Objective To explore the impact of number of positive regional lymph nodes (nPRLN) in N1 stage on the prognosis of non-small cell lung cancer (NSCLC) patients. Methods Patients with TxN1M0 stage NSCLC who underwent lobectomy and mediastinal lymph node dissection from 2010 to 2015 were screened from SEER database (17 Regs, 2022nov sub). The optimal cutoff value of nPRLN was determined using X-tile software, and patients were divided into 2 groups according to the cutoff value: a nPRLN≤optimal cutoff group and a nPRLN>optimal cutoff group. The influence of confounding factors was minimized by propensity score matching (PSM) at a ratio of 1 : 1. Kaplan-Meier curves and Cox proportional hazards models were used to evaluate overall survival (OS) and lung cancer-specific survival (LCSS) of patients. Results A total of 1316 patients with TxN1M0 stage NSCLC were included, including 662 males and 654 females, with a median age of 67 (60, 73) years. The optimal cutoff value of nPRLN was 3, with 1165 patients in the nPRLN≤3 group and 151 patients in the nPRLN>3 group. After PSM, there were 138 patients in each group. Regardless of before or after PSM, OS and LCSS of patients in the nPRLN≤3 group were superior to those in the nPRLN>3 group (P<0.001). N1 stage nPRLN>3 was an independent prognostic risk factor for OS [HR=1.52, 95%CI (1.22, 1.89), P<0.001] and LCSS [HR=1.72, 95%CI (1.36, 2.18), P<0.001]. Conclusion N1 stage nPRLN>3 is an independent prognostic risk factor for NSCLC patients in TxN1M0 stage, which may provide new evidence for future revision of TNM staging N1 stage subclassification.

Objective: The cross sectional study aimed to identify predominant co-occurrence patterns among six common mental health issues in college students, so as to provide empirical basis for designing targeted interventions. Methods: From October 2024, a total of 9 837 students from 4 universities in Xiangtan City, Hunan Province, participated in the current study by multistage random cluster sampling method. Participants completed self report measures, including the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder 7 item Scale (GAD-7), Young s Internet Addiction Diagnostic Questionnaire, the Adolescent Insomnia Symptom Self rating Scale, the Ottawa Self injury Inventory, and the Brief Community Assessment of Psychic Experiences Questionnaire. Demographic and co-occurrence characteristics were first compared using Chi square or trend Chi-square tests, followed by application of the Apriori algorithm to mine association rules for primary co-occurrence patterns. Results: The detection rate of co-occuring the common mental health issues was 46.44%. The detection rate was significantly higher in female than in male students (50.42%, 43.61%; χ 2=44.46) and in students from rural versus urban areas (47.22%, 44.60%; χ 2=5.67) (both P <0.05). Significant differences were observed among freshmen, sophomores, juniors, and seniors (46.63%, 48.35%, 45.05% , 43.66%, respectively; χ 2=9.22, P <0.05), although no statistically significant trend was detected ( χ 2 trend =3.75, P = 0.05 ). Association rule mining identified “anxiety + depression” “anxiety + psychotic experiences + depression” and “anxiety + sleep disorder + depression” as the combinations with the highest support. In addition, “anxiety+depression+Internet addiction+psychotic experiences =>sleep disorder (>= refered to the occurrence of the latter item under the condition that the former item occurs)” and “anxiety + depression+Internet addiction=>sleep disorder” were combinations with relatively high confidence. Conclusions Co-occurrence of these mental health issues among college students is high and exhibits diverse patterns. Strategies to address this burden should prioritize integrated interventions that target these specific combinations of factors.

Primary cilia—those solitary, microtubule-based projections extending from the surface of most eukaryotic cells—are increasingly recognized not merely as cellular appendages, but as sophisticated signaling hubs. By compartmentalizing specific receptors (e.g., GPCRs) and effectors within a microdomain guarded by the transition zone, these organelles function effectively as high-gain sensors capable of integrating mechanical stimuli with metabolic cues. In this review, we examine the pivotal role of primary cilia across the nervous, bone-vascular, and renal landscapes, arguing for a unified “mechano-metabolic coupling” framework. Here, conserved ciliary modules are not static; rather, they are differentially deployed to uphold systemic homeostasis. Within the central nervous system, we position primary cilia as upstream integrators. We highlight how hypothalamic neuronal cilia concentrate metabolic receptors, such as the melanocortin 4 receptor (MC4R), to interpret energy status. Moreover, the recent identification of serotonergic “axon-cilium synapses” points to a direct mode of neurotransmission, wherein 5-HT6 receptors drive nuclear signaling and chromatin accessibility to rapidly modulate gene expression. Through these mechanisms, central cilia modulate sympathetic tone and neuroendocrine output, effectively establishing the mechanical and metabolic “boundary conditions” under which peripheral organs operate. Dysfunction in these central hubs is linked to obesity and neurodevelopmental disorders, including Bardet-Biedl syndrome. In peripheral tissues, cilia serve as versatile mechanotransducers that convert physical forces into biochemical responses. Regarding the bone-vascular system, we discuss the translation of mechanical loads and fluid shear stress into structural remodeling. In osteoblasts, specifically, ciliary integrity is intrinsically linked to cholesterol and glucose metabolism, fine-tuning the balance between Hedgehog and Wnt/β-catenin signaling to govern osteogenesis and bone repair. A similar dynamic exists in the vasculature, where endothelial cilia sense shear stress to modulate KLF4 expression and endothelial-to-mesenchymal transition—processes critical for valvulogenesis and vascular remodeling. Meanwhile, in the kidney, tubular cilia act as terminal effectors within a “shear-cilia-metabolism” axis. Here, fluid shear stress engages ciliary signaling to trigger AMPK-mediated lipophagy and mitochondrial biogenesis, thereby securing the ATP supply required for solute transport. Notably, dysregulation of this axis leads to metabolic reprogramming and aberrant proliferation, acting as a hallmark driver of cystogenesis in polycystic kidney disease (PKD). Crucially, this review attempts to dissect the often-conflated logic of cross-system integration by distinguishing 3 non-equivalent pathways: direct communication via ciliary extracellular vesicles, though this remains largely hypothetical in long-range signaling; “physiology-mediated cascades”, where ciliary dysfunction in a single organ—such as the kidney—precipitates systemic pathology through hemodynamic and metabolic shifts (e.g., altered blood pressure, fluid volume, or uremic toxins); and “parallel molecular defects”, where shared genetic mutations in ubiquitous components like the IFT machinery cause simultaneous, independent failures across multiple organ systems. Building on these distinctions, we propose a nested-loop model that links central set-points with peripheral feedback via physiological variables. Furthermore, we construct a “causality-to-translation” roadmap that pinpoints structural repair (e.g., targeting IFT assembly) and metabolic rescue (e.g., AMPK activation or autophagy induction) as promising therapeutic avenues. Ultimately, this framework provides a theoretical basis for deciphering the shared pathological mechanisms of multisystem ciliopathies, offering a strategic guide for the development of targeted interventions that go beyond symptomatic treatment.

Objective To establish an individualized nomogram model and evaluate its efficacy to provide a possible evaluation basis for the prognosis of lower third and abdominal part of oesophageal adenocarcinoma (EAC). Methods Lower third and abdominal part of EAC patients from 2010 to 2015 were chosen from the SEER Research Plus Database (17 Regs, 2022nov sub). The patients were randomly allocated to the training cohort and the internal validation cohort with a ratio of 7∶3 using bootstrap resampling. The Cox proportional hazards regression analysis was used to determine significant contributors to overall survival (OS) in EAC patients, which would be elected to construct the nomogram prediction model. C-index, calibration curve and receiver operating characteristic (ROC) curve were performed to evaluate its efficacy. Finally, the efficacy to evaluate the OS of EAC patients was compared between the nomogram prediction model and TNM staging system. Results In total, 3945 patients with lower third and abdominal part of EAC were enrolled, including 3475 males and 470 females with a median age of 65 (57-72) years. The 2761 patients were allocated to the training cohort and the remaining 1184 patients to the internal validation cohort. In the training and the internal validation cohorts, the C-index of the nomogram model was 0.705 and 0.713, respectively. Meanwhile, the calibration curve also suggested that the nomogram model had a strong capability of predicting 1-, 3-, and 5-year OS rates of EAC patients. The nomogram also had a higher efficacy than the TNM staging system in predicting 1-, 3-, and 5-year OS rates of EAC patients. Conclusion This nomogram prediction model has a high efficiency for predicting OS in the patients with lower third and abdominal part of EAC, which is higher than that of the current TNM staging system.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

The minor purchasing process and mode of some hospital were introduced,and the implementation of the hospital's minor purchasing projects in the past year was analyzed.The causes for high failure rate of purchasing were pointed out including long interval between project creation and procurement,unreasonable demand presentation,insufficient demand demonstration and lack of active participation of suppliers.Some suggestions were put forward such as timely adjustment of demands,strengthening of demand demonstration,improvement of supplier motivation and enhancement of procurement process management,which were of great significance for increasing the success rate of minor purchasing of the hospital.[Chinese Medical Equipment Journal,2025,46(8):91-95]

Objective To explore and identify the nutritional/inflammatory indicator with the highest predictive potential for overall survival(OS)in postoperative tumor patients so as to provide guidance for postoperative rehabilitation of tumor patients.Methods Data from 3 191 surgical patients were collected,including 15 nutritional/inflammatory indicators.The maximum selection rank statistic method was used to calculate the optimal cut-off values for continuous indicators.The Kaplan-Meier method was used to assess OS,and Cox proportional hazards models were used to analyze the association between the aforementioned 15 indicators and survival.The predictive value of these 15 indicators was evaluated with receiver operating characteristic(ROC)curves and C-index.Results Multivariate analysis showed that all 15 indicators were significantly associated with poorer OS in surgical patients(P＜0.05 for all).Time-dependent area under the curve(AUC)and C-index analysis indicated that 3 indicators with the highest predictive potential in OS in postoperative tumor patients were the nutritional risk index(NRI)(C-index:0.597),C-reactive protein-to-albumin ratio(CAR)(C-index:0.587),and C-reactive protein-to-lymphocyte ratio(CLR)(C-index:0.587).The optimal cut-off value for NRI was determined to be 104.31(i.e.,NRI＜104.31 suggests malnutrition)with the maximum selection rank statistic method,the optimal cut-off value for CAR to be 0.05(i.e.,CAR≥0.05 suggests a strong inflammatory response,often accompanied by malnutrition),and the optimal cut-off value for CLR to be 1.18(i.e.,CLR≥1.18 suggests a strong inflammatory response).Subgroup analysis indicated that NRI,CAR,and CLR had good correlation with tumor staging,and there were significant differences between tumor node metastasis(TNM)Ⅲ/Ⅳ stage patients and TNM Ⅰ/Ⅱ stage patients when there was a strong inflammatory response or malnutrition.Conclusion In postoperative tumor patients,NRI,CLR,and CAR have high prognostic value.Combining these with the patient's clinical stage,it enables more precise guidance for clinical diagnosis and treatment strategies.

Objective:To explore the structural equation model to explore the levels of work-related musculoskeletal disorders (WMSDs) and various risk factors in the feet and ankle of China's occupational population, providing scientific basis for for preventing WMSDs in feet and ankles.Methods:Data of 73497 national occupational epidemiological cases were selected from June 2018 to December 2023 used the Chinese version of the Electronic Questionnaire on Musculoskeletal Disorders. The adverse ergonomic factors and their source classification standard and confirmatory factor analysis were used to investigate foot and ankle WMSDs and their related risk factors (including individual factors, work organization, work posture, work type, fatigue, etc.) in key occupational groups in China, and structural equation model hypothesis, fitting, verification, and path and intermediary effect analysis were carried out. The model fit evaluation indexes included Chi-square specific degrees of freedom ( χ2/ df), gauge fit index (NFI), Tucker Lewis index (TLI), goodness of Fit index (GFI), adjusted Goodness of Fit index (AGFI) and approximate root mean square error (RMSEA) . Results:A total of 73497 occupational workers were surveyed, with local muscle fatigue and WMSDs incidence rates in the feet and ankles being 17.17% and 12.06%, respectively. The fitting index of the adjusted structural equation model basically meets the standard (GFI=1, AGFI=1, RMESA=0.042, NFI=0.716, TLI=0.663). The top three factors affecting feet and ankle WMSDs are feet and ankle muscle fatigue, work type, and work organization, with standardized path coefficients of 0.221, 0.105, and 0.095, respectively. The top two factors affecting feet and ankle muscle fatigue are work organization and work type, with standardized path coefficients of 0.548 and 0.383, respectively. Feet and ankle muscle fatigue, work type, work organization, and work posture have a direct effect on feet and ankle WMSDs, with effect values of 0.221, 0.105, 0.095, and 0.077, respectively. The organization and type of work can also have indirect effects through feet and ankle muscle fatigue, with effect values of 0.121 and 0.084, respectively.Conclusion:Feet and ankle muscle fatigue has a direct impact on WMSDs, and plays a mediating role between ankle and ankle WMSDs caused by work organization and work type. Feet and ankle muscle fatigue is an important pathway leading to feet and ankle WMSDs. It is recommended that employers and managers detect job fatigue early and take corresponding prevention and intervention measures, which can play a key role in preventing feet and ankle WMSDs.

Medical ultrasound technology is widely used for diagnosis and therapy in clinical practice.Ultrasound probes,which are directly contact with patients,pose a potential risk of pathogen transmission.This expert consen-sus was developed by a multidisciplinary team based on international guidelines,standards in China,and the results of a national survey,aiming to reduce the risk of healthcare-associated infection through standardizing reprocessing of medical ultrasound probes,and formulating consensus recommendations with the Delphi method.The consensus clarifies the reprocessing principles for three types of ultrasound probes of different infection risks:external-use ul-trasound probes,interventional percutaneous ultrasound probes,and internal-use ultrasound probes,puts forward systematic suggestions on the reprocessing standards and disinfection levels of ultrasound probe isolation covers and coupling agents,the reprocessing procedures and methods of ultrasound probes,as well as architectural layout and management of reprocessing,so as to provide a scientific prevention and control framework for ensuring ultrasound diagnosis and therapy safety.

Objective To construct 5 machine-learning models and compare their performance in predicting the associations between pre-pregnancy socio-psycho-behavioral exposures of both spouses and preconception outcomes.Methods Based on Chongqing Preconception Reproductive Health and Birth Outcome Cohort of volunteers recruited from Chongqing Health Center for Women and Children during January 2019 and March 2022,5 447 couples were recruited and surveyed through interviewer-interview for the demographic and social-psychological-behavioral data of both spouses(221 variables).According to the inclusion and exclusion criteria,4 097 couples were finally included,and randomly assigned into a training set(n=2 867 spouses)and a validation set(n=1 230 spouses)at a ratio of 7∶3.Feature analysis and collinear screening were applied to select the potential exposure factors.In consideration of difficulty to carry out semen parameters analysis in primary healthcare institutions,feature Set 1 including sperm parameters and feature Set 2 excluding semen parameters were constructed by including or excluding sperm quality simultaneously in the training set and the validation set.Five algorithms,that is,Logistic Regression,Naive Bayes,Random Forest,Gradient Boosting Machine,and Support Vector Machine,were used to construct preconception outcome prediction models,and the parameters of each model were optimized using random search combined with grid search.The predictive performance of each model was compared using precision,recall,F1 score,area under the receiver operating characteristic curve(AUC),and calibration curve.The optimal model was then selected by comparing the changes in the predictive ability of the questionnaire data for fertility outcomes with or without semen parameters.Results There were 24 variables screened out in feature Set 1,and 16 variables in feature Set 2.In feature Set 1,the gradient boosting machine performed better,with a relatively higher AUC value(0.651)and better F1 score(0.61).The logistic regression model performed stably(AUC value=0.647)and was suitable as the reference model.The random forest(AUC value=0.641),Naive Bayes(AUC value=0.641),and support vector machine(AUC value=0.634)performed second-best.By utilizing the gradient boosting machine,comparable results were found between the predictions from feature sets with or without semen parameters,as in feature Set 1,the AUC value of its validation set was 0.651(95%CI:0.629～0.681),the prediction accuracy was 0.63,the recall rate was 0.65,and the average precision value F1 was 0.61;and in feature Set 2,the AUC value of its validation set was 0.649(95%CI:0.624～0.663),and both the calibration curves were close to the ideal curve.The prediction results indicated that in feature Set 1,the features highly negatively correlated with preconception outcomes were female age,male age,and no pregnancy within 1 year without contraception,while the features highly positively correlated with preconception outcomes were female pregnancy history,total sperm vitality,and use of contraceptive measures before enrollment.Conclusion Among the 5 machine-learning algorithms performed in this cohort data,the gradient boosting machine shows slightly better performance.There are 24 factors being associated with preconception outcomes in both spouses,and the performance of the simplified model excluding semen parameters is not significantly declined.It is feasible to use machine-learning methods to predict human preconception outcomes through social-psychological-behavioral questionnaires.