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.

Depression is a heterogeneous mental disorder in which the interaction between genetic susceptibility and environ-mental factors plays a key role in its pathogenesis.Although the specific mechanisms of the disease still need to be thoroughly studied and elucidated,there is now a broad consensus that epi-genetic markers have a central influence on its mechanism of ac-tion.DNA methylation of brain-derived neurotrophic factor(BD-NF)is not only regarded as a promising epigenetic biomarker of pathology,but may also help predict the efficacy of antidepres-sants.In this paper we reviewed the gene structure of BDNF and its DNA methylation regulation mechanism,and also analyzed the changes of DNA methylation of this factor in depression pa-tients and animal models,aiming to provide new ideas and theo-retical support for clinical research.

Objective To explore the role of X chromosome encoded epigenetic regulator UTX in NK cell-mediated anti-tumor activity.Methods Male Ncr1-iCre mice were crossed with female UTXfl/fl mice to generate F1 Ncr1-iCre+UTXfl/-male mice,which were further crossed with female UTXfl/fl mice to obtain male Ncr1-iCre-UTX fl/-control mice(M-Con)and NK-specific deletion of UTX male mice Ncr1-iCre+UTXfl/-(M-KO),as well as female Ncr1-iCre-UTXfl/fl control mice(F-Con)and UTX-deficient female mice Ncr1-iCre+UTXfl/fl(F-KO).UTX-deficient mice were injected with melanoma cell line B16F10 via tail vein to observe pulmonary metastatic tumor nodules.Moreover,flow cytometry was applied to detect the proportion and quantity of pulmonary NK cells(CD3-CD19-NK1.1+),maturation makers KLRG1 and CD11b,activation receptors NKG2D and CD69,and effector molecules,including perforin,granzyme B,CD107a,and IFN-γ.Then pulmonary NK cells were sorted and co-cultured with B16F10 cells,and the apoptosis of the melanoma cells was measured with flow cytometry.Results Compared with the M-Con mice,the M-KO mice presented less number of pulmonary tumor nodules(P<0.05),increased proportion and quantity of NK cells in the tumor microenvironment(P<0.01),though no obvious changes in the ratio of NK maturation makers KLRG1 to CD11b,enhanced expression level of cytotoxic molecule perforin(P<0.01),but no changes in the expression of effector molecule granzyme B,degranulation marker CD107a and cytokine IFN-γ in NK cells.Co-culture of NK cells and B16F10 cells promoted the apoptosis of tumor cells(P<0.05).Compared with the F-Con mice,the F-KO mice had no statistical difference in the number of pulmonary tumor nodules,but larger proportion and number of NK cells(P<0.05),decreased ratio of KLRG1 to CD11b(P<0.01),elevated level of perforin but decreased levels of granzyme B,CD107a and IFN-γ in NK cells(P<0.01).The co-culture of NK cells and B16F10 cells reduced the apoptosis of tumor cells in F-KO female mice(P<0.05).Conclusion NK-specific deletion of UTX regulates pulmonary metastasis of melanoma in a sex-dependent manner.

Bone grafting is a primary method for treating bone defects. Among various graft materials, xenogeneic bone substitutes are widely used in clinical practice due to their abundant sources, convenient processing and storage, and avoidance of secondary surgeries. With the advancement of domestic production and the limitations of imported products, an increasing number of bone filling or grafting substitute materials isentering clinical trials. Relevant experts have drafted this consensus to enhance the management of medical device clinical trials, protect the rights of participants, and ensure the scientific and effective execution of trials. It summarizes clinical experience in aspects, such as design principles, participant inclusion/exclusion criteria, observation periods, efficacy evaluation metrics, safety assessment indicators, and quality control, to provide guidance for professionals in the field.
Humans ; Bone Substitutes/therapeutic use* ; Randomized Controlled Trials as Topic/methods* ; Consensus ; Bone Transplantation ; Research Design

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 explore the intervention mechanism of Shangke Lengtongtie on cold hyperalgesia in KOA mice based on the HMGB1/CXCL12/CXCR4 signaling axis.METHODS Monosodium iodoacetate(MIA)was used for the intra-articular injec-tion into the knee joint to establish mice model of knee osteoarthritis(KOA).Peripheral blood monocytes were extracted from mice,cultured,and then reinfused into the tail vein of the mice.Subsequently,in vivo animal imaging was used to observe the recruitment sites of these monocytes.The cold hyperalgesia threshold was measured at various time points in each group of mice.Hematoxylin and eosin(HE)staining was used to evaluate the level of synovial pathological changes.ELISA was employed to detect the expression of in-flammatory factors IL-1β,TNF-α,and pain mediators CGRP and Substance P in mouse serum.Western blot and qPCR methods were used to detect the protein and gene expression of cold hyperalgesia-related indicators such as TRPA1,TRPM8,HMGB1,CXCL12,CXCR4,Collagen Ⅰ,and Netrin-1 in synovial tissue,as well as DCC in dorsal root ganglia(DRG)tissue.RESULTS In vivo ima-ging showed that after the monocytes were reinfused into KOA mice,they were recruited to the knee joint area,with the HMGB1 group exhibiting a greater recruitment of circulating monocytes at the knee joint.Additionally,compared to the control group,the KOA group and HMGB1 group showed inflammatory pathological changes in the synovium,increased expression of serum inflammatory factors and pain mediators,reduced cold hyperalgesia threshold,and upregulated protein and gene expression of cold hyperalgesia-related indica-tors in synovial and DRG tissues.The changes were more significant in the HMGB1 group compared to the KOA group(P<0.05).Af-ter treatment with Shangke Lengtongtie or GL intervention,synovial inflammation was alleviated,serum inflammatory factors and pain mediators decreased,cold hyperalgesia threshold increased,and the upregulation of cold hyperalgesia-related indicator protein and gene expression levels was significantly reversed(P<0.05).CONCLUSION Shangke Lengtongtie exerts a beneficial effect on the mitigation of synovitis and cold hyperalgesia in KOA mice,a therapeutic mechanism that possibly mediated through the inhibition of the HMGB1/CXCL12/CXCR4 signaling axis.