Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 15 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 →BMC palliative care
Factors associated with psychological distress among end-of-life care volunteers: a systematic review of quantitative and qualitative evidence
BACKGROUND: Volunteers are integral to end-of-life care, providing emotional, spiritual, and practical support. However, they often face emotionally demanding situations with limited training and supervision compared to professionals. Given the limited and fragmented literature on psychological distress experienced by end-of-life volunteers, this systematic review aimed to synthesise existing quantitative and qualitative evidence to identify factors associated with psychological distress. METHODS: We conducted a systematic literature review including qualitative and quantitative evidence. Five databases (MEDLINE, EMBASE, PsycINFO, Cochrane Database and Web of Science) were searched for original studies, complemented by citation and reference searches. Study quality was assessed using the Qualsyst tool. Quantitative findings were synthesised using an algorithm to evaluate evidence strength, and qualitative data were integrated through thematic meta-synthesis. RESULTS: Twenty-six studies (20 quantitative and 6 qualitative studies) met inclusion criteria. Quantitative research examined 49 volunteer-related, 18 service-related, and one volunteer-patient-interaction-related factor associated with anxiety, death anxiety, depression, burnout, and/or perceived stress. Moderate-strength evidence indicated that death anxiety was negatively associated with better health and well-being but unrelated to age, volunteer experience, or training. Furthermore, depression was negatively associated with volunteer training. Qualitative evidence was scarce, but highlighted additional patient-, interaction-, and service-level mechanisms. CONCLUSION: This review identifies a small, methodologically diverse evidence base on factors associated with psychological distress in end-of-life care volunteers. Quantitative evidence suggests a potential protective association between training and depression, though substantial heterogeneity limits firm conclusions. Limited qualitative evidence revealed patient-, interaction- and service-level factors that are rarely captured quantitatively. Robust theory-guided longitudinal studies are needed to better understand distress and resilience in this under-researched group.
28 June 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 →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 →Journal of medical Internet research
Virtual Reality Interventions for Stress Reduction in the General Population: Systematic Review and Meta-Analysis of Randomized Controlled Trials
BACKGROUND: Increasing mental demands across multiple life domains underscore the importance of effective individual stress management to mitigate the adverse health consequences of chronic stress. Growing evidence suggests that virtual reality (VR) interventions constitute an effective approach to stress reduction. OBJECTIVE: This systematic review and meta-analysis aimed to examine and compare application areas of VR interventions for stress reduction in the general population and to identify potential predictors of effectiveness based on sample characteristics and intervention design. METHODS: Five databases (MEDLINE, CINAHL, CENTRAL, PsycInfo, and Web of Science) were systematically searched for randomized controlled trials investigating the effectiveness of VR interventions for stress reduction in the general population. Studies were included if they primarily focused on stress reduction, included a neutral control condition, and reported a validated measurement of perceived stress. Trials targeting mental disorders or those conducted in the context of medical procedures were excluded. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias using the Cochrane Collaboration's tool. Effects were synthesized using pooled standardized mean differences, and relevant predictors were evaluated through subgroup analyses and meta-regressions. RESULTS: A total of 55 relevant studies met the inclusion criteria, with 37 investigating single-session and 18 multisession interventions (ranging from 1 to 42 sessions over 2 days to 6 months). The meta-analysis included 39 studies with 4024 participants (sample sizes 24-409; mean ages 19.2-70.6 years). Intervention types included VR-based nature exposure (21), biophilic architectural elements (6), guided meditation (9), interactive tasks (4), and other approaches (1). On average, VR interventions significantly reduced perceived stress level (-0.55, 95% CI -0.70 to -0.40; P<.001; I2=76%; 95% prediction interval [PI] -1.32 to 0.23), anxiety (-0.88, 95% CI -1.23 to -0.54; P<.001; I2=87%; 95% PI -2.07 to 0.31), and depression (-0.34, 95% CI -0.47 to -0.21; P<.001; I2=0%; 95% PI -0.47 to -0.21), and enhanced positive emotion (-0.64, 95% CI -0.84 to -0.46; P<.001; I2=62%; 95% PI -1.28 to -0.02). In addition, the decrease in systolic blood pressure was significant (-0.20, 95% CI -0.37 to -0.04; P=.02; 95% PI -0.37 to -0.04). Univariate linear regression analyses identified multiple sessions (R²=15%, P=.02) as a significant predictor of VR-based stress reduction. Further analyses of technical and content-related characteristics revealed that a higher image refresh rate (t15=-2.36, 2-tailed; P=.04) was associated with stronger intervention effects. CONCLUSIONS: This review highlights the considerable potential of VR interventions for reducing psychological stress in the general population and extends recent evidence by quantifying the effects of a broad range of VR-based stress reduction approaches and, for the first time, comparing diverse intervention characteristics. VR represents a promising alternative, particularly in contexts where access to real-life nature or conventional relaxation methods is limited or resource-intensive.
26 May 2026
Read appraisal →Medicine
Effects of a psychological capital intervention on emergency nurses' work engagement and turnover intention: A randomized controlled trial
BACKGROUND: Emergency nurses are exposed to high occupational stress, which leads to decreased psychological capital, reduced work engagement, and high turnover intention. Evidence on targeted psychological capital intervention (PCI) for this population remains limited. METHODS: A parallel randomized controlled trial was conducted. A total of 122 emergency nurses from a tertiary hospital in Zhejiang Province were randomly assigned to the intervention group (6-week PCI, n = 61) or the control group (routine training, n = 61). Outcomes were measured at baseline (T0), post-intervention (T1), 1 month (T2), and 3 months (T3) using the Psychological Capital Questionnaire, Utrecht Work Engagement Scale, and Turnover Intention Scale. RESULTS: Baseline characteristics and outcome scores were comparable between groups (all P > .05). At T1 to T3, the intervention group had significantly higher Psychological Capital Questionnaire and Utrecht Work Engagement Scale scores and lower Turnover Intention Scale scores than the control group (all P < .001). Psychological capital was positively correlated with work engagement (R = 0.604, P < .001) and negatively correlated with turnover intention (r = -0.624, P < .001). CONCLUSION: This 6-week PCI program significantly improves psychological capital and work engagement, and reduces turnover intention among emergency nurses, with effects sustained for at least 3 months. These findings support the utility of PCI as a psychological management strategy for emergency nursing staff.
25 May 2026
Read appraisal →Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Mycobacterium tuberculosis Transmission in Healthcare Settings: A Systematic Review of Frequency, Risk Factors, and Exposure Conditions
BACKGROUND: Uncertainty regarding factors that govern Mycobacterium tuberculosis transmission in healthcare settings leads to broad and variable exposure definitions and low-yield contact investigations. We systematically reviewed data on M. tuberculosis transmission in healthcare to inform risk stratification and exposure definitions. METHODS: We searched MEDLINE, Embase, CINAHL, and Cochrane databases from inception through December 2024 for studies describing M. tuberculosis transmission from adult patients to healthcare personnel (HCP) or patients. We tabulated data on transmission frequency, circumstances, and risk factors. RESULTS: Of 6695 studies screened, 86 met inclusion criteria, encompassing 1083 source patients, 35 698 exposed individuals, and 2517 secondary cases (7.1% overall transmission risk, 3.8% among studies published since the US Centers for Disease Control and Prevention last updated healthcare ventilation standards in 2005). Transmission was associated with prolonged contact between HCP and source patients (median duration 24 hours, interquartile range [IQR] 24-25 hours) and between patients in shared rooms (median duration 24 hours, IQR 12-24 hours), clinical care without N95 respirator (1232 transmissions/10 149 exposures that included data on face covering use, 12.1%), frequent direct care (185 transmissions/410 exposures, 45.1%), and lack of negative pressure ventilation (1906 transmissions/5670 exposures, 33.6%). Only 8/116 (6.9%) transmissions with documented exposure durations were associated with <8 hours of cumulative exposure and all in poorly ventilated spaces. CONCLUSIONS: Mycobacterium tuberculosis transmission in healthcare is uncommon, particularly in spaces that meet current healthcare ventilation standards. Risk factors for transmission include prolonged exposure, close contact, lack of respirators, and poor ventilation. Defining exposures as >8 hours without a respirator should identify most transmissions and could improve the efficiency of contact investigations.
22 May 2026
Read appraisal →Neurosurgical review
The impact of augmented reality on radiation exposure during spine surgery: a systematic review
BACKGROUND: Augmented reality (AR) has rapidly emerged as an important navigational technology in spine surgery, with growing interest in its potential to reduce dependence on fluoroscopy and mitigate radiation exposure for both patients and operating room staff. METHODS: This systematic review examined current evidence on radiation-related outcomes of AR-assisted spine procedures. A PRISMA-guided search of PubMed, Embase, and Scopus identified twelve eligible studies, including randomized trials, prospective and retrospective clinical cohorts, and cadaveric or synthetic-model investigations. RESULTS: Across the included studies, AR was consistently associated with reduced fluoroscopy use, with several studies demonstrating statistically significant reductions in exposure time compared with conventional fluoroscopy. Two clinical studies directly measured occupational radiation and reported substantially lower staff doses when AR-based navigation and optimized shielding strategies were employed. Patient radiation exposure was similarly decreased in most studies, particularly when AR was integrated with low-dose cone-beam CT protocols. Operative time findings were mixed, reflecting early learning curves and variability in AR systems, but accuracy remained high across platforms. CONCLUSION: Current evidence suggests that AR may reduce intraoperative radiation exposure without compromising workflow or surgical precision, available studies are limited by small sample sizes, heterogenous methodologies, and a paucity of direct staff-dosimetry data. Larger, high-quality multicenter studies are needed to clarify the magnitude of AR's radiation-sparing benefits and to define its role in modern spine surgery.
19 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 →Journal of radiological protection : official journal of the Society for Radiological Protection
Fast occupational upper-limb radiation dose prediction using machine learning and Monte Carlo simulation
Interventional procedures expose physicians to scattered radiation, particularly to their upper extremities, posing occupational health risks. Existing extremity dosimeters such as thermoluminescents, optically stimulated luminescence rings and active personal dosimeters provide limited spatial information, exhibit angular and energy dependence and offer little or no real-time feedback. This study develops machine learning (ML) models to estimate radiation dose values at discrete upper-limb locations using Monte Carlo (MC)-derived data and procedure-specific parameters. A dataset of 10 000 MC dose maps was generated under varied clinical and geometric conditions. After log-transformation and normalisation, several ML models, including deep neural networks and tree-based regressor, were trained and assessed using five-fold cross-validation. Mean absolute error and relative error (RE) were evaluated on the original dose scale, and an ensemble of the three best-performing models was constructed to improve robustness. The ensemble consistently outperformed individual models, achieving an average RE of 3.69% and demonstrating stable performance across anatomical regions and dose levels. Highest accuracy was obtained for standard beam geometries, whereas larger discrepancies occurred in extreme configurations with steep dose gradients. Predicted dose patterns were consistent with the expected distributions across the upper-limb regions. The findings demonstrate the feasibility of ML-based extremity dose estimation in interventional environments. The proposed ensemble provides a rapid (around 10 ms), scalable alternative to full MC simulations, enabling near real-time predictions of upper-limb occupational dose and supporting optimisation of radiation protection practices in image-guided procedures.
16 May 2026
Read appraisal →PloS one
Smartphone-based accurate pothole depth estimation using monocular RGB and LiDAR-guided deep learning
The maintenance of the road infrastructure is vital to ensure public safety and the efficiency of transportation, but the unpredictable and widespread occurrence of potholes often hinders continuous monitoring. Traditional detection approaches typically rely on expensive equipment or manual inspections, rendering them impractical for large-scale, real-time deployment. To overcome these limitations, we present a low-cost and scalable pothole assessment framework that leverages the built-in LiDAR sensors available in modern smartphones in combination with a hybrid deep learning pipeline. The proposed system fuses RGB imagery with LiDAR-guided refinement to achieve precise depth estimation and accurate pothole segmentation, further enhanced through post-processing for improved boundary alignment. A comprehensive dataset of 25,000 RGB-LiDAR image pairs, collected under various road and lighting conditions, was curated to train and evaluate the framework. Extensive experiments demonstrate that our approach achieves high accuracy in both depth estimation and pothole delineation, significantly outperforming existing methods. This work highlights the potential of lightweight smartphone-based solutions for real-time automated road condition assessment, offering a practical and economical tool for modern infrastructure management.
12 May 2026
Read appraisal →PloS one
Unsupervised industrial image defect detection based on autoencoder and GANs
In the process of intelligentization in modern manufacturing, especially in industrial fields such as automobile manufacturing, semiconductor production, and electronic product assembly, product quality control is crucial. Traditional defect detection methods face problems such as supervised learning methods relying on a large amount of labeled data, weak generalization ability, and high cost. In the process of intelligent manufacturing, industrial product quality control is a key link to ensure production safety and product consistency. Especially in typical industrial scenarios such as automobile manufacturing, semiconductor production, and electronic product assembly, traditional defect detection methods are difficult to meet the needs of actual production lines for high-precision and high-efficiency detection due to their reliance on a large amount of labeled data, weak generalization ability, and high cost. To solve the problem of defect detection in small and zero sample scenarios in these industries, improve detection sensitivity and localization accuracy, and enhance the model's generalization ability to unknown defects, a unsupervised industrial image defect detection method based on autoencoder and Generative Adversarial Networks (GANs) is proposed. This study constructs an Multi-level Deep feature Adaptive fusion AutoEncoder (MDAAE) module, extracts multi-scale features through Pre-Trained Convolutional Neural Backbone Networks (PTCNBN), introduces Attention Mechanism (AM) to dynamically calculate feature weights, and achieves feature fusion and reconstruction. Meanwhile, the self-AM is fused to improve the GANs. The self-attention module efficiently captures long-range dependencies and generates an adversarial network training objective function to optimize the generator's unsaturated loss. The results showed that the Area Under the Curve (AUC) of the Recall-Precision curve of the research method reached 93.6 ± 0.5%, and its F1-Score value exceeded 0.890 ± 0.003 in defect types such as scratches and dents. In practical applications, the inference time of the research method remained stable at 3.0 ± 0.2 GB of central processor memory, and the Fréchet Inception Distance (FID) value fully converged at the 2230th iteration, with a stable FID value of 3. The false alarm rate was only 8.6 ± 0.7% under strong light conditions. The proposed UIIDD method based on autoencoder and GANs had good robustness, generalization ability, reliability, and efficiency. This study effectively solves the problems of poor robustness, weak generalization ability, and high cost of traditional defect detection methods.
12 Apr 2026
Read appraisal →PloS one
A computer vision approach for the grading of cotton base load ages in measuring the performance of washing machine.
The performance testing standards for washing machines specify clear requirements regarding the age of the base load used. To enable non-contact detection of the service life of the test fabric and thereby improve the consistency of washing machine performance test results, this study employed computer vision techniques to investigate the feasibility of using image features of the base load for age grading. Base load samples underwent 1-100 accelerated washing cycles were categorized into five degradation stages (C1-C5 were used to represent base loads with ages of 1-20 cycles, 21-40 cycles, 41-60 cycles, 61-80 cycles, and 81-100 cycles, respectively). Wrinkle information and plain weave structure information were extracted from base load images, from which color, texture and area features were obtained. In addition, k-nearest neighbors (kNN), multilayer perceptron (MLP), linear discriminant analysis (LDA), and logistic regression (LR) classifiers were trained to grade the age of base load. As a result, LR classifier demonstrated robust overall performance, achieving accuracy of 0.75, 0.75, 0.62, 0.75, and 1.00 for C1, C2, C3, C4, and C5, respectively. Models utilizing exclusively plain weave structure-derived features consistently outperformed those using only wrinkle-derived features across all classifiers. These results validate computer vision as an effective tool for objective base load aging assessment, offering significant potential to streamline washing machine testing protocols and enhance sustainability. Future work can be focused on expanding sample sizes and exploring mobile-based implementation.
12 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
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