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.

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.

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 provide anatomical,radiological,and clinical diagnostic and therapeutic references for ankylosing spondylitis and spinal surgical operations.Methods Non-measurement spinal observations,X-ray examinations,and measurements were performed on two spinal specimens,along with digital image acquisition and processing.Results The first specimen included thoracic vertebra 7(T7)to lumbar vertebra 3(L3),with an average total length of 29.7 cm;the second specimen ranged from cervical vertebra 7(C7)to lumbar vertebra 2(L2),with an average total length of 38.3 cm.The specimens showed partial or complete calcification of ligaments,ossification of the small joints and intervertebral discs,and osteoporosis;The anterior-posterior diameter(width)of the vertebral foramen was narrower than that of a normal adult,while most of the superior-inferior diameter(height)was wider.Radiographically,the anterior longitudinal ligament calcification appeared as dot-like or striated,but it was actually flaky in the actual specimens.The specimens provided views of the facet joints,costovertebral joints,and intervertebral foramina that was difficult to demonstrate on two-dimensional X-ray images.Conclusion As ankylosing spondylitis progresses,the range of motion in spinal bending and rotation decreases,as does the extent of thoracic expansion,thereby affecting respiration and complicating procedures such as intraspinal anesthesia and sacral canal injections.In terms of diagnosis,bone specimens and X-ray films allow us to understand the development process and severity of ankylosing spondylitis more directly and accurately.

Objective To investigate the clinical outcomes of cranioplasty with polyether ether ketone(PEEK)or titanium after large craniectomy in patients.Methods Clinical data of 150 patients undergoing skull repair due to large frontotemporal skull defect in our hospital from April 2018 to June 2022 were retrospectively analyzed,and they were divided into titanium mesh group and PEEK group according to different repair materials.The conditions of surgical site infection,bleeding,subcutaneous effusion,seizure,implant rupture or exposure in the two groups were compared.Results In the PEEK group,96.3％of patients needed to implant the repair material under the temporal muscle,which was significantly higher than that in the titanium mesh group(78.1％)(P＜0.05).There were no significant differences in postoperative complications including infection,bleeding,seizure,implant rupture or leakage between the two groups(P＞0.05).However,the incidence of postoperative subcutaneous effusion in PEEK group was higher than that in titanium mesh group(14.8％VS4.2％,P＜0.05),and the difference was statistically significant.Conclusion Both titanium and PEEK can be used in cranioplasty for patients with large frontotemporal cranial defects.Subcutaneous effusion is common in patients underwent cranioplasty with PEEK postoperatively,which needs to be paid more attention.

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.