The global distribution of medical resources is uneven and organ shortages are becoming increasingly serious. ASEAN countries have been working hard to explore and promote local organ transplantation in order to alleviate the serious imbalance between organ donation and organ transplantation needs. However, the development of cadaveric organ donation varies among ASEAN countries, and the cadaveric organ donation rate in most countries is generally low. Since 1991, China and ASEAN have evolved from dialogue to strategic cooperation, then to a community with a shared future, and further to a comprehensive strategic partnership, all demonstrating broad prospects for cooperation. This article analyzes the current situation and challenges of organ donation and transplantation in ASEAN countries, combining field visits and its own experience, and proposes strategies for strengthening international cooperation, optimizing policy environment, enhancing technical capabilities, and increasing public awareness in the field of organ donation and transplantation under the China-ASEAN development strategy framework. The aim is to build a more equitable, efficient, and sustainable organ donation and transplantation system, contributing to the realization of global public health security and a community of common health for mankind.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Tripterygium wilfordii is a traditional Chinese medicinal herb belonging to the genus Tripterygium in the Celastraceae family， which has the effects of clearing heat and detoxifying， dispelling wind and dampness， and invigorating blood circulation to relieve pain， and is used to treat diseases such as rheumatoid arthritis， glomerulonephritis， nephrotic syndrome， lupus erythematosus， scabies， and stubborn tinea. Its chemical composition is diverse. Among them， triptolide（TP） is one of the main active and toxic components of T. wilfordii. It has significant biological activities such as anti-inflammation， anti-tumor， and immunosuppression. However， it causes serious adverse reactions such as liver and kidney function damage and reproductive system disorders. At the same time， TP has poor water solubility and low bioavailability， and the enhancement of bioavailability by increasing the dosage undoubtedly improves the exposure of the drug in non-target organs， leading to the occurrence of adverse reactions， and these largely limit the clinical application of TP. Based on this， this article extracted relevant data from the Web of Science， PubMed， and China National Knowledge Infrastructure（CNKI） databases， summarized the research on the adverse reactions of TP in recent years， and reviewed the progress of toxicity reduction research from the perspectives of structural modification， novel drug delivery systems， and compatibility. Structural modification can precisely alter the chemical structure of TP， reduce the activity of its toxic groups， and retain its biological activity while fundamentally reducing the occurrence of adverse reactions. New drug delivery systems can achieve targeted delivery of TP， increase its concentration in target organs， and reduce its exposure in non-target organs， thereby enhancing therapeutic efficacy and reducing adverse effects. In addition， the combination of TP with Chinese medicine compound， single-flavored Chinese medicine or monomer can reduce the adverse effects of TP and enhance the efficacy to different degrees， which is of clinical value. This paper systematically explains attenuation research from the above three perspectives， aiming to provide a theoretical basis for the full utilization of biological activity and drug development of TP.

Objective To investigate and analyze clinical characteristics and related factors of elderly patients with type 2 diabetes mellitus (T2DM) and frailty. Methods A total of 310 elderly patients with T2DM admitted to the hospital from January 2023 to June 2024 were selected as the research subjects. Their general information and disease-related information was collected through questionnaires. The Fried Frailty Scale was used to evaluate frailty status, and the patients were divided into frailty group and non-frailty group based on the Fried Frailty Scale score. Factors related to T2DM with frailty in the elderly were analyzed. Results The incidence of frailty in this study was 31.61% (98/310), and those without frailty accounted for 68.39% (212/310). There were statistically significant differences between the two groups in terms of age, body mass index (BMI), Self-rating Depression Scale (SDS) score, number of chronic complications, glycosylated hemoglobin (HbA1c) level, hemoglobin level, Mini-Nutritional Assessment-Short Form (MNA-SF) score, and Charlson Comorbidity Index (CCI) score (P<0.05). Multivariate logistic regression analysis showed that age, HbA1c level, SDS score, MNA-SF score, and CCI score were risk factors for frailty in elderly patients with T2DM (P<0.05). Conclusion The incidence of frailty is relatively high in elderly patients with T2DM. It is influenced by factors such as age , SDS score , HbA1c level , MNA-SF score and CCI score, and deserves clinical attention.

Objective To explore the reliability and safety of continuous monitoring of vital signs in patients using wireless wearable monitoring devices after video-assisted thoracoscopic surgery (VATS) for lung cancer. Methods The patients undergoing VATS for lung cancer in West China Hospital, Sichuan University from May to August 2023 were prospectively enrolled. Both wireless wearable and traditional wired devices were used to monitor the vital signs of patients after surgery. Spearman correlation analysis, paired sample t test and ratio Bland-Altman method were used to test the correlation, difference and consistency of monitoring data measured by the two devices. The effective monitoring rate of the wireless wearable device within 12 hours was calculated to test the reliability of its continuous monitoring. Results A total of 20 patients were enrolled, including 15 females and 5 males with an average age of 46.20±11.52 years. Data collected by the two monitoring devices were significantly correlated (P<0.001). Respiratory rate and blood oxygen saturation data collected by the two devices showed no statistical difference (P>0.05), while heart rate measured by wireless wearable device was slightly lower (=−0.307±1.073, P<0.001), and the blood pressure (=1.259±5.354, P<0.001) and body temperature(=0.115±0.231, P<0.001) were slightly higher. The mean ratios of heart rate, respiratory rate, blood oxygen saturation, blood pressure and body temperature collected by the two devices were 0.996, 1.004, 1.000, 1.014, and 1.003, respectively. The 95% limits of agreement (LoA) and 95% confidence interval of 95%LoA of each indicator were within the clinically acceptable limit. The effective monitoring rate of each vital signs within 12 hours was above 98%. Conclusion The wireless wearable device has a high accuracy and reliability for continuous monitoring vital signs of patients after VATS for lung cancer, which provides a security guarantee for subsequent large-scale clinical application and further research.

Objective To investigate the signal abnormality of conus medullaris in patients with overactive bladder(OAB)and un-deractive bladder(UAB)by MRI diffusion tensor imaging(DTI). Methods From May,2021 to April,2023,23 patients with lower urinary tract dysfunction without trauma and supraspi-nal lesions were enrolled(case group).All patients underwent imaging urodynamics and pelvic floor electromy-ography.Based on the bladder contraction during the filling phase of urodynamics,the patients were divided into UAB group and OAB group.Eight healthy subjects were included as the control group.All participants under-went T10 to L5 spinal segment MRI scans and DTI scans.The position of conus medullaris was determined by comparing the DTI sequences with the MRI scans.The fractional anisotropy(FA),apparent diffusion coefficient(ADC),and relative anisotropy(RA)of the conus medullaris intermediate segment were compared. Results Twelve cases were in UAB group,and eleven in OAB goup.Abnormalities were found in the pelvic floor elec-tromyography in the case group.There was significant difference in sacral reflex arc nerve conduction testing be-tween UAB and OAB groups(P = 0.036).Compared with the control group,ADC increased(t = 2.185,P = 0.037)in the case group;FA decreased(t = 3.439,P = 0.005)and ADC increased(t = 4.582,P<0.001)in UAB group. Conclusion DTI is helpful to find the potential lesion of spinal cord in patients with lower urinary tract dysfunction.FA and ADC are valuable indicators for the diagnosis of conus medullaris injury.