Clinical practice guidelines represent the best recommendations for patient care. They are developed through systematically reviewing currently available clinical evidence and weighing the relative benefits and risks of various interventions. However, clinical practice guidelines have to go through a long translation cycle from development and revision to clinical promotion and application, facing problems such as scattered distribution, high duplication rate, and low actual utilization. At present, the clinical practice guideline information platform can directly or indirectly solve the problems related to the lengthy revision cycles, decentralized dissemination and limited application of clinical practice guidelines. Therefore, this paper systematically examines different types of clinical practice guideline information platforms and investigates their corresponding challenges and emerging trends in platform design, data integration, and practical implementation, with the aim of clarifying the current status of this field and providing valuable reference for future research on clinical practice guideline information platforms.

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.

ObjectiveMagnetoencephalography (MEG), a non-invasive neuroimaging technique, meticulously captures the magnetic fields emanating from brain electrical activity. Compared with MEG based on superconducting quantum interference devices (SQUID), MEG based on optically pump magnetometer (OPM) has the advantages of higher sensitivity, better spatial resolution and lower cost. However, most of the current studies are clinical studies, and there is a lack of animal studies on MEG based on OPM technology. Pain, a multifaceted sensory and emotional phenomenon, induces intricate alterations in brain activity, exhibiting notable sex differences. Despite clinical revelations of pain-related neuronal activity through MEG, specific properties remain elusive, and comprehensive laboratory studies on pain-associated brain activity alterations are lacking. The aim of this study was to investigate the effects of inflammatory pain (induced by Complete Freund’s Adjuvant (CFA)) on brain activity in a rat model using the MEG technique, to analysis changes in brain activity during pain perception, and to explore sex differences in pain-related MEG signaling. MethodsThis study utilized adult male and female Sprague-Dawley rats. Inflammatory pain was induced via intraplantar injection of CFA (100 μl, 50% in saline) in the left hind paw, with control groups receiving saline. Pain behavior was assessed using von Frey filaments at baseline and 1 h post-injection. For MEG recording, anesthetized rats had an OPM positioned on their head within a magnetic shield, undergoing two 15-minute sessions: a 5-minute baseline followed by a 10-minute mechanical stimulation phase. Data analysis included artifact removal and time-frequency analysis of spontaneous brain activity using accumulated spectrograms, generating spectrograms focused on the 4-30 Hz frequency range. ResultsMEG recordings in anesthetized rats during resting states and hind paw mechanical stimulation were compared, before and after saline/CFA injections. Mechanical stimulation elevated alpha activity in both male and female rats pre- and post-saline/CFA injections. Saline/CFA injections augmented average power in both sexes compared to pre-injection states. Remarkably, female rats exhibited higher average spectral power 1 h after CFA injection than after saline injection during resting states. Furthermore, despite comparable pain thresholds measured by classical pain behavioral tests post-CFA treatment, female rats displayed higher average power than males in the resting state after CFA injection. ConclusionThese results imply an enhanced perception of inflammatory pain in female rats compared to their male counterparts. Our study exhibits sex differences in alpha activities following CFA injection, highlighting heightened brain alpha activity in female rats during acute inflammatory pain in the resting state. Our study provides a method for OPM-based MEG recordings to be used to study brain activity in anaesthetized animals. In addition, the findings of this study contribute to a deeper understanding of pain-related neural activity and pain sex differences.

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.

Aim To study the permeability of salidro-side(Sal)to the blood brain barrier(BBB)by high-performance liquid chromatography electrospray ioniza-tion tandem mass spectrometry(UPLC-ESI-MS-MS),and to explore the target and mechanism of Sal in the treatment of ischemic stroke(IS)by network pharma-cology,molecular docking technique and animal exper-iment.Methods UPLC-ESI-MS/MS was used to study the BBB penetration of Sal.Multiple databases were used to predict the target of Sal and the disease target of IS,GO and KEGG enrichment analysis were performed and verified by molecular docking technique and animal experiments.Results After Sal adminis-tration to normal rats and MCAO rats,Sal prototype and the metabolite tyrosol were detected in plasma and brain tissue of rats.A total of 191 targets were identi-fied by network pharmacology,the enrichment analysis of GO mainly involved in the biological processes of proteolysis and positive regulation of cell migration,and the analysis of KEGG pathway suggested that PI3K-Akt,MAPK,FOXO and other signaling path-ways played a key role in the treatment of IS by Sal The results of molecular docking showed that Sal had good binding ability with the core target of docking,and the results of animal experiments showed that Sal could significantly improve the neurologic impairment of MCAO rats,the number of Nissl-positive cells in is-chemic side significantly increased,and the expression of VEGF,EGFR and IGF1 increased,while the ex-pression of IL-6 and MMP9 was inhibited.Conclu-sions Sal is able to penetrate the BBB and enter the central nervous system for its pharmacological effects.Network pharmacology predicts the core targets of Sal in the treatment of IS,including VEGFA,EGFR,IL-6,MMP9,IGF1,CASP3,ALB,SRC.The effects of Sal on some core targets can be verified by animal ex-periments,to provide a reference for further study of the mechanism of Sal in the treatment of IS.

Objective To explore clinical effect of single small incision with honeycomb titanium plate in treating acute acromioclavicular dislocation.Methods The clinical data of 40 patients with acute acromioclavicular dislocation admitted from December 2019 to December 2021 were retrospectively analyzed and divided into two groups according to different surgical methods.Among them,20 patients were fixed with single small incision with honeycomb titanium plate(titanium plate group),including 11 males and 9 females,aged from 23 to 65 years old with an average of(47.40±12.58)years old;12 patients on the left side,8 patients on the right side;11 patients with type Ⅲ,3 patients with type Ⅳ,and 6 patients with type Ⅴ according to Rockwood classification.Twenty patients were fixed with clavicular hook plate(clavicular hook group),including 8 males and 12 females,aged from 24 to 65 years old with an average of(48.40±12.08)years old;12 patients on the left side,8 patients on the right side;10 patients with type Ⅲ,2 patients with type Ⅳ,and 8 patients with type V according to Rockwood classifica-tion.Operative time,incision length,intraoperative blood loss,hospital stay,visual analogue scale(VAS)and Constant-Murley score of shoulder joint function were compared between two groups.Anteroposterior radiographs of the affected shoulder joint were recorded before,immediately and 6 months after surgery,and the coracoclavicular distance was measured and compared.Results Both groups of patients were successfully completed operation without serious complications.All patients were fol-lowed up for 6 to 15 months with an average of(11.9±4.8)months.There were no incisional infection,internal plant fracture or failure,bone tunnel fracture and other complications occurred.The incision length of titanium plate group(35.90±3.14)mm was significantly shorter than that of clavicular hook group(49.30±3.79)mm(P＜0.05).There were no significant difference in operative time,intraoperative blood loss and hospital stay between two groups(P＞0.05).At 1 and 3 months after operation,VAS of titanium plate group was lower than that of clavicular hook group(P＜0.05).Connstant-Murley scores in titanium plate group at 1,3 and 6 months after operation were(86.80±1.36),(91.60±2.32)and(94.90±2.22),respectively;and in clavicular hook group were(78.45±5.47),(85.55±2.01)and(90.25±1.92),which were higher than that of clavicular hook group(P＜0.05).There was no significant difference in coracoclavicular distance between two groups immediately and 6 months after op-eration(P＞0.05).Conclusion For the treatment of acute acromioclavicular joint dislocation,single small incision combined with honeycomb titanium plate have advantages of shorter incision,fast recovery of shoulder joint function without the second operation,and has good satisfaction of patient.

Objective To explore clinical effect of modified Chinese-way technique under shoulder arthroscopy in treating massive rotator cuff tears.Methods From January 2019 to June 2022,22 patients with massive rotator cuff tears who underwent arthroscopic rotator cuff repair with improved Chinese-way technique,including 10 males and 12 females,aged from 46 to 76 years old with an average of(64.14±7.45)years old;the courses of disease ranged from 5 to 14 months with an average of(8.32±2.42)months;19 patients were complete repaired,and 3 patients were partial repaired.Visual analogue scale(VAS)and University of California at Los Angeles(UCLA)scale were used to evaluate pain and function of shoulder joint preopera-tively and 1 year postoperatively.Postoperative complications,the integrity of reconstructed tissue structure and the size of sub-acromial space were observed.Results All patients were followed up from 12 to 34 months with an average of(17.14±5.93)months.Re-tear were occurred in 4 patients during MRI follow-up,but clinical symptoms of patients were improved significant-ly and they were satisfied with the treatment,the others were no complications such as incision infection,peripheral nerve in-jury,loosening and falling off of internal fixation anchors.Preoperative and 1 year after operation VAS were(8.05±1.12)and(1.82±1.50),UCLA scores were(7.45±1.65)and(31.41±2.87)respectively,and the difference was statistically significant(P＜0.05).Conclusion The modified Chinese-way technique under shoulder arthroscopy for the massive rotator cuff tear could relieve pain obviously and recovery postoperative function well,with satisfactory curative effect.