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Background Previous studies have suggested that exposure to air pollution may increase stroke risk, but the results remain inconsistent. Evidence of more recent studies is highly warranted, especially gas air pollutants. Methods We searched PubMed, Embase, and Web of Science to identify studies till February 2020 and conducted a meta-analysis on the association between air pollution (PM 2.5 , particulate matter with aerodynamic diameter less than 2.5 μm; PM 10 , particulate matter with aerodynamic diameter less than 10 μm; NO 2 , nitrogen dioxide; SO 2 , sulfur dioxide; CO, carbon monoxide; O 3 , ozone) and stroke (hospital admission, incidence, and mortality). Fixed- or random-effects model was used to calculate pooled odds ratios (OR)/hazard ratio (HR) and their 95% confidence intervals (CI) for a 10 μg/m 3 increase in air pollutant concentration. Results A total of 68 studies conducted from more than 23 million participants were included in our meta-analysis. Meta-analyses showed significant associations of all six air pollutants and stroke hospital admission (e.g., PM 2.5 : OR = 1.008 (95% CI 1.005, 1.011); NO 2 : OR = 1.023 (95% CI 1.015, 1.030), per 10 μg/m 3 increases in air pollutant concentration). Exposure to PM 2.5 , SO 2 , and NO 2 was associated with increased risks of stroke incidence (PM 2.5 : HR = 1.048 (95% CI 1.020, 1.076); SO 2 : HR = 1.002 (95% CI 1.000, 1.003); NO 2 : HR = 1.002 (95% CI 1.000, 1.003), respectively). However, no significant differences were found in associations of PM 10 , CO, O 3 , and stroke incidence. Except for CO and O 3 , we found that higher level of air pollution (PM 2.5 , PM 10 , SO 2 , and NO 2 ) exposure was associated with higher stroke mortality (e.g., PM 10 : OR = 1.006 (95% CI 1.003, 1.010), SO 2 : OR = 1.006 (95% CI 1.005, 1.008). Conclusions Exposure to air pollution was positively associated with an increased risk of stroke hospital admission (PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3 ), incidence (PM 2.5 , SO 2 , and NO 2 ), and mortality (PM 2.5 , PM 10 , SO 2 , and NO 2 ). Our study would provide a more comprehensive evidence of air pollution and stroke, especially SO 2 and NO 2 .

Background Due to the class imbalance issue faced when Alzheimer’s disease (AD) develops from normal cognition (NC) to mild cognitive impairment (MCI), present clinical practice is met with challenges regarding the auxiliary diagnosis of AD using machine learning (ML). This leads to low diagnosis performance. We aimed to construct an interpretable framework, extreme gradient boosting-Shapley additive explanations (XGBoost-SHAP), to handle the imbalance among different AD progression statuses at the algorithmic level. We also sought to achieve multiclassification of NC, MCI, and AD. Methods We obtained patient data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, including clinical information, neuropsychological test results, neuroimaging-derived biomarkers, and APOE-ε4 gene statuses. First, three feature selection algorithms were applied, and they were then included in the XGBoost algorithm. Due to the imbalance among the three classes, we changed the sample weight distribution to achieve multiclassification of NC, MCI, and AD. Then, the SHAP method was linked to XGBoost to form an interpretable framework. This framework utilized attribution ideas that quantified the impacts of model predictions into numerical values and analysed them based on their directions and sizes. Subsequently, the top 10 features (optimal subset) were used to simplify the clinical decision-making process, and their performance was compared with that of a random forest (RF), Bagging, AdaBoost, and a naive Bayes (NB) classifier. Finally, the National Alzheimer’s Coordinating Center (NACC) dataset was employed to assess the impact path consistency of the features within the optimal subset. Results Compared to the RF, Bagging, AdaBoost, NB and XGBoost (unweighted), the interpretable framework had higher classification performance with accuracy improvements of 0.74%, 0.74%, 1.46%, 13.18%, and 0.83%, respectively. The framework achieved high sensitivity (81.21%/74.85%), specificity (92.18%/89.86%), accuracy (87.57%/80.52%), area under the receiver operating characteristic curve (AUC) (0.91/0.88), positive clinical utility index (0.71/0.56), and negative clinical utility index (0.75/0.68) on the ADNI and NACC datasets, respectively. In the ADNI dataset, the top 10 features were found to have varying associations with the risk of AD onset based on their SHAP values. Specifically, the higher SHAP values of CDRSB , ADAS13 , ADAS11 , ventricle volume , ADASQ4 , and FAQ were associated with higher risks of AD onset. Conversely, the higher SHAP values of LDELTOTAL , mPACCdigit , RAVLT_immediate , and MMSE were associated with lower risks of AD onset. Similar results were found for the NACC dataset. Conclusions The proposed interpretable framework contributes to achieving excellent performance in imbalanced AD multiclassification tasks and provides scientific guidance (optimal subset) for clinical decision-making, thereby facilitating disease management and offering new research ideas for optimizing AD prevention and treatment programs.

To assess the direct and indirect effects of patient or caregiver factors on caregiver burden of caring for community-residing people with mild Alzheimer's disease (AD). We conducted a cross-sectional study of patients diagnosed with AD from two hospitals and three communities in Taiyuan, China and their caregivers. For this survey, 200 patients with mild AD and their caregivers were selected. Caregivers were asked to provide sociodemographic information including age, gender, relationship with the patient, level of education, and number of contact hours per week with the patient. Caregiver burden was assessed using the Caregivers Burden Inventory. The caregivers also completed other measures including the Positive Aspects of Caregiving, the Family Adaptation, Partnership, Growth, Affection, and Resolve, and the Social Support Rating Scale. The patients with AD completed the Montreal Cognitive Assessment; their caregivers completed the Activities of Daily Living Scale and a questionnaire about the patients' Behavioral and Psychological symptoms of Dementia. The main outcome in this study was caregiver burden. The care receivers' level of cognitive function, physical function, and behavioral problems were treated as original stress; the primary appraisal variable was measured as the number of hours of caregiving in the previous week reported by the caregiver. Mediator variables included perceived social support, family function, and caregiving experience. Path analysis was used to build the interrelationship among caregiver burden and patient or caregiver factors. A lower level of cognitive function in patients (r = -0.28, p<0.001) and longer hours of caregiving (r = 0.17, p = 0.019) were related to increased caregiver burden. Greater social support (r = -0.23, p<0.001), family function (r = -0.17, p = 0.015) and caregiving experience (r = -0.16, p = 0.012) were related to decreased caregiver burden. Social support (r = 0.16, p = 0.040) and family function (r = 0.25, p = 0.002) were directly related to patients' level of cognitive functioning, but were mediator factors between level of cognitive function in patients and caregiver burden. Similarly, social support was a mediator factor between patients' daily function (r = -0.23, p = 0.004) and caregiver burden; while caregiving experience mediated the link between behavioral and psychological symptoms in patients (r = 0.36, p<0.001) and caregiver burden. Level of cognitive function and hours of caregiving were directly related to caregiver's burden. Social support, family function and caregiving experience could mediate the relationship between patient factors and caregiver burden. Focusing on patient factors and promoting caregiver care will be helpful in lowering the perceived burden of caregiving.

To develop and validate an integrated 2.5D deep learning (DL) and Radiomics model using gadoxetic acid-enhanced MRI hepatobiliary phase (HBP) images combined with clinical features for preoperative prediction of microvascular invasion (MVI) and high Ki-67 expression (>20%) dual positivity in hepatocellular carcinoma (HCC). This retrospective study included 235 pathologically confirmed HCC patients categorized as MVI/Ki-67 double-positive (n = 129) or non-double-positive (n = 106). Clinical data (tumor diameter, AFP, GGT, differentiation grade, etc.) and HBP MRI images were collected. Tumor ROIs were segmented on HBP images. A 2.5D DL approach utilized axial, sagittal, and coronal planes of the largest tumor cross-section. LASSO regression selected key features from clinical, radiomic, and DL feature sets. Multivariate logistic regression identified independent predictors, and a nomogram was built. Model performance was evaluated via ROC curves, calibration plots, DCA, confusion matrices, and waterfall plots. Assessment of early recurrence within 2 years after HCC surgery was performed using alpha-fetoprotein (AFP) levels and imaging examinations. Significant intergroup differences existed in tumor diameter, AFP, GGT, and differentiation grade (P < 0.05). LASSO selected 38 key features (7 clinical, 23 DL, 8 radiomic). Multivariate analysis confirmed the derived clinical feature score, DL_Radscore, and radiomics Radscore as independent predictors of dual positivity. The integrated nomogram model (combining 2.5D DL, radiomics, and clinical features) achieved optimal prediction performance: AUROC, sensitivity, specificity, precision, accuracy, and F1-score values of 0.939, 0.793, 0.940, 0.942, 0.859, and 0.861, respectively.Calibration curves demonstrated good agreement, and DCA indicated clinical utility. Furthermore, postoperative follow-up confirmed that the MVI/Ki-67 dual-positive group exhibited a significantly higher early recurrence rate compared to the non-dual-positive group (P < 0.05). The integrated MRI 2.5D DL model synergizing radiomics and clinical features surpasses single-modality models for preoperative prediction of MVI/Ki-67 dual positivity in HCC. This tool shows strong potential for enhancing HCC risk stratification and guiding personalized treatment planning.

Background Individualized prediction of health outcomes supports clinical medicine and decision making. Our primary objective was to offer a comprehensive survey of methods for the dynamic prediction of Alzheimer’s disease (AD), encompassing both conventional statistical methods and deep learning techniques. Methods Articles were sourced from PubMed, Embase and Web of Science databases using keywords related to dynamic prediction of AD. A set of criteria was developed to identify included studies. The correlation information for the construction of models was extracted. Results We identified four methodological frameworks for dynamic prediction from 18 studies with two-stage model ( n  = 3), joint model ( n  = 11), landmark model ( n  = 2) and deep learning ( n  = 2). We reported and summarized the specific construction of models and their applications. Conclusions Each framework possesses distinctive principles and attendant benefits. The dynamic prediction models excel in predicting the prognosis of individual patients in a real-time manner, surpassing the limitations of traditional baseline-only prediction models. Future work should consider various data types, complex longitudinal data, missing data, assumption violations, survival outcomes, and interpretability of models.

The study aims to investigate the potential of training efficient deep learning models by using 2.5D (2.5-Dimension) masks of sICH. Furthermore, it intends to evaluate and compare the predictive performance of a joint model incorporating four types of features with standalone 2.5D deep learning, radiomics, radiology, and clinical models for early expansion in sICH. A total of 254 sICH patients were enrolled retrospectively and divided into two groups according to whether the hematoma was enlarged or not. The 2.5D mask of sICH is constructed with the maximum axial, coronal and sagittal planes of the hematoma, which is used to train the deep learning model and extract deep learning features. Predictive models were built on clinic, radiology, radiomics and deep learning features separately and four type features jointly. The diagnostic performance of each model was measured using the receiver operating characteristic curve (AUC), Accuracy, Recall, F1 and decision curve analysis (DCA). The AUCs of the clinic model, radiology model, radiomics model, deep learning model, joint model, and nomogram model on the train set (training and Cross-validation) were 0.639, 0.682, 0.859, 0.807, 0.939, and 0.942, respectively, while the AUCs on the test set (external validation) were 0.680, 0.758, 0.802, 0.857, 0.929, and 0.926. Decision curve analysis showed that the joint model was superior to the other models and demonstrated good consistency between the predicted probability of early hematoma expansion and the actual occurrence probability. Our study demonstrates that the joint model is a more efficient and robust prediction model, as verified by multicenter data. This finding highlights the potential clinical utility of a multifactorial prediction model that integrates various data sources for prognostication in patients with intracerebral hemorrhage. The Critical Relevance Statement: Combining 2.5D deep learning features with clinic features, radiology markers, and radiomics signatures to establish a joint model enabling physicians to conduct better-individualized assessments the risk of early expansion of sICH.

Objective Shift workers face elevated risk of sleep disruption and reduced well-being, yet the pathways linking physical activity to life satisfaction under rotating shift systems remain underexplored. This study examined how physical activity (PA) relates to life satisfaction (LS) among 1,465 migrant manufacturing workers in Dongguan on monthly rotating dual-shift schedules, testing sequential mediation by sleep quality (SQ) and subjective well-being (SWB) and moderation by the sleep environment (SE). Methods We used cross-sectional survey data and estimated mediation and moderated-mediation models with PLS-SEM. Analyses reported path coefficients, explained variance (R²), interaction effects, and 95% confidence intervals to quantify direct and indirect associations between PA and LS. Results Higher PA was associated with greater LS both directly (β = 0.530, p  < 0.001) and indirectly through SE (PA → SE → LS, β = 0.187, p  < 0.001), SQ (PA → SQ → LS, β = 0.161, p  < 0.001), SWB (PA → SWB → LS, β = 0.044, p  < 0.001), and a sequential chain (PA → SQ → SWB → LS, β = 0.032, p  < 0.001). Notably, SE significantly moderated the sequential mediation (interaction β = 0.180, p  < 0.001), such that more favorable sleep environments amplified PA’s indirect effects via sleep and well-being. The full model explained 41% of variance in LS (R² = 0.409), with the moderator contributing a significant additional increment (ΔR² = 0.028, p  < 0.001). Conclusion At the population level, sleep quality emerges as a central conduit linking physical activity to life satisfaction, while the sleep environment conditions the extent to which behavioral gains translate into improved well-being. For shift-working populations, interventions that combine promotion of physical activity with practical improvements to sleep conditions (for example, dormitory upgrades, sleep hygiene programs, or timing-based activity guidance) are likely to produce more durable benefits than single-domain approaches. Because the study is cross-sectional, longitudinal or experimental research is needed to confirm causal directions and to test phased or timing-sensitive interventions.

Background Timely prediction of cognitive decline in patients with Mild Cognitive Impairment (MCI) is crucial for guiding optimal therapeutic interventions. In this study, we aimed to develop a deep survival analysis model that leverages longitudinal, multi-modal data to estimate the probability of dementia conversion, thereby facilitating personalized treatment planning in clinical practice. Methods We employed a deep neural network model specifically designed for survival analysis to predict the progression from MCI to Alzheimer’s Disease (AD). The model integrated longitudinal biomarkers, including neuropsychological assessments and neuroimaging measures, along with baseline demographic characteristics and genetic risk factors, using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Results This study enrolled 922 baseline MCI patients for analysis. The predictive performance was evaluated using a test set at time intervals  = 1, 2, 3, 4 years from the landmark time s = 1. The prognostic model exhibited outstanding predictive capability, attaining cdAUC values of 0.9089 ± 0.01 alongside BS of 0.1651 ± 0.01 with  = 1 year on the test set, when all variable sets were incorporated into the time-dependent Cox survival neural network (tdCoxSNN) model. Through feature significance evaluation, the Functional Activities Questionnaire (FAQ) emerged as the most influential predictive element. Conclusions By systematically integrating diverse longitudinal biomarkers, we developed a dynamic prediction model for MCI using deep survival analysis. This approach enables accurate individual risk stratification, facilitates the early identification of high-risk individuals, and supports informed, personalized clinical decision-making.

Objective: We wished to explore Parkinson’s disease (PD) subtypes by clustering analysis based on the multimodal magnetic resonance imaging (MRI) indices amplitude of low-frequency fluctuation (ALFF) and gray matter volume (GMV). Then, we analyzed the differences between PD subtypes. Methods: Eighty-six PD patients and 44 healthy controls (HCs) were recruited. We extracted ALFF and GMV according to the Anatomical Automatic Labeling (AAL) partition using Data Processing and Analysis for Brain Imaging (DPABI) software. The Ward linkage method was used for hierarchical clustering analysis. DPABI was employed to compare differences in ALFF and GMV between groups. Results: Two subtypes of PD were identified. The “diffuse malignant subtype” was characterized by reduced ALFF in the visual-related cortex and extensive reduction of GMV with severe impairment in motor function and cognitive function. The “mild subtype” was characterized by increased ALFF in the frontal lobe, temporal lobe, and sensorimotor cortex, and a slight decrease in GMV with mild impairment of motor function and cognitive function. Conclusion: Hierarchical clustering analysis based on multimodal MRI indices could be employed to identify two PD subtypes. These two PD subtypes showed different neurodegenerative patterns upon imaging.

Apart from the classic features, it is almost unknown whether there exist other new pathological features during pre-metastatic niche formation in hepatocellular carcinoma (HCC). Our previous works have highlighted the contribution of increased matrix stiffness to lung pre-metastatic niche formation and metastasis in HCC. However, whether increased matrix stiffness influences glucose metabolism and supply of lung pre-metastatic niche remains largely unclear. Here we uncover the underlying mechanism by which matrix stiffness-tuned exosomal miRNAs as the major contributor modulate glucose enrichment during lung pre-metastatic niche formation through decreasing the glucose uptake and consumption of lung fibroblasts and increasing angiogenesis and vascular permeability. Our findings suggest that glucose enrichment, a new characteristic of the lung pre-metastatic niche triggered by matrix stiffness-tuned exosomal miRNAs, is essential for the colonization and survival of metastatic tumor cells, as well as subsequent metastatic foci growth. The mechanisms involved in the formation of the premetastatic niche in hepatocellular carcinoma (HCC) are not completely elucidated. Here, the authors show that matrix stiffness in HCC induces cancer-exosome release which increases glucose availability in the lung premetastatic niche favouring metastasis formation and growing.