk levels. Results reveal that seafood represents the dominant global dietary MP risk source, with mollusca contributing the highest predicted F/B-related risk signal. Spatial stratification indicates that the United States and Ecuador were classified into the highest model-derived tier based on national mean predicted F/B values, followed by high-risk regions such as Vietnam, Turkey, and Ireland, while countries in the Middle East and North Africa remain at moderate risk. These geographic contrasts suggest the combined influence of dietary structure and regional exposure intensity. Overall, we present a biomarker-anchored, machine-learning-enabled screening framework that links dietary MP exposure to predicted changes in a dysbiosis-associated indicator (F/B). This approach can support monitoring prioritization, identification of data gaps, and hypothesis generation for future validation studies.
15 June 2026
Read appraisal →Environmental research
Air pollution and adverse birth outcomes: A systematic review of fine critical windows
Epidemiological studies have reported associations between prenatal exposure to outdoor air pollution and adverse pregnancy outcomes. However, most analyses assessed exposure over the entire pregnancy or by trimester, potentially missing specific critical windows of vulnerability that recent statistical methods can identify. The objective of this study was to summarise studies examining critical windows of exposure to outdoor air pollution in relation to adverse pregnancy outcomes. This systematic review (PROSPERO-CRD42024523345) followed PRISMA recommendations using PubMed, Embase and CENTRAL databases. The search was performed on April 2024 and udpated on February 2025. Eligible studies analysed critical windows of association between air pollutants (PM2.5, PM10, NO2, O3) and the following outcomes: prematurity, low birth weight, intrahepatic cholestasis of pregnancy, gestational diabetes, pre-eclampsia, eclampsia, gestational hypertension and stillbirth. Two independent reviewers screened titles, abstracts and full texts. Results were synthesised narratively. Out of 3631 articles, 20 studies were included. All were conducted in China or the United States and relied almost exclusively on distributed lag models (DLM or DLNM). Heterogeneity was observed across pathologies and between studies addressing the same outcome. The most consistent finding concerned preterm birth, for which the end of the second trimester (weeks 17-28) was often identified as a critical window. No studies addressed intrahepatic cholestasis of pregnancy or stillbirth. Conclusions. The variability in results reflects distinct pathophysiological mechanisms, differences in exposure levels, or individual characteristics. Precisely identifying critical windows of environmental exposure during pregnancy appears essential to improve perinatal environmental prevention and guide public health action.
15 June 2026
Read appraisal →Journal of hazardous materials
Prenatal 6:2 Cl-PFESA exposure and birth weight: A systematic review and meta-analysis
Most existing systematic reviews and meta-analyses on PFAS have focused on legacy compounds, yet with less attention on PFAS alternatives and mixtures. Chlorinated polyfluorinated ether sulfonic acids (Cl-PFESAs) have been introduced as PFOS alternatives. Among these, 6:2 Cl-PFESA has accumulated persistent environmental residues and has recently emerged as a significant health concern. We aim to evaluate the association between prenatal 6:2 Cl-PFESA exposure and birth weight using a meta-analysis. A systematic review identified 12 studies reporting birth weight in relation to 6:2 Cl-PFESA biomarker measures, including maternal serum and umbilical cord samples. Risk of bias and study sensitivity were assessed, and overall effects were estimated by random-effects model. Stratified analyses were conducted by study confidence, sample size, and exposure level. The overall pooled estimate showed no statistically significant association between 6:2 Cl-PFESA exposure and birth weight. However, subgroup analyses revealed a consistent reduction in birth weight per ln-unit increase in 6:2 Cl-PFESA in studies with large sample sizes (β = -20.05 g; 95% CI: -33.13, -6.97), high methodological confidence (β = -16.74 g; 95% CI: -26.42, -7.06), or higher exposure levels (β = -16.15 g; 95% CI: -25.43, -6.87), all showing low between-study heterogeneity. Sensitivity analysis excluding the study contributing most to heterogeneity indicated a 15.57 g decrease in birth weight per ln ng/mL increase in 6:2 Cl-PFESA (95% CI: -24.74, -6.40). Although current evidence suggests that prenatal 6:2 Cl-PFESA exposure may adversely affect fetal growth, well-designed prospective studies with standardized exposure assessments are needed to confirm this potential risk.
15 June 2026
Read appraisal →Journal of global health
Residential green space mitigates genetic susceptibility to incident irritable bowel syndrome in a prospective cohort of 376 749 individuals
BACKGROUND: Environmental exposures may influence irritable bowel syndrome (IBS) pathogenesis, but the interplay between genetic risk, residential green/blue space, and IBS incidence remains unexplored. METHODS: We aimed to study the association between green/blue space and incident IBS, and to assess effect modification by genetic susceptibility. The study included 376 749 UK Biobank participants at baseline. Residential green space was measured using the normalised difference vegetation index (NDVI), and blue space was defined by distance to the nearest water bodies. For time-to-event analyses, we used Cox proportional hazards models, polygenic risk scores (PRS), and mediation analysis to test gene-environment interactions. Results are presented as hazard ratios (HRs) with 95% confidence intervals (95% CIs). RESULTS: During a follow-up of 11.73 years, 7091 incident IBS cases were documented. Higher residential green space exposure was associated with reduced risk of incident IBS. Specifically, within a 1000 m buffer, each interquartile range (IQR) increase in NDVI corresponded to a 5% lower IBS risk (HR = 0.95; 95% CI = 0.91-0.98), with consistent effects observed at 800 m and 500 m buffers. Conversely, a greater distance to water bodies was associated with reduced IBS risk. Stratification by PRS revealed a significant interaction: increased green space (1000 m buffer) reduced IBS risk by 8% exclusively among high-genetic-risk individuals (HR = 0.92; 95% CI = 0.88-0.96), whereas no interaction was observed for blue space. Moreover, physical activity mediated the association between green space and IBS. CONCLUSIONS: Increased residential green space exposure is associated with reduced IBS incidence, particularly among individuals with high genetic susceptibility. These findings underscore the importance of urban greening as a public health strategy to mitigate IBS risk, highlighting the potential for targeted environmental interventions to offset genetic predispositions.
13 June 2026
Read appraisal →Medicine
Environmental toxicant glyphosate induces cardiotoxicity: New insights from network toxicology, integrated machine learning, molecular modeling and multidimensional bioinformatics analysis
Glyphosate is one of the world's most widely used herbicide, yet the causal relationship between glyphosate and cardiotoxicity remains inadequate. To elucidate the cardiotoxic mechanisms of glyphosate, an integrative strategy was developed in this work, which combines computational toxicometry, predictive machine learning, and atomic-level molecular modeling. Initially, glyphosate's systematic toxicological profile was evaluated using ProTox-3.0 and ADMETlab 3.0. Candidate genes associated with glyphosate were obtained from 3 separate databases. Target genes associated with cardiotoxicity were integrated from 2 databases. Intersection analysis identified 113 core candidate genes associated with glyphosate and cardiotoxicity. Functional enrichment-related analyses were employed and revealed that the target genes were significantly involved in key biological processes such as ERBB3 signaling pathways. In order to refine the candidate genes to a more precise gene pool, net topology analyses were employed and identified 16 key feature genes. We subsequently integrated machine learning algorithms and validated the results using the GEO database, ultimately identifying 4 hub genes, AKT1, IL1B, BRCA1, and PTGS2. Finally, we applied molecular docking analysis and revealed strong binding affinities, which suggested direct interaction mechanisms in glyphosate-induced cardiotoxicity. In summary, our study establishes a comprehensive mechanistic framework for glyphosate-induced cardiotoxicity.
31 May 2026
Read appraisal →Renal failure
Unveiling the role of air pollution in diabetic kidney disease: an integrated study combining network toxicology, machine learning, and Mendelian randomization
Diabetic kidney disease (DKD) is a major and severe complication associated with diabetes. Air pollution is not only an independent risk factor for metabolic disorders but also an "accelerator" of DKD progression. This study seeks to investigate the molecular pathways connecting air pollution to DKD. Multiple databases were integrated to obtain potential target genes of 10 common air pollutants. The gene expression omnibus (GEO) database was employed to acquire DKD datasets. Differential expression analysis and weighted correlation network analysis (WGCNA) were performed to identify DKD-related genes. 12 machine learning algorithms were utilized to generate 113 unique predictive models, which were employed to select hub genes. Subsequently, MR, single-cell, gene set enrichment analysis (GSEA), and immune infiltration analyses were performed, followed by molecular docking of hub genes with air pollutants. A total of 714 targets were identified from 10 air pollutants, and 80 potential targets were identified from DKD transcriptomic data. Machine learning methods identified 5 hub genes that are closely associated with DKD. Mendelian randomization (MR) analysis indicated that, among the five hub genes, only NOS3 demonstrated a statistically significant causal association with DKD. Immune infiltration analysis found that hub genes were closely related to immune cells. Molecular docking validation indicated that certain air pollutants can stably bind with hub genes such as NOS3 and PTGS2. Air pollutants may be linked to alterations in various biological processes, potentially involving key genes such as ADH5, CASP3, NOS3, PTGS2, and SDHB, including potential metabolic reprogramming, inflammatory processes, and immune microenvironment changes.
26 May 2026
Read appraisal →The Science of the total environment
Ambient and personal air monitoring using low-cost sensors to identify elevated air pollution in vulnerable communities
In the United States, regulatory air monitoring networks are designed to assess regional air quality but do not capture neighborhood-level variability in pollutant concentrations. Low-cost air monitors may be one approach to address these gaps. The objective of this study was to evaluate whether neighborhood-level and personal monitoring identified increased concentrations of fine particulate matter (PM2.5) exposures in the neighborhood of Lower Price Hill (LPH), Cincinnati, Ohio, compared with regional regulatory monitors. We deployed a network of 12 PurpleAir monitors across LPH from April-June 2022 and calibrated measurements to a nearby U.S. EPA regulatory monitor. PM2.5 concentrations and Air Quality Index (AQI) values measured by the PurpleAir sensors were compared with two EPA monitoring sites. Additionally, three residents conducted 7-day personal monitoring using wearable AirBeam devices. Calibrated PurpleAir data demonstrated significantly increased PM2.5 concentrations at four of the 12 PurpleAir sites and increased AQI at 11 of the 12 PurpleAir sites in LPH compared to EPA regulatory sites. PurpleAir data also found that LPH experienced up to six days where AQI exceeded healthy levels (>50) but the EPA regulatory monitor did not exceed 50. Personal monitoring revealed substantial variability, with two of three participants exceeding the EPA 24-hour outdoor PM2.5 standard (>35 μg/m3) on all study days, indicating exposures not measured by outdoor stationary monitoring. Overall, regulatory monitoring may underestimate neighborhood-level and personal PM2.5 exposures in LPH. Community-based and personal monitoring provides novel insights into localized exposure disparities and may improve risk communication and inform interventions in vulnerable communities.
25 May 2026
Read appraisal →Current urology reports
Association Between Ambient Temperature and Urolithiasis: A Systematic Review and Meta-analysis
PURPOSE OF REVIEW: The global incidence of urolithiasis has risen in recent decades. Emerging evidence suggests that higher ambient temperatures are linked to an increased risk of kidney-stone-related events, including acute renal colic, likely through physiological mechanisms such as increased urinary calcium excretion and greater supersaturation of calcium oxalate and calcium phosphate. This study aims to systematically review existing evidence on the relationship between ambient temperature and urolithiasis risk. RECENT FINDINGS: Of 892 records identified, 46 studies met the inclusion criteria for the systematic review, and 16 were included in the meta-analysis. Most studies reported a positive association between higher ambient temperatures and increased kidney stone-related events, with seasonal peaks occurring during summer months in the Northern Hemisphere and from December to March in Australia. Considerable heterogeneity was observed in exposure definitions, meteorological data sources, outcome ascertainment, and lag structures. Reported effect estimates ranged from RR 1.10 to 2.54. Meta-analysis showed that higher ambient temperature was associated with an increased risk of urolithiasis presentation (pooled RR 1.31, 95% CI 1.21-1.43; I²=86.7%). Subgroup analyses showed pooled RRs of 1.27 (95% CI 1.18-1.37) for stone colic, 1.33 (95% CI 1.06-1.67) for emergency visits, and 1.36 (95% CI 0.98-1.90) for hospitalizations. Associations were stronger at medium and long lag periods than at short lags. Most studies were rated as high risk of bias, primarily because of confounding and outcome measurement concerns. Sensitivity analyses supported the robustness of the findings. This systematic review highlights an association between higher ambient temperatures and increased presentations for kidney stone disease. Addressing environmental factors may be a valuable strategy for reducing nephrolithiasis incidence.
25 May 2026
Read appraisal →Current environmental health reports
Industrial Pollution and Health in Louisiana: A Systematic Review of Quantitative and Qualitative Studies
PURPOSE OF REVIEW: Louisiana has one of the largest concentrations of petrochemical industry in the USA. Many studies have assessed patterns of industrial pollution and health in Louisiana; we aim to systematically review this evidence. We systematically searched PubMed, Web of Science, Embase, APA PsycInfo, and GreenFILE for peer-reviewed papers published 1999-2024 that reported geographical variation in health or industrial pollution, and/or tested for an association between the two in Louisiana. We used Covidence to support standardized review and extraction. RECENT FINDINGS: We identified 2485 non-duplicate papers in our search; 53 met the inclusion criteria. Most reported quantitative findings. All studies of industrial pollution described air pollution (some also described other pollution). Studies described various health outcomes, including cancer, respiratory health, mortality, and COVID-19. Overall, people who lived closer to industrial activity had higher pollution exposure and worse health. Black and lower-income residents were exposed to more industrial activity than white and higher-income residents. Twenty-one studies assessed statistical associations between industrial pollution and health; many found an association. Twenty-one studies were quantitative and adjusted for confounding, 29 studies did not adjust for confounding (including qualitative studies), and three studies did not adjust for confounding and had authors with industry ties. Evidence suggests that there is a higher burden of air pollution and worse health outcomes in industrialized areas of Louisiana. While there was some evidence of significant associations between industrial pollution and health outcomes, research with larger sample sizes and improved pollution exposure measurements could be informative.
23 May 2026
Read appraisal →Occupational and environmental medicine
Lymphoid neoplasms and benzene exposure using a revised classification scheme: systematic review and meta-analysis
Lymphoid neoplasms are a diverse group of cancers derived from lymphocytes, with classification recently updated by the 5th edition of the WHO Classification of Haematolymphoid Tumours (WHO-HAEM5). Benzene, a well-known carcinogen, is widely used in industries and environmental exposures vary; however, its association with specific lymphoid neoplasm subtypes remains unclear due to historical classification challenges. This study aimed to clarify the risk of lymphoid neoplasms related to benzene exposure using the updated WHO-HAEM5 framework.A systematic review and meta-analysis were conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Eligible studies were comparative human studies evaluating benzene exposure and lymphoid neoplasm risk, with sufficient data for risk estimates. Inclusion criteria specified study designs, participants, exposures and outcomes based on clinical diagnoses. Data were independently extracted by multiple reviewers. The main outcome was risk of lymphoid neoplasms and subtypes analysed by random-effects meta-analysis with heterogeneity and bias assessments.From 1488 records, 95 studies met criteria (65 occupational and 30 environmental exposure). Benzene exposure significantly increased overall lymphoid neoplasm risk (RR 1.26; 95% CI 1.18 to 1.35). B-cell neoplasms showed elevated risk (RR 1.26; 95% CI 1.16 to 1.37), including mature B-cell neoplasms, Hodgkin lymphoma and plasma cell neoplasms. T-cell and natural killer cell neoplasm risk was not significantly increased overall. Occupational exposures conferred higher risks with lower heterogeneity than environmental exposures.Benzene exposure is strongly associated with increased risk of lymphoid neoplasms, particularly B-cell subtypes, supported by the refined WHO-HAEM5 classification reducing heterogeneity. This study underscores the importance of biologically informed disease classification and the need for targeted occupational safety and environmental health policies.The review protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (Registration ID: CRD420251063844) .
23 May 2026
Read appraisal →Environmental monitoring and assessment
Air pollution-related health impacts from domestic waste burning and associated interventions: the merits of a traditional versus machine learning scoping review methodology
Regularly updating repositories of air pollution impacts on health is essential for evidence-based policies, interventions, and progress monitoring. However, this is often time-consuming and labour-intensive. Automation can ensure up-to-date evidence; reduce retrieval, screening time, and costs; and make information accessible to non-academic stakeholders. This study investigated whether a machine learning approach can perform any stages of a traditional scoping review on the health impacts, policies, and interventions related to air pollution due to domestic waste burning. A traditional approach was conducted in parallel with machine learning methods enabling a comparison of the efficiency and quality of the partially automated approach against the manual review. Findings from the two approaches were compared and the final set of included articles were considered for (1) reported impacts of waste burning on health outcomes and (2) recommendations on solutions and interventions to prevent/reduce adverse effects on health from waste burning. We found a range of health impacts associated with waste burning, including low birth weight, hypertensive disorders of pregnancy, adverse respiratory outcomes like asthma and wheeze, cancer risk, and mortality. Few studies proposed solutions or evaluated the effectiveness of interventions. The ML approach showed a tendency towards false positives, which are preferable to false negatives (where relevant papers were excluded). Results showed that the model can conduct initial searches and decisions for the review. However, the articles included in the model should be screened manually for final acceptance. Therefore, we propose a hybrid approach be used until the automated model can be further refined.
19 May 2026
Read appraisal →Environmental research
Association between air pollution and chronic cough: Evidence from a systematic review and meta-analysis of observational studies
BACKGROUND: Chronic cough is a common and debilitating respiratory symptom, yet its association with air pollution remains insufficiently understood. METHODS: We conducted a systematic review and meta-analysis of observational studies evaluating the association between air pollution and chronic cough. Searches of Medline, Embase, Scopus, and GeoBase were completed up to February 14, 2025. Studies were eligible if they reported quantitative associations between air pollution and exposures and chronic cough outcomes. Two reviewers independently screened studies, assessed quality, and extracted data. Random-effects models were used to estimate pooled odds ratios (ORs) with 95% confidence intervals. RESULTS: Of 1250 articles screened, 53 studies were reviewed, and eight were included in meta-analysis. Pooled ORs per 10 μg/m3 increase in exposure were 1.10 (95% CI: 0.99-1.22) for PM2.5 and 1.04 (95% CI: 1.02-1.06) for NO2. Heterogeneity was low to moderate (I2: 18.9% for PM2.5; I2: 0.0% for NO2), and there was no evidence of publication bias (Egger's test p = 0.98 and 0.47, respectively). Stratified analyses revealed stronger associations in adults and males. Evidence on progression from chronic cough to more severe respiratory conditions was limited, highlighting a critical gap for evidence-informed clinical follow-up and public guidance to prevent progression. CONCLUSION: Our findings highlight that chronic cough is an overlooked consequence of air pollution exposure and emphasize the need for health care professionals to consider environmental factors when managing persistent cough. Strengthening air quality control and early detection strategies could help reduce the respiratory disease burden.
18 May 2026
Read appraisal →Environmental pollution (Barking, Essex : 1987)
A systematic review of low-level ambient fine particulate matter (PM2.5) exposures and adverse cardiovascular health outcomes
Associations between exposure to fine particulate matter and impacts in the cardiorespiratory system are well-established, but associations are expected to vary based on exposure duration and concentration. Domestic and international agencies set standards and guidelines for PM2.5 in part to protect public health. This systematic review explores if associations exist between adverse cardiovascular endpoints and PM2.5 exposure when the concentrations are below regulatory standards. In total, 1349 documents were screened, with 95 articles meeting criteria (i.e. peer-reviewed, PM2.5 concentrations reported, cardiovascular endpoints measured). Of the included articles, 67% (64 articles) observed significant associations with adverse cardiovascular endpoints with variable findings based on specific cardiovascular endpoints (e.g. direct measurements - 69% of articles, cardiovascular endpoints - 44% of articles, events and mortality - 76% of articles, and biomarkers - 67% of articles). While not universally consistent, significant associations were observed between PM2.5 exposure and adverse cardiovascular effects, even at levels deemed compliant with current regulations. This review highlights the necessity for further research across diverse geographical settings and populations to clarify the implications of low-level PM2.5 exposure on cardiovascular health. The findings advocate for a review of regulatory standards for PM2.5 exposure in the context of cardiovascular health.
17 May 2026
Read appraisal →Environmental research
Mental health benefits of specific blue space types and characteristics: A systematic evidence map.
Natural environments support mental health and wellbeing, yet limited evidence exists about which types or characteristics are most beneficial. This is particularly true for blue spaces (natural or manmade outdoor environments featuring water). This evidence map investigates the mental health and wellbeing benefits associated with specific blue space types (e.g. coast, river) and characteristics (e.g. sounds). Following PRISMA guidelines, specific keywords were used to search for articles published from inception to March 2025. A total of 139 papers were included, examining the relationships between specific blue space types or characteristics and mental health. Coastal environments were the most frequently studied blue space (94 studies). Affect was the most assessed mental health outcome (74 studies). Most studies were conducted in European and Asian countries. Most studies compared blue spaces to urban or a green space environments, rather than other blue spaces. Only six studies directly compared different blue space types within the same study. Overall, the current evidence base does not allow for comparisons between different blue space types or characteristics, due to the limited number of direct comparisons and substantial heterogeneity across study designs, outcome measures, geographical contexts, blue space descriptions, and study populations. Future research should prioritize direct comparisons between different blue spaces, preferably including the detailed descriptions and analyses of specific characteristics, and examine the role of amount and type of exposure to better inform evidence-based recommendations for blue spaces and mental health.
16 May 2026
Read appraisal →The British journal of general practice : the journal of the Royal College of General Practitioners
Pollution to prevention: digital decision support for air pollution and exercise counselling in general practice.
BACKGROUND: Outdoor exercise supports physical, mental, and social wellbeing. However, exposure to air pollution during activity can increase cardiorespiratory risk. The Royal College of Physicians (A Breath of Fresh Air, 2025) urges clinicians to address environmental determinants of health, including air quality, during routine care, including annual checks. Patients increasingly use digital tools providing air-quality data and actionable behavioural recommendations, yet the quality, evidence base, and integration of these tools into clinical workflows remain unclear. AIM: To evaluate digital tools that help patients modify exercise behaviour based on air-pollution exposure to inform the development of a custom chatbot to support exercise counselling in primary care. METHOD: A scoping review of app libraries, grey literature, and peer-reviewed studies identified digital tools providing air-quality data with actionable exercise recommendations. Data extraction covered functionality, pollutants, validation, and quality using the Mobile App Rating Scale (MARS) and Behaviour Change Technique Taxonomy (BCTTv1). RESULTS: Seven tools were identified: IQAir, Plume Labs, BreezoMeter, EPA AirNow, PurpleAir, airTEXT, and AirTrack. MARS scores ranged from 2.9 to 4.3 (mean = 3.6/5.0), highest for functionality and aesthetics but lower for information accuracy and engagement. Behaviour change content was limited to general health information, and no tools demonstrated formal validation or clinical integration. CONCLUSION: Existing tools provide accessible air-quality data but lack individualised recommendations for exercise. Insights informed a custom chatbot embedding air-quality and exercise advice into GP cardiorespiratory annual reviews. Next steps include further development of the custom chatbot and pilot implementation in general practice to assess feasibility, clinician uptake, and patient outcomes.
16 May 2026
Read appraisal →Environmental health : a global access science source
Building the model: a review of input structures in extreme temperature-health
BACKGROUND: The health impacts of extreme temperatures have been extensively studied through epidemiological models. However, limited attention has been paid to the specification of these models, particularly regarding input structure and model selection. Although exposure metrics and statistical techniques have evolved over time, a comprehensive synthesis of the variables included in these models, and the rationale behind their inclusion, is still lacking. This gap limits the comparability of studies and may compromise the robustness of temperature-health evidence. METHODS: We conducted a systematic review of peer-reviewed studies published between 2014 and 2024 that employed quantitative epidemiological models to estimate the association between extreme temperatures and health outcomes. Following PRISMA guidelines, we selected 119 studies through searches conducted in Scopus, PubMed, and Web of Science. Each study was analysed in terms of spatial coverage, modelling framework, and model inputs. Inputs were classified into six functional groups: thermal exposures; environmental covariates (including both non-thermal meteorological variables and air pollutants); temporal controls; socio-demographic factors; health system indicators; and built environment characteristics. RESULTS: Substantial heterogeneity was observed in both input selection and model specification. Daily mean temperature was the dominant exposure metric, though rarely justified over alternatives. Environmental covariates were inconsistently included: while relative humidity was frequent, other meteorological modifiers and air pollutants were often omitted without clear rationale. Temporal adjustments were common but heterogeneous. Distributed lag non-linear models were the prevailing framework, varying greatly in lag structure, spline specification, and covariate integration. Socio-economic, health, and infrastructural indicators appeared in less than one third of studies, typically as effect modifiers in meta-regression analyses, highlighting uneven integration of contextual determinants. No consensus currently exists on what constitutes a minimum model specification necessary to ensure reliable and interpretable effect estimates. CONCLUSIONS: Current temperature and health modelling remains fragmented, with notable variability in input specification and transparency. Strengthening methodological coherence through clearer guidance on input selection is essential. Greater integration of socio-economic and infrastructural variables would further enhance models' capacity to capture contextual vulnerability. To ensure reliability and policy relevance, future research should develop shared guidelines for input specification, define minimum modelling standards, and promote transparent reporting of analytical decisions.
11 May 2026
Read appraisal →Environmental science. Processes & impacts
Machine learning-driven QSAR models for the prediction of metabolic mechanisms and thyroid hormone-disrupting effects of emerging pollutants in the human body: a case study of bisphenol analogues
Research on theoretical prediction methods for elucidating the reaction mechanisms and thyroid hormone-disrupting effects of emerging pollutants in the human body faces significant challenges. The application of in silico methods utilizing machine learning is increasingly recognized as an effective strategy to determine the mechanisms of reactions catalyzed by human cytochrome P450 enzymes (CYPs) and to predict the adverse effects of emerging pollutants. Bisphenol analogues (BPs) represent one of the most important endocrine disruptors. These compounds can cause serious effects on the ecological environment and human health. Herein, density functional theory (DFT) calculations was employed to investigate the reaction mechanisms of the O-addition and H-abstraction of 20 BPs by human CYPs. A machine-learning-integrated quantitative structure-activity relationship (ML-QSAR) framework was established to predict the energy barriers of three types of reactions. Moreover, binding affinities were calculated between BPs (including their metabolites) and TRβ-LBD to evaluate their thyroid hormone-disrupting effects. The binding affinity data of BPs to TRβ-LBD were used as a training set to develop a ML-QSAR model for efficient prediction of potentially hazardous BPs. A ML-QSAR model with an R2 of 0.869 was established to predict their thyroid hormone-disrupting effects. The study enhances our understanding of CYP catalytic mechanisms involving BPs and provides a reference for the future design of environmentally friendly BPs. The predictive model developed for thyroid hormone-disrupting effects can aid in identifying the adverse effects of emerging BPs, supporting more effective management of toxic compounds.
4 May 2026
Read appraisal →Environmental science. Processes & impacts
Adverse outcome pathway-informed machine learning for predicting vascular toxicity of emerging organic pollutants
Emerging organic pollutants (EOPs) are increasingly detected in wastewater and pose potential vascular toxicity risks that remain inadequately assessed in current regulatory frameworks. This study developed an adverse outcome pathway (AOP)-informed machine learning approach to evaluate vascular toxicity for 312 EOPs. By integrating ToxCast high-throughput bioassay data with Morgan fingerprints, we trained fourteen multilayer perceptron (MLP) models targeting key events in AOP 509, including Nrf2 inhibition, oxidative stress, mitochondrial dysfunction, apoptosis, endothelial impairment, and angiogenesis disruption. Our optimized models achieved high predictive accuracy (70-95%), enabling activity classification for both tested and untested chemicals. The predicted activation profiles prioritized chemicals such as ketoconazole, sertraline, and miconazole, with literature evidence supporting their vascular toxicity potential. This AOP-guided modelling framework demonstrates how integrating mechanistic pathways with machine learning can inform chemical risk assessment and prioritization, supporting hazard evaluation and environmental decision-making for pollutants with limited toxicological data.
4 May 2026
Read appraisal →Journal of chemical information and modeling
Development, Evaluation and Application of a Multi-Representation Fusion Model for Accurate Prediction of Per- and Polyfluoroalkyl Substance (PFAS) Binding to Plasma Proteins
Per- and polyfluoroalkyl substances (PFAS) constitute a large and structurally diverse class of man-made chemicals. Their strong carbon-fluorine (C-F) bonds confer high environmental persistence, bioaccumulation, and various associated toxicities. As amphiphilic compounds, most PFAS bind to proteins and accumulate in protein-rich tissues, with such bioaccumulation exerting significant adverse impacts on human health. Accurate evaluation of the binding status between PFAS and proteins constitutes an essential step in health risk assessment. Traditional experiments and certain modeling approaches for analyzing PFAS bioaccumulation suffer from drawbacks such as time-consuming processes, high costs, or inadequate capture of molecular structural information, while existing machine learning-based prediction methods rely on single molecular representation, making it difficult to comprehensively encode the structural information on PFAS. Here, we propose MURNet, a multirepresentation fusion network model integrating chemical descriptors, 2D molecular graphs, and molecular fingerprints to predict PFAS-plasma protein binding. Compared with the state-of-the-art baseline models, MURNet achieves the optimal comprehensive performance. The multirepresentation fusion strategy generates higher-quality molecular features. Tanimoto similarity applicability domain analysis demonstrates MURNet's capability to reliably predict PFAS homologues. Case studies reveal the effectiveness of MURNet in screening PFAS with potential binding affinity to human serum albumin (HSA).
2 May 2026
Read appraisal →BMJ open
Association between ambient air pollution exposure levels and the risk of developing pulmonary embolism in the Chinese population: findings from the Chinese Cardiovascular Association (CCA) Database-Chest Pain Centre
OBJECTIVE: To analyse the associations between exposure to different air pollutants and the morbidity of pulmonary embolism (PE). DESIGN: Time series study with a distribution lag non-linear model. SETTING: 275 cities in China, 1 January 2015 to 31 March 2022. PARTICIPANTS: 27 369 cases of PE. MAIN OUTCOME MEASURES: The associations between exposure to different air pollutants and the morbidity of PE. RESULTS: During the study period, 27 369 cases of PE were recorded. PM2.5(Particulate Matter 2.5), PM10(Particulate Matter 10) and NO2(Nitrogen Dioxide) exposure levels were associated with an increased risk of developing PE in the single-pollutant model. PM2.5 exposure caused the greatest risk of developing PE at lag 1 in the single-day lag model, with a relative risk (RR) value of 1.033 (95% CI 1.007 to 1.058). PM10 and NO2 exposure had the greatest effects at lag 0, with RR values of 1.038 (95% CI 1.016 to 1.059) and 1.039 (95% CI 1.009 to 1.068), respectively. No associations were found between CO(Carbon Monoxide), O3(Ozone) or SO2(Sulfur Dioxide) exposure levels and the risk of developing PE. PM2.5, PM10 and NO2 exposure levels were most strongly associated with the development of PE at lag 01 in the cumulative lag model, with RR values of 1.052 (95% CI 1.009 to 1.095), 1.053 (95% CI 1.020 to 1.086) and 1.058 (95% CI 1.005 to 1.111), respectively. CONCLUSIONS: Short-term exposure to PM2.5, PM10 and NO2 can increase the risk of developing PE, especially in females. The effects of PM2.5 and PM10 exposure were more significant in cold seasons and in North China.
17 Apr 2026
Read appraisal →Journal of the American Heart Association
Exposure to Volatile Organic Compounds and Cardiovascular Diseases: A Systematic Review and Meta-Analysis
BACKGROUND: Environmental pollution is an often-neglected contributor to cardiovascular disease (CVD). Volatile organic compounds (VOCs) are a subset of ubiquitous pollutants. Recent evidence has suggested exposure to VOCs as a possible contributor to CVD. We aimed to review the evidence for such a link between VOC exposure and cardiovascular outcomes. METHODS: PubMed, Web of Science, and Scopus were systematically searched for ecological and observational studies evaluating associations between exposure to VOCs and cardiovascular outcomes. The available evidence was synthesized qualitatively, and meta-analyses were conducted wherever multiple estimates were identified for a shared association. Sensitivity analyses were performed to evaluate the influence of individual studies on the pooled estimates. RESULTS: Thirty-six studies were included in the review. Pooled random-effects estimates for associations between VOC mixtures (odds ratio, 1.57 [95% CI, 0.79-3.11])/toluene, ethylbenzene, and xylene and CVD trended toward a positive effect while being imprecise. The pooled estimate for the association between exposure to benzene and CVD death also trended toward a positive effect (meta relative risk, 1.09 [95% CI, 0.99-1.21]; I2=91.4 [95% prediction interval, 0.82-1.46]). However, serious inconsistencies were noted on the significance and the magnitude of association. Fixed-effect analyses indicated significant positive effects in all evaluated associations. CONCLUSIONS: Evidence from studies evaluating ambient and individual VOC exposures suggests a possible association with increased CVD and CVD death. However, the heterogeneity noted between specific VOCs' effects, possible confounding, limited long-term exposure assessment, and sparse evaluation of mixture/copollutant effects warrant cautious interpretation of these findings and highlight the need for future high-quality prospective studies.
8 Apr 2026
Read appraisal →Environmental science & technology
Molecular Representation and Closed-Loop Validation for Toxicity Assessment of Organic Compounds in Ambient Air PM2.5
Although the health impacts of fine particulate matter (PM2.5) are primarily attributed to its chemically diverse composition rather than to mass concentration, assessing the toxicity of PM2.5 constituent compounds remains highly challenging due to chemical complexity and limited experimental scalability. This study introduces an interpretable machine learning (ML) framework using a curated A549 cytotoxicity data set (19,841 compounds) that integrates customized Molecular Access System (MACCS) fingerprints, six base models, and a stacking ensemble meta-model, all optimized with a biobjective strategy to assess the toxicity of organic compounds in PM2.5. The stacking ensemble model demonstrated satisfactory performance (test AUC > 0.8), exhibiting good generalization, adaptability, and robustness. Nontargeted analysis generated and experimentally validated a prediction set from the Hong Kong PM2.5 samples (51 of 387 compounds confirmed from 13 classes), demonstrating broad applicability on an independent Nanjing PM2.5 set (572 compounds). Key substructures driving toxicity, identified through a SHapley Additive exPlanations (SHAP) analysis and cell experimental validation, revealed that PM2.5 compounds with aromatic rings and nitrogen-based functional groups (e.g., aromatic α,β-unsaturated ketones, aromatic amines, aromatic nitro compounds, and tertiary amines) likely contribute to high toxicity. The derived "structure-toxicity" rules narrow the search space from thousands of compounds to those containing critical substructures, enabling efficient prioritization of toxic components and providing a foundation for improving model specificity and predictive accuracy in future studies.
8 Apr 2026
Read appraisal →Cardiovascular toxicology
Association Between Environmental Chemicals Phthalates and Stroke Among the US Adults: A Cross-Sectional Study
Stroke has a multifactorial etiology, and phthalates, as widely used environmental chemicals, may play an underexplored role in cerebrovascular health. This study aimed to evaluate the association between urinary phthalate metabolites and stroke risk using data from the National Health and Nutrition Examination Survey (NHANES), complemented by network toxicology, molecular docking, and machine learning approaches. We analyzed data from 10,771 adult participants in NHANES (2003-2018) who had complete records of stroke status, urinary phthalates, and clinical covariates. Multivariable logistic regression and restricted cubic spline (RCS) models were used to assess associations. We constructed protein-protein interaction networks and performed molecular docking to identify potential toxicological targets. Five machine learning algorithms were employed to develop predictive models for phthalate-related stroke risk. Higher levels of MECPP, MCPP, MNBP, and MEOHP were significantly associated with increased stroke prevalence. Network toxicology identified SRC as a key target, and molecular docking confirmed strong binding affinity between MBZP and SRC. Among the machine learning methods, Random Forest achieved the highest predictive performance (AUC = 0.833). Notably, moderate coffee consumption (≤ 170 g/day) was significantly associated with a reduced risk of stroke (OR = 0.63, 95%CI: 0.51-0.77), suggesting potential dietary mitigation against phthalate-related stroke risk. Our findings demonstrate that certain phthalates may increase stroke risk via molecular pathways involving SRC. Furthermore, moderate coffee consumption may mitigate the adverse cerebrovascular effects of these environmental pollutants.
5 Apr 2026
Read appraisal →Neurology
Long-Term Exposure to Ambient Air Pollution and Incident Amyotrophic Lateral Sclerosis: A Prospective Cohort Analysis of the UK Biobank
BACKGROUND AND OBJECTIVES: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with a complex etiology. Although a range of genetic and lifestyle factors have been implicated, the potential role of environmental airborne pollution exposure is uncertain. This study examined the association between long-term ambient exposure to air pollutants and the incidence of ALS in UK Biobank participants. METHODS: This prospective cohort study was based on the UK Biobank participants aged 40-69 years. The analytical sample comprised participants free of ALS at baseline and had complete data on air pollution exposure. Long-term exposure (2006-2021) to nitrogen dioxide (NO2), nitrogen oxides (NOX), fine particulate matter (PM2.5; <2.5 µm), and coarse particulate matter (PM10; <10 µm) was assessed using data from the UK Department for Environment, Food and Rural Affairs at a spatial resolution of 1 × 1 km. To evaluate the association between these pollutants and ALS risk, we used multivariable time-varying Cox proportional hazards models. Several sensitivity analyses were conducted to assess the robustness of the results. We also examined for gene-environment interaction stratified by C9orf72 status and UNC13A genotype. RESULTS: Among the 501,308 participants with a mean age of 56.5 (SD 8.1) years at baseline, 272,764 (54.4%) were female. Over a median follow-up of 8.4 years, 687 individuals developed ALS. We did not observe any associations for any of the examined pollutants and ALS risk. Specifically, the hazard ratios per SD increment for PM10, PM2.5, NOX, and NO2 were 1.03 (95% CI 0.92-1.15), 1.00 (95% CI 0.88-1.14), 1.01 (95% CI 0.90-1.13), and 1.00 (95% CI 0.89-1.12), respectively. Individuals living in areas with the highest tertile of air pollutant exposure, compared with those in the lowest tertile, did not show a higher risk of ALS across any of the pollutants examined (p for trend >0.05). Restricted cubic spline analyses revealed no nonlinear associations between air pollution and ALS risk (all p for nonlinearity >0.05). These results remained robust in various subgroup and sensitivity analyses. No evidence of gene-environment interaction was found. DISCUSSION: In this large population-based study with high statistical power, ambient air pollution was not a risk factor for the development of ALS.
5 Apr 2026
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