OBJECTIVE To investigate the effects of subanesthetic dose of esketamine on postoperative anxiety and recovery in patients undergoing laparoscopic cholecystectomy. METHODS A total of 200 patients scheduled for laparoscopic cholecystectomy at Suzhou Ninth Hospital Affiliated to Soochow University from January 2023 to December 2024 were randomly assigned to control group （n＝100） and observation group （n＝100）. One minute before the initiation of anesthesia， patients in the control group received intravenous injections of Propofol emulsion injection， Sufentanil citrate injection， and Succinylcholine chloride injection. On this basis， patients in the observation group received an intravenous injection of Esketamine hydrochloride injection. The anxiety status of patients in both groups was compared， along with their general intraoperative conditions （including sufentanil dosage， duration of pneumoperitoneum， operative time， anesthesia time， and extubation time）， postoperative recovery， incidence of adverse reactions， and the need for dezocine rescue analgesia. Heart rate and mean arterial pressure， entropy index （state entropy and response entropy）， inflammatory marker levels [interleukin-6 （IL-6） and C-reactive protein （CRP）]， numerical rating scale （NRS） for pain intensity were compared between the two groups at different time points. RESULTS No significant differences were found between the two groups in pneumoperitoneum duration， operative time， anesthesia time，extubation time， incidence of postoperative dry mouth， entropy index or length of stay in the post-anesthesia care unit （P＞0.05）. Compared with the control group， the observation group showed significantly lower postoperative STAI-S scores， reduced intraoperative sufentanil consumption， decreased incidence of postoperative nausea， vomiting， and shivering， the need for dezocine rescue analgesia， as well as lower plasma IL-6 and CRP levels at 24 h after surgery， and NRS （P＜0.05）. The heart rate and mean arterial pressure of patients in the observation group at the start of surgery， end of surgery， and during extubation were all significantly higher than those in the control group （P＜0.05）. CONCLUSIONS Subanesthetic dose of esketamine can effectively alleviate postoperative anxiety， reduce intraoperative opioid consumption， suppress postoperative inflammatory response， relieve postoperative pain， and promote recovery in patients undergoing laparoscopic cholecystectomy.

Myasthenia gravis is an autoimmune disorder characterized by impaired neuromuscular transmission. Thymectomy is one of the therapeutic options for acetylcholine receptor antibody-positive myasthenia gravis patients. The quality of perioperative care is directly associated with surgical safety and patient outcomes. However, there is currently a lack of specialized nursing consensus or guidelines specifically addressing the care of these patients domestically or internationally. To promote the standardization and normalization of perioperative nursing care for myasthenia gravis patients undergoing thymectomy and to ensure treatment efficacy, a panel of 57 experts from relevant fields was convened. Based on evidence-based medicine and clinical practice experience, discussions were held on various aspects including condition assessment, nutritional support, medication management, and airway care, resulting in a consensus with 18 final recommendations by using the Delphi method through two rounds of expert consultation. This consensus aims to provide a scientific reference for the perioperative nursing care of myasthenia gravis patients undergoing thymectomy.

ObjectiveTo systematically review the modeling methods and analyzes the model alignment with clinical features of primary dysmenorrhea （PD） in both traditional Chinese medicine （TCM） and western medicine， providing theoretical and practical guidance for establishing the animal models of PD that better reflect the diagnostic and therapeutic characteristics of both TCM and western medicine. MethodsThe literature on PD animal models was searched against domestic and international databases such as PubMed， CNKI， and Wanfang Data. According to the diagnostic criteria of TCM and western medicine， the modeling methods in the literature were summarized， evaluated for strengths and weaknesses， and systematically assessed for clinical concordance rates to identify suitable reference models. ResultsThe available animal models of PD showed the average clinical concordance rates of 43.64% and 61.27% with the clinical features in TCM and western medicine， respectively. Commonly used modeling methods included estrogen administration， physical stimulation， and surgical intervention， with the estrogen combined with oxytocin model and the ice-water bath model being the most studied. The model of Qi stagnation and blood stasis syndrome that was established with the comprehensive stimulation method demonstrated the highest clinical concordance rate. ConclusionCurrent PD animal models primarily replicate dysmenorrhea and simulate menstruation， but they differ from human menstruation to some extent and cannot fully reflect the pathogenesis and physiological characteristics of PD. Moreover， except the cold coagulation and dampness stagnation syndrome and Qi stagnation and blood stasis syndrome， no animal models for other TCM syndromes have been reported， which limits comprehensive TCM research on this disease to a certain extent.

ObjectiveTo systematically review the modeling methods and analyzes the model alignment with clinical features of primary dysmenorrhea （PD） in both traditional Chinese medicine （TCM） and western medicine， providing theoretical and practical guidance for establishing the animal models of PD that better reflect the diagnostic and therapeutic characteristics of both TCM and western medicine. MethodsThe literature on PD animal models was searched against domestic and international databases such as PubMed， CNKI， and Wanfang Data. According to the diagnostic criteria of TCM and western medicine， the modeling methods in the literature were summarized， evaluated for strengths and weaknesses， and systematically assessed for clinical concordance rates to identify suitable reference models. ResultsThe available animal models of PD showed the average clinical concordance rates of 43.64% and 61.27% with the clinical features in TCM and western medicine， respectively. Commonly used modeling methods included estrogen administration， physical stimulation， and surgical intervention， with the estrogen combined with oxytocin model and the ice-water bath model being the most studied. The model of Qi stagnation and blood stasis syndrome that was established with the comprehensive stimulation method demonstrated the highest clinical concordance rate. ConclusionCurrent PD animal models primarily replicate dysmenorrhea and simulate menstruation， but they differ from human menstruation to some extent and cannot fully reflect the pathogenesis and physiological characteristics of PD. Moreover， except the cold coagulation and dampness stagnation syndrome and Qi stagnation and blood stasis syndrome， no animal models for other TCM syndromes have been reported， which limits comprehensive TCM research on this disease to a certain extent.

BackgroundNon-suicidal self-injury （NSSI） behavior among adolescents has become a global public health concern. Anxiety and depression are considered key factors influencing NSSI behavior， while social support may play a protective role in alleviating emotional and behavioral issues. However， existing research has primarily focused on the direct impact of individual factors on NSSI behavior， with insufficient exploration of the combined effects of anxiety， depression and social support. ObjectiveTo investigate the direct effect of anxiety on NSSI， the mediating role of depression and the moderating role of social support in relationship between anxiety and NSSI behavior， thus to provide references for the prevention and intervention of NSSI behavior among adolescents. MethodsIn February 2022， a total of 40 820 students in grades 7 to 12 across 10 middle schools in a district of Chengdu were selected as participants， and they were assessed using Generalized Anxiety Disorder Scale-7 item （GAD-7）， Patient's Health Questionnaire Depression Scale-9 item （PHQ-9）， Social Support Scale for Urban Students （SSSUS） and Adolescent Self-Harm Scale （ASHS）. Pearson correlation analysis was conducted to examine the correlations between scale scores among adolescents with NSSI behaviors. Mediation and moderation analyses were performed using Process 3.5 in SPSS， and the significance was tested with bootstrapping. The interaction was visualized by using simple slope analysis. ResultsAmong 34 534 （84.60%） valid respondents， 542 adolescents （1.57%） reported engaging in NSSI behavior. Significant differences in gender， GAD-7 scores， PHQ-9 scores， and SSSUS scores were observed between NSSI behavior group and non-NSSI group （χ²/t=62.889， 71.120， 94.365， -41.464， P<0.01）.Adolesents with NSSI showed positive correlations between GAD-7 scores and both ASHS and PHQ-9 scores （r=0.158， 0.166， P<0.01）. PHQ-9 scores were positively correlated with ASHS scores （r=0.364， P<0.01）， but negatively correlated with SSSUS scores （r=-0.290， P<0.01）. SSSUS scores were negatively correlated with ASHS scores （r=-0.247， P<0.01）. Depression partially mediated the relationship between anxiety and NSSI behavior， with an effect size of 0.544 （95% CI： 0.162~0.944）， accounting for 35.79% of the total effect. Social support moderated the relationship between depression and NSSI bahavior， with an effect value of -0.082 （95% CI： -0.135~-0.029）. ConclusionAnxiety not only directly influences NSSI bahavior among adolescents， also indirectly exacerbates it through depression， while social support mitigates the impact of depression on NSSI behavior. ［Funded by Youth Project of National Natural Science Foundation of China （number， 82401812）； Project of Health Commission of Sichuan Province （number， 24LCYJPT18）］

Background The association between occupational physical activity (OPA) and cardiometabolic risk factors remains controversial, potentially due to differences in the associations between OPA and various cardiometabolic indicators, as well as the lack of a clearly defined optimal OPA range for multiple-indicator synergistic benefits. Objective To investigate the relationship between OPA and cardiometabolic risk factors in individuals at high risk of cardiovascular disease (CVD) in Hubei Province, and to explore an optimal OPA range for multi-indicator improvements. Methods Data were derived from the Hubei Province dataset of the China Health Evaluation And Risk Reduction Through Nationwide Teamwork from 2015 to 2023, including 19028 high-risk CVD individuals. OPA (metabolic equivalent·h·d⁻¹, MET·h·d⁻¹) was assessed using the China Kadoorie Biobank physical activity questionnaire, anthropometric measurements (height, weight, waist circumference) and cardiometabolic indicators (blood pressure, fasting blood glucose, lipid profiles) were measured. Multivariate logistic regression was used to analyze the association between logOPA quartiles (Q1-Q4) and abnormal cardiometabolic indicators, while restricted cubic spline (RCS) models were employed to explore their dose-response relationships. Subgroup and interaction analyses were also conducted by gender. Results The median OPA of all participants was 12.86 MET·h·d⁻¹ (male: 14.40 MET·h·d⁻¹, female: 11.14 MET·h·d⁻¹). After adjusting for confounders, compared with the Q1 group, logOPA in the Q4 group was associated with 20% (OR=0.80, 95%CI: 0.72, 0.89), 14% (OR=0.86, 95%CI: 0.78, 0.96), and 13% (OR=0.87, 95%CI: 0.79, 0.95) reduction in the risk of abdominal obesity, low high-density lipoprotein cholesterol (HDL-C), and metabolic syndrome (MS), respectively, and 33% (OR=1.33, 95%CI: 1.18, 1.49), 33% (OR=1.33, 95%CI: 1.18, 1.50), and 38% (OR=1.38, 95%CI: 1.21, 1.57) increase in the risk of high total cholesterol (TC), high low-density lipoprotein cholesterol (LDL-C), and high non-HDL-C, respectively. Similar trends were observed in males and females, with significant positive associations of logOPA with high TC and high non-HDL-C in males (P_interaction<0.05). The risk of low HDL-C decreased rapidly when logOPA was more than 2.213 (OPA>9.14 MET·h·d⁻¹), showing an inverted L-shaped trend. The risk of high TC, high LDL-C, and high non-HDL-C increased beyond logOPA thresholds of 3.244, 2.476, and 3.377 (OPA >25.64, 11.89, and 29.28 MET·h·d⁻¹), respectively, showing a J-shaped trend. However, logOPA exhibited a U-shaped nonlinear dose-response relationship with blood pressure and fasting blood glucose abnormalities (P_non-linear<0.05) , with minimal risks at logOPA 2.969 and 2.372 (OPA of 19.47 and 10.72 MET·h·d⁻¹). Conclusion OPA exhibits heterogeneous dose-response patterns across cardiometabolic indicators. Integrating the inverted L-, J-, and U-shaped association patterns, we suggest an optimal OPA range of 9.14−25.64 MET·h·d⁻¹ for multiple cardiometabolic indicators in total individuals at high-risk of CVD, with ranges of 9.61−31.34 MET·h·d⁻¹ for males and 8.97−22.87 MET·h·d⁻¹ for females. These findings provide critical evidence for developing OPA interventions strategies targeting high-risk population for CVD.

As the volume of medical research using large language models (LLM) surges, the need for standardized and transparent reporting standards becomes increasingly critical. In January 2025, Nature Medicine published statement titled by TRIPOD-LLM reporting guideline for studies using large language models. This represents the first comprehensive reporting framework specifically tailored for studies that develop prediction models based on LLM. It comprises a checklist with 19 main items (encompassing 50 sub-items), a flowchart, and an abstract checklist (containing 12 items). This article provides an interpretation of TRIPOD-LLM’s development methods, primary content, scope, and the specific details of its items. The goal is to help researchers, clinicians, editors, and healthcare decision-makers to deeply understand and correctly apply TRIPOD-LLM, thereby improving the quality and transparency of LLM medical research reporting and promoting the standardized and ethical integration of LLM into healthcare.

Objective To identify the independent predictors of programmed death-1 (PD-1) inhibitor-related endocrine adverse events and construct a clinically usable risk prediction model. Methods A total of 302 patients with solid tumors treated with PD-1 inhibitors were retrospectively enrolled. According to the presence or absence of endocrine immune-related adverse events (irAEs), the patients were divided into case group and control group. The clinical and laboratory indexes were compared between the two groups. Multivariable logistic regression was used to confirm independent predictors of endocrine irAEs. The nomogram was constructed, while the receiver operating characteristic (ROC) curve was used to test the prediction performance of the model. Results The overall incidence of endocrine irAEs was 21.9% (66/302), and the incidence of hypothyroidism was 19.5% (59/302). The age, PD-1 inhibitors, free thyroxine, thyroid peroxidase antibody (TPOAb), thyroglobulin, amylase, lymphocyte subset CD3 expression were statistically different between the two groups (P＜0.05). Multivariable logistic regression showed that higher expression of lymphocyte subset CD3 was a protective factor to prevent endocrine irAEs occurrence (P＝0.004), while age＜60 years, higher TPOAb and use of pembrolizumab were independent risk factors of endocrine irAEs (P＜0.05). The nomogram model thus constructed, and when the threshold probability of the model exceeded 0.1, its net benefit was higher. ROC curve showed that the AUC of the model to predict endocrine irAEs was 0.760. The prediction result of the model was highly consistent with the actual result. Conclusions The age, type of PD-1 inhibitor, baseline TPOAb level, and baseline CD3 expression can independently predict endocrine irAEs occurrence or not. The nomogram model based on this model has good predictive efficiency, which can provide reference for early identification of high-risk patients and immunotherapy management.

Objective To analyze the characteristics of adverse drug reactions （ADR） reported in Sixth People’s Hospital South Campus, Shanghai Jiaotong University from 2021 to 2023, to provide reference for promoting rational clinical drug use. Methods ADR data reported in our hospital were collected retrospectively, including patients’ basic information, drugs causing adverse reactions, types of adverse reactions and outcomes. Descriptive analysis methods were used to summarize and analyze the data. Results A total of 979 cases of ADR were reported in our hospital from 2021 to 2023. The highest proportion of patients with ADR occurred in the age range of 31 to 50, and more male patients （63.5%）. The top five drugs involved with adverse reactions were antibiotics （48.8%）, Chinese medicine injections（19.2%）, vitamins（7.5%）, Chinese traditional medicine（7.2%）, equine tetanus immunoglobulin（6.3%）. Among antibiotics, cefuroxime, ceftazidime and cefotiam were the majority. The organs/systems involved in all ADR were mainly skin and accessories damage （55.4%）. The clinical manifestations were rash, itching, and maculopapular rash. Conclusion From 2021 to 2023, the most common drugs causing adverse drug reactions in our hospital were mainly antibacterial drugs, and the rational clinical use of antibacterial drugs still needs to be concerned.

This comprehensive review systematically explores the multifaceted applications, inherent challenges, and promising future directions of artificial intelligence (AI) within the medical domain. It meticulously examines AI's specific contributions to basic medical research, disease prevention, intelligent diagnosis, treatment, rehabilitation, nursing, and health management. Furthermore, the review delves into AI's innovative practices and pivotal roles in clinical trials, hospital administration, medical education, as well as the realms of medical ethics and policy formulation. Notably, the review identifies several key challenges confronting AI in healthcare, encompassing issues such as inadequate algorithm transparency, data privacy concerns, absent regulatory standards, and incomplete risk assessment frameworks. Looking ahead, the future trajectory of AI in healthcare encompasses enhancing algorithm interpretability, propelling generative AI applications, establishing robust data-sharing mechanisms, refining regulatory policies and standards, nurturing interdisciplinary talent, fostering collaboration among industry, academia, and medical institutions, and advancing inclusive, personalized precision medicine. Emphasizing the synergy between AI and emerging technologies like 5G, big data, and cloud computing, this review anticipates a new era of intelligent collaboration and inclusive sharing in healthcare. Through a multidimensional analysis, it presents a holistic overview of AI's medical applications and development prospects, catering to researchers, practitioners, and policymakers in the healthcare sector. Ultimately, this review aims to catalyze the deep integration and innovative deployment of AI technology in healthcare, thereby driving the sustainable advancement of smart healthcare.