ObjectiveTo explore the academic characteristics of contemporary renowned Chinese medicine masters in treating diabetic kidney disease （DKD） from the perspectives of principles， methods， formulas， and medications. MethodsIn strict accordance with the Systematic Review of Text and Opinion （SrTO） process developed by the Joanna Briggs Institute （JBI）， an Australian evidence-based healthcare center， the databases including China National Knowledge Infrastructure （CNKI）， VIP Database， Wanfang Data， and China Biomedical Literature Service System （SinoMed） were searched. Based on predefined inclusion and exclusion criteria， text information extraction， quality evaluation， and text information synthesis were conducted sequentially. The data were analyzed and presented in the form of text and figures. ResultsA total of 215 articles related to 43 contemporary renowned experts in the fields of Chinese medicine nephrology and endocrinology were included. The study found that the academic thoughts of these masters in the treatment of DKD are extensive， involving multiple levels such as disease understanding， therapeutic strategies， formula application， and medication use. In terms of disease understanding， the primary pathogenesis is characterized by deficiency in the root and excess in the manifestation. It is emphasized that internal factors， such as congenital endowment deficiency， interact with external factors such as improper diet， emotional disturbances， invasion of exogenous pathogens， and delayed or inappropriate treatment， to jointly induce the disease. This further gives rise to various pathogenetic theories， including obstruction of renal collaterals by blood stasis， toxin-induced damage to renal collaterals， latent wind disturbing the kidney， and internal heat leading to mass formation. In terms of therapeutic strategies and medication use， the principal treatment method is to replenish Qi and nourish Yin. Stage-based and syndrome-differentiated treatments are advocated. Flexible use of insect-derived drugs and wind-dispelling drugs is emphasized， along with proficiency in applying classical formulas and drug pairs. Integrated internal and external treatments， as well as the combined application of multiple therapeutic approaches， are commonly employed for comprehensive management. Meanwhile， the concept of "preventive treatment of disease" is upheld， and individualized long-term management of patients is advocated. ConclusionThrough the SrTO process， the academic thoughts of contemporary renowned Chinese medicine masters in the treatment of DKD have been systematically and standardly synthesized， providing a scientific and standardized basis for future theoretical exploration.

ObjectiveTo explore the academic characteristics of contemporary renowned Chinese medicine masters in treating diabetic kidney disease （DKD） from the perspectives of principles， methods， formulas， and medications. MethodsIn strict accordance with the Systematic Review of Text and Opinion （SrTO） process developed by the Joanna Briggs Institute （JBI）， an Australian evidence-based healthcare center， the databases including China National Knowledge Infrastructure （CNKI）， VIP Database， Wanfang Data， and China Biomedical Literature Service System （SinoMed） were searched. Based on predefined inclusion and exclusion criteria， text information extraction， quality evaluation， and text information synthesis were conducted sequentially. The data were analyzed and presented in the form of text and figures. ResultsA total of 215 articles related to 43 contemporary renowned experts in the fields of Chinese medicine nephrology and endocrinology were included. The study found that the academic thoughts of these masters in the treatment of DKD are extensive， involving multiple levels such as disease understanding， therapeutic strategies， formula application， and medication use. In terms of disease understanding， the primary pathogenesis is characterized by deficiency in the root and excess in the manifestation. It is emphasized that internal factors， such as congenital endowment deficiency， interact with external factors such as improper diet， emotional disturbances， invasion of exogenous pathogens， and delayed or inappropriate treatment， to jointly induce the disease. This further gives rise to various pathogenetic theories， including obstruction of renal collaterals by blood stasis， toxin-induced damage to renal collaterals， latent wind disturbing the kidney， and internal heat leading to mass formation. In terms of therapeutic strategies and medication use， the principal treatment method is to replenish Qi and nourish Yin. Stage-based and syndrome-differentiated treatments are advocated. Flexible use of insect-derived drugs and wind-dispelling drugs is emphasized， along with proficiency in applying classical formulas and drug pairs. Integrated internal and external treatments， as well as the combined application of multiple therapeutic approaches， are commonly employed for comprehensive management. Meanwhile， the concept of "preventive treatment of disease" is upheld， and individualized long-term management of patients is advocated. ConclusionThrough the SrTO process， the academic thoughts of contemporary renowned Chinese medicine masters in the treatment of DKD have been systematically and standardly synthesized， providing a scientific and standardized basis for future theoretical exploration.

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.