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.

Based on latent structure model and association rule analysis, this study investigates the prescription patterns used by professor YANG Zhong-qi in treating hypertension with traditional Chinese medicine(TCM) and infers the associated TCM syndromes, providing a reference for clinical syndrome differentiation and treatment. The observation window spanned from January 8, 2013, to June 26, 2024, during which qualified herbal decoction prescriptions meeting efficacy criteria were extracted from the outpatient medical record system of the First Affiliated Hospital of Guangzhou University of Chinese Medicine and compiled into a standardized database. Statistical analysis of high-frequency herbs included frequency counts and herbal property-channel tropism analysis. Latent structure modeling and association rule analysis were performed using R 4.3.2 and Lantern 5.0 software to identify core herbal combinations and infer TCM syndrome patterns. A total of 2 436 TCM prescriptions were included in the study, involving 263 drugs with a cumulative frequency of 29 783. High-frequency herbs comprised Uncariae Ramulus cum Uncis, Poria, Glycyrrhizae Radix et Rhizoma, Puerariae Lobatae Radix, and Alismatis Rhizoma, predominantly categorized as deficiency-tonifying, heat-clearing, and blood-activating and stasis-resolving herbs. Latent structure analysis identified 18 latent variables, 74 latent classes, 5 comprehensive clustering models, and 15 core herbal combinations, suggesting that the core syndrome clusters include liver Yang hyperactivity pattern, Yin deficiency with Yang hyperactivity pattern, phlegm-stasis intermingling pattern, and liver-kidney insufficiency pattern. Association rule analysis revealed 22 robust association rules. RESULTS:: indicate that hypertension manifests as a deficiency-rooted excess manifestation, significantly associated with functional dysregulation of the liver, lung, spleen-stomach, heart, and kidney. Key pathogenic mechanisms involve liver Yang hyperactivity, phlegm-stasis interaction, and liver-kidney insufficiency. Therapeutic strategies should prioritize liver-calming, spleen-fortifying, and deficiency-tonifying principles, supplemented by dynamic regulation of Qi-blood and Yin-Yang balance according to syndrome evolution, alongside pathogen-eliminating methods such as phlegm-resolving and stasis-dispelling. Synergistic interventions like mind-tranquilizing therapies should be tailored to individual conditions.
Hypertension/drug therapy* ; Drugs, Chinese Herbal/therapeutic use* ; Humans ; Medicine, Chinese Traditional ; Drug Prescriptions ; Latent Class Analysis

Digital technologies have become an integral part of complete denture restoration. With advancement in computer-aided design and computer-aided manufacturing (CAD/CAM), tools such as intraoral scanning, facial scanning, 3D printing, and numerical control machining are reshaping the workflow of complete denture restoration. Unlike conventional methods that rely heavily on clinical experience and manual techniques, digital technologies offer greater precision, predictability, and efficacy. They also streamline the process by reducing the number of patient visits and improving overall comfort. Despite these improvements, the clinical application of digital complete denture restoration still faces challenges that require further standardization. The major issues include appropriate case selection, establishing consistent digital workflows, and evaluating long-term outcomes. To address these challenges and provide clinical guidance for practitioners, this expert consensus outlines the principles, advantages, and limitations of digital complete denture technology. The aim of this review was to offer practical recommendations on indications, clinical procedures and precautions, evaluation metrics, and outcome assessment to support digital restoration of complete denture in clinical practice.
Humans ; Denture, Complete ; Computer-Aided Design ; Denture Design/methods* ; Consensus ; Printing, Three-Dimensional

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.

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.