Chenyu Sun

Microplastic pollution may influence neurodevelopment, yet its relationship with attention‐deficit/hyperactivity disorder (ADHD) remains unclear. We integrated a pediatric cohort, multi‐omics analyses, network toxicology, and animal experiments to investigate polyethylene (PE) microplastic exposure in ADHD vulnerability. Among 90 children, PE was the predominant polymer detected in blood and was more abundant in children with ADHD. Higher PE burden was associated with greater symptom severity, increased IL‐6, and reduced antioxidant capacity. Multi‐omics and network analyses converged on PI3K/AKT1/Nrf2 signaling. In a perinatal nicotine mouse model, developmental PE co‐exposure exacerbated ADHD‐like behaviors, dopaminergic dysfunction, and oxidative injury. Melatonin partially alleviated these abnormalities by restoring PI3K/AKT1/GSK3β and Nrf2 signaling, whereas AAV‐mediated PI3K knockdown weakened its protective effects. Collectively, the human findings identify circulating PE burden as an environmental correlate of ADHD‐related neurodevelopmental vulnerability, while the animal experiments provide evidence that developmental PE co‐exposure can aggravate ADHD‐like abnormalities in a susceptible background.

Background and aims: Machine learning (ML) is increasingly applied to the National Health and Nutrition Examination Survey (NHANES) to classify cardiovascular disease (CVD), but whether it outperforms logistic regression, and whether dietary intake adds information beyond clinical variables, is uncertain. Because NHANES is cross-sectional, such models classify prevalent disease rather than predict future risk. We developed and internally validated explainable ML models for prevalent, self-reported CVD in US adults using a leakage-safe pipeline, compared six algorithms against conventional logistic regression, and compared clinical-only, dietary-only and combined predictor sets. Methods: We analysed 19,404 adults aged ≥20 years from NHANES 2011–2018 with complete self-reported CVD status (heart failure, coronary heart disease, angina, myocardial infarction or stroke), at least one 24-hour dietary recall, and examination data. The sample was split 80:20 before any data-dependent processing. Within the development set, multivariate imputation, correlation filtering, LASSO selection and 1:1 down-sampling were nested inside repeated 5-fold cross-validation (2 repeats) to tune six algorithms (elastic-net logistic regression, random forest, naive Bayes, support-vector machine, XGBoost, LightGBM). Performance was assessed once on the test set using ROC-AUC, PR-AUC, Brier score, calibration intercept/slope, and decision-curve analysis, with bootstrap confidence intervals (CIs). Predicted probabilities were corrected for down-sampling. SHAP values were computed for the final LightGBM models. Results: Prevalent CVD was reported by 2,117 participants (10.9%). The combined LightGBM model discriminated well (ROC-AUC 0.843 [0.825–0.861]; PR-AUC 0.409 [0.364–0.462] vs. a 0.109 prevalence baseline) and, after down-sampling correction, was adequately calibrated (intercept 0.043 [−0.074–0.151], slope 0.989 [0.906–1.086], Brier 0.078 [0.072–0.084]), with net benefit over classify-all/none across thresholds of 1–50%. Cross-validated and test ROC-AUC were similar (0.862 and 0.843), and discrimination was similar across sex, race/ethnicity, education and income groups but lower within age strata. In the unweighted test set the six algorithms did not outperform conventional logistic regression (LightGBM minus conventional: 0.009 [−0.000 to 0.020]), and clinical predictors carried nearly all discriminative information (combined minus clinical: −0.0004 [−0.004 to 0.003]); dietary-only discrimination was poor (0.587 [0.558–0.615]). Age, hypertension, total cholesterol, poverty-income ratio, kidney function and red-cell distribution width had the largest SHAP attributions. Conclusions: Explainable gradient-boosting models classified prevalent, self-reported CVD with good discrimination but not clearly better than logistic regression, and dietary intake added little beyond clinical measures. Cross-sectional design, self-reported outcome and lack of external validation limit these models to epidemiological description and hypothesis generation, and support simple clinical models as the starting point for prospective analyses.

Introduction: Cardiogenic shock (CS) carries high morbidity and mortality. However, evidence for mechanical circulatory support (MCS) in non‐ischemic CS remains limited, as prior trials have focused on infarct‐related shock. Methods: We performed a retrospective cross‐sectional study using the Nationwide Inpatient Sample (2017–2019), identifying hospitalizations with a principal diagnosis of CS and excluding acute myocardial infarction. Patients were categorized into mutually exclusive groups: no MCS, intra‐aortic balloon pump (IABP) only, Impella only, or extracorporeal membrane oxygenation (ECMO) only. Primary outcomes were in‐hospital mortality, length of stay (LOS), and total hospital cost. Survey‐weighted multivariable models adjusted for demographics, hospital characteristics, and comorbidity burden using Elixhauser and Charlson indices. Results: Among 104 045 hospitalizations, 6040 received MCS. IABP was most common, followed by Impella and ECMO. Among patients receiving a single MCS strategy, overall in‐hospital mortality was 29.5%., with lower mortality in the IABP group (19.9%) compared with Impella (37.4%) and ECMO (44.3%). After adjustment, IABP was associated with lower observed in‐hospital mortality versus no MCS (aOR 0.58, 95% CI 0.53–0.64; p < 0.01), whereas Impella and ECMO showed increased aOR. IABP and ECMO were associated with longer LOS, while Impella was not. All MCS strategies were associated with higher costs, with ECMO showing the greatest increase (cost ratio 3.30, 95% CI 3.12–3.50). Conclusion: In non‐ischemic CS, MCS strategies demonstrate distinct profiles of mortality and resource utilization. IABP was associated with lower observed in‐hospital mortality, whereas all MCS were linked to higher costs, highlighting heterogeneity in outcomes and the need for further research.

Differences in mental health services covered by U.S. insurance payers are a public health concern. Mental health benefits are required under the Affordable Care Act, and parity laws have long aimed to ensure equitable access across insurance types. The authors examined the association between insurance coverage (Medicaid or private), hospitalization for a principal psychiatric diagnosis, and patient outcomes. A retrospective cross-sectional analysis was conducted by using the 2017-2019 Nationwide Inpatient Sample for adults ages 18-64 with Medicaid or private insurance. Mental disorders, length of stay (LOS), hospital charges, and in-hospital mortality rates were analyzed. Survey-weighted multivariable regressions adjusted for demographic characteristics, hospital characteristics (i.e., region and size as number of beds), and somatic comorbid conditions were performed. Among 7,428,110 unweighted hospital discharges (weighted=37.1 million), 11.9% of Medicaid and 5.5% of privately insured hospitalizations (610,394 unweighted and 3,051,969 weighted discharges) involved a principal psychiatric diagnosis (adjusted OR=2.35, 95% CI=2.25-2.44, p<0.001). Compared with private insurance, Medicaid was associated with a longer LOS and higher charges among hospitalizations for a principal psychiatric diagnosis. Subgroup analyses revealed longer LOS and higher charges for major depressive, substance use, and posttraumatic stress disorders. For anxiety disorders, longer LOS under Medicaid was not associated with higher charges. No differences in in-hospital mortality rate were identified. Compared with private insurance, Medicaid was found to cover a higher proportion of hospitalizations for a principal psychiatric diagnosis, longer LOS, and higher cost. These findings likely reflect differences in patient needs but may also be due to programmatic differences.

As a consequence of global industrial growth, microplastics (plastic fragments < 5 mm) have become ubiquitous environmental contaminants, prompting serious questions about their impact on human health. Beyond the established risks of ingestion, the inhalation of these airborne particles is now a primary focus, especially regarding potential effects on the neurological system. Emerging evidence from laboratory and animal models shows that inhaled microplastics can penetrate the brain. Once there, they can trigger a cascade of harmful effects, including neuroinflammation, oxidative stress, and deficits in learning and memory. Among the general population, children are uniquely vulnerable to microplastic exposure due to their developing physiological systems, which are particularly susceptible to the chemical and physical hazards posed by these particles. Nevertheless, current scientific understanding of the health consequences of microplastic exposure remains limited, with a substantial knowledge gap concerning the long-term effects of respiratory microplastic exposure on pediatric populations, particularly those with pre-existing neurological conditions such as epilepsy. This study investigates the relationship between chronic respiratory exposure to polystyrene microplastics and seizure severity, with the aim of establishing a potential exposure-metabolite-gene regulatory network. We hypothesize that prolonged respiratory mciroplatsic exposure induces systemic oxidative stress and inflammation, which subsequently disrupts lipid metabolism, alters gene expression profiles, triggers ferroptosis, and ultimately exacerbates seizure manifestations.This study highlights three key findings. First, chronic respiratory microplastic exposure induces systemic oxidative stress and inflammation, posing substantial health risks. Second, integrated metabolomics and Mendelian randomization analyses reveal that this exposure disrupts lipid metabolism, with metabolic perturbations strongly associated with ferroptosis activation and increased seizure severity. Third, multi-omics approaches coupled with in vivo validation confirm that microplastics disrupt lipid homeostasis, dysregulate ferroptosis-related gene expression, and exacerbate seizure manifestations. Notably, our data identify melatonin as a promising therapeutic candidate for mitigating these adverse effects. Collectively, these findings substantially advance the understanding of microplastic-induced neurotoxicity and reveal actionable molecular targets for potential therapeutic interventions.

In terms of precise diagnosis and risk assessment, this special issue highlights pivotal progress from morphological judgment to quantitative prediction. Artificial intelligence models based on multimodal imaging and clinical data have significantly improved diagnostic accuracy and prognostic prediction. For instance, KneeXNet integrates graph convolutional networks (GCNs) with a multi-scale feature fusion module to enable automated, high-precision MRI diagnosis of knee injuries (Sun et Overall, the 24 research papers in this special issue cover a complete research chain, ranging from competitive sports injuries to age-related fragility fractures, from surgical techniques to rehabilitation protocols, and from fundamental mechanisms to clinical translation. They fully embody the interdisciplinary innovation philosophy of clinical demand-oriented, engineering technology-enabled, and functional recovery-targeted. These findings not only address numerous current clinical dilemmas but also lay a solid foundation for intelligent diagnosis, digital orthopedics, regenerative repair and smart rehabilitation. They also demonstrate that medical and engineering innovations will drive earlier, smarter, more precise, more efficient and personalized prevention and treatment in sports medicine. Moving forward, the diagnosis and management of sports injuries will continue to evolve toward full-process digitalization, normalized regenerative medicine, interpretable artificial intelligence, and integrated injury prevention and health maintenance.We hope readers can gain more than just technological progress, but also a clear perspective on how to solve practical clinical problems and foster broader interdisciplinary collaboration through this issue. Ultimately, we aim to enable all individuals affected by sports injuries-whether elite athletes or the general public-to truly benefit from these innovations, allowing them to exercise safely, recover efficiently, and return to their cherished lives with dignity. This embodies the original mission of sports medicine and fuels our ongoing research and endeavor.

As an emerging tire wear-derived environmental contaminant, 6PPD-quinone (6PPDQ) has raised significant concerns regarding its neurotoxic potential, particularly for children exposed to recycled tire crumb rubber in playgrounds. However, the molecular mechanisms by which 6PPDQ influences neurological disorders such as epilepsy remain poorly understood. In this study, we employed an integrative framework combining network toxicology, bulk analysis of human epileptic brain tissues, Mendelian randomization, and molecular dynamics simulations to elucidate these mechanisms. Our findings, validated through CETSA-WB and SPR, identify 6PPDQ as a direct ligand that binds to and stabilizes neuronal TP53. Through a synergistic double-hit mechanism, 6PPDQ directly engages the TP53 pathway while simultaneously triggering microglial interleukin-6 secretion. These converging pathways lead to the suppression of the master antioxidant regulator Nrf2, resulting in glutathione depletion, excessive reactive oxygen species accumulation, and exacerbated neuronal damage under excitotoxic stress. Experimental validation using glutamate-induced HT22 cell models and microglia–neuron crosstalk systems confirmed that targeting the TP53/Nrf2 axis or scavenging ROS significantly attenuates 6PPDQ-induced neurotoxicity. Our findings highlight critical risks to pediatric neurological health posed by tire-derived contaminants and identify the TP53/Nrf2 axis as a promising therapeutic target. Furthermore, this work provides a robust scientific basis for refining risk assessment frameworks and developing regulatory strategies to mitigate environmental exposure to 6PPDQ.