Objective: To characterize perioperative dynamic changes in immune-cell phenotypes and inflammatory cytokines in patients undergoing CPB (cardiopulmonary bypass) cardiac surgery, and to explore their associations with postoperative outcomes. Methods: In this prospective cohort study, 120 adult patients who underwent elective cardiac surgery under CPB at West China Hospital from May 2022 to March 2023 were enrolled. Perioperative immune-cell phenotypes and concentrations of 40 inflammation-related cytokines were measured. The primary outcomes were the sequential organ failure assessment (SOFA) score at 24 h after surgery and ΔSOFA (the peak SOFA score within 48 h after surgery minus the preoperative SOFA score). Secondary outcomes included major adverse cardiovascular events (MACE), acute kidney injury (AKI), respiratory failure, severe liver injury, and infection. Results: The mean age of enrolled patients was 57±10 years. Of these, 52% (62/120) were male and 90% (108/120) underwent valve surgery. During the rewarming to the end of CPB, neutrophil counts rapidly increased (7.39×10 /L vs preoperative 3.07×10 /L, P<0.001), with significant upregulation of CD11b (7.30×10 /L vs preoperative 3.05×10 /L, P<0.001) and CD54 (7.15×10 /L vs preoperative 2.99×10 /L, P<0.001). Lymphocyte counts increased at the end of CPB (1.75×10 /L vs preoperative 1.12×10 /L, P<0.001) but decreased significantly at 24 h after surgery (0.59×10 /L vs preoperative 1.12×10 /L, P<0.001). Plasma analysis showed that multiple pro-inflammatory cytokines increased during CPB and remained elevated up to 24 h after surgery; five chemokines and the anti-inflammatory cytokine IL-10 peaked at the end of CPB. The SOFA score increased from 1 (1, 2) preoperatively to 7 (5, 10) at 24 h after surgery, with a ΔSOFA of 6 (4, 8). Within 30 days after surgery, 48 patients (40.0%) developed AKI, 17 (14.2%) developed infection, 4 (3.3%) developed severe liver injury, 3 (2.5%) developed respiratory failure, and 3 (2.5%) experienced MACE. During the 2-year follow-up, 8 patients (6.7%) experienced MACE and 5 (4.2%) died. Conclusion: Multi-organ dysfunction is common after cardiac surgery under CPB (median ΔSOFA, 6), accompanied by perioperative activation of multiple immune-cell subsets and upregulation of pro-inflammatory, anti-inflammatory, and chemotactic mediators. This study provides data-driven evidence and research clues for further investigation of the associations between CPB-related immune perturbations and postoperative organ dysfunction and clinical outcomes.

Stroke is the leading cause of death and disability among adults in China， and its common complications include digestive system abnormalities， cognitive impairment， depression， stroke-associated pneumonia， and hemiplegia. The combination of traditional Chinese and Western medicine has great potential in treating post-stroke complications. Xinglou Chengqitang （XLCQT） is a representative prescription of alleviating the disease in the upper part by treating the lower part. It has definite therapeutic effect and high safety. Clinically， XLCQT is often used to treat stroke and its complications. However， the quantity and quality of clinical trials of XLCQT in treating post-stroke complications need to be improved. Additionally， since the basic research is weak， the material basis and multi-target mechanism for the efficacy of this prescription are unknown. This article reviews XLCQT in terms of the pharmacodynamic basis， medicinal properties， safety evaluation， and progress in clinical research and mechanisms in treating post-stroke complications. This article summarizes 22 key active ingredients of XLCQT in treating acute stroke complicated with syndrome of phlegm heat and fu-organ excess. Among these key active ingredients， resveratrol， kaempferol， luteolin， chrysoeriol， apigenin， （+）-catechin， and adenosine have good pharmacokinetic properties and high bioavailability. The mechanisms of XLCQT in treating post-stroke complications are complex， including inflammatory response， brain-gut axis， hypothalamic-pituitary-adrenal （HPA） axis， intestinal flora， neurotrophic factors， autophagy， oxidative stress， and free radical damage. This review helps to deeply understand the pharmacodynamic basis and mechanisms of XLCQT in treating post-stroke complications and provides a theoretical basis for the clinical application of XLCQT against post-stroke complications and the development of drugs.

Objective: Patients with atherosclerotic cardiovascular disease (ASCVD) following percutaneous coronary intervention (PCI) are classified as very-high-risk individuals in cardiovascular disease (CVD) risk stratification. The distribution pattern of traditional Chinese medicine (TCM) syndromes in this patient population, as well as its association with blood lipid profiles and clinical prognosis, remains unclear. The present prospective cohort study aims to investigate these correlations, thereby providing insights to enrich the research fields. Methods: We enrolled consecutive patients with ASCVD who underwent PCI at the Integrated Cardiology Unit of China-Japan Friendship Hospital between September 1, 2020 and December 31, 2022. Demographics and clinical characteristics, signs and symptoms defining each TCM syndrome, and fasting venous blood samples were collected at baseline and follow up or upon major adverse cardiovascular events (MACEs). We analyzed the correlation between TCM syndromes, blood lipid profiles, and MACEs, and developed a new joint prognostic model incorporating both TCM syndromes and blood lipids using logistic regression. The analyses were based on detailed baseline and one-year follow-up data. Results: A per-protocol analysis was performed on 586 patients with complete data ultimately. During the one-year follow-up, 174 patients (29.69%) experienced a MACE. We performed statistical analyses on comorbidities, medication, and biochemical indicators across groups defined by TCM syndrome differentiation. When comparing different TCM syndromes, no significant differences were found in age, body mass index (BMI), history of revascularization, comorbidities, family history of CVD, smoking or drinking, or statin intensity (P > 0.05). Patients with intertwined phlegm and blood stasis syndrome exhibited significantly higher levels of total cholesterol (TC, 5.27 ± 1.18 mmol/L, P < 0.001), triglyceride (TG, 1.96 ± 1.33 mmol/L, P = 0.008), low-density lipoprotein cholesterol (LDL-C, 3.35 ± 0.79 mmol/L, P < 0.001), and high-density lipoprotein cholesterol (HDL-C, 1.24 ± 0.81 mmol/L, P < 0.001) compared with those with other TCM syndromes combined. A multivariable logistic regression model was constructed to predict MACEs. The model included TCM syndrome type [with intertwined phlegm and blood stasis as a predictor, adjusted odds ratio (OR) = 1.413, 95% confidence interval (CI): 0.517 – 3.864, P = 0.501], age (adjusted OR = 0.97, 95% CI: 0.955 – 1.001, P = 0.057), male gender (adjusted OR = 0.698, 95% CI: 0.416 – 1.170, P = 0.173), TC (adjusted OR = 1.004, 95% CI: 0.513 – 1.965, P = 0.990), and LDL-C (adjusted OR = 5.825, 95% CI: 2.214 – 15.326, P < 0.001). This model demonstrated good discriminatory ability for MACEs in post-PCI ASCVD patients [the area under the receiver operating characteristic (ROC) curve (AUC) = 0.865, 95% CI: 0.816 – 0.914]. Conclusion The intertwined phlegm and blood stasis TCM syndrome is associated with a distinct atherogenic lipid profile characterized by elevated levels of TC and LDL-C. The prognostic model that incorporates this TCM syndrome type along with conventional lipid parameters (TC and LDL-C) shows good discriminatory ability for predicting MACEs in ASCVD patients after PCI, underscoring the potential clinical utility of integrating TCM syndrome differentiation into CVD risk assessment.

Objective To explore the prevalence of depressive symptoms in postoperative patients with ovarian cancer and to analyze its influencing factors from multiple dimensions, including clinical characteristics, psychological factors, and laboratory indicators. Methods A cross-sectional study was conducted, which enrolled 235 postoperative patients with ovarian cancer. Depressive status was assessed using the patient health questionnaire, and the demographic, pathological, and medical record data of the patients were collected using the generalized anxiety disorder scale, Pittsburgh sleep quality index, European organization for research and treatment of cancer quality of life questionnaire core 30, and ECOG performance status score. Peripheral blood tumor marker (CA125), routine blood test, lymphocyte subsets, and serum cytokine levels were measured. Univariate and multivariate binary logistic regression analysis were used for statistical analysis. Results The prevalence of depression in postoperative patients with ovarian cancer was 39.15% (92/235). Univariate analysis showed that ECOG score ≥ 2 points, pain, anxiety, poor sleep quality, low quality of life, low life satisfaction, tumor recurrence, six or more cycles of chemotherapy, as well as higher levels of CA125, NLR, and NAR, and lower hemoglobin levels were significantly associated with depression (all P<0.05). Multivariate binary Logistic regression analysis showed that anxiety (OR=1.975, 95%CI: 1.231-3.170), sleep efficiency (OR=4.181, 95%CI: 1.211-14.43), sleep latency (OR=34.806, 95%CI: 4.258-284.542), ECOG performance status score, cognitive function (OR=0.918, 95%CI: 0.868-0.97), and life satisfaction were independent risk factors for depression (all P<0.05). Laboratory indicators were not independent influencing factors in the multivariate Logistic regression model. Conclusion Depression in postoperative patients with ovarian cancer is influenced by physiological, psychological, and social factors. Clinical management should focus on patients with anxiety, sleep disorders, poor physical condition, and low life satisfaction, and a comprehensive prevention and treatment strategy centered on psychological intervention and taking into account symptom management and social support should be implemented.

Background Long working hours, as a common risk factor for occupational stress, is closely related to the occurrence of depressive symptoms. Understanding how long working hours affect occupational stress and depressive symptoms will inform occupational health interventions. Objective To quantify the impact of long working hours exposure on occupational stress and depressive symptoms among Internet industry employees, translate black-box outputs into actionable insights, and demonstrate the value of interpretable machine learning for early-warning occupational-health surveillance. Methods A dataset was derived from a cross-sectional survey involving 2866 internet industry employees in China. This survey was part of the project Risk Assessment Of Long Working Hour Exposure And Its Adverse Health Effects, conducted by the National Institute for Occupational Health and Poisoning Control, Chinese Center for Disease Control and Prevention, from 2021 to 2023. Working hours, occupational stress and depressive symptoms were quantified with a set of structured questionnaires including the Core Occupational Stress Scale and the Patient Health Questionnaire. Pairwise associations were screened by Mantel tests and variance-inflation factors. Key predictors identified through feature selection were fed into six machine-learning risk-prediction models. Visual interpretation was provided by feature importance, Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), while directed causal effects and intervention impacts of prolonged working hours exposure on occupational stress and depressive symptoms were dissected with causal explanation of features techniques. Results The positive rates of occupational stress and depressive symptoms among internet employees were 12.9% and 77.8% respectively. Twelve core features for occupational stress and nine for depressive symptoms were retained after selection. After these features were supplied to six predictive algorithms and evaluated on five metrics, the Light Gradient Boosting Machine (LGBM) achieved the highest accuracy—0.89 for occupational stress and 0.79 for depressive symptoms on the hold-out test set. The feature-importance rankings converged on fatigue accumulation and life satisfaction as dominant drivers for both outcomes, whereas weekly working hours and daily overtime emerged as the principal exposure-related predictors. The SHAP summary plots revealed that longer weekly hours and daily overtime systematically elevated the probability of occupational stress. The causal feature explanation further quantified that ascending one category in weekly working hours increased the probability of occupational stress by 7.04%. Conclusion Exposure to long working hours is associated with both occupational stress and depressive symptoms among internet industry employees. Interpretable machine-learning frameworks translate these associations into transparent, defensible drivers, enabling precise identification of the pivotal factors and their interplay. This evidence base equips occupational-health practitioners with actionable insights for designing targeted prevention and intervention strategies.

BACKGROUND: Most studies have evaluated disability-adjusted life years (DALYs) of colorectal cancer (CRC) patients based on a set of generic disability weights (DWs). This study aimed to apply local CRC-stage-specific DWs to estimate the burden of DALYs for CRC (CRC-DALYs) in populations in China and consider the influence of local screening coverage of CRC. METHODS: A prevalence-based model was constructed using data from various sources. Years lived with disability (YLDs) were estimated mainly via cumulative prevalence data (based on CRC incidence rates, population numbers, and survival rates), stage-specific proportions of CRC, and DWs of the local population. Years of life lost (YLLs) were calculated based on the CRC mortality rates and standard life expectancies. CRC incidence and mortality rates for the years 2020, 2025, and 2030 were estimated by joinpoint regression, and the corresponding DALYs were predicted. The main assumption was made for CRC screening coverage. Sensitivity analyses were used to assess the impact of population, DWs, and coverage. RESULTS: In 2017, among the Chinese population, the estimated number of CRC-DALYs was 4,303,314 (11.9% for YLDs). If CRC screening coverage rate in China (2.3%) remains unchanged, the overall DALYs in 2030 are predicted to increase by 37.2% (45.1% of those aged ≥65 years). More optimistically, the DALYs would then decrease by 0.7% in 2030 (from 5,902,454 to 5,860,200) if the coverage could be increased to 25.0%. A sensitivity analysis revealed that using local DWs would change the base-case values by 5.7%. CONCLUSIONS The estimated CRC-DALYs in China using population-specific DWs were considerably lower (with a higher percentage of YLDs) than the global burden of disease (GBD) estimates (5,865,004, of 4.6% for YLDs), suggesting the impact extent of applying local parameters. Sustainable scale-up CRC screening needs to be in place to moderate the growth trend of CRC-DALYs in China.
Humans ; Colorectal Neoplasms/diagnosis* ; China/epidemiology* ; Disability-Adjusted Life Years ; Male ; Prevalence ; Female ; Middle Aged ; Aged ; Early Detection of Cancer ; Quality-Adjusted Life Years ; Adult ; Incidence

(±)-Talapyrones A-F (1-6), six pairs of dimeric polyketide enantiomers featuring unusual 6/6/6 and 6/6/6/5 ring systems, were isolated from the fungus Talaromyces adpressus. Their structures were determined by spectroscopic analysis and HR-ESI-MS data, and their absolute configurations were elucidated using a modified Mosher's method and electronic circular dichroism (ECD) calculations. (±)-Talapyrones A-F (1-6) possess a 6/6/6 tricyclic skeleton, presumably formed through a Michael addition reaction between one molecule of α-pyrone derivative and one molecule of C₈ poly-β-keto chain. In addition, compounds 2/3 and 4/5 are two pairs of C-18 epimers, respectively. Putative biosynthetic pathways of 1-6 were discussed.
Polyketides/isolation & purification* ; Talaromyces/chemistry* ; Stereoisomerism ; Molecular Structure ; Circular Dichroism ; Pyrones/chemistry*

Background Occupational stress has emerged as a critical public health concern affecting the physical and mental well-being of workers in the manufacturing sector. However, researchers typically evaluate its core driver—cumulative fatigue—using a crude binary “present/absent” variable, thereby overlooking the high-dimensional complexity and heterogeneity inherent in fatigue characteristics. This oversimplification constrains both the precision and predictive performance of occupational stress risk assessment model. Objective Leveraging a data-driven approach, to survey data on cumulative fatigue among manufacturing employees, and then use this new classification to develop and validate an occupational stress prediction model, with an ultimate aim of enhancing the accuracy and effectiveness of occupational stress assessment. Methods A set of cross-sectional survey data on 3871 manufacturing employees in 2021 were derived from the “Long working hours exposure and its adverse health effect risk assessment” program of the National Institute for Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention. Occupational stress was assessed using the Core Occupational Stress Measurement Scale, while cumulative fatigue was evaluated via the Worker’s Self-Diagnostic Questionnaire for Fatigue Accumulation. Boruta method was applied to screen core variables from 20 occupational stress influencing factors. Dimensionality reduction of cumulative fatigue features was performed using a Bayesian sparse autoencoder (BASE), followed by comparison and application of clustering methods to achieve multi-class classification of the reduced features. Six machine learning classification models were then selected including logistic regression, support vector machine, decision tree, random forest, adaptive boosting, and lightweight gradient boosting machine (LightGBM) to construct and compare two occupational stress prediction models involving cumulative fatigue combined with ten other core variables: one utilizing the original binary cumulative fatigue label as a core variable, and another employing the novel fatigue classification proposed herein as a core variable. Results The positive rate of occupational stress among the manufacturing employees was 38.9%. The Boruta method identified 11 core variables of occupational stress: depressive symptoms, cumulative fatigue, age, length of service, tenure in current position, average weekly working hours, average daily overtime hours, average monthly income, low levels of exercise, life satisfaction, and sleep quality. The BSAE reduced the original cumulative fatigue factors to 12 dimensions, while the K-means clustering grouped the reduced fatigue features into three categories: no fatigue, moderate fatigue, and sever fatigue. Six occupational stress prediction models were constructed using the three-category labels of cumulative fatigue and ten additional factors. The results indicated that the LightGBM model performed best, achieving an area under the curve (AUC) of 0.78, an accuracy of 0.77, and an F1 score of 0.72. Compared with the model incorporating the traditional binary labels for cumulative fatigue, the best prediction AUC (0.72), accuracy (0.66), and F1 score (0.65) improved by 6%, 11%, and 7%, respectively. Conclusion Cumulative fatigue is a significant predictor of occupational stress among manufacturing employees. Applying data dimensionality reduction combined with three-class cluster analysis to characterize cumulative fatigue improves the performance of occupational stress prediction models. From a data-driven perspective, machine learning methods demonstrate strengths in processing complex datasets, capturing nonlinear relationships, and generating predictions based on key variables. This study therefore confirms that refined processing of core factors substantially enhances the predictive capability of machine learning models in assessing occupational stress risk in the manufacturing workforce.

Objective: To establish anin vitromodel of ferroptosis induced by Erastin and RAS-selective lethal 3(RSL3) in hepatoma cells, and to provide theoretical basis for the development of novel therapeutic strategies for HCC. Methods: Hepatoma cells(HCCLM3, HepG2, Hep3B, Huh7 and PLC/PRF/5) in logarithmic growth phase were treated with Erastin(0-40 μmol/L) and RSL3(0-10 μmol/L) at double concentrations respectively. After 24 h, CCK-8 method was used to detect cell viability, draw growth curve, calculate IC50, and HCC cells sensitive to inducers were selected for follow-up experiments. The effect of inducer on the state of hepatoma cells was observed under light microscope, and immunoblotting and flow cytometry were used to verify whether the ferroptotic modelin vitrowas successfully constructed. Results: Huh7, Hep3B and HepG2 cells were sensitive to Erastin and RSL3, but HCCLM3 and PLC/PRF/5 were insensitive to Erastin and RSL3. When the concentration of Erastin and RSL3 reached the maximum, the survival rate was still above 65%. Huh7, Hep3B and HepG2 cells were selected for subsequent experiments. Compared with the control group, the expression of Glutathione peroxidase 4(GPX4), a ferroptotic marker, was down-regulated in a concentration-dependent manner. In Huh7, Hep3B and HepG2 cells, lipid reactive oxygen species(ROS) levels significantly increased after 24 h treatment with 10 μmol/L and 20 μmol/L Erastin, respectively; in Huh7 cells, lipid ROS levels significantly increased after 24 h treatment with 0.5 μmol/L and 1 μmol/L RSL3, respectively; in Hep3B and HepG2 cells, lipid ROS levels significantly increased after 24 h treatment with 1 μmol/L and 2 μmol/L RSL3, respectively, compared with control group. Conclusion Huh7, Hep3B and HepG2 cells are highly sensitive to Erastin and RSL3. Huh7, Hep3B and HepG2 cells treated with 10 μmol/L Erastin for 24 h are good models for simulating ferroptosis induced by Erastinin vitro, Huh7 cells treated with 0.5 μmol/L RSL3 for 24 h and Hep3B and HepG2 cells treated with 1 μmol/L RSL3 for 24 h are good models for simulating ferroptosis induced by RSL3in vitro.

Neuroendocrine carcinoma (NEC) represents a category of malignant tumors originating from neuroendocrine cells. Given that NEC cells exhibit characteristics of both neural and endocrine cells, they can hijack neuronal signaling pathways and dynamically regulate the expression of neuronal lineage markers during tumor metastasis, thereby constructing a microenvironment conducive to tumor growth and metastasis. Conversely, alterations in the tumor microenvironment can enhance the interactions between neurons and tumor cells, ultimately synergistically promoting the metastasis of NEC. This review highlights recent advancements in the field of cancer neuroscience, uncovering neuronal lineage markers in NEC that facilitate tumor dissemination through mediating crosstalk, bidirectional communication, and synergistic interactions between tumor cells and the nervous system. Consequently, the latest findings in tumor neuroscience have enriched our understanding of the biological mechanisms underlying tumor metastasis, opening new research avenues for a deeper comprehension of the complex biological processes involved in tumor metastasis, particularly brain metastasis. This review provides a comprehensive review of the crosstalk between tumor cells and neural signaling in the metastasis of NEC.
.
Humans ; Carcinoma, Neuroendocrine/metabolism* ; Signal Transduction ; Animals ; Neoplasm Metastasis ; Neurons/pathology* ; Tumor Microenvironment ; Cell Communication