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.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

AIM To investigate the effects of Polygonum cuspidatum and polydatin on lipid deposition in adipose tissue of high-fat diet-induced obese mice.METHODS Forty male C57BL/6J mice were randomly assigned to either a control group(10 mice)fed standard chow or a diet-induced obesity(DIO)group(30 mice)fed a high-fat diet for 8 weeks.The successful mouse models were randomly assigned to the model group,the polydatin group(250 mg/kg)and the P.cuspidatum group(4.5 g/kg),with 8 mice in each group,to resume their high-fat diet during the following 8 weeks corresponding drug administration by gavage.Weekly body weight measurements were recorded for all mice.Serum TG,TC and LDL levels were quantified post-treatment.Histopathological assessment of adipose tissue was performed using HE staining.The mRNA expressions of AMPK,SREBP-1c and FAS in adipose tissue were analyzed by RT-qPCR.The protein expressions of p-AMPK,SREBP-1c and FAS in adipose tissue was detected by Western blot.RESULTS Compared to the control group,the model group displayed significantly higher body weight,inguinal fat weight and epididymal fat weight(P＜0.05);elevated serum TG,TC and LDL levels(P＜0.05);markedly enlarged volumes of inguinal and epididymal adipocytes(P＜0.01);reduced p-AMPK protein expression in inguinal adipose tissue(P＜0.01);and upregulated mRNA and protein expressions of SREBP-1c and FAS(P＜0.05,P＜0.01).Compared to the model group,both the P.cuspidatum group and polygonin group exhibited significantly reduced body weight and inguinal fat weight(P＜0.05);decreased serum TG and TC levels(P＜0.05);reduced inguinal adipocyte size(P＜0.01);elevated p-AMPK protein expression in inguinal adipose tissue(P＜0.01);and downregulated mRNA and protein expressions of SREBP-1c and FAS(P＜0.05,P＜0.01).CONCLUSION P.cuspidatum and polydatin significantly increases p-AMPK expression while decreasing SREBP-1c and FAS levels in adipose tissue.This regulatory effect likely contributes to reduction of body weight in obese mice through suppression of lipogenesis.

Progressive pulmonary fibrosis (PPF) is a progressive and lethal condition with few effective treatment options. Improvements in quality of life for patients with PPF remain limited even while receiving treatment with approved antifibrotic drugs. Traditional Chinese medicine (TCM) has the potential to improve cough, dyspnea and fatigue symptoms of patients with PPF. TCM treatments are typically diverse and individualized, requiring urgent development of efficient and precise design strategies to identify effective treatment options. We designed an innovative Bayesian adaptive two-stage trial, hoping to provide new ideas for the rapid evaluation of the effectiveness of TCM in PPF. An open-label, two-stage, adaptive Bayesian randomized controlled trial will be conducted in China. Based on Bayesian methods, the trial will employ response-adaptive randomization to allocate patients to study groups based on data collected over the course of the trial. The adaptive Bayesian trial design will employ a Bayesian hierarchical model with "stopping" and "continuation" criteria once a predetermined posterior probability of superiority or futility and a decision threshold are reached. The trial can be implemented more efficiently by sharing the master protocol and organizational management mechanisms of the sub-trial we have implemented. The primary patient-reported outcome is a change in the Leicester Cough Questionnaire score, reflecting an improvement in cough-specific quality of life. The adaptive Bayesian trial design may be a promising method to facilitate the rapid clinical evaluation of TCM effectiveness for PPF, and will provide an example for how to evaluate TCM effectiveness in rare and refractory diseases. However, due to the complexity of the trial implementation, sufficient simulation analysis by professional statistical analysts is required to construct a Bayesian response-adaptive randomization procedure for timely response. Moreover, detailed standard operating procedures need to be developed to ensure the feasibility of the trial implementation. Please cite this article as: Zhang C, Nie YS, Zhang CT, Yang HJ, Zhang HR, Xiao W, Cui GF, Li J, Li SJ, Huang QS, Yan SY. An adaptive Bayesian randomized controlled trial of traditional Chinese medicine in progressive pulmonary fibrosis: Rationale and study design. J Integr Med. 2025; 23(2): 138-145.
Female ; Humans ; Male ; Bayes Theorem ; Disease Progression ; Drugs, Chinese Herbal/therapeutic use* ; Medicine, Chinese Traditional/methods* ; Pulmonary Fibrosis/therapy* ; Quality of Life ; Randomized Controlled Trials as Topic ; Research Design ; Adaptive Clinical Trials as Topic

Geniposidic acid(GA), a natural iridoid, exists in the roots, stems, leaves, flowers, bark, fruits, and seeds of medicinal plants of Rubiaceae, Eucommiaceae, and Plantaginaceae. Modern pharmacological studies have revealed that GA has multiple pharmacological activities, including organ-protective, anti-inflammatory, antioxidative, anti-osteoporosis, anti-neurodegenerative, and anti-cardiovascular effects. GA can enhance cell/organism defenses by upregulating key anti-inflammatory and antioxidant cytokines, while downregulating key node proteins in pro-inflammatory signaling pathways such as AhR and TLR4/MyD88, thereby exerting pharmacological effects such as organ protection. Pharmacokinetic investigations have suggested that after oral administration, GA can be distributed in multiple organs(kidney, liver, heart, spleen, lung, etc.). In addition, the pharmacokinetic behavior of GA could be significantly altered under disease conditions, as demonstrated by a marked increase in systematic exposure. This article comprehensively summarizes the reported pharmacological activities and mechanisms and systematically analyzes the pharmacokinetic characteristics and key parameters of GA, with the aim of providing a theoretical basis and scientific reference for the precise clinical application of GA-related Chinese patent medicines, as well as for the investigation and development of innovative drugs based on GA.
Humans ; Drugs, Chinese Herbal/chemistry* ; Animals ; Iridoid Glucosides/chemistry* ; Plants, Medicinal/chemistry* ; Anti-Inflammatory Agents/pharmacology*

Objective To explore how to organically integrate the human anatomy curriculum with medical imaging,thereby enhancing medical students' spatial understanding and 3D reconstruction skills,and strengthening their anatomical foundation and clinical competence.This approach aims to bridge the gap between basic science and clinical practice while cultivating clinical thinking abilities.Methods In this study,the medical imaging knowledge was introduced into the anatomy curriculum in Peking University,enabling students to better understand the human body structure and its relationship to the clinical practice with aid of the ultrasound and MRI method.After the course concluded,we evaluated the examination result and learning satisfaction data from the anatomy course.Results The result showed that students provided positive feedback,showing increased interest in learning,enhanced initiative,significant improvement in their anatomy grades(P＜0.01),and a notable enhancement in their ability to apply basic knowledge to solve clinical problems(P＜0.05).Conclusion The integrated teaching approach of medical imaging and human anatomy courses provides innovative ideas and practical method for medical students to learn the basic medical course and enhance their clinical skills in the future.

Objective To propose a dynamic early warning model based on machine learning methods and validate its predi-ctive efficacy so as to achieve precise assessment and early warning of mortality risk in patients with traumatic hypothermia.Methods Firstly,a total of 480 patients who met inclusion criteria were retrospectively selected from the eICU database and randomly divided into training and test sets at an 8∶2 ratio.Secondly,physiological parameters were extracted from these patients,and five machine learning algorithms including XGBoost,AdaBoost,LightGBM,logistic regression(LR)and random forest(RF)were employed respectively to develop dynamic mortality risk warning models for traumatic hypothermia patients,utilizing a 1-hour observation window.Thirdly,receiver operating characteristic curves(ROC)were plotted using the test set data and the effects of different warning windows on the model performance were analyzed by calculating the AUC.Finally,the interpretability of the models was analyzed using the SHapley Additive exPlanations(SHAP)algorithm to elucidate the contribution of each feature to predictive performance.Results The optimal warning window for the dynamic warning model constructed using the eICU database was 12 hours,and in case of 12-hour warning window the logistic regression model achieved the highest AUC of 0.935 and showed optimal predictive performance.The results of the interpretability analysis by the SHAP algorithm showed that body temperature was the feature that had the greatest impact on the model results,and its reduction was positively correlated with the increased risk of death.Conclusion The machine learning-based dynamic warning model for mortality risk in traumatic hypothermia patients enables real-time dynamic risk assessment,providing robust support for clinicians to identify the patient's condition changes at an early stage and references for the adjustment of clinical treatment programs.[Chinese Medical Equipment Journal,2025,46(3):9-14]

Objective To propose a dynamic early warning model based on machine learning methods and validate its predi-ctive efficacy so as to achieve precise assessment and early warning of mortality risk in patients with traumatic hypothermia.Methods Firstly,a total of 480 patients who met inclusion criteria were retrospectively selected from the eICU database and randomly divided into training and test sets at an 8∶2 ratio.Secondly,physiological parameters were extracted from these patients,and five machine learning algorithms including XGBoost,AdaBoost,LightGBM,logistic regression(LR)and random forest(RF)were employed respectively to develop dynamic mortality risk warning models for traumatic hypothermia patients,utilizing a 1-hour observation window.Thirdly,receiver operating characteristic curves(ROC)were plotted using the test set data and the effects of different warning windows on the model performance were analyzed by calculating the AUC.Finally,the interpretability of the models was analyzed using the SHapley Additive exPlanations(SHAP)algorithm to elucidate the contribution of each feature to predictive performance.Results The optimal warning window for the dynamic warning model constructed using the eICU database was 12 hours,and in case of 12-hour warning window the logistic regression model achieved the highest AUC of 0.935 and showed optimal predictive performance.The results of the interpretability analysis by the SHAP algorithm showed that body temperature was the feature that had the greatest impact on the model results,and its reduction was positively correlated with the increased risk of death.Conclusion The machine learning-based dynamic warning model for mortality risk in traumatic hypothermia patients enables real-time dynamic risk assessment,providing robust support for clinicians to identify the patient's condition changes at an early stage and references for the adjustment of clinical treatment programs.[Chinese Medical Equipment Journal,2025,46(3):9-14]

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.

AIM To investigate the effects of Polygonum cuspidatum and polydatin on lipid deposition in adipose tissue of high-fat diet-induced obese mice.METHODS Forty male C57BL/6J mice were randomly assigned to either a control group(10 mice)fed standard chow or a diet-induced obesity(DIO)group(30 mice)fed a high-fat diet for 8 weeks.The successful mouse models were randomly assigned to the model group,the polydatin group(250 mg/kg)and the P.cuspidatum group(4.5 g/kg),with 8 mice in each group,to resume their high-fat diet during the following 8 weeks corresponding drug administration by gavage.Weekly body weight measurements were recorded for all mice.Serum TG,TC and LDL levels were quantified post-treatment.Histopathological assessment of adipose tissue was performed using HE staining.The mRNA expressions of AMPK,SREBP-1c and FAS in adipose tissue were analyzed by RT-qPCR.The protein expressions of p-AMPK,SREBP-1c and FAS in adipose tissue was detected by Western blot.RESULTS Compared to the control group,the model group displayed significantly higher body weight,inguinal fat weight and epididymal fat weight(P＜0.05);elevated serum TG,TC and LDL levels(P＜0.05);markedly enlarged volumes of inguinal and epididymal adipocytes(P＜0.01);reduced p-AMPK protein expression in inguinal adipose tissue(P＜0.01);and upregulated mRNA and protein expressions of SREBP-1c and FAS(P＜0.05,P＜0.01).Compared to the model group,both the P.cuspidatum group and polygonin group exhibited significantly reduced body weight and inguinal fat weight(P＜0.05);decreased serum TG and TC levels(P＜0.05);reduced inguinal adipocyte size(P＜0.01);elevated p-AMPK protein expression in inguinal adipose tissue(P＜0.01);and downregulated mRNA and protein expressions of SREBP-1c and FAS(P＜0.05,P＜0.01).CONCLUSION P.cuspidatum and polydatin significantly increases p-AMPK expression while decreasing SREBP-1c and FAS levels in adipose tissue.This regulatory effect likely contributes to reduction of body weight in obese mice through suppression of lipogenesis.