Objective To investigate the post-discharge exercise behavior and factors influencing moderate to vigorous intensity physical activity (MVPA) in patients undergoing lung surgery. Methods A total of 2874 patients from the large prospective, observational perioperative lung symptom study cohort (CN-PRO-Lung 3) in the Department of Thoracic Surgery at Sichuan Cancer Hospital between April 7, 2021, and January 31, 2024, were selected as the survey subjects. A survey was conducted using the Investigation of Exercise Behavior after Lung Surgery questionnaire and the International Physical Activity Questionnaire-Short Form (IPAQ-SF) among patients who underwent lung surgery. Binary logistic regression was used to analyze the factors influencing patients’ engagement in MVPA. Results A total of 702 patients were surveyed, including 252 males and 450 females, with an average age of (52.4±10.2) years. Patients with lung cancer accounted for 85.9%. Only 36.0% of the patients had regular exercise habits, while 42.3% did not engage in any physical activity. The three main barriers for postoperative exercise were physical discomfort (pain, coughing, shortness of breath, etc, 54.7%), lack of professional guidance (41.7%), and concerns about the surgical wound (28.9%). The proportions of patients engaging in vigorous, moderate, and low-intensity physical activity were 5.7%, 28.2%, and 66.1%, respectively. Multivariate analysis showed that patients with a personal annual income ≥50000 yuan (OR=1.52, 95%CI 1.01-2.29, P=0.044), high school education or above (OR=1.92, 95%CI 1.33-2.76, P<0.001), and lobectomy (OR=1.44, 95%CI 1.02-2.03, P=0.037) engaged in more MVPA. Conclusion Patients undergoing lung surgery have inadequate physical activity after discharge, particularly lacking in MVPA. Patients with higher income, higher educational levels, and lobectomy are more frequently engaged in MVPA. Measures such as symptom control, providing exercise guidance, and enhancing education on wound care may potentially improve the inadequate physical activity in lung surgery patients after discharge.

Objective: To explore whether the long-term and frequent use of citrate anticoagulants negatively affects the bone metabolism balance of female frequent plateletpheresis donors, so as to better protect their health. Methods: A total of 65 female plateletpheresis donors and 55 female whole-blood donors from Guangzhou Blood Center (May to December 2024) were enrolled as experimental and control groups respectively, stratified into age subgroups (18-39 years and 40-60 years). Serum levels of 25-hydroxyvitamin D [25(OH)D], procollagen type I N-terminal propeptide (PINP), osteocalcin (OC), and type I collagen carboxy-terminal telopeptide (CTX) were measured. Differences in bone metabolism markers between experimental and control groups across age subgroups were compared. ANOVA was used to analyze dose-response relationships between donation age, annual apheresis donation frequency, and biochemical indicators. Results: In the 40-60 age subgroup, 25(OH)D levels were significantly lower in the experimental group (P<0.05), exhibiting a linear increase with age and a linear decrease with annual donation frequency. No significant differences in CTX or PINP levels were observed between experimental and control groups in either age subgroup. Conclusion: High-frequency plateletpheresis donation does not disrupt bone metabolic balance in female donors. However, it is associated with reduced vitamin D levels in female donors aged >40 years, potentially increasing the risk of osteoporosis. Vitamin D supplementation is recommended for high-frequency female plateletpheresis donors in this age group.

A medical device detection method based on improved YOLOv5s was proposed to solve the problem of medical device stacking and further improve the detection accuracy of medical devices.The proposed method uses C2f module to optimize YOLOv5s network for improving the detection accuracy,introduces squeeze-and-excitation network into the feature fusion network for improving the model's attention to effective information,and constructs α-DIOU by introducing Alpha intersection union ratio(α-IOU)on the basis of the distance-intersection over union(DIOU)loss function,which makes the bounding box regression more accurate and enables the accurate detection of medical devices in the image.Experimental results show that the improved model has precision,recall rate and mean average precision of 81.8%,93.7%and 91.5%,respectively,for medical device detection on the validation set,which are 3.2%,3.4%and 4.6%higher than YOLOv5s model.The proposed method is simple and effective,and is expected to provide new ideas for the detection methods of medical devices.

AIM To evaluate the quality of Rubi Fructus.METHODS UPLC-Q-TOF-MS/MS was adopted in the component identification,after which the HPLC fingerprints were established,cluster analysis,principal component analysis and orthogonal partial least squares discriminant analysis were used for chemical pattern recognition.and the contents of chlorogenic acid,ferulic acid,ellagic acid,isoquercitrin,kaempferol-3-O-rutinoside,astragalin,tiliroside quercetin,kaempferol were determined.RESULTS Total 34 constituents were identified.There were 19 common peaks in the fingerprints for 31 batches of medicinal materials with the similarities of more than 0.8.Wild varieties and cultivated varieties,and medicinal materials from different producing areas could be distinguished;4 principal components demonstrated the accumulative variance contribution rate of 84.142％;8 differential components were screened,2 of which were ellagic acid and astragalin.Ellagic acid and astragalin displayed higher contents in the wild varieties than those in the cultivated varieties(P＜0.05,P＜0.01).CONCLUSION UPLC-Q-TOF-MS/MS,HPLC fingerprints combined with content determination can be used for the quality control of Rubi Fructus.

Objective:This study aims to systematically review the current evaluation paradigms of conversational AI in healthcare and provide insights to facilitate the development of a comprehensive evaluation framework and methodological advancements in this field.Methods:A systematic review was conducted by searching the PubMed and Web of Science databases to analyze the existing evaluation paradigms of healthcare conversational AI,including evaluation subjects,assessment metrics,and evaluation methodologies.Results:A total of 60 studies were included in this review.The findings indicate that most evaluation subjects focus on general-purpose large language models.The assessment metrics cover five key dimensions:technical performance,information quality,clinical effectiveness,user experience,and ethics and safety.However,there were significant differences in the evaluation criteria used in existing studies.There were also issues such as a low degree of alignment between the evaluation questions and the application scenarios,as well as a lack of diversity in the roles of the evaluators.Conclusions:The current evaluation framework for healthcare conversational AI remains underdeveloped.Future improvements should focus on broadening model coverage,enhancing the comprehensiveness of evaluation indicators,standardizing evaluation methods,improving the operationalizability of test content,and expanding the scalability of evaluation languages.

AIM To evaluate the quality of Rubi Fructus.METHODS UPLC-Q-TOF-MS/MS was adopted in the component identification,after which the HPLC fingerprints were established,cluster analysis,principal component analysis and orthogonal partial least squares discriminant analysis were used for chemical pattern recognition.and the contents of chlorogenic acid,ferulic acid,ellagic acid,isoquercitrin,kaempferol-3-O-rutinoside,astragalin,tiliroside quercetin,kaempferol were determined.RESULTS Total 34 constituents were identified.There were 19 common peaks in the fingerprints for 31 batches of medicinal materials with the similarities of more than 0.8.Wild varieties and cultivated varieties,and medicinal materials from different producing areas could be distinguished;4 principal components demonstrated the accumulative variance contribution rate of 84.142％;8 differential components were screened,2 of which were ellagic acid and astragalin.Ellagic acid and astragalin displayed higher contents in the wild varieties than those in the cultivated varieties(P＜0.05,P＜0.01).CONCLUSION UPLC-Q-TOF-MS/MS,HPLC fingerprints combined with content determination can be used for the quality control of Rubi Fructus.

A medical device detection method based on improved YOLOv5s was proposed to solve the problem of medical device stacking and further improve the detection accuracy of medical devices.The proposed method uses C2f module to optimize YOLOv5s network for improving the detection accuracy,introduces squeeze-and-excitation network into the feature fusion network for improving the model's attention to effective information,and constructs α-DIOU by introducing Alpha intersection union ratio(α-IOU)on the basis of the distance-intersection over union(DIOU)loss function,which makes the bounding box regression more accurate and enables the accurate detection of medical devices in the image.Experimental results show that the improved model has precision,recall rate and mean average precision of 81.8%,93.7%and 91.5%,respectively,for medical device detection on the validation set,which are 3.2%,3.4%and 4.6%higher than YOLOv5s model.The proposed method is simple and effective,and is expected to provide new ideas for the detection methods of medical devices.

Objective:This study aims to systematically review the current evaluation paradigms of conversational AI in healthcare and provide insights to facilitate the development of a comprehensive evaluation framework and methodological advancements in this field.Methods:A systematic review was conducted by searching the PubMed and Web of Science databases to analyze the existing evaluation paradigms of healthcare conversational AI,including evaluation subjects,assessment metrics,and evaluation methodologies.Results:A total of 60 studies were included in this review.The findings indicate that most evaluation subjects focus on general-purpose large language models.The assessment metrics cover five key dimensions:technical performance,information quality,clinical effectiveness,user experience,and ethics and safety.However,there were significant differences in the evaluation criteria used in existing studies.There were also issues such as a low degree of alignment between the evaluation questions and the application scenarios,as well as a lack of diversity in the roles of the evaluators.Conclusions:The current evaluation framework for healthcare conversational AI remains underdeveloped.Future improvements should focus on broadening model coverage,enhancing the comprehensiveness of evaluation indicators,standardizing evaluation methods,improving the operationalizability of test content,and expanding the scalability of evaluation languages.

For middle-aged and elderly patients with conditions such as spinal fractures and degenerative spinal diseases, spinal internal fixation is a core surgical procedure for reconstructing spinal stability, heavily relying on the biomechanical stability provided by pedicle screw systems. Whereas, these patients are often complicated by osteoporosis that can significantly compromise the stability of the bone-pedicle screw interface, leading to a marked increase in pedicle screw loosening and surgical failure rates. The bone cement-augmented pedicle screw technique, which involves injecting bone cement into the vertebral body or screw trajectory to optimize the mechanical properties of the bone-pedicle screw composite, has been proven to significantly enhance fixation strength and effectively prevent screw-related failures, thereby reducing the incidence of internal fixation failure in high-risk populations undergoing spinal fusion. However, the widespread clinical application of this technique has faced challenges such as inaccurate clinical decision-making (indication and contraindication selection), non-standardized operative practices, and insufficient awareness of complication prevention, resulting in considerable variability in clinical outcomes and even severe complications. To address this, Prof. Luo Fei from First Affiliated Hospital of Army Medical University initiated the project and the Chinese Association Orthopaedic Surgeons organized relevant experts to develop the Evidence-based clinical practice guideline for bone cement-augmented pedicle screw technique ( version 2025), based on current evidence. The guidelines put forward 8 recommendations regarding the clinical value, scope of application, and operational standards of the technique, aiming to provide evidence-based medical support and technical standardization for clinical decision-making.