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:This investigation sought to delineate the associations among colorectal adenomatous polyps, diabetes, and biomolecules involved in glucose metabolism.Method:Data were collected from 40 patients who underwent endoscopic polypectomy at the Endoscopy Department of Shandong Cancer Hospital between June 2019 and September 2021. This cohort included 27 patients with inflammatory polyps and 13 with adenomatous polyps. We assessed fasting insulin (Fins), fasting blood glucose (FBG), and the mRNA expressions of fibroblast growth factor 19 (FGF-19) and insulin-like growth factor 1 (IGF-1) in the polyp tissues. Both univariate and multivariate logistic regression analyses were employed to ascertain the determinants influencing the emergence of adenomatous polyps. From these analyses, a predictive nomogram was constructed to forecast the occurrence of adenomatous polyps, and evaluations on the discriminative capacity, calibration, and clinical utility of the model were conducted.Results:The adenomatous polyp group exhibited markedly elevated levels of glucose, insulin, FGF-19, and IGF-1, with respective concentrations of (8.67±2.70) mmol/L, (12.72±7.69) μU/L, 2.20±1.88, and 1.36±0.69. These figures were significantly higher compared to the inflammatory polyp group, which showed levels of (5.51±0.72) mmol/L, (5.49±2.68) μU/L, 0.53±0.97, and 0.41±0.46, respectively, P=0.001. Multivariate logistic regression revealed that the relative expression of IGF-1 served as an independent risk factor for the development of colorectal adenomatous polyps ( OR=5.622, 95% CI:1.085-29.126). The nomogram displayed a C-index of 0.849, indicating substantial discriminative capability. The calibration curve affirmed the model's accuracy in aligning predicted probabilities with actual outcomes, and the clinical decision curve demonstrated thepractical clinical applicability of the model. Conclusions:There was a significant correlation between the occurrence of colorectal adenomatous polyps and glucose metabolic pathways. Individuals with diabetes showed a higher propensity to develop such polyps.

Objective:This investigation sought to delineate the associations among colorectal adenomatous polyps, diabetes, and biomolecules involved in glucose metabolism.Method:Data were collected from 40 patients who underwent endoscopic polypectomy at the Endoscopy Department of Shandong Cancer Hospital between June 2019 and September 2021. This cohort included 27 patients with inflammatory polyps and 13 with adenomatous polyps. We assessed fasting insulin (Fins), fasting blood glucose (FBG), and the mRNA expressions of fibroblast growth factor 19 (FGF-19) and insulin-like growth factor 1 (IGF-1) in the polyp tissues. Both univariate and multivariate logistic regression analyses were employed to ascertain the determinants influencing the emergence of adenomatous polyps. From these analyses, a predictive nomogram was constructed to forecast the occurrence of adenomatous polyps, and evaluations on the discriminative capacity, calibration, and clinical utility of the model were conducted.Results:The adenomatous polyp group exhibited markedly elevated levels of glucose, insulin, FGF-19, and IGF-1, with respective concentrations of (8.67±2.70) mmol/L, (12.72±7.69) μU/L, 2.20±1.88, and 1.36±0.69. These figures were significantly higher compared to the inflammatory polyp group, which showed levels of (5.51±0.72) mmol/L, (5.49±2.68) μU/L, 0.53±0.97, and 0.41±0.46, respectively, P=0.001. Multivariate logistic regression revealed that the relative expression of IGF-1 served as an independent risk factor for the development of colorectal adenomatous polyps ( OR=5.622, 95% CI:1.085-29.126). The nomogram displayed a C-index of 0.849, indicating substantial discriminative capability. The calibration curve affirmed the model's accuracy in aligning predicted probabilities with actual outcomes, and the clinical decision curve demonstrated thepractical clinical applicability of the model. Conclusions:There was a significant correlation between the occurrence of colorectal adenomatous polyps and glucose metabolic pathways. Individuals with diabetes showed a higher propensity to develop such polyps.

Endoplasmic reticulum is an important organelle in eukaryotic cells,which is responsible for the folding,processing and transportation of secretory proteins.A variety of stimuli inside and outside cells can lead to the accumulation of misfolded or unfolded proteins in the endoplasmic reticulum,resulting in abnormal structure and function of the endoplasmic reticulum,which is called endoplasmic reticulum stress(ERS).Endoplasmic reticulum autophagy is an important endogenous mechanism to alleviate ERS.It is often considered as a cell protective procedure,which participates in many important physiological processes,such as metabolism,immune response,inflammatory response and cell proliferation.Endoplasmic reticulum autophagy is an important endogenous protective mechanism to alleviate endoplasmic reticulum stress and restore the endoplasmic reticulum homeostasis,through eliminating redundant and disabled endoplasmic reticulum membrane and macromolecular protein complexes,which is critical to cell function and fate.This paper reviews the types of endoplasmic reticulum autophagy,related specific receptors,main regulatory mechanisms,and its role and significance in the related diseases.

Objective To retrospectively analyze the ultra-fast track anesthesia(UFTA)methods and perioperative anesthesia management experiences of transcatheter mitral valve edge-to-edge repair(TEER)in the treatment of functional mitral regurgitant.Methods In this retrospective study,patients underwent the TEER procedure and received UFTA in Fuwai Yunnan Hospital,from May 2022 to September 2022 for heart failure combined with moderate to severe or severe functional mitral regurgitant were included.Baseline,preoperative complications,cardial function and anesthesia classification,amino-terminal probrain natriuretic peptide(NT-proBNP),ultrasound examination results,surgery time,extubation time,intraoperative anesthetic and vasoactive drug,complications related to TEER and UFTA,perioperative,and postoperative 30-day and one-year follow-up data were collected.All perioperative clinical data were recorded and analyzed.Results A total of 30 patients were enrolled,11 patients(36.7％)were female,mean age was(63.6±6.1)years,NYHA classification IV 14 patients(46.7％),left ventricular ejection fraction(LVEF)(36.0±8.1)％,the end-diastolic volume of the left ventricle(66.0±8.2)mm,mitral regurgitation 4+14 patients(56.7％),3+17 patients(43.3％),NT-proBNP(1 934.1±1 973.5)pg/ml,1 patient(3.3％)used high-dose vasoactive drugs during surgery.All patients did not experience nausea,vomiting,delirium,respiratory depression,perioperative transesophageal echocardiography-related gastrointestinal bleeding,pericardial effusion,cerebrovascular accidents,emergency surgery or secondary intervention,or other serious adverse events within 24 hours after surgery.No 30-day all-cause death occurred;the mean postoperative hospital stay was(7.4±2.8)days.All patients completed one-year follow-up,LVEF(37.6±11.1)％,the end-diastolic volume of the left ventricle(63.2±8.6)mm,mitral regurgitation 2+7 patients(23.3％),1+23 patients(76.7％),NT-proBNP(1 949.2±2 576.6)pg/ml.Conclusions Ultra-fast track anesthesia can be safely applied to TEER in treating functional mitral regurgitant patients.

Aim To investigate the regulatory mecha-nism of butin on the proliferation,migration,cycle blockage and pyroptosis related inflammatory factors in human fibroblast-like synoviocytes of rheumatoid arthri-tis(HFLS-RA).Methods Cell proliferation,migra-tion and invasion were studied using cell migration and invasion assays.Cell cycle was detected by flow cytom-etry,and the expression of the pyroptosis-associated in-flammatory factors IL-1β,IL-18,caspase-1 and caspase-3 was detected by ELISA,RT-qPCR and West-ern blot.Results Migration and invasion experiments showed that the cell proliferation rate of the butin group was lower than that of the blank control group(P＜0.05).Cell cycle analysis demonstrated that in the G0/G1 phase,the DNA expression was elevated in the medium and high-dose groups of butin(P＜0.05),while in the G2 and S phases,the DNA expression was reduced in the medium and high-dose groups of butin(P＜0.05).The results of ELISA,RT-qPCR and Western blot assay revealed that the expression of IL-1β,IL-1 8,caspase-1,and caspase-3 decreased in the butin group compared with the IL-1β+caspase-3 in-hibitor group(P＜0.05).Conclusions Butin inhib-its HFLS-RA proliferation by inhibiting the synthesis of inflammatory vesicles by caspase-1 in the pyroptosis pathway,thereby reducing the production and release of inflammatory factors such as IL-1β and IL-18 down-stream of the pathway,and also inhibits HFLS-RA pro-liferation by exerting a significant blocking effect in the G1 phase,which may be one of the potential mecha-nisms of butin in the treatment of RA.