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.

ObjectiveTo explore the current status and issues regarding the application of ancient books in clinical practice guidelines and expert consensus of traditional Chinese medicine （TCM） published in China， and to provide methodological recommendations for the incorporation of ancient books in the development of TCM guidelines. MethodsWe searched China National Knowledge Infrastructure （CNKI）， WanFang Data， VIP， SinoMed， PubMed， Embase， as well as six industry websites including China Association of Chinese Medicine， National Group Standards Information Platform， and Chinese Association of the Integration of Traditional and Western Medicine，etc. TCM clinical practice guidelines or expert consensus issued during January 1st， 2017， to November 26th， 2022 were searched. Clinical practice guidelines or expert consensus that explicitly referred to ancient books were included， and the content regarding the searching for ancient books， sources of access to ancient books， methods of evaluating the level of evidence， methods of evaluating the level of recommendation， and methods of evaluating the evidence for the ancient books were analysed. ResultsA total of 1,215 TCM clinical practice guidelines or expert consensus were retrieved， with 442 articles explicitly mentioning the application of ancient books， including 300 （67.87%） clinical practice guidelines and 142 （32.13%） expert consensus. Sixty of the 442 publications explicitly reported that ancient books searching had been conducted （13.57%）； among these 60 publications 27 （45.00%） explicitly reported ancient books searching strategies， and the most frequent method was manual searching with a total of 24 articles （40.00%）. The most popular search source was Chinese Medical Dictionary， a TCM classics database， with a total of 18 articles. 197 articles （44.57%） explicitly reported the evaluation criteria for the level of evidence， of which 141 articles （71.57%） involved the evaluation criteria for the ancient books； 413 articles （93.44%） mentioned ancient books in the recommendations， and only the source of formula name was mentioned in 409 （99.03%） of the publications. ConclusionThe current application of ancient books in TCM clinical practice guidelines and expert consensus is limited， with issues of non-standard searching and evaluation methods. Standar-dization and uniformity are needed in evidence grading and recommendation standards. Future research should clarify the scope and methods of applying ancient book， emphasize their integration with modern research evidence， and enhance their value and quality in the development of TCM clinical practice guidelines.

Objective To analyze the effect of fisetin against venous thrombosis in rats.Methods Seventy SD rats were randomly divided into the following groups:sham-operation group,model group,fisetin 45 mg/kg,15 mg/kg,5 mg/kg groups,and aspirin group(47 mg/kg).The corresponding medication was administered by gavage once a day consecutively(the sham-operation group and the model group were given 0.5％carboxymethyl cellulose sodium solution with 10 mL/kg,respectively)for 7 consecutive days.One hour after the last administration,the rats were anesthetized,the lower part of the intersection of inferior vena cava and left renal vein was ligated with silk thread(no ligation in the sham-operation group),and the abdominal wall was sutured.Two hours later,the abdominal cavity was reopened,the other venous branches 1.5 cm away from the ligation site were closed with the artery clamp,and blood was collected from the abdominal aorta.The anticoagulant ratio of 3.8％sodium citrate∶whole blood was 1∶9.The venous thrombus 1 cm down from the ligation point of the intersection of inferior vena cava and left renal vein was cut and the thrombus was separated.The residual blood was dried with filter paper,weighed and recorded.Plasma was taken after anticoagulant blood centrifugation.The levels of plasma antithrombin-Ⅲ(AT-Ⅲ),protease C(PC),plasminogen(PLG),and plasminogen activator inhibitor(PAI-1)were detected by ELISA kits.Results Compared with the model group,the weight of thrombus in fisetin 45 mg/kg group and aspirin 47 mg/kg group decreased(P＜0.01).The content of AT-Ⅲ in three fisetin groups increased(all P＜0.05).The content of PC in fisetin 45 mg/kg increased(P＜0.05).The content of PLG and PAI-1 in fisetin 45 mg/kg group decreased(both P＜0.05).Conclusion Fisetin has the effect against venous thrombosis in vivo,and the effect is related to the upregulation of AT-Ⅲ and PC and the downregulation of PLG and PAI-1.

Humans ; Clinical Relevance ; Leukemia, Myeloid, Acute ; Transcription Factors ; Neoplasm Proteins ; Antigens, CD ; GPI-Linked Proteins

Objective: To understand the antimicrobial resistance and genome characteristics of Campylobacter isolates recovered from retailed poultry meat samples in 20 provinces in China in 2020. Methods: In 2020, 265 Campylobacter strains including 244 Campylobacter jejuni and 21 Campylobacter coli collected from retailed poultry meat samples in China were tested for antimicrobial resistance to 9 antimicrobial compounds by using the agar dilution method. Forty-two selected isolates were sent for whole genome sequencing and 38 high-quality genomes were analyzed for their antimicrobial resistance genes, virulence genes, sequence types and genetic diversity. Results: The resistance rates of Campylobacter isolates from poultry meats to tetracycline, nalidixic acid and ciprofloxacin were the highest (84%-100%), with 53.2% of the isolates showing multidrug resistance in this study. The resistance rates of C. coli to erythromycin, azithromycin, telithromycin, gentamicin and clindamycin were significantly higher than those of C. jejuni (P<0.05). The resistance genes conferring resistance to β-lactams (100%, 38/38), quinolones (94.7%, 36/38), tetracycline (81.6%, 31/38) and aminoglycosides (50%, 19/38) were the most frequently detected among 38 Campylobacter genomes. C. jejuni carried more virulence genes than C. coli. In total, 19 and 17 sequence types (ST) were obtained from 20 sequenced C. jejuni and 18 C. coli isolates, respectively, including 5 novel STs. The isolates showed a high genetic diversity based on their sequence types. Conclusion: The phenomenon of antimicrobial resistance in Campylobacter from poultry meat sources in China is relatively serious, and resistance and virulence genes are widely distributed in Campylobacter. There is genetic diversity in Campylobacter.
Humans ; Animals ; Anti-Bacterial Agents/pharmacology* ; Campylobacter/genetics* ; Poultry ; Drug Resistance, Bacterial/genetics* ; Genomics ; China ; Tetracycline

Objective: To understand the antimicrobial resistance and genome characteristics of Campylobacter isolates recovered from retailed poultry meat samples in 20 provinces in China in 2020. Methods: In 2020, 265 Campylobacter strains including 244 Campylobacter jejuni and 21 Campylobacter coli collected from retailed poultry meat samples in China were tested for antimicrobial resistance to 9 antimicrobial compounds by using the agar dilution method. Forty-two selected isolates were sent for whole genome sequencing and 38 high-quality genomes were analyzed for their antimicrobial resistance genes, virulence genes, sequence types and genetic diversity. Results: The resistance rates of Campylobacter isolates from poultry meats to tetracycline, nalidixic acid and ciprofloxacin were the highest (84%-100%), with 53.2% of the isolates showing multidrug resistance in this study. The resistance rates of C. coli to erythromycin, azithromycin, telithromycin, gentamicin and clindamycin were significantly higher than those of C. jejuni (P<0.05). The resistance genes conferring resistance to β-lactams (100%, 38/38), quinolones (94.7%, 36/38), tetracycline (81.6%, 31/38) and aminoglycosides (50%, 19/38) were the most frequently detected among 38 Campylobacter genomes. C. jejuni carried more virulence genes than C. coli. In total, 19 and 17 sequence types (ST) were obtained from 20 sequenced C. jejuni and 18 C. coli isolates, respectively, including 5 novel STs. The isolates showed a high genetic diversity based on their sequence types. Conclusion: The phenomenon of antimicrobial resistance in Campylobacter from poultry meat sources in China is relatively serious, and resistance and virulence genes are widely distributed in Campylobacter. There is genetic diversity in Campylobacter.
Humans ; Animals ; Anti-Bacterial Agents/pharmacology* ; Campylobacter/genetics* ; Poultry ; Drug Resistance, Bacterial/genetics* ; Genomics ; China ; Tetracycline