Aim To systematically investigate the ac-tive components,targets,and regulatory pathways of Po-lygonum capitatum in intervening atherosclerosis(AS)through network pharmacology,molecular docking and animal experiments.Methods Active components of Polygonum capitatum and AS-related targets were screened and identified through database searches.Protein-protein interaction(PPI)network analysis was performed using the STRING database,followed by GO and KEGG enrichment analyses via the David plat-form.Molecular docking validation was conducted with AutoDock.An AS model was established in Syrian golden hamsters fed a high-fat diet.Predicted pathways and targets were validated using qPCR,ELISA,and histopathological assessment of aortic and hepatic tis-sues via HE staining.Results Network pharmacology identified 27 potential active components of Polygonum capitatum(primarily flavonoids such as quercetin and luteolin)and 110 drug-disease intersection targets,in-cluding core targets MMP-9,ALB,and AKT1.GO and KEGG analyses enriched 593 and 125 pathways,re-spectively,with the NF-κB inflammatory pathway,TNF signaling pathway and lipid metabolism/atherosclerosis pathways highlighted as key mechanisms.Animal ex-periments demonstrated that Polygonum capitatum im-proved serum lipid profiles(reduced TC,TG,LDL-C)in AS hamsters,suppressed the MMP-9/NF-κB signa-ling pathway(downregulated MMP-9,p65 phosphoryla-tion,TNF-α,and IL-6),and inhibited VSMC synthetic phenotypic transformation(upregulated α-SMA and myocardin)by downregulating MCPIP1.Additionally,Polygonum capitatum ameliorated aortic lesions and he-patic lipid deposition in AS hamsters.Conclusions Polygonum capitatum alleviates AS by synergistically regulating the MMP-9/NF-κB/MCPIP1 axis through flavonoid components,suppressing vascular inflammato-ry cascades and maintaining VSMC contractile pheno-types.This reflects Polygonum capitatum's multi-com-ponent,multi-pathway,and multi-target characteristics in combating AS.

Objective:To analyze the short-term efficacy and safety of robot-assisted laparoscopic partial nephrectomy（RAPN）for non-metastatic pathological stage T 3a renal cell carcinoma. Methods:The clinical and pathological data of 45 patients with pathologically confirmed non-metastatic T 3a renal cell carcinoma who underwent RAPN at Sun Yat-sen University Cancer Center between January 2016 and December 2023 were retrospectively reviewed. There were 30 males and 15 females. The average age of the cohort was（54.3±10.7）years，and the average clinical tumor diameter was（4.9±1.8）cm. Of all the patients，35（77.8%）were asymptomatic，7（15.6%）presented with hematuria，and 3（6.7%）presented with lumbar pain. Preoperative imaging assessed 34 patients（75.6%）as having clinical stage T 3a，all suspected of involving the collecting system or perirenal fat invasion；the remaining 11 patients（24.4%）were assessed as having stage T 1-2 disease. The median R.E.N.A.L. nephrectomy score was 8.0（7.0，10.0）. A history of hypertension，diabetes，or chronic kidney disease was present in 18 patients（40.0%）. The primary endpoint was progression-free survival，and the secondary endpoints included postoperative complications and short-term renal function outcomes. Survival curve was estimated using the Kaplan-Meier method，and renal function comparisons were made using the paired t-test. Results:The RAPN was performed through a transabdominal approach in 32 patients（71.1%），with a median estimated blood loss of 150.0（50.0，300.0）ml. Seven（15.6%）patients required intraoperative blood transfusion. The median length of postoperative hospital stay was 4.0（4.0，6.0）days. Postoperative complications occurred in 6 patients（13.3%），including 5（11.1%）with mild complications and 1（2.2%）with a severe complication. Renal function returned to baseline in 24 of 39 evaluable patients（61.5%），while 3 patients（7.7%）developed surgery-related chronic kidney disease 3 to 12 months postoperatively，but none required dialysis. The median follow-up time was 31.8（22.7，50.9）months，12（26.7%）patients received programmed cell death protein 1 inhibitor adjuvant therapy postoperatively. During follow-up，3 patients experienced tumor recurrence，the 3-year progression-free survival rate of the entire cohort was 95.4%.Conclusions:For some carefully selected patients with T 3a renal cell carcinoma，RAPN performed by experienced surgeons is a feasible and safe option，providing excellent short-term oncological outcomes，complication control，and renal function recovery. The long-term efficacy remains to be seen.

Objective:To present an evaluation indicator system for access to cancer screening services.Methods:The evaluation indicator pool was constructed through a scoping review. The theoretical framework was constructed based on the multi-source indicators, and the qualitative expert consultation method was employed to form the initial version of the three-level evaluation indicator system. Delphi expert consultation method was conducted in two rounds to evaluate the relevance, importance, and availability of the proposed evaluation indicator system. The expert positive coefficient, authority coefficient, coordination degree of expert opinions, and concentration of expert opinions were subjected to analysis. Subsequently, the three-level evaluation indicator system for access to cancer screening services was adjusted and determined based on the boundary value method and the open opinions of experts. Finally, the combination weight method was employed to determine the weight.Results:The initial version of the indicator system comprised 3 primary (first-level) indicators, 11 secondary (second-level) indicators, and 46 tertiary (third-level) indicators. Delphi expert consultation was conducted for the initial version, and 17 experts ultimately completed it, exhibiting a positive coefficient of 100% and an authority coefficient of 0.87. In comparison to the initial round of consultation, Kendall's W coefficient ranges (0.15-0.43, all P<0.05) of relevance, importance, and availability scores for each tertiary indicator in the second round exhibited an improvement. The analysis of the importance dimension indicates that expert opinions are also more concentrated, as evidenced by an increase of 8.5% and 7.0% in the proportion of the tertiary indicators with an arithmetic mean above 8 and a full mark ratio above 0.5, respectively. The final evaluation indicator system comprises three primary indicators, with the weights of structure evaluation, process evaluation, and outcome evaluation being 0.338, 0.378, and 0.285, respectively. It also comprises 11 secondary indicators and 45 tertiary indicators. Conclusions:The evaluation indicator system developed in this article can be an effective evaluation tool for quantitative comparison of access to cancer screening services across different populations, cancer types, and before and after intervention. Furthermore, it is recommended that the system undergo continuous optimization concerning its application.

Objective:To present an evaluation indicator system for access to cancer screening services.Methods:The evaluation indicator pool was constructed through a scoping review. The theoretical framework was constructed based on the multi-source indicators, and the qualitative expert consultation method was employed to form the initial version of the three-level evaluation indicator system. Delphi expert consultation method was conducted in two rounds to evaluate the relevance, importance, and availability of the proposed evaluation indicator system. The expert positive coefficient, authority coefficient, coordination degree of expert opinions, and concentration of expert opinions were subjected to analysis. Subsequently, the three-level evaluation indicator system for access to cancer screening services was adjusted and determined based on the boundary value method and the open opinions of experts. Finally, the combination weight method was employed to determine the weight.Results:The initial version of the indicator system comprised 3 primary (first-level) indicators, 11 secondary (second-level) indicators, and 46 tertiary (third-level) indicators. Delphi expert consultation was conducted for the initial version, and 17 experts ultimately completed it, exhibiting a positive coefficient of 100% and an authority coefficient of 0.87. In comparison to the initial round of consultation, Kendall's W coefficient ranges (0.15-0.43, all P<0.05) of relevance, importance, and availability scores for each tertiary indicator in the second round exhibited an improvement. The analysis of the importance dimension indicates that expert opinions are also more concentrated, as evidenced by an increase of 8.5% and 7.0% in the proportion of the tertiary indicators with an arithmetic mean above 8 and a full mark ratio above 0.5, respectively. The final evaluation indicator system comprises three primary indicators, with the weights of structure evaluation, process evaluation, and outcome evaluation being 0.338, 0.378, and 0.285, respectively. It also comprises 11 secondary indicators and 45 tertiary indicators. Conclusions:The evaluation indicator system developed in this article can be an effective evaluation tool for quantitative comparison of access to cancer screening services across different populations, cancer types, and before and after intervention. Furthermore, it is recommended that the system undergo continuous optimization concerning its application.

Syringa pinnatifolia, belonging to the family Oleaceae, is a species endemic to China. It is predominantly distributed in the Helan Mountains region of Inner Mongolia and Ningxia of China. The peeled roots, stems, and thick branches have been used as a distinctive Mongolian medicinal material known as "Shan-chen-xiang", which has effects such as suppressing "khii", clearing heat, and relieving pain and is employed for the treatment of cardiovascular and pulmonary diseases and joint pain. Over the past five years, significant increase was achieved in research on chemical constituents and pharmacological effects. There were a total of 130 new constituents reported, covering sesquiterpenoids, lignans, and alkaloids. Its effects of anti-myocardial ischemia, anti-cerebral ischemia/reperfusion, sedation, and analgesia were revealed, and the mechanisms of agarwood formation were also investigated. To better understand its medical value and potential of clinical application, this review updates the research progress in recent five years focusing on the chemical constituents and pharmacological effects of S. pinnatifolia, providing reference for subsequent research on active ingredient and support for its innovative application in modern medicine system.
Medicine, Mongolian Traditional ; Humans ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Syringa/chemistry*

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.