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Background The triglyceride–glucose (TyG) index, which is a reliable surrogate marker of insulin resistance (IR), has been associated with cardiovascular diseases. However, evidence of the impact of the TyG index on the severity of coronary artery disease (CAD) is limited. This study investigated the relationship between the TyG index and CAD severity of individuals with different glucose metabolic statuses. Methods This study enrolled 2792 participants with CAD in China between January 1, 2018 and December 31, 2021. All participants were divided into groups according to the tertiles of the TyG index as follows: T1 group, TyG index < 6.87; T2 group, TyG index ≥ 6.87 to < 7.38; and T3 group, TyG index ≥ 7.38. The glucose metabolic status was classified as normal glucose regulation, pre-diabetes mellitus (pre-DM), and diabetes mellitus according to the standards of the American Diabetes Association. CAD severity was determined by the number of stenotic vessels (single-vessel CAD versus multi-vessel CAD). Results We observed a significant relationship between the TyG index and incidence of multi-vessel CAD. After adjusting for sex, age, body mass index, smoking habits, alcohol consumption, hypertension, estimated glomerular filtration rate, antiplatelet drug use, antilipidemic drug use, and antihypertensive drug use in the logistic regression model, the TyG index was still an independent risk factor for multi-vessel CAD. Additionally, the highest tertile of the TyG group (T3 group) was correlated with a 1.496-fold risk of multi-vessel CAD compared with the lowest tertile of the TyG group (T1 group) (odds ratio [OR], 1.496; 95% confidence interval [CI], 1.183–1.893; P < 0.001) in the multivariable logistic regression model. Furthermore, a dose–response relationship was observed between the TyG index and CAD severity (non-linear P = 0.314). In the subgroup analysis of different glucose metabolic statuses, the T3 group (OR, 1.541; 95% CI 1.013–2.344; P = 0.043) were associated with a significantly higher risk of multi-vessel CAD in individuals with pre-DM. Conclusions An increased TyG index was associated with a higher risk of multi-vessel CAD. Our study indicated that TyG as an estimation index for evaluating IR could be a valuable predictor of CAD severity, especially for individuals with pre-DM.

Insulin resistance (IR) has a strong association with acute ischemic stroke (AIS) occurrence and poor prognosis of afflicted patients. However, the relation between early neurological deterioration (END) risk and IR in elderly and middle-aged patients remains to be thoroughly studied. Here, we investigated the relationship between four indicators of IR and the risk of END in middle-aged patients patients with AIS. The study retrospectively analyzed 1696 elderly and middle-aged patients having AIS between January 2019 and June 2023. Within 7 days of admission, the patients were then stratified relying upon alternations in the National Institutes of Health Stroke Scale. Subsequently, we employed logistic regression analyses for assessing each index correlation with END on the basis of the tertiles of TyG index (TyGI), triglyceride to high-density lipoprotein ratio (TG/HDL), TyG-BMI, alongside IR metabolic score (METS-IR). These four indicators were significantly heightened in the END group (n = 680) in comparison to the non-END group (n = 1016). When grouping using tertiles, the four aforementioned indicators emerged as independent risk factors for END occurrence, whether or not adjusted for confounding factors. The results revealed a progressive elevation in END occurrence risk with the rise in the tertile of each indicator. Finally, we utilized receiver operating characteristic (ROC) curves for assessing the indicators' predictive power. TyG-BMI, TyGI, TG/HDL, and METS-IRs’ area under the curve (AUC) were, respectively, 0.736 (95% CI: 0.712–0.761; P  < 0.001), 0. 694 (95% CI: 0.668–0.721; P  < 0.001), 0.684 (95% CI: 0.658–0.711; P  < 0.001), and 0.722 (95% CI: 0.697–0.747; P  < 0.001). IR is associated with END risk in middle-aged AIS patients. TyG-BMI, TyGI, TG/HDL, and METS-IR are independent risk factors of END in elderly and middle-aged AIS patients. Simultaneously, these four IR indicators have significant predictive power for END.

Background: Drug recommendation is a crucial application of artificial intelligence in medical practice. Although many models have been proposed to solve this task, two challenges remain unresolved: (i) most existing models use all historical visits as input, overlooking fine-grained correlations between historical and current information; (ii) Electronic Health Records (EHRs) are underutilized, with only partial information considered to describe patient conditions. To tackle the challenges, we propose a novel drug recommendation model, denoted by EDRMM, which incorporates multi-granularity and multi-attribute information into representation learning. We develop a longitudinal attribute-level history selection mechanism to effectively identify fine-grained historical information that is highly relevant to a patient’s current clinical conditions. We analyze the impact of key Electronic Health Record (EHR) attributes, demonstrating that incorporating such attributes into patient representations can further boost performance. We also design an adaptive global Drug–Drug Interaction (DDI) risk regularization term for the DDI loss function to better balance accuracy and safety during training. Results: Experimental results show that our model achieves state-of-the-art performance on a widely used MIMIC-III dataset. Conclusions: EDRMM overcomes two key drug recommendation limitations through three innovations: (1) Dynamic attribute-level history selection, which retrieves relevant features and filters out noise, (2) The integration of multi-attribute EHR with attribute-specific encoding strategies to generate comprehensive patient representations, and (3) Hybrid optimization balancing accuracy and safety via adaptive DDI regularization. The combination of these three innovations enables the proposed EDRMM to achieve the best recommendation performance on the MIMIC-III dataset.

The current standard of care (SOC) for whole breast radiotherapy (WBRT) in the US is conventional tangential photon fields. Advanced WBRT techniques may provide similar tumor control and better normal tissue sparing, but it is controversial whether the medical benefits of an advanced technology are significant enough to justify its higher cost. To analyze the cost-effectiveness of six advanced WBRT techniques compared with SOC. We developed a Markov model to simulate health states for one cohort of women (65-year-old) with early-stage breast cancer over 15 years after WBRT. The cost effectiveness analyses of field-in-field (FIF), hybrid intensity modulated radiotherapy (IMRT), full IMRT, standard volumetric modulated arc therapy (STD-VMAT), multiple arc VMAT (MA-VMAT), non-coplanar VMAT (NC-VMAT) compared with SOC were performed with both tumor control and radiogenic side effects considered. Transition probabilities and utilities for each health state were obtained from literature. Costs incurred by payers were adopted from literature and Medicare data. Quality-adjusted life years (QALYs) and incremental cost-effectiveness ratio (ICER) were calculated. One-way sensitivity analyses and probabilistic sensitivity analyses (PSA) were performed to evaluate the impact of uncertainties on the final results. FIF has the lowest ICER value of 1,511 $/QALY. The one-way analyses show that the cost-effectiveness of advanced WBRT techniques is most sensitive to the probability of developing contralateral breast cancer. PSAs show that SOC is more cost effective than almost all advanced WBRT techniques at a willingness-to-pay (WTP) threshold of 50,000 $/QALY, while FIF, hybrid IMRT and MA-VMAT are more cost-effective than SOC with a probability of 59.2%, 72.3% and 72.6% at a WTP threshold of 100,000 $/QALY, respectively. FIF might be the most cost-effective option for WBRT patients at a WTP threshold of 50,000 $/QALY, while hybrid IMRT and MA-VMAT might be the most cost-effective options at a WTP threshold of 100,000 $/QALY.

Background Stress hyperglycemia is strongly associated with poor clinical outcomes in patients with acute coronary syndrome (ACS). Recently, the stress hyperglycemia ratio (SHR) has been proposed to represent relative hyperglycemia. Studies regarding the relationship between SHR and mortality in coronary artery disease (CAD) are limited. This study aimed to clarify the association between SHR and in-hospital mortality in patients with CAD. Methods A total of 19,929 patients with CAD who were hospitalized in Beijing Hospital were enrolled in this study. Patients with an estimated glomerular filtration rate < 30 ml/min, cancer, or missing blood glucose/HbA1c data were excluded; therefore, 8,196 patients were included in the final analysis. The patients were divided into three groups based on tertiles of SHR: T1 group (SHR < 0.725, n = 2,732), T2 group (0.725 ≤ SHR < 0.832, n = 2,730), and T3 group (SHR ≥ 0.832, n = 2,734). The primary endpoint was in-hospital mortality. Results The overall in-hospital mortality rate was 0.91% (n = 74). After adjusting for covariates, SHR was significantly associated with in-hospital mortality in patients with CAD [odds ratio (OR) = 17.038; 95% confidence interval (CI) = 9.668–30.027; P <  0.001], and the T3 group had a higher risk of in-hospital mortality (OR = 4.901; 95% CI = 2.583–9.297; P <  0.001) compared with T1 group. In the subgroup analysis, the T3 group had an increased risk of mortality among patients with pre-diabetes mellitus (pre-DM) (OR = 9.670; 95% CI = 1.886–49.571; P  = 0.007) and diabetes mellitus (DM) (OR = 5.023; 95% CI = 2.371–10.640; P < 0.001) after adjustments for covariates. The relationship between SHR and in-hospital mortality among patients with ACS and chronic coronary syndrome was consistent with the main finding. SHR and in-hospital mortality exhibited a dose-response relationship, and the risk of in-hospital mortality increased when the SHR index was above 1.20. Moreover, the area under the curve of SHR for predicting in-hospital mortality in patients with CAD was 0.741. Conclusion SHR is significantly associated with in-hospital mortality in patients with CAD. SHR may be an effective predictor of in-hospital mortality in patients with CAD, especially for those with pre-DM and DM.

Through literature research method and questionnaire survey method, this paper explores the relationship between philanthropic activities and ecotourism consumption intention. This paper uses a seven-point scale to measure variables and adopts questionnaire analysis method to collect data after reading relevant literature. After using regression analysis to analyze the data, this paper finds that: (1) Corporate’s cross-border and domestic philanthropy reduce consumers’ ecotourism consumption intention, and cross-border philanthropy reduce consumers’ ecotourism consumption intention more than domestic philanthropy; (2) This effect is mediated by the moral licensing; (3) Patriotic feeling moderates the relationship between corporate philanthropy and moral licensing. The higher the consumer’s patriotic feeling, the stronger the negative impact of corporate cross-border philanthropy on moral licensing, and the stronger the positive impact of corporate domestic philanthropy on moral licensing; (4) Cultural identity moderates the relationship between corporate philanthropy and moral licensing.

In this paper, the roles of upward counterfactual thinking and their perception of COVID-19 risk in the influence of social comparison tendency on travel satisfaction was investigated. The study follows the design of 3 (social comparison tendency: high, low and medium) by 2 (perceived risk degree of COVID-19: high and low). The findings are as follows: (1) There are significant differences in the tourism satisfaction of individuals with different social comparison tendencies: the tourism satisfaction of individuals with low social comparison tendencies is significantly higher than that of the other two groups, and the tourism satisfaction of individuals with high social comparison tendencies is higher than that of individuals with medium social comparison tendencies; (2) The influence of social comparison tendency on tourism satisfaction is mediated by upward counterfactual thinking and moderated by the perception of COVID-19 risk. These findings help the tourism industry to better understand consumers' psychological needs and behavior patterns, so as to design tourism products and marketing strategies that better meet consumers' expectations. Especially after the epidemic, understanding consumers' risk perception and its impact on satisfaction can help the tourism industry to cope with the potential health crisis more effectively, improving the quality of tourism services and enhance consumers' confidence.

Excessive daytime sleepiness (EDS), which is common in Parkinson's disease (PD), has been reported to exacerbate gait disturbance in patients with PD, but there is a lack of objective assessment, as well as an unknown specific mechanism. The purpose of our study is to explore the relationship between EDS and gait parameters. Sixty-one patients with PD were recruited and divided into the EDS group (  = 29) and the non-EDS group (  = 32) based on the scores of the Epworth Sleepiness Scale (ESS). The gait metrics of the two groups were then assessed by wearable devices and compared under various walking scenarios. Compared with the non-EDS group, the EDS group showed significantly shorter step lengths and stride lengths, slower walk speed and gait speed, reduced shank-max forward swing and sagittal angular velocity, and increased phase coordination indices and mean duration of turns. Pearson correlation analysis revealed a significant association between ESS scores and various gait parameters. Furthermore, multiple linear regression analysis confirmed that EDS is an independent factor influencing gait in patients with PD. EDS was independently associated with gait disturbances in patients with PD, suggesting that EDS symptoms warrant serious attention in clinical practice.

Background This study aims to systematically elucidate the molecular mechanisms underlying idiopathic pulmonary fibrosis (IPF), with a specific focus on the regulatory role of the nucleic acid-binding protein Y-box binding protein 1 (YBX1) in alveolar type II epithelial cells (AT2) and its association with disease progression. Additionally, the study integrates virtual screening and molecular dynamics (MD) simulations to identify small-molecule compounds targeting YBX1, thereby providing both mechanistic insights and therapeutic candidates for IPF. Methods We employed integrative multi-omics analysis and bioinformatics approaches to identify IPF-associated signature genes, construct a diagnostic model and risk scoring system, and establish YBX1 as a central regulatory node. Single-cell RNA sequencing (scRNA-seq) data were used to characterize AT2 cell heterogeneity and developmental trajectories, highlighting the dynamic expression pattern of YBX1 during cell fate transitions. Cell–cell communication analysis elucidated YBX1’s potential involvement in immunomodulatory signaling, particularly between AT2 cells and M2 macrophages. Mendelian randomization was applied to infer the causal relationship between YBX1 expression and lung function indices. The expression and functional role of YBX1 were further validated using independent clinical cohorts and in vitro cell models. Structure-based virtual screening was performed to identify candidate compounds targeting YBX1, followed by MD simulations to assess binding stability and infer potential mechanisms of action. Results YBX1 emerged as a key molecular signature of IPF with strong diagnostic potential and a prominent role in modulating immune cell infiltration. scRNA-seq revealed significant AT2 cell subtype diversity, with YBX1 dynamically expressed along differentiation trajectories. Intercellular communication analysis suggested that YBX1 may mediate indirect signaling between AT2 cells and M2 macrophages, potentially influencing the immune microenvironment and fibrotic progression. Mendelian randomization supported a significant positive causal relationship between YBX1 expression and pulmonary function, suggesting a protective role. Both clinical samples and cell-based assays confirmed YBX1 downregulation in fibrotic lung tissue, and its restoration improved mitochondrial function and enhanced antioxidant capacity. Virtual screening identified several small molecules with high binding affinity to functional domains of YBX1. MD simulations further supported the structural stability of YBX1–ligand complexes and suggested conformational regulation as a potential mechanism of action. Conclusion This study delineates the pivotal role of YBX1 in the pathogenesis of IPF, highlighting its function in maintaining alveolar epithelial cell homeostasis and regulating disease progression. The identification of YBX1-targeting compounds through virtual screening and MD simulations offers a rational framework for the development of targeted therapies, advancing the translational potential of YBX1 as a diagnostic and therapeutic target in IPF.

Background Early Parkinson’s disease (PD) presents with subtle symptoms and lacks specific diagnostic methods. Clinical diagnosis primarily relies on subjective assessment, with confirmation often occurring at mid-to-late stages. Therefore, identifying objective and quantifiable biomarkers to assist in early PD diagnosis and intervention is of significant clinical value. Method This study recruited 20 early PD patients and 18 healthy controls who performed a visually guided motor-cognitive task (VMC) under three visual feedback gain conditions (low, medium, and high), while electroencephalography (EEG) and grip strength data were recorded simultaneously. Subsequently, EEG microstate analysis was used to extract temporal dynamic parameters (Duration, Occurrence, Coverage, and Transition probability), while grip strength parameters (RMSE, CV) were computed. Finally, machine learning models were developed using EEG microstate parameters, grip strength parameters, and their multimodal fusion features to systematically evaluate classification performance in distinguishing early PD patients from healthy individuals. Result In the resting-state, PD patients exhibited a significant increase in the Occurrence and Coverage of microstate A, while the Coverage of microstate B significantly decreased. During the VMC task, PD patients exhibited a microstate pattern significantly different from that of HC. PD patients exhibited significantly lower temporal parameters for microstate B but higher values for microstate A and C under medium/high gain conditions, which was exactly the opposite of the performance at low gain. Moreover, PD patients exhibited poorer grip performance across all gain conditions. Finally, compared with the grip strength parameter model and the multidimensional parameter model, the microstate parameter model based on the VMC task demonstrated higher sensitivity in identifying early PD patients. At low, medium, and high visual feedback gains, the highest classification accuracy reached 100% in all cases. Notably, under high-gain conditions, all classification algorithms achieved 100% accuracy. Conclusion This study, through EEG microstate analysis, revealed abnormal brain network dynamics in early PD patients under resting-state and various visual feedback gain conditions. The EEG microstate parameter model based on the VMC task exhibited high sensitivity in identifying early PD patients, particularly under high gain conditions. These findings suggest that EEG microstate parameters during the VMC task hold potential as objective biomarkers for early PD diagnosis and have significant clinical value.