BACKGROUND:The optimal surgical technique for treating posterior cruciate ligament tibial attachment point avulsion injuries is debatable.With the application and maturity of arthroscopic surgery,it has great prospects in the diagnosis and treatment of posterior cruciate ligament tibial attachment point avulsion fractures. OBJECTIVE:To summarize the application and progress of arthroscopic technology in the treatment of posterior cruciate ligament tibial attachment point avulsion fractures,including different arthroscopic treatment methods,surgical approach,tibial tunnel design,suture material selection,and internal fixation implant selection. METHODS:Relevant literature was retrieved from PubMed,Web of Science,and ScienceDirect databases through computers.The search period was from January 2003 to November 2023.Chinese search terms were"posterior cruciate ligament,posterior cruciate ligament,avulsion fracture,arthroscopy".English search terms were"posterior cruciate ligament,injury,fracture,tibia,arthroscopic,operation,fixation,treatment".Totally 97 articles were included for review. RESULTS AND CONCLUSION:Arthroscopic technology provides a reliable treatment for posterior cruciate ligament tibial attachment point avulsion fractures.Arthroscopic treatment for avulsion fractures of the tibial attachment point of the cruciate ligament can be divided into several categories based on the type of approach,suture material,and the number of approaches used for suture and fixation of the tibial tunnel:arthroscopic suture fixation combined with autologous graftenhancement and reconstruction,arthroscopic multicross band suture bridge fixation,arthroscopic strong thread fixation,and arthroscopic direct anterior posterior suture suspension fixation.In various studies,commonly used outcome measures include knee range of motion,Lysholm scale,International Knee Documentation Committee,and KT-2000 arthrometer.Studies have shown that at the last follow-up,the score results showed significant improvement compared to surgery.In the radiological follow-up results of various arthroscopic techniques,all studies have shown satisfactory results.During the follow-up process,all types of patients who received arthroscopic treatment for cruciate ligament tibial attachment point avulsion fractures did not experience serious complications,such as traumatic arthritis,neurovascular injury,perioperative wound infection,thrombosis,and nonunion of fractures.

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.

Thirty-one phenolic constituents were isolated and purified from the 95% ethanol extract of Sanguisorbae Radix by using various chromatographic techniques, including macroporous resin, silica gel, ODS, Sephadex LH-20 and semi-preparative HPLC. Their structures were elucidated by physicochemical properties, spectroscopic data (MS and NMR) and electronic circular dichroism (ECD) spectra, and identified as 3-methoxyl-2S,3S-epoxyflavanone (1a), 3-methoxyl-2R,3R-epoxyflavanone (1b), longifoin B (2), longifoin C (3), eriodictyol (4), naringenin (5), liquiritigenin (6), 5,3ʹ-dihydroxy-7,4ʹ-dimethoxyflavanone (7), naringenin-7-O-β-D-glucopyranoside (8), dihydroquercetin (9), dihydrokaempferol (10), (-)-garbanzol (11), (2R,3R)-4-methoxyl-distylin (12), kaempferol (13), quercetin (14), α,4,2′,4′-tetrahydroxydihydrochalcone (15), phloretin (16), (+)-catechin (17), ethyl (+)-cyanidan-3-ol-8-carboxylate (18), phyllocoumarin (19), methyl 3-methoxy-4,5-dihydroxybenzoate (20), 4,5-dimethoxy-3-hydroxybenzoic acid methyl ester (21), 3,4′-di-O-methylellagic acid (22), 3,4,3′-O-trimethylellagic acid (23), 3,3ʹ,4ʹ-O-trimethylellagic acid-4-O-β-D-xyloside (24), (3R)-thunberginol C (25), resveratrol (26), 1-hydroxypinoresinol (27), (7S,8S)-3-methoxy-3′,7-epoxy-8,4′-oxyneoligna-4,9,9′-triol (28), emodin-8-O-β-D-glucoside (29), phloracetophenone (30) and 4-(4′-hydroxyphenyl)-butan-2-one (31). Among them, compound 1a and 1b is a pair of new flavonoid enantiomers, compounds 2 and 3 are a pair of new epimers, while compounds 4, 5, 6, 9, 10, 13, 16 and 26 were obtained from S. officinalis for the first time, compounds 7, 8, 27, 30 and 31 were isolated for the first time from the S. officinalis genus, and compounds 11, 12, 15, 18, 19, 25, 28 and 29 were isolated for the first time from the Rosaceae. The antioxidant activities of compounds 1-24 were evaluated by activating the Nrf2 transcriptional pathway, which were measured by the dual-luciferase reporter gene assay in 293T cells. Compounds 4, 6-10, 12, 14, 17, 19, 20 and 22-24 showed significant Nrf2 agonistic effect compared with the control group at 25 μmol·L^-1, which provided reference for the research of their antioxidant activity.