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 effect of Qideng Mingmu capsule on the formation and remodeling of retinal neovascularization in mice with oxygen-induced retinopathy (OIR).Methods:Thirty-six postnatal day 7 (P7)SPF grade C57BL/6J pups were divided into normal group, OIR group, Qideng Mingmu capsule group and apatinib group by random number table method, with 9 mice in each group.The mice in the normal group were raised in normal environment.The mice in the other three groups were fed in hyperoxic environment of (75±2)% oxygen concentration for 5 days from P7 to P12 and then were fed in normal environment for 5 days from P12 to P17 to establish the OIR model.From P12, mice in Qideng Mingmu capsule group and apatinib group were given intragastric administration of Qideng Mingmu capsule (900 mg/kg) and vascular endothelial growth factor receptor 2 inhibitor apatinib (70 mg/kg) respectively, once a day for 5 consecutive days.On P17, paraffin sections of mouse eyeballs were made and stained with hematoxylin-eosin to count the number of vascular endothelial cells that broke through the internal limiting membrane.The retinal slices were prepared and stained with FITC-dextran to quantify the retinal non-perfusion area, neovascularization density and total vascular density.The distribution and fluorescence intensity of retinal vascular endothelial cell marker CD31 and pericyte marker α-smooth muscle actin (α-SMA) were observed by double immunofluorescence staining.Immunohistochemical staining was used to detect the expression and distribution of retinal hypoxia inducible factor-1α (HIF-1α) and vascular endothelial cadherin (VE-cadherin).The use and care of animals were in accordance with the Regulations on the Management of Laboratory Animals issued by the Ministry of Science and Technology.This study was approved by the Animal Ethics Committee of Chengdu University of Traditional Chinese Medicine (No.2019-30).Results:The number of vascular endothelial cells breaking through the internal limiting membrane in normal group, OIR group, Qideng Mingmu capsule group and apatinib group were (2.83±4.40), (37.33±5.43), (23.83±6.79) and (14.00±9.34), respectively, with a statistically significant overall difference ( F=28.313, P<0.001).There were more vascular endothelial cells breaking through internal limiting membrane in OIR group than in normal group, Qideng Mingmu capsule group and apatinib group, showing statistically significant differences (all at P<0.05).In the observation of mouse retinal slices, there were large non-perfusion areas, neovascularization buds and disordered distribution of blood vessels in OIR group.The distribution of blood vessels was more uniform and the areas of non-perfusion and neovascularization were smaller in Qideng Mingmu capsule group and apatinib group than in OIR group.The relative area of central retinal non-perfusion area and neovascularization density were significantly lower in normal group, Qideng Mingmu capsule group and apatinib group than in OIR group (all at P<0.05).The immunofluorescence intensity of CD31 and the absorbance value of HIF-1α were significantly lower, and the immunofluorescence intensity of α-SMA and the absorbance value of VE-cadherin were significantly higher in normal group, Qideng Mingmu capsule group and apatinib group than in OIR group (all at P<0.05). Conclusions:Qideng Mingmu capsule can inhibit retinal neovascularization formation, increase vascular pericyte coverage, relieve retinal hypoxia and increase vascular integrity in OIR mice.It can protect the retinal vessels of OIR mice.

Based on the story of Chinese idiom, (a magical and skilled form of craftsmanship) as the breakthrough point, this paper discusses the both (cook) and the experienced acupuncture practitioner have the same high skills and explores the potential relationship between mind-regulation in treatment with acupuncture and flow theory. It is believed that the skills of ancient acupuncture practitioner in mind-regulation with acupuncture is not only a kind of "Tao" mode, but also a state of "flow". By the discussion on mind-regulation and flow theory, modern people may have more clear recognition on the mind regulation in treatment with acupuncture so as to better determine the therapeutic methods of acupuncture for mind-regulation.

Objective: To explore the key genes and potential therapeutic drugs for osteoarthritis (OA) by bioinformatics.Method: The microarray data GSE55235 was downloaded from the data platform of gene expression omnibus (GEO) and the differentially expressed genes were screened by R language software (3.5.0).Then,the differentially expressed genes were subjected to gene ontology (GO) enrichment analysis and Kyoto encyclopedia of genes and genomes (KEGG) signaling pathway analysis with David online database.The protein-protein interaction was analyzed by String 10.5 online database and visual editing was analyzed by Cytoscape v3.6.1 software.Subnetwork module analysis was utilized by MCODE plugin to screen the core genes in the process of OA.Finally,small molecule drugs with potential treatment for OA were analyzed by connectivity map (CMap) database.Result: A total of 556 differentially expressed genes were screened,among which 252 were up-regulated and 304 were down-regulated.These genes were mainly involved in extracellular matrix (ECM) organization,inflammatory response,cell adhesion,immune response,collagen binding,etc.The analysis of KEGG pathway showed that differential genes were mainly involved in ECM-receptor interaction,phosphatidylinositol 3 kinase-protein kinase B (PI3K/Akt) signaling pathway and osteoclast differentiation.Some genes,such as interleukin-6(IL-6),JUN,vascular endothelial growth factor α(VEGFA),FOS,MYC and early growth response gene-1(EGR-1),activating transcription factor-3(ATF-3),playing critical role in the process of OA were identified by protein-protein interaction.Some potential small molecular drugs for the treatment of OA have also been screened,such as lycorine and anisomycin.Conclusion: The selected key genes may be targets for the diagnosis of OA or potential targets for the treatment of OA,and the selected small molecular drugs can be developed as the key drugs for the treatment of OA.

Objective To evaluate the effect of propofol on interleukin-1β (IL-1β)-induced increase in monolayer permeability of human umbilical vein endothelial cells (HUVECs).Methods Primary HUVECs were cultured and purified by immuno-magnetic separation.The expression of VE-cadherin in endothelial cells was determined by immunofluorescence.The HUVEC monolayer permeability was detected by the Transwell system.The cells were seeded on the upper chamber (2 × 105 cells/well) and cultured for 3 days after confluence.The cells were treated in two ways.The cells were randomly divided into 6 groups (n =36 each) and 5 of the 6 groups treated with 1,2,5,10 and 20 ng/ml IL-1β for 24 h except for control group.The cells were also randomly divided into 5 groups (n =30 each) and 4 of the 5 groups were pretreated with 0,10,50 and 100 μmol/L propofol for 30 min,and then treated with 10 ng/ml IL-1β for 24 h except for control group.The cells were radomly divided into 3 groups (n =18 each) and 2 of the 3 groups were pretreated with 50 μmol/L propofol for 30 min,and then treated with 10 ng/ml IL-1β for 24 h or 30 min.The expression of occludin protien,p38 mitogen activiated protienkinase (p38 MAPK) and phosphorylated p38 MAPK (p-p38 MAPK) was determined by Western blot.Results Compared with control group,5,10 and 20 ng/ml IL-1β significantly increased HUVEC monolayer permeability in a concentration-dependent manner (P ＜ 0.05 or 0.01).10,50 and 100 μmol/L propofol inhibited IL-1 β-induced increase in the permeability of HUVEC monolayer permeability in a concentration-dependent manner (P ＜ 0.01).IL-1β could down-regulate HUVEC occludin protein expression,and activate p38MAPK signaling pathway,and propofol inhibited IL-1β-induced down-regulation of HUVEC occludin protein expression and activation of p38 MAPK signaling pathway (P ＜ 0.01).Conclusion Propofol can alleviate IL-1β-induced increase in the permeability of HUVEC monolayer via inhibiting activation of p38 MAPK signaling pathway.

Objective To study the inducement of U251 glioblastoma cell apoptosis in vivo through up-regulating PUMA expresion and knocking down miR-221/222, and explore its mechanism.Methods Nude mouse xenograft models were established in 5-week-old BALB/c nude mice by subcutaneous vaccination of U251 glioblastomas; 1 week later, they were treated with intratumoral injection of lipofcctamine-mediated miRNA-221/222 antisense oligonucleotides (GroupA), nonsense sequences (Group B) and controls (Group C),respectively (n=8).The tumor growth was monitored until the end of observation period (28 d after the treatment) and pathological changes of the glioblastoma tissues were observed by HE staining at the end of observation.Fluorescence in situ hybridization (FISH) and real-time PCR were employed to measure the miR-221 and miR-222 expressions. Terminal deoxynucleotidyl transferase-mediated uridine 5'-triphosphate-biotin nick end labeling (TUNEL) assay was used to detect the apoptosis of glioblastomas.Immunohistochemistry and Westem blotting were used to detect the expressions of PUMA,bax,bcl-2 and p53 in removed tumor specimens. Results The volume in Group A was significantly smaller than that of those in group B and group C 6-28 dater treatment (P=0.006). The miR-221 and miR-222 mRNA expressions in Group A were significantly decreased as compared with those of those in group B and group C.HE staining indicated that decreased heteromorphism and reduced number of new vessels in Group A were noted as compared with those in group B and group C.The cell apoptotic index in Group A was significantly higher than that in group B and group C (P＜0.05).Immunohistochemistry showed that the expression levels of PUMA and bax in Group A was significantly up-regulated as compared with those in group B and group C, while the expression of bcl-2 in Group A was significantly down-regulated as compared with that in group B and group C; and no significant changes were noted in the p53 expression. Conclusion By up-regulating PUMA expresion,knocking down miR-221/222 can induce U251 glioma apoptosis in vivo.