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.

ObjectiveDetection and quantification of RNA synthesis in cells is a widely used technique for monitoring cell viability, health, and metabolic rate.After exposure to environmental stimuli, both the internal reference gene and target gene would be degraded. As a result, it is imperative to consider the accurate capture of nascent RNA and the detection of transcriptional levels of RNA following environmental stimulation. This study aims to create a Click Chemistry method that utilizes its property to capture nascent RNA from total RNA that was stimulated by the environment. MethodsThe new RNA was labeled with 5-ethyluridine (5-EU) instead of uracil, and the azido-biotin medium ligand was connected to the magnetic sphere using a combination of “Click Chemistry” and magnetic bead screening. Then the new RNA was captured and the transcription rate of 16S rRNA was detected by fluorescence molecular beacon (M.B.) and quantitative reverse transcription PCR (qRT-PCR). ResultsThe bacterial nascent RNA captured by “Click Chemistry” screening can be used as a reverse transcription template to form cDNA. Combined with the fluorescent molecular beacon M.B.1, the synthesis rate of rRNA at 37℃ is 1.2 times higher than that at 15℃. The 16S rRNA gene and cspI gene can be detected by fluorescent quantitative PCR,it was found that the measured relative gene expression changes were significantly enhanced at 25℃ and 16℃ when analyzed with nascent RNA rather than total RNA, enabling accurate detection of RNA transcription rates. ConclusionCompared to other article reported experimental methods that utilize screening magnetic columns, the technical scheme employed in this study is more suitable for bacteria, and the operation steps are simple and easy to implement, making it an effective RNA capture method for researchers.

Background::Although overnight fasting is recommended prior to endoscopic retrograde cholangiopancreatography (ERCP), the benefits and safety of high-carbohydrate fluid diet (CFD) intake 2 h before ERCP remain unclear. This study aimed to analyze whether high-CFD intake 2 h before ERCP can be safe and accelerate patients’ recovery.Methods::This prospective, multicenter, randomized controlled trial involved 15 tertiary ERCP centers. A total of 1330 patients were randomized into CFD group ( n = 665) and fasting group ( n = 665). The CFD group received 400 mL of maltodextrin orally 2 h before ERCP, while the control group abstained from food/water overnight (>6 h) before ERCP. All ERCP procedures were performed using deep sedation with intravenous propofol. The investigators were blinded but not the patients. The primary outcomes included postoperative fatigue and abdominal pain score, and the secondary outcomes included complications and changes in metabolic indicators. The outcomes were analyzed according to a modified intention-to-treat principle. Results::The post-ERCP fatigue scores were significantly lower at 4 h (4.1 ± 2.6 vs. 4.8 ± 2.8, t = 4.23, P <0.001) and 20 h (2.4 ± 2.1 vs. 3.4 ± 2.4, t= 7.94, P <0.001) in the CFD group, with least-squares mean differences of 0.48 (95% confidence interval [CI]: 0.26–0.71, P <0.001) and 0.76 (95% CI: 0.57–0.95, P <0.001), respectively. The 4-h pain scores (2.1 ± 1.7 vs. 2.2 ± 1.7, t = 2.60, P = 0.009, with a least-squares mean difference of 0.21 [95% CI: 0.05–0.37]) and positive urine ketone levels (7.7% [39/509] vs. 15.4% [82/533], χ2 = 15.13, P <0.001) were lower in the CFD group. The CFD group had significantly less cholangitis (2.1% [13/634] vs. 4.0% [26/658], χ2 = 3.99, P = 0.046) but not pancreatitis (5.5% [35/634] vs. 6.5% [43/658], χ2 = 0.59, P = 0.444). Subgroup analysis revealed that CFD reduced the incidence of complications in patients with native papilla (odds ratio [OR]: 0.61, 95% CI: 0.39–0.95, P = 0.028) in the multivariable models. Conclusion::Ingesting 400 mL of CFD 2 h before ERCP is safe, with a reduction in post-ERCP fatigue, abdominal pain, and cholangitis during recovery.Trail Registration::ClinicalTrials.gov, No. NCT03075280.

Objective To observe the clinical efficacy of"triple-posture positive bone-setting"chiropractic manipulation combined with Tongluo Huoxue Formula for the treatment of lumbar spinal stenosis(LSS)with qi deficiency and blood stasis syndrome.Methods Sixty patients with LSS of qi deficiency and blood stasis type were randomly divided into trial group and control group,with 30 cases in each group.The trial group was treated with"triple-posture positive bone-setting"chiropractic manipulation(a chiropractic manipulation performed under the positive cooperation of the patients at three postures)combined with Tongluo Huoxue Formula,while the control group was treated with"triple-posture positive bone-setting"chiropractic manipulation combined with conventional western medicine.The course of treatment for the two groups covered 4 weeks.Before and after treatment,the patients of the two groups were observed in the changes of pain visual analogue scale(VAS)score,Japanese Orthopedic Association(JOA)score of lumbar function,Oswestry Disability Index(ODI)score,straight-leg raising test results and serum interleukin 6(IL-6)and C-reactive protein(CRP)levels.After treatment,the clinical efficacy and safety of the two groups were evaluated.Results(1)After 4 weeks of treatment,the total effective rate of the trial group was 96.67％(29/30)and that of the control group was 63.33％(19/30).The intergroup comparison(tested by Fisher's exact test)showed that the clinical efficacy of the trial group was significantly superior to that of the control group(P＜0.05).(2)After treatment,the lumbar function indicators of pain VAS scores and ODI scores in the trial group were significantly lower(P＜0.05),and the JOA scores were significantly higher than those before treatment(P＜0.05),while in the control group,only the ODI scores were significantly lower than those before treatment(P＜0.05).The intergroup comparison showed that the decrease of VAS and ODI scores and the increase of JOA scores in the trial group were significantly superior to those in the control group(P＜0.05 or P＜0.01).(3)After treatment,the Laseque s sign of the trial group was significantly improved compared with that before treatment(P＜0.05),while no significant improvement was presented in the control group(P＞0.05).The intergroup comparison showed that the improvement of Laseque's sign in the trial group was significantly superior to that in the control group(P＜0.01).(4)After treatment,the levels of serum inflammatory factors of IL-6 and CRP in the two groups were lower than those before treatment(P＜0.05),and the decrease of serum IL-6 level in the trial group was significantly superior to that in the control group(P＜0.05),but CRP level in the two groups after treatment did not differ from that before treatment,no statistically significant difference was shown between the two groups after treatment,either(P＞0.05).(5)The incidence of adverse reactions in the trial group was 6.67％(2/30)and that in the control group was 13.33％(4/30),and the intergroup comparison(by Fisher's exact test)showed that there was no significant difference between the two groups(P＞0.05).Conclusion The therapeutic effect of"triple-posture positive bone-setting"chiropractic manipulation combined with Tongluo Huoxue Formula exert certain effect for the treatment of LSS patients with qi deficiency and blood stasis syndrome,and it has more obvious advantages in improving the lumbar function,promoting the rehabilitation of the patients,and lowering the level of serum inflammatory factors than"triple-posture positive bone-setting"chiropractic manipulation combined with conventional western medication.

The imprint desorption electrospray photoionization mass spectrometry was employed to locally image the spatial distribution of chemical components in dried tobacco leaves after initial curing. The relative content distribution of different chemical components was obtained in tobacco leaves. The application of imprinting method could transfer tobacco internal compounds to the surface of porous polytetrafluoroethylene plate,which realized the detection and visual analysis of tobacco internal substances. Besides,the imprint desorption electrospray ionization/post-photoionization (Imprint DESI/PI) mass spectrometry imaging technique had the advantages of non-polarity discrimination,soft ionization and high ionization efficiency for plant samples,and could simultaneously detect and image rich compounds in tobacco samples. A total of 40 kinds of chemical components including alkaloids,amino acids,sugars,acids,ketones and phenols were identified based on high resolution mass spectrometry. The results showed that the representative chemical components of tobacco,such as alkaloids,amino acids and sugars,were mainly distributed near the leaf tip from the vertical analysis and at the left and right leaf edges from the horizontal analysis. Amadori compound (1-Deoxy-1-L-proline-d-fructose) was detected,and the content of Amadori was found to be consistent with that of free amino acid (proline). In addition,the technique was further used to study the climate spot disease area of tobacco,and it was found that the compounds had specific distribution in the climate spot area,which further proved the superiority of this method in studying the growth stress of tobacco leaves.

A new method was developed for simultaneous detection of total 19 kinds of organophosphate esters(OPEs)and their diester metabolites(di-OPEs)in human serum(1.0 mL)and urine(1.5 mL)with low volume of samples.The target compounds were determined using ultra-high performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)after acetonitrile liquid-liquid extraction combined with purification using an ENVI-18 solid-phase extraction(SPE)column.OPEs and di-OPEs were separated using a Shim-pack GIST C18 column(100 mm×2.1 mm,2 μm)with a Shim-pack GIST-HP(G)C18 guard column.An electrospray ionization source(ESI)was employed in mass spectrometry analysis,with positive/negative ion mode using the multiple reaction monitoring(MRM).All target compounds were separated within 15 min,and exhibited good linear relationships in the concentration range of 2-100 ng/mL,with correlation coefficients(R2)above 0.994.The method detection limits(MDL)in serum ranged from 0.001 to 0.178 ng/mL and the MDL in urine ranged from 0.001 to 0.119 ng/mL.The recoveries of the analytes spiked in serum and urine matrices at two concentration levels were 30.5%-126.8%,with the relative standard deviations(RSDs)ranged from 1%to 23%.In addition,paired serum and urine samples from 11 patients were analyzed.For all samples tested,the internal standards of OPEs exhibited recoveries between 61%and 114%,whereas the internal standards for di-OPEs had recoveries ranging from 43%to 103%.OPEs and di-OPEs exhibited high detection frequencies in 22 serum and urine samples.Triethyl phosphate(TEP),tributyl phosphate(TBP),tris(2-ethylhexyl)phosphate(TEHP),tris(2-butoxyethyl)phosphate(TBEP),tris(1-chloro-2-propyl)phosphate(TCIPP),triphenyl phosphate(TPHP),tri-m-tolyl-phosphate(TMTP)and 2-ethylhexyl diphenyl phosphate(EHDPP)were universally detected in all serum samples.TCIPP was identified at the highest concentrations(median 0.548 ng/mL)in serum samples.In urine samples,the detection frequency for 12 kinds of target compounds reached 100%.Notably,TBP emerged as the predominant OPE in urine,demonstrating a median concentration of 0.506 ng/mL.Regarding di-OPEs,bis(2-chloroethyl)phosphate(BCEP)and bis(2-butoxyethyl)hydrogen phosphate(BBOEP)were the most abundant in urine,with median concentrations of 6.404 and 2.136 ng/mL,respectively.The total concentrations of OPEs and di-OPEs in serum and urine were 1.580-3.843 ng/mL and 5.149-17.537 ng/mL,respectively.These results not only confirmed the effectiveness of the method in detection of OPEs and di-OPEs in biological matrices,but also revealed the widespread presence of OPE compounds in human body and pointed to potential exposure risks.