This paper reviewed the development history of doctor-patient shared decision-making （SDM） at home and abroad， emphasizing the importance of cross-cultural analysis in constructing a Chinese doctor-patient SDM model. It also delved into the relationship between Western “individualistic” sociocultural values and doctor-patient SDM， as well as the influence of China’s “collectivist” sociocultural values on doctor-patient SDM， revealing significant disparities in doctor-patient SDM models under distinct sociocultural contexts. Although the doctor-patient SDM theory in China originated from the West， this theory requires profound “collision” and adaptation with local Chinese culture to form a localized theory suited to China’s national conditions. Through cross-cultural adaptation and integrating China’s familism tradition and medical ethics concepts， the future construction of the doctor-patient SDM model in China should emphasize family members’ involvement and seek cultural balance to facilitate its widespread application in clinical practice.

Objective To analyze the effect of releasing the lower pulmonary ligament on right residual lung expansion after right upper lobe resection under different body mass index (BMI) levels. Methods The clinical data of patients who underwent thoracoscopic right upper lobe resection in the First Affiliated Hospital with Nanjing Medical University from 2021 to 2022 were retrospectively analyzed. Patients were divided into a group A (17 kg/m2＜BMI≤23 kg/m2), a group B (23 kg/m2＜BMI≤29 kg/m2) and a group C (BMI＞29 kg/m2) according to BMI. The presence of residual cavity was judged by chest X-ray at 7-10 days after operation, the degree of compensation change of the right main bronchus angle was measured, and the changes in lung volume were determined by CT three-dimensional reconstruction. Results A total of 157 patients who underwent thoracoscopic right upper lobe resection were included, including 71 males and 86 females, with an average age of (59.7±11.2) years. There were 50 patients in the group A, 75 patients in the group B, and 32 patients in the group C. In the group A, compared with those without releasing the lower pulmonary ligament, patients with releasing had a lower incidence of postoperative residual cavity (P=0.016), greater changes in bronchus angle (P<0.001), and smaller changes in lung volume (P<0.001). In the group B and C, there was no significant effect of releasing the lower pulmonary ligament on postoperative residual cavity, bronchus angle, and lung volume changes (P>0.05). Conclusion For patients with thin and long body shape and low BMI, releasing the lower pulmonary ligament is helpful to promote the expansion of the residual lung after right upper lobe resection and reduce the occurrence of postoperative residual cavity in patients.

With the rapid development of deep learning(DL)technology,its potential applications in the medical field have become increasingly prominent.As a refractory disease,osteonecrosis of the femoral head(ONFH)has certain limitations in traditional diagnostic and therapeutic approaches.The application of DL technology is expected to overcome these limitations and improve diagnosis and treatment outcomes.At present,the applications of DL models-including enhancing image clarity,improving diagnostic accuracy and efficiency,conducting prognostic evaluations,optimizing preoperative planning,assisting intraoperative imaging,and customizing personalized treatment plans-have fully demonstrated their tremendous potential in the diagnosis and treatment of ONFH.This review summarizes the current application status of DL in ONFH diagnosis and treatment,aiming to provide references and insights for future related research.

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.

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]

Aim To explore the mechanism of Epimedii folium-Astmgali radix activating the SCF/c-kit signa-ling pathway to activate the PI3K/Akt signaling path-way and its effect on sperm production and vitality in oligoasthenospermia.Methods Sixty male SD rats were used to establish a model of oligoasthenospermia with cyclophosphamide.They were randomly divided into six groups:experimental group(further divided into high,medium,and low dose group),model group,control group and blank group.The oligoasthenosper-mia model was established by using cyclophosphamide in experimental group,levocarnitine group and model group.The rats in the high,medium,and low dose group of the experimental group were orally adminis-tered Epimedii folium-Astmgali radix extract at doses of 800,400,and 200 mg·kg-1,respectively,Once daily for 35 days.Rats of the control group were orally ad-ministered 250 mg·kg-1·d-1 of levocarnitine,Once daily for 35 days.ELISA was used to detect serum of T,E2,FSH,and LH.Western blot and IHC staining were used to detect the expression of SCF,c-kit,Bcl-2,Bax,PI3K,and Akt proteins in rat testicular tissues.Sperm activity is examined by microscopy.The testicu-lar tissue structure and cell morphology of rats in each group were observed.Results Compared with the model group,Epimedii folium-Astmgali radix increased the sperm density,total viability rate,and vitality(P＜0.05,P＜0.01),decreased sperm apoptosis rate and LH,T,and E2 levels(P＜0.05,P＜0.01),decreased Bax protein expression in testicular tissue(P＜0.01),and increased Bcl-2,SCF,c-Kit,PI3K,and Akt protein expression(P＜0.05,P＜0.01);it increased the number of germ cells,thickened basement membrane,and significantly improved seminiferous tubule mor-phology,even showing germ cells at different develop-mental stages and mature sperm.Conclusions Epi-medii folium-Astmgali radix has a significant therapeu-tic effect on oligoasthenospermia in rats.Its mechanism may be related to the activation of the SCF/c-kit signa-ling pathway to activate the PI3K/Akt signaling path-way promoting the proliferation and differentiation of germ cells,and promoting sperm production,maturation and motility.

Aim To explore the mechanism of Epimedii folium-Astmgali radix activating the SCF/c-kit signa-ling pathway to activate the PI3K/Akt signaling path-way and its effect on sperm production and vitality in oligoasthenospermia.Methods Sixty male SD rats were used to establish a model of oligoasthenospermia with cyclophosphamide.They were randomly divided into six groups:experimental group(further divided into high,medium,and low dose group),model group,control group and blank group.The oligoasthenosper-mia model was established by using cyclophosphamide in experimental group,levocarnitine group and model group.The rats in the high,medium,and low dose group of the experimental group were orally adminis-tered Epimedii folium-Astmgali radix extract at doses of 800,400,and 200 mg·kg-1,respectively,Once daily for 35 days.Rats of the control group were orally ad-ministered 250 mg·kg-1·d-1 of levocarnitine,Once daily for 35 days.ELISA was used to detect serum of T,E2,FSH,and LH.Western blot and IHC staining were used to detect the expression of SCF,c-kit,Bcl-2,Bax,PI3K,and Akt proteins in rat testicular tissues.Sperm activity is examined by microscopy.The testicu-lar tissue structure and cell morphology of rats in each group were observed.Results Compared with the model group,Epimedii folium-Astmgali radix increased the sperm density,total viability rate,and vitality(P＜0.05,P＜0.01),decreased sperm apoptosis rate and LH,T,and E2 levels(P＜0.05,P＜0.01),decreased Bax protein expression in testicular tissue(P＜0.01),and increased Bcl-2,SCF,c-Kit,PI3K,and Akt protein expression(P＜0.05,P＜0.01);it increased the number of germ cells,thickened basement membrane,and significantly improved seminiferous tubule mor-phology,even showing germ cells at different develop-mental stages and mature sperm.Conclusions Epi-medii folium-Astmgali radix has a significant therapeu-tic effect on oligoasthenospermia in rats.Its mechanism may be related to the activation of the SCF/c-kit signa-ling pathway to activate the PI3K/Akt signaling path-way promoting the proliferation and differentiation of germ cells,and promoting sperm production,maturation and motility.

Objective To predict the lymph node metastasis status of patients with invasive pulmonary adenocarcinoma by constructing machine learning models based on primary tumor radiomics, peritumoral radiomics, and habitat radiomics, and to evaluate the predictive performance and generalization ability of different imaging features. Methods A retrospective analysis was performed on the clinical data of 1 263 patients with invasive pulmonary adenocarcinoma who underwent surgery at the Department of Thoracic Surgery, Jiangsu Province Hospital, from 2016 to 2019. Habitat regions were delineated by applying K-means clustering (average cluster number of 2) to the grayscale values of CT images. The peritumoral region was defined as a uniformly expanded area of 3 mm around the primary tumor. The primary tumor region was automatically segmented using V-net combined with manual correction and annotation. Subsequently, radiomics features were extracted based on these regions, and stacked machine learning models were constructed. Model performance was evaluated on the training, testing, and internal validation sets using the area under the receiver operating characteristic curve (AUC), F1 score, recall, and precision. Results After excluding patients who did not meet the screening criteria, a total of 651 patients were included. The training set consisted of 468 patients (181 males, 287 females) with an average age of (58.39±11.23) years, ranging from 29 to 78 years, the testing set included 140 patients (56 males, 84 females) with an average age of (58.81±10.70) years, ranging from 34 to 82 years, and the internal validation set comprised 43 patients (14 males, 29 females) with an average age of (60.16±10.68) years, ranging from 29 to 78 years. Although the habitat radiomics model did not show the optimal performance in the training set, it exhibited superior performance in the internal validation set, with an AUC of 0.952 [95%CI (0.87, 1.00)], an F1 score of 84.62%, and a precision-recall AUC of 0.892, outperforming the models based on the primary tumor and peritumoral regions. Conclusion The model constructed based on habitat radiomics demonstrated superior performance in the internal validation set, suggesting its potential for better generalization ability and clinical application in predicting lymph node metastasis status in pulmonary adenocarcinoma.

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]

As an innovative dosage form, traditional Chinese medicine(TCM) dry powder inhalers have emerged as a focal point in the research and development of new preparations due to its high efficiency, safety, and bioavailability. This paper systematically reviewed the relevant literature and patents associated with TCM dry powder inhalers to analyze the origins and the current research and development status. Furthermore, this paper probed into the research and development ideas of TCM dry powder inhalers regarding clinical positioning, prescription screening, and druggability. Additionally, the paper thoroughly analyzed the technical barriers in druggability studies and elaborated on corresponding research techniques and coping measures. Furthermore, it emphasized the need for improved regulations and policies governing TCM dry powder inhalers, advocated for strengthened oversight, and called for the establishment of a scientific quality evaluation system. Measures such as promoting production-education-research collaboration, enhancing personnel training, and fostering international exchanges were proposed to provide a scientific and systematic reference for the future research, development, and application of TCM dry powder inhalers, thereby facilitating the rapid modernization of TCM.
Humans ; Dry Powder Inhalers/trends* ; Drugs, Chinese Herbal/chemistry* ; Medicine, Chinese Traditional/instrumentation* ; Administration, Inhalation