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 analyse and predict the tendencies of using erythrocyte blood in Changsha based on the autoregressive integrated moving average (ARIMA) model, long short-term memory (LSTM) and ARIMA-LSTM combination model, so as to provide reliable basis for designing a feasible and effective blood inventory management strategy. [Methods] The data of erythrocyte usage from hospitals in Changsha between January 2012 and December 2023 were collected, and ARIMA model, LSTM model and ARIMA-LSTM combination model were established. The actual erythrocyte consumption from January to May 2024 were used to assess and verify the prediction effect of the models. The extrapolation prediction accuracy of the models were tested using two evaluation indicators: mean absolute percentage error (MAPE) and root mean square error (RMSE), and then the prediction performance of the model was compared. [Results] The RMSE of LSTM model, optimal model ARIMA(1,1,1)(1,1,1)12 and ARIMA-LSTM combination model were respectively 5 206.66, 3 096.43 and 2 745.75, and the MAPE were 18.78%,11.54% and 9.76% respectively, which indicated that the ARIMA-LSTM combination model was more accurate than the ARIMA model and LSTM model, and the prediction results was basically consistent with the actual situation. [Conclusion] The ARIMA-LSTM model can better predict the clinical erythrocyte consumption in Changsha in the short term.

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.

Background: Intradermal microdroplet injections of botulinum toxin type-A (BoNT/A) effectively ameliorate rosacea-related angiogenesis, but the mechanism remains unclear. Objective: To explore the anti-angiogenesis of BoNT/A in the rosacea-like mouse model and to measure the secretion of vascular endothelial growth factor (VEGF) by mast cells. Methods: A rosacea-like mouse model was induced by LL37 in both Mas-related G-proteincoupled receptor B2 conditional knockout (MrgprB2 −/− ) mice and wild-type (WT) mice, then treated with BoNT/A and/or Apatinib. The abundance of endothelial cells and mast cells in mouse skin was determined using dual immunofluorescence staining. The VEGF levels in supernatants and cell lysates of laboratory of allergic disease 2 (LAD2) mast cells were assessed using reverse transcription quantitative polymerase chain reaction, western blots, and enzyme-linked immunosorbent assay. The effect of conditioned medium (CM) collected from LAD2 on human umbilical vein endothelial cells (HUVECs) was determined using tube formation assays. The number of proliferative cells was confirmed using the 5-ethynyl-2’-deoxyuridine incorporation assays.The effect of BoNT/A on the internalization of Mas-related G-protein-coupled receptor X2 (MRGPRX2) was detected using flow cytometry and immunofluorescence staining. Results: LL37-induced rosacea-like skin manifestations were significantly alleviated in MrgprB2 −/− mice compared to WT controls. BoNT/A mitigated the LL37-induced secretion of VEGF by LAD2. The CM from BoNT/A-treated LAD2 inhibited HUVEC proliferation and tube formation. The LAD2 cells co-treated with LL37 and BoNT/A exhibited dramatically enhanced MRGPRX2 internalization. Conclusion BoNT/A enhances LL37-mediated MRGPRX2 internalization in mast cells, thereby reducing VEGF secretion and neovascularization and improving facial flushing symptom in rosacea.

Objective To explore the construction and practice of an intelligent prevention and treatment system for venous thromboembolism (VTE) in grassroots hospitals. Methods Based on relevant guidelines and expert consensuses on VTE prevention and treatment, domestic and foreign literature was reviewed. A research and development team composed of clinical experts in VTE prevention and treatment, medical and nursing quality management experts, and information engineers conducted investigations and research in surrounding grassroots hospitals. Through evidence-based research and surveys, the team identified relevant business needs, user needs, and functional requirements of grassroots hospitals, and finally formulated a detailed design plan. The main program of system was written in Java. The interface obtained data from the hospital's data platform through Webservice and view interfaces. To prevent issues of repeated data extraction when multiple applications perform time tasks to assess the same patient during later server usage and expansion, the XXL-JOB distributed task scheduling platform was adopted to handle VTE assessments by medical staff. Results After the clinical application of the intelligent VTE prevention and treatment system, the bleeding risk assessment rate increased from 26.20% at the initial system launch in January 2023 to 83.04% by the end of 2023. In January 2023, the implementation rates of mechanical prevention, pharmacological prevention, and combined prevention for medium-to-high-risk VTE patients were 21.39%, 16.39%, and 5.26%, respectively, which increased to 51.75%, 25.50%, and 25.65% in December 2023. Conclusion The VTE prevention and treatment software system developed by grassroots hospitals can improve development efficiency, enhance the clinical practicality of the system, reduce the workload of medical staff, promote standardization and normalization in VTE prevention and treatment, strengthen closed-loop management of medical quality for VTE as a single disease, and effectively improve the prevention and treatment capabilities and levels of VTE within hospitals.

Objective To observe the progression of intestinal fibrosis in chronic radiation intestinal injury(CRII)and study the protective mechanisms of autophagy agonist rapamycin on intestinal fibrosis in CRII.Methods Thirty C57/B6male mice were randomly divided into the control group(CO group),the radiation group(SR group)and the rapamycin intervention group(RI group).The CO group was not treated.In SR group,the CRII model(single dose of9Gy radiation)was established first,and the samples were taken after 3months.In RI group,the rats were treated with rapamycin(2mg/kg,intraperitoneal injection)for 1 week after modeling,other treat-ments were the same as that in SR group.Hematoxylin-eosin staining and Masson staining were used to evaluate the degree of intestinal mucosal injury and intestinal fibrosis.Enzyme-linked immunosorbent assay was used to detect the serum levels of interleukin-1 β(IL-1β)and interleukin-6(IL-6).The level of intestinal α-smooth muscle actin(α-SMA)was detected by immunohistochemistry.The levels of transforming growth factor-β1(TGF-β1),connective tissue growth factor(CTGF)and autophagy-related proteins(p62 and LC3)were detected by Western blot.Results Histopathological staining showed that compared with CO group,the intestinal muco-sal damage was aggravated(P＜0.05),and the degree of intestinal fibrosis was increased in SR group(P＜0.01).Compared with SR group,the intestinal mucosal damages were relieved(P＜0.05),and the intestinal fibrosis was greatly decreased in RI group(P＜0.01).Compared with the CO group,the levels of IL-1 β and IL-6 in the SR group were significantly increased(P＜0.01),while those in the RI group significantly decreased compared with the SR group(P＜0.01).The results of immunohistochemistry and Western blot showed that the expression levels of α-SMA,TGF-β1 and CTGF in the SR group were greatly higher than those in the CO group(P＜0.05),while significantly lower in the RI group than those in the SR group(P＜0.05).The expression of autophagy indexes in SR group were lower than that in the CO group(P＜0.05),and significantly higher in the RI group than that in the SR group(P＜0.05).Conclusion Rapamycin-induced autophagy could improve the process of intestinal fibrosis in CRII,and the mechanism may be related to the inhibition the differentiation and function of intestinal myofibroblasts and reduce the inflammation of intestine.

Chronic lumbar and back pain caused by degenerative vertebral endplates presents a challenging issue for pa-tients and clinicians.As a new minimally invasive spinal treatment method,radiofrequency ablation of vertebral basal nerve in bone can denature the corresponding vertebral basal nerve through radiofrequency ablation of degenerative vertebral endplate.It blocks the nociceptive signal transmission of the vertebral base nerve,thereby alleviating the symptoms of low back pain caused by the degenerative vertebral endplate.At present,many foreign articles have reported the operation principle,opera-tion method,clinical efficacy and related complications of radiofrequency ablation of the vertebral basal nerve.The main pur-pose of this paper is to conduct a comprehensive analysis of the current relevant research,and provide a reference for the follow-up clinical research.