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:Exercise has a regulatory effect on intestinal flora and glucose metabolism,but the effects of high-intensity intermittent exercise on intestinal flora and glucose metabolism in patients with type 2 diabetes mellitus are unclear. OBJECTIVE:To investigate the effects of high-intensity intermittent exercise on glucose metabolism and intestinal flora in patients with type 2 diabetes mellitus. METHODS:Eleven patients with type 2 diabetes mellitus were recruited,among which,two were lost to the follow-up and nine were finally enrolled.High-intensity intermittent exercise intervention was conducted 3 times per week for 6 continuous weeks.Fasting blood and fecal samples were collected before and after the intervention.Glucose metabolism indexes were detected in the blood samples,and intestinal flora was detected in the fecal samples.Changes in glucose metabolism indexes and intestinal flora indexes of the patients with type 2 diabetes mellitus before and after the intervention were compared. RESULTS AND CONCLUSION:After 6 weeks of high-intensity intermittent exercise intervention,fasting blood glucose and glycosylated serum protein levels in patients were significantly reduced(P<0.05),and fasting insulin,although not significantly changed,was decreased compared with before intervention.Alpha diversity analysis showed that the diversity(Shannon index),richness(Chao index)and coverage(Coverage index)did not change significantly.Venn diagrams showed that the relative abundance of Bacteroidetes,Actinobacteria,Proteobacteria,and Fusobacteria in the intestinal flora of the patients increased,and the relative abundance of Firmicutes decreased,and a significant decrease was seen in Ruminococcus_torques and Ruminococcus_gnavus in the Firmicutes,which were both positively correlated with the abnormalities of the glycemic metabolism-related indicators,as well as with other disease development.All these findings indicate that high-intensity intermittent exercise intervention has an improvement effect on the glycemic metabolism-related indexes of patients with type 2 diabetes mellitus,and the abundance of beneficial flora in the intestinal tract increases,and the abundance of harmful flora decreased,enhancing the stability of the intestinal flora in patients.

Twelve compounds were isolated from the rice fermentation extracts of Penicillium expansum GY618 by silica column chromatography, Sephadex gel column chromatography, ODS column chromatography and semi preparative HPLC methods. They were determined as 11-hydroxyl-penicitrinone F (1), penicitrinone F (2), betulin (3), erythrodiol (4), ergosterol (5), ergost-5α,8α-epidioxy-6,22-dien-3β-ol (6), (5α,8α-epidioxy-(22E,24R)-ergosta-6,9(11),22-trien-3β-ol) (7), 5α,8α-epidioxy-(22E,24R)-23-methylergosta-6,22-dien-3β-ol (8), 5α,8α-epidioxy- 23,24(R)-dimethylcholesta-6,9(11),22-trien-3β-ol (9), dankasterone A (10), (17R)-4-hydroxy-17-methylincisterol (11) and ergosta-4,6,8(14),22-tetraen-3-one (12), through mass spectrometer, nuclear magnetic resonance (NMR) and comparison with the literature. Compound 1 was a new compound and compounds 2-4, 6-12 were isolated from Penicillium expansum fungus for the first time. The tyrosinase inhibitory activity experiment showed that compounds 1, 3 and 12 showed certain inhibitory activity against tyrosinase with IC₅₀ values of (75 ± 9), (69 ± 8) and (64 ± 2) μmol·L^-1, respectively. The IC₅₀ of other compounds were all greater than 100 μmol·L^-1, while IC₅₀ of the positive control kojic acid was (46 ± 4) μmol·L^-1.

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 clinical characteristics and diagnosis and treatment plans of neurofibromatosis type 1 (NF1), and to provide references for clinical diagnosis and treatment. Methods : The clinical manifestations and treatment of an 8-year-old female patient with NF1 was reported. A literature review was conducted to summarize the clinical characteristics and therapeutic strategies of NF1. Multiple NF1s occurred on the right cheek, orbit, and eyelid, and recurred after surgical resection. The tumor caused ptosis, incomplete closure, and vision loss in the upper eyelid of the right eye. After a multidisciplinary assessment determined that radical resection was not feasible, selumetinib sulfate targeted therapy was adopted (25 mg, Po, bid), 28 days constitute one treatment course, and 14 courses have been completed, combined with symptomatic ocular treatments, such as Befusu. Result: The follow-up showed that the tumor volume did not continue to increase (stable disease), the uncorrected vision of the right eye improved (0.05 vs 0.1), and no drug-related adverse reactions occurred during the treatment period. The literature review summarizes the diverse clinical manifestations of NF1, with café-au-lait macules, multiple neurofibromas, and Lisch nodules being hallmark features. Currently, surgical intervention remains the most commonly employed and primary therapeutic approach for NF1; however, for patients who do not meet the criteria for surgery, alternative treatment strategies should be considered. MEK inhibitors, such as selumetinib, demonstrate significant efficacy in inhibiting the growth of NF1-associated plexiform neurofibromas, with tumor volume reductions of at least 20% observed in 70% of pediatric patients in the SPRINT clinical trial. Furthermore, these inhibitors exhibit favorable long-term safety profiles. Conclusion Café-au-lait macules, multiple neurofibromas, and Lisch nodules are hallmark features of NF1. Selumetinib is safe and effective for NF1 in the head and neck of children, and it is the preferred treatment option for patients who are not suitable for surgery. Long-term follow-up monitoring of tumor changes and drug safety is required.

Aim To analyze serum exosome sequencing data from patients with coronary heart disease(CHD)and normal subjects by using bioinformatics-related methods to construct a competitive endogenous ln-cRNA-miRNA-mRNA(ceRNA)regulatory network,to mine the predicted potential Chinese medicines,and to perform preliminary validation of the biological processes and core Chinese medicines involved in the ceRNA network.Methods We used exoRbase data-base to obtain the expression matrix of differential genes,combined with the raw letter method to con-struct the ceRNA network,and performed GO analysis and KEGG analysis on the differential mRNAs in the network,and used COREMINE database to predict the biological processes and core target genes involved in the ceRNA network,and to screen the herbal medi-cines with potential therapeutic effects;AVECs oxida-tive damage cell model was constructed in vitro,and the cytoskeleton,tube-forming function,cell prolifera-tion,LDH leakage rate,ROS level and p-AKT,AKT,p-PI3K and AKT protein expression were examined to verify the action pathways and targets of the core Chi-nese medicine Salvia miltiorrhiza for the treatment of coronary heart disease.Results Compared with nor-mal subjects,395 mRNAs,80 miRNAs,60 lncRNA differential genes,and 80 miRNAs were predicted in serum exosomes of coronary heart disease,and the constructed ceRNA sub-network,mainly consisted of 21 lncRNAs,80 miRNAs,and 82 mRNAs;AKT1,VEGFA,IL1B and other genes in the network.The abnormally expressed mRNAs were involved in biologi-cal processes such as oxidative stress and signaling pathways such as PI3 K/Akt,and Dan Shen,Chuanx-iong and Panax notoginseng were most closely related to exosome-mediated biological processes and core genes in coronary heart disease.The active ingredients of tanshinone ⅡA,the core Chinese medicine,could pro-mote vascular endothelial cell proliferation,tube for-mation,skeleton formation and repair,reduce LDH leakage rate and ROS level,and promote the expres-sion of p-AKT and p-PI3K protein.Conclusion There is a complex ceRNA regulatory network trans-duction in coronary artery disease serum exosomes,and traditional Chinese medicine can be used to treat CHD through multi-target intervention,and Dan Shen,Chuanxiong and Panax notoginseng are expected to be candidate sources of traditional Chinese medicine,a-mong which the active ingredient of Dan Shen,tanshi-none ⅡA,activates PI3 K/Akt signaling pathway to play a protective role against oxidative stress-injured cells,and treats CHD.

Objective To study the expression of long noncoding RNA Homeobox D gene cluster antisense growth-associated long noncoding RNA(lncRNA HAGLR)and its downstream target genes in osteoporosis(OP)-tibial fracture(TF)rats,and to explore the effect and mechanism of lncRNA HAGLR on osteogenic differentiation of rat bone marrow mesenchymal stem cells(MSCs).Methods A total of 30 SD female rats were randomly divided into the sham group,the OP group and the OP-TF group,with 10 rats in each group.Serum alkaline phosphatase(ALP)and tartrate-resistant acid phosphatase(TRAP)levels of rats were detected by ELISA.Rats MSC cell line R7500 was induced by osteogenic differentiation induction medium and divided into the MSC group and the Osteogenic-MSC group.R7500 was individually transfected with pcDNA-HAGLR,pcDNA-NC,miR-19a-3p mimic,mimic negative control(NC mimic),miR-19a-3p inhibitor and negative control of miR-19a-3p inhibitor(NC inhibitor),and divided into corresponding groups.The dual luciferase gene report experiment was used to verify the targeting relationship between lncRNA HAGLR and miR-19a-3p and bone morphogenetic protein 2(BMP2)and miR-19a-3p.The expressions of lncRNA HAGLR and miR-19a-3p in each group were detected by qRT-PCR.The expressions of BMP2,ALP,collagen Ⅰ(COL-Ⅰ),osteocalcin(OCN),and osteopontin(OPN)were detected by Western blot.ALP staining and AR staining were used to detect the osteogenic differentiation ability of MSC.Results The serum ALP and TRAP levels in the OP group and the OP-TF group were higher than those in the sham group,and the differences were statistically significant(P<0.05).There was no significant difference in the expression levels of lncRNA HAGLR,miR-19a-3p or BMP2 of tibia tissue between the OP group and the sham group(P>0.05),while the expression levels of lncRNA HAGLR and BMP2 of tibia tissue in the OP-TF group were significantly lower than those in the sham group and the OP group(P<0.05),the expression level of miR-19a-3p of tibia tissue in the OP-TF group was higher than those in the sham group and the OP group(P<0.05).Compared with the MSC group,the expression level of lncRNA HAGLR in the Osteogenic-MSC group was significantly increased(P<0.05),while the expression of miR-19a-3p was decreased(P<0.05).The dual luciferase gene report experiment verified that lncRNA HAGLR has a targeting relationship with miR-19a-3p,and miR-19a-3p has a targeting relationship with BMP2.The expression level of miR-19a-3p in the pcDNA-HAGLR group was lower than that in the pcDNA-NC group(P<0.05).There was no significant difference in the expression level of lncRNA HAGLR between the miR-19a-3p mimic group and the NC mimic group(P>0.05).Compared with the NC mimic group,the expression level of BMP2 protein in the miR-19a-3p mimic group was decreased(P<0.05),while the expression level of miR-19a-3p was increased(P<0.05).The cells in the pcDNA-HAGLR group had stronger osteogenic differentiation ability and higher ALP activity than those in the pcDNA-NC group(P<0.05).The cells in the miR-19a-3p inhibitor group had stronger osteogenic differentiation ability and higher ALP activity than those in the NC inhibitor group(P<0.05).Conclusion The expression of lncRNA HAGLR and BMP2 is decreased and the expression of miR-19a-3p is increased in rats with tibial fracture.Overexpression of lncRNA HAGLR promotes osteogenic differentiation of rat MSCs by targeting the miR-19a-3p/BMP2 axis.