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.

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.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

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.

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.

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.

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.