Cardiometabolic disease is one of the leading causes of morbidity and mortality in adults. In recent years, the prevalence of cardiometabolic disease has increased year by year, becoming a major problem in the global disease burden. The occurrence and development of cardiometabolic risk are affected by many factors. Shift work as an occupational hazard has been widely concerned. Nurses are a typical population for shift work, but few studies have looked at the association between their shift work and cardiometabolic risk. This article reviewed the current situation of cardiometabolic risk and the research progress on the association between shift work and cardiometabolic risk among nurses. The results showed that cardiometabolic risk is prevalent in the nurse population. Shift work is associated with cardiometabolic risks such as metabolic syndrome, cardiovascular disease, diabetes, and dyslipidemia, and is mediated by circadian rhythm disorder, hormone secretion disorder, and stress response. However, the causal relationship between shift work and cardiometabolic risk is still unclear, and further prospective cohort studies should be conducted to improve the understanding of the impact of shift work on cardiometabolic risk in order to improve the cardiometabolic health status of nurses.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Hypercoagulable state (HCS) after kidney transplantation is one of the common and serious complications in kidney transplant recipients, which has attracted increasing attention in recent years. HCS refers to the abnormal and excessive activation of blood coagulation function, leading to the increased risk of thrombosis. After kidney transplantation, the combined effects of hemodynamic changes, surgical trauma and severe rejection increase the incidence of HCS, not only raising the risk of thrombosis but also potentially causing graft failure and affecting the postoperative survival rate of patients. This article reviews the influencing factors, clinical manifestations, diagnostic methods and preventive strategies of HCS after kidney transplantation, aiming to provide a theoretical basis for optimizing perioperative management and improving the prognosis of patients.

ObjectiveTo investigate the effect of Danggui Shaoyaosan on ferroptosis in the rat model of non-alcoholic fatty liver disease （NAFLD） and explore the underlying mechanism based on the nuclear factor E₂-related factor 2 （Nrf2）/solute carrier family 7 member 11 （SLC7A11）/glutathione peroxidase 4 （GPX4） signaling pathway. MethodsThe sixty SD rats were randomly grouped as follows： control， model， Yishanfu （0.144 g·kg^-1）， and low-， medium-， and high-dose （2.44， 4.88， and 9.76 g·kg^-1， respectively） Danggui Shaoyaosan. A high-fat diet was used to establish the rat model of NAFLD. After 12 weeks of modeling， rats were treated with corresponding agents for 4 weeks. Then， the body weight and liver weight were measured， and the liver index was calculated. At the same time， serum and liver samples were collected. The levels or activities of total cholesterol （TC）， triglycerides （TG）， alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， and Fe²⁺ in the serum and TC， TG， free fatty acids （FFA）， malondialdehyde （MDA）， superoxide dismutase （SOD）， glutathione peroxidase （GPX）， and Fe²⁺ in the liver were measured. Hematoxylin-eosin staining and oil red O staining were employed to observe the pathological changes in the liver. Immunofluorescence was used to assess the reactive oxygen species （ROS） content in the liver. Mitochondrial morphology was observed by transmission electron microscopy. The protein levels of Nrf2， SLC7A11， GPX4， transferrin receptor 1 （TFR1）， and divalent metal transporter 1 （DMT1） in the liver were determined by Western blot. ResultsCompared with the control group， the model group showed increases in the body weight， liver weight， liver index， levels or activities of TC， TG， ALT， AST， and Fe²⁺ in the serum， levels of TC， TG， FFA， MDA， Fe²⁺， and ROS in the liver， and protein levels of TFR1 and DMT1 in the liver （P<0.01）， and decreases in the activities of SOD， GPX and the protein levels of Nrf2， SLC7A11， and GPX4 in the liver （P<0.05， P<0.01）. Meanwhile， the liver tissue in the model group presented steatosis， iron deposition， mitochondrial shrinkage， and blurred or swollen mitochondrial cristae. Compared with the model group， all doses of Danggui Shaoyaosan reduced the body weight， liver weight， liver index， levels or activities of TC， TG， ALT， AST， and Fe²⁺ in the serum， levels of TC， TG， FFA， MDA， Fe²⁺， and ROS in the liver， and protein levels of TFR1 and DMT1 in the liver （P<0.01）， while increasing the activities of SOD and GPX and the protein levels of Nrf2， SLC7A11， and GPX4 in the liver （P<0.01）. Furthermore， Danggui Shaoyaosan alleviated steatosis， iron deposition， and mitochondrial damage in the liver. ConclusionDanggui Shaoyaosan may inhibit lipid peroxidation and ferroptosis by activating the Nrf2/SLC7A11/GPX4 signaling pathway to treat NAFLD.

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 investigate the effects of joint function screening and correction on intervention efficacy of prevention and assessment score of training injuries in new recruits.Methods A randomized controlled study was conducted on 265 new recruits subjected from two organizational units of an army unit with cluster sampling.Based on entire organizational unit,the participants were randomly divided into a control group(n=132)and an experimental group(n=133).The experimental group received joint function screening and corrective movement training,which was subsequently applied in the new recruit training,while the control group underwent training according to conventional methods.Joint function were collected before and after training.The demographic data,assessment score of training,and incidence of training injuries were collected through the participant's own organizational unit.Receiver operating characteristic(ROC)curve was plotted to evaluate the efficacy of joint function screening in predicting training injuries,and binary logistic regression and general linear regression analyses were applied to verify the correlation of joint function screening score with training injuries and assessment score of training.Results After new training,the score of joint function screening was significantly higher in the experimental group than the control group(16.62±1.87 vs 14.92±2.58,P<0.001).And the score was obviously increased in the experimental group(16.62±1.87 vs 12.82±1.98,P<0.001)and the control group(14.92±2.58 vs 12.95±1.81,P<0.001)when compared with the corresponding score before training.The area under the ROC curve(AUC)of joint function screening in predicting training injuries was 0.762(95%CI:0.694～0.830),indicating good predictive efficacy.During the new training process,the incidence of training injuries in the experimental group(13.53%)was significantly lower than that in the control group(24.24%,Chi-square=4.963,P=0.026).Binary logistic regression analysis showed that the pre-training assessment score of joint function screening was an important influencing factor for training injuries in new recruits(OR=0.552,95%CI:0.413～0.660,P<0.001).The experimental group obtained notably higher mean assessment score than the control group[733.00(716.00,752.75)vs 728.79(710.46,744.28),P=0.027].Linear regression analysis revealed a correlation between post-training score of joint function screening and the assessment score of newly trained personnel(P<0.001).Conclusion Joint function screening and correction for newly trained personnel can effectively prevent training-related injuries during the new training period,and correcting joint function through training can effectively improve the assessment score of newly trained personnel.

Objective To explore the prevention effectiveness of a mode,conducting targeted corrective training based on motor dysfunction detected by military joint function screening,on military training injuries in new recruits during recruit basic training in order to cope with the high incidence of military training injuries among them.Method A military personnel joint function screening was conducted on the new recruits in a training base of Joint Logistics Support Force.Based on the results of screening,the new recruits with a single action score of 1 and a total score of<10 were subjected and served as corrective training participants.Through cluster sampling,the new recruits were randomly divided into an experimental group(n=223)and a control group(n=223).The control group were trained according to the regular training plan,while the experimental group completed their designated training tasks and a 2-month corrective training for motor dysfunction at the same time.The entire corrective training process was carried out by our key personnel who had received relevant training.Medical records of the medical security department of the experimental unit were collected and evaluated for the injury situation.The score of joint function screening was analyzed using independent sample t test.The incidence of training injuries was analyzed using Chi-square test or Fisher's exact test.Result After corrective training,the score of joint function was higher in the experimental group than the score before training(15.12±2.13 vs 10.58±2.83),and the score was also higher than that of the control group(15.12±2.13 vs 14.19±1.97,P<0.05).During the recruit basic training period,the incidence of training injuries was notably lower in the experimental group than the control group(12.5％vs 34.5％,Chi-square=5.469,P=0.001).Only for those who were injured during the training,11 people(39.2％)in the experimental group scored less than 10,which was obviously lower than the 49 people(63.6％)in the control group(Chi-square=4.972,P=0.026).Conclusion Our mode,corrective training based on the results of military personnel joint function screening,can effectively reduce the incidence of military training injuries in new recruits during recruit basic training,and exerts a good preventive effect against military training injuries.

Objective To identify recruit movement dysfunction based on military joint function screening and assessment,implement targeted corrective training,explore the impact of this assessment-correction system on knowledge-attitude-belief-practice(KABP)related factors,and scientifically evaluate its efficacy in preventing recruit military training injuries within the knowledge-attitude-belief-practice theoretical framework.Methods A cluster randomized controlled trial was conducted at a recruit training base of the Joint Logistics Support Force from March to May 2025,enrolling 446 recruits.Participants were randomly assigned to an experimental group(n=223)or control group(n=223)using a random number table.The control group followed the routine training program,while the experimental group additionally received a 2-month targeted corrective training for movement dysfunction alongside the established training tasks.Knowledge-attitude-belief-practice questionnaires were administered to both groups at the initial,intermediate,and advanced stages of the corrective training.Univariate logistic regression was used to preliminarily screen KABP-related factors,and a multivariate logistic regression model was further constructed to analyze the role of KABP factors in the corrective training.Results The experimental group had a cumulative training injury incidence of 40 cases(17.9%),which was significantly lower than that of the control group(83 cases,37.2%;χ2=20.757,P<0.001).The experimental group showed varying degrees of improvement in knowledge,attitude and belief,and practice dimensions(P<0.05),while the control group exhibited no significant changes in the three KABP dimensions across the three surveys.Logistic regression analysis revealed:In the first round,total practice score was significantly negatively associated with training injury incidence rate(OR=0.863,95%CI:0.822～0.906,P<0.001),whereas knowledge and attitude-belief dimensions showed no significant association;In the second round,both total knowledge score(OR=0.925,95%CI:0.903～0.946,P<0.001)and total practice score(OR=0.906,95%CI:0.874～0.940,P<0.001)significantly reduced the risk of military training injuries,with attitude-belief dimension still showing no significant effect;In the third round,all three KABP dimensions were significantly negatively associated with military training injury incidence rate(knowledge:OR=0.905,95%CI:0.884～0.926,P<0.001;attitude and belief:OR=0.942,95%CI:0.899～0.988,P=0.013;behavior:OR=0.882,95%CI:0.841～0.924,P<0.001).Conclusion Joint function screening and corrective training can significantly reduce the incidence of recruit training injuries,primarily by optimizing knowledge mastery and movement behavior;belief cultivation,however,requires long-term practical accumulation.

Integrated metabolomic and lipidomic profiling,utilizing liquid chromatography coupled with high-resolution mass spectrometry(LC-HRMS),has emerged as a pivotal strategy for biomarker discovery.However,the inherent polarity disparity between metabolites and lipids complicates simultaneous analysis.To address this,a dual-stationary phase polarity-extended liquid chromatography(PELC)system was developed,which surpassed conventional one-dimensional LC(1D-LC)by enabling comprehensive coverage of both polar and non-polar compounds within a single injection.This system enhanced chromatographic resolution,peak capacity,and throughput while minimizing analytical variability.Extracellular vesicles(EVs),lipid bilayer-enclosed nanoparticles ubiquitously present in biofluids,had gained prominence as reservoirs of cancer biomarkers due to their cargo stability and pathophysiological relevance.Herein,the application of PELC-HRMS for concurrent metabolome-lipidome profiling in EVs was pioneered.A total of 193 metabolites were identified using this technique coupled with MS-DIAL software and Human Metabolome Database.Subsequently,this technique was employed to explore potential biomarkers for prostate cancer(PCa).Multivariate analysis identified 17 differentially abundant metabolites in PCa,implicating dysregulated pathways including purine metabolism,starch and sucrose metabolism,galactose metabolism,cysteine and methionine metabolism,and biosynthesis of unsaturated fatty acids.Notably,creatine(AUC=0.92)and DG 42:5(AUC=0.80)demonstrated robust diagnostic efficacy,attributable to their broad polarity ranges and EV-specific enrichment.This study established PELC as a high-fidelity platform for multi-omics integration in complex biospecimens,advancing mechanistic insights into metabolic rewiring and disease pathophysiology.

Ketamine is a dissociative anesthetic.It is clinically used as a surgical anesthetic or anes-thetic inducer and has a certain degree of mental dependence.Its abuse can lead to nerve damage,ad-verse emotional reactions and other toxic side effects.The primary mechanism by which ketamine exerts its pharmacological effects is to block N-methyl-D-aspartate receptors(NMDAR).It also functions through pathways such as α-amino-3-hydroxy-5-methyl-4-isox-azolepropionic acid receptors(AMPAR),opioid receptors,γ-aminobutyric acid(GABA)receptors,monoaminergic receptors,cholinergic recep-tors,hyperpolarization-activated cyclic nucleotide-gated(HCN)channels,voltage-gated sodium channels,and L-type voltage-dependent calcium channels(VDCC).This article summarizes the molecular mecha-nism and toxic effects of ketamine's pharmacological functions,in order to provide a basis for foren-sic applications such as the identification of symptomatic phenotypes of ketamine toxic effects and the identification of ketamine abuse.