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.

OBJECTIVE: To investigate the accuracy and safety of pedicle screw placement using 3D-printed auxiliary guides in scoliosis correction surgery for adolescents. METHODS: A retrospective analysis was conducted on the clinical data of 51 patients who underwent posterior scoliosis correction surgery from January 2020 to March 2023. Among them, there were 35 cases of adolescent idiopathic scoliosis and 16 cases of congenital scoliosis. The patients were divided into two groups based on the auxiliary tool used:the 3D-printed auxiliary guide screw placement group (3D printing group) and the free-hand screw placement group (free-hand group, without auxiliary tools). The 3D printing group included 32 patients (12 males and 20 females) with an average age of (12.59±2.60) years;the free-hand group included 19 patients (7 males and 12 females) with an average age of (14.58±3.53) years. The two groups were compared in terms of screw placement accuracy and safety, spinal correction rate, intraoperative blood loss, number of intraoperative fluoroscopies, operation time, hospital stay, and preoperative and last follow-up scores of the Scoliosis Research Society-22 (SRS-22) questionnaire. RESULTS: A total of 707 pedicle screws were placed in the two groups, with 441 screws in the 3D printing group and 266 screws in the free-hand group. All patients in both groups successfully completed the surgery. There was a statistically significant difference in operation time between the two groups (P<0.05). The screw placement accuracy rate of the 3D printing group was 95.46% (421/441), among which the Grade A placement rate was 89.34% (394/441);the screw placement accuracy rate of the free-hand group was 86.47% (230/266), with a Grade A placement rate of 73.31% (195/266). There were statistically significant differences in the accuracy of Grade A, B, and C screw placements between the two groups (P<0.05), while no statistically significant differences were observed in intraoperative blood loss, number of fluoroscopies, correction rate, or hospital stay (P>0.05). In the SRS-22 questionnaire scores, the scores of functional status and activity ability, self-image, mental status, and pain of patients in each group at the last follow-up were significantly improved compared with those before surgery (P<0.05), but there were no statistically significant differences in all scores between the two groups (P>0.05). CONCLUSION In scoliosis correction surgery, compared with traditional free-hand screw placement, the use of 3D-printed auxiliary guides for screw placement significantly improves the accuracy and safety of screw placement and shortens the operation time.
Humans ; Male ; Scoliosis/surgery* ; Female ; Adolescent ; Printing, Three-Dimensional ; Retrospective Studies ; Pedicle Screws ; Child

BACKGROUND: Medical informatics accumulated vast amounts of data for clinical diagnosis and treatment. However, limited access to follow-up data and the difficulty in integrating data across diverse platforms continue to pose significant barriers to clinical research progress. In response, our research team has embarked on the development of a specialized clinical research database for cardiology, thereby establishing a comprehensive digital platform that facilitates both clinical decision-making and research endeavors. METHODS: The database incorporated actual clinical data from patients who received treatment at the Cardiovascular Medicine Department of Chinese PLA General Hospital from 2012 to 2021. It included comprehensive data on patients' basic information, medical history, non-invasive imaging studies, laboratory test results, as well as peri-procedural information related to interventional surgeries, extracted from the Hospital Information System. Additionally, an innovative artificial intelligence (AI)-powered interactive follow-up system had been developed, ensuring that nearly all myocardial infarction patients received at least one post-discharge follow-up, thereby achieving comprehensive data management throughout the entire care continuum for high-risk patients. RESULTS: This database integrates extensive cross-sectional and longitudinal patient data, with a focus on higher-risk acute coronary syndrome patients. It achieves the integration of structured and unstructured clinical data, while innovatively incorporating AI and automatic speech recognition technologies to enhance data integration and workflow efficiency. It creates a comprehensive patient view, thereby improving diagnostic and follow-up quality, and provides high-quality data to support clinical research. Despite limitations in unstructured data standardization and biological sample integrity, the database's development is accompanied by ongoing optimization efforts. CONCLUSION The cardiovascular specialty clinical database is a comprehensive digital archive integrating clinical treatment and research, which facilitates the digital and intelligent transformation of clinical diagnosis and treatment processes. It supports clinical decision-making and offers data support and potential research directions for the specialized management of cardiovascular diseases.

Objective To evaluate the changes in postoperative plasma β-endorphin(β-EP)levels in patients who had received perioperative electroacupuncture(EA)treatment in 10 randomized controlled trials(RCTs)and examine the impact of EA on postoperative pain.Methods This meta-analysis evaluated the changes in plasma β-EP levels and visual analog scale(VAS)12,24 and 48 hours after surgery in patients receiving perioperative EA.It also assessed the changes in plasma serotonin(5-hydroxytryptamine,5-HT)and prostaglandin E2(PGE2)levels at 24 hours postsurgery.A comprehensive search was conducted in the China National Knowledge Infrastructure(CNKI),Wanfang,Chongqing VIP database,Chinese Biomedical Database(CBM),Web of Science,and PubMed databases.RCTs on perioperative EA and β-EP published from the inception of the websites up to July 25,2023,were retrieved.Effect size aggregation,literature quality assessment,and bias analysis were performed using RevMan 5.3 software,and sensitivity analysis was conducted via R 4.3.1.Results A total of 10 RCTs involving 706 patients were included.EA in conjunction with conventional anesthesia significantly increased plasma β-EP levels at 12 hours postsurgery[standard mean difference(SMD)=2.79,95%CI(1.85,3.72),Z=5.81,P＜0.00001],24hours postsurgery[SMD=1.87,95%CI(0.9,2.83),Z=3.79,P=0.0001],and 48 hours postsurgery[SMD=2.02,95%CI(1.49,2.54),Z=7.50,P＜0.00001].EA reduced plasma PGE2 levels at 24 hours postsurgery and plasma 5-HT levels at 24 hours postsurgery,and the VAS at 12,24 and 48 hours after surgery also decreased.Conclusion These findings suggest that perioperative EA markedly elevates plasma β-EP levels,reduces pain-inducing factors in plasma,and effectively alleviates acute postoperative pain.

Objective To explore the effect of Wenyang Huazhuo Tongluo recipe on pulmonary microvascular injury in mice with scleroderma based on mitophagy.Methods Fifty mice were randomly divided into blank control group(0.9％NaCl,by gavage),control group(0.9％NaCl,by gavage),model group,Wenyang Huazhuo Tongluo recipe group(47mg·kg-1·d-1 Wenyang Huazhuo Tongluo recipe by gavage),positive control group(10 mg·kg-1·d-1 KC7F2 dissolved in phosphate buffer solution intraperitoneal injection),continuous administration for 4 weeks.The expression levels of in vitro membrane translocation enzyme 20(TOMM20),hypoxia inducible factor-1α(HIF-1α),B cell lymphoma-2/adenovirus E1B-19 kDa interacting protein 3(BNIP3),PTEN inducible muscle enzyme protein 1(PINK1)and E3 ubiquitin ligase(Parkin)in lung tissue were detected by immunohistochemistry(IHC).Western blot(WB)was used to detect the expression levels of mitophagy-related proteins(TOMM20,LC3B)and HIF-1α/BNIP3/PINK1/Parkin pathway proteins in pulmonary microvascular endothelial cells.Results The relative content of HIF-1α in microvascular endothelial cells of lung tissue in the control group,model group,Wenyang Huazhuo Tongluo recipe group and positive control group were 0.17±0.02,0.98±0.01,0.66±0.03 and 0.48±0.01;the relative content of BNIP3 were 0.40±0.02,0.74±0.01,0.56±0.01 and 0.60±0.02;the relative content of PINK1 were 0.26±0.04,0.88±0.01,0.65±0.02 and 0.67±0.02;the relative contents of Parkin were 0.33±0.02,0.89±0.01,0.65±0.02 and 0.77±0.02;the relative contents of TOMM20 were 1.10±0.02,0.58±0.01,1.02±0.01 and 0.98±0.03;the relative contents of LC3B-Ⅰ/LC3B-Ⅱ were 0.24±0.01,0.80±0.01,0.53±0.02 and 0.70±0.02,respectively.The content of HIF-1α,BNIP3,PINK1,Parkin and LC3B-Ⅰ/LC3B-Ⅱ in model group was higher than those in control group.Wenyang Huazhuo Tongluo recipe can effectively reduce its content.The content of TOMM20 in the model group was lower than that in control group,and Wenyang Huazhuo Tongluo recipe can effectively increase its content.Conclusion Wenyang Huazhuo Tongluo recipe may inhibit mitophagy and improve SSc pulmonary microvascular injury by increasing TOMM20 and inhibiting the protein expression of LC3B and HIF-1α/BNIP3/PINK1/Parkin signaling pathway.

Monosodium urate (MSU)-induced the gouty arthritis (GA) model was used to investigate the effect of Nod-like receptor protein 3 (NLRP3) inhibitor N14 in alleviating GA. Firstly, the effect of NLRP3 inhibitor N14 on the viability of mouse monocyte macrophage J774A.1 was examined by the cell counting kit-8 (CCK-8) assay. The expression of mature interleukin 1β (IL-1β) and cysteinyl aspartate specific proteinase-1 (caspase-1) p20 in the cell supernatant and the expression of NLRP3, caspase-1 and pro-IL-1β proteins in the cell lysates was detected by Western blot for the inhibitory effect of N14 on the MSU-induced NLRP3 inflammasome activation in J774A.1 cells. Animal behavioral tests were used to detect redness, swelling, heat and pain in mice with gouty arthritis. Hematoxylin-eosin (H&E) staining revealed pathologic changes and inflammatory infiltration in foot sections. Protein expression of NLRP3, caspase-1, and pro-IL-1β in mouse hind paw tissues were assessed by Western blot. The effect of N14 on the plasma levels of alanine transaminase (ALT), aspartate transaminase (AST), creatinine (CRE), urea, and uric acid (UA) was investigated by the MSU-induced gouty arthritis model in mice. All animal experiments in this paper were approved by the Scientific Ethics Review Board of Qingdao Marine Biomedical Research Institute (grant No. E-MBWNL-2024-20). The experimental results showed that N14 did not exhibit cytotoxicity in mouse monocyte macrophage J774A.1 cells at concentrations up to 100 μmol·L^-1, and N14 effectively prevented MSU-induced activation of NLRP3 inflammasome. In the mouse gouty arthritis model, N14 significantly ameliorated the redness, swelling, heat and pain caused by GA, and down-regulated the levels of NLRP3 inflammasome-associated proteins in mouse hind paw tissues. Meanwhile, N14 appeared to be well tolerated, as it did not significantly affect various biochemical indices in mouse plasma. In conclusion, N14 effectively alleviated GA in mice by inhibiting the NLRP3 inflammasome pathway, which is important for both prevention and treatment of related diseases.