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.

The circadian clock plays a critical role in regulating various physiological processes, including gene expression, metabolic regulation, immune response, and the sleep-wake cycle in living organisms. Post-translational modifications (PTMs) are crucial regulatory mechanisms to maintain the precise oscillation of the circadian clock. By modulating the stability, activity, cell localization and protein-protein interactions of core clock proteins, PTMs enable these proteins to respond dynamically to environmental and intracellular changes, thereby sustaining the periodic oscillations of the circadian clock. Different types of PTMs exert their effects through distincting molecular mechanisms, collectively ensuring the proper function of the circadian system. This review systematically summarized several major types of PTMs, including phosphorylation, acetylation, ubiquitination, SUMOylation and oxidative modification, and overviewed their roles in regulating the core clock proteins and the associated pathways, with the goals of providing a theoretical foundation for the deeper understanding of clock mechanisms and the treatment of diseases associated with circadian disruption.
Protein Processing, Post-Translational/physiology* ; Circadian Rhythm/physiology* ; Humans ; Animals ; CLOCK Proteins/physiology* ; Circadian Clocks/physiology* ; Phosphorylation ; Acetylation ; Ubiquitination ; Sumoylation

Objective:To analyze the immune reconstitution after BTKi treatment in patients with chronic lymphocytic leukemia(CLL).Methods:The clinical and laboratorial data of 59 CLL patients admitted from January 2017 to March 2022 in Fujian Medical University Union Hospital were collected and analyzed retrospectively.Results:The median age of 59 CLL patients was 60.5(36-78).After one year of BTKi treatment,the CLL clones(CD5+/CD19+)of 51 cases(86.4％)were significantly reduced,in which the number of cloned-B cells decreased significantly from(46±6.1)× 109/L to(2.3±0.4)× 109/L(P=0.0013).But there was no significant change in the number of non-cloned B cells(CD19+minus CD5+/CD19+).After BTKi treatment,IgA increased significantly from(0.75±0.09)g/L to(1.31±0.1)g/L(P＜0.001),while IgG and IgM decreased from(8.1±0.2)g/L and(0.52±0.6)g/L to(7.1±0.1)g/L and(0.47±0.1)g/L,respectively(P＜0.001,P=0.002).BTKi treatment resulted in a significant change in T cell subpopulation of CLL patients,which manifested as both a decrease in total number of T cells from(2.1±0.1)× 109/L to(1.6±0.4)× 109/L and NK/T cells from(0.11±0.1)× 109/L to(0.07±0.01)× 109/L(P=0.042,P=0.038),both an increase in number of CD4+cells from(0.15±6.1)× 109/L to(0.19±0.4)× 109/L and CD8+cells from(0.27±0.01)× 109/L to(0.41±0.08)× 109/L(both P＜0.001).BTKi treatment also up-regulated the expression of interleukin(IL)-2 while down-regulated IL-4 and interferon(IFN)-γ.However,the expression of IL-6,IL-10,and tumor necrosis factor(TNF)-α did not change significantly.BTKi treatment could also restored the diversity of TCR and BCR in CLL patients,especially obviously in those patients with complete remission(CR)than those with partial remission(PR).Before and after BTKi treatment,Shannon index of TCR in patients with CR was 0.02±0.008 and 0.14±0.001(P＜0.001),while in patients with PR was 0.01±0.03 and 0.05±0.02(P＞0.05),respectively.Shannon index of BCR in patients with CR was 0.19±0.003 and 0.33±0.15(P＜0.001),while in patients with PR was 0.15±0.009 and 0.23±0.18(P＜0.05),respectively.Conclusions:BTKi treatment can shrink the clone size in CLL patients,promote the expression of IgA,increase the number of functional T cells,and regulate the secretion of cytokines such as IL-2,IL-4,and IFN-γ.BTKi also promote the recovery of diversity of TCR and BCR.BTKi treatment contributes to the reconstitution of immune function in CLL patients.

Objective: To evaluate the efficacy and safety of hybutimibe monotherapy or in combination with atorvastatin in the treatment of primary hypercholesterolemia. Methods: This was a multicenter, randomized, double-blind, double-dummy, parallel-controlled phase Ⅲ clinical trial of patients with untreated primary hypercholesterolemia from 41 centers in China between August 2015 and April 2019. Patients were randomly assigned, at a ratio of 1∶1∶1∶1∶1∶1, to the atorvastatin 10 mg group (group A), hybutimibe 20 mg group (group B), hybutimibe 20 mg plus atorvastatin 10 mg group (group C), hybutimibe 10 mg group (group D), hybutimibe 10 mg plus atorvastatin 10 mg group (group E), and placebo group (group F). After a dietary run-in period for at least 4 weeks, all patients were administered orally once a day according to their groups. The treatment period was 12 weeks after the first dose of the study drug, and efficacy and safety were evaluated at weeks 2, 4, 8, and 12. After the treatment period, patients voluntarily entered the long-term safety evaluation period and continued the assigned treatment (those in group F were randomly assigned to group B or D), with 40 weeks' observation. The primary endpoint was the percent change in low density lipoprotein cholesterol (LDL-C) from baseline at week 12. Secondary endpoints included the percent changes in high density lipoprotein cholesterol (HDL-C), triglyceride (TG), apolipoprotein B (Apo B) at week 12 and changes of the four above-mentioned lipid indicators at weeks 18, 24, 38, and 52. Safety was evaluated during the whole treatment period. Results: Totally, 727 patients were included in the treatment period with a mean age of (55.0±9.3) years old, including 253 males. No statistical differences were observed among the groups in demographics, comorbidities, and baseline blood lipid levels. At week 12, the percent changes in LDL-C were significantly different among groups A to F (all P<0.01). Compared to atorvastatin alone, hybutimibe combined with atorvastatin could further improve LDL-C, TG, and Apo B (all P<0.05). Furthermore, there was no significant difference in percent changes in LDL-C at week 12 between group C and group E (P=0.991 7). During the long-term evaluation period, there were intergroup statistical differences in changes of LDL-C, TG and Apo B at 18, 24, 38, and 52 weeks from baseline among the statins group (group A), hybutimibe group (groups B, D, and F), and combination group (groups C and E) (all P<0.01), with the best effect observed in the combination group. The incidence of adverse events was 64.2% in the statins group, 61.7% in the hybutimibe group, and 71.0% in the combination group during the long-term evaluation period. No treatment-related serious adverse events or adverse events leading to death occurred during the 52-week study period. Conclusions: Hybutimibe combined with atorvastatin showed confirmatory efficacy in patients with untreated primary hypercholesterolemia, which could further enhance the efficacy on the basis of atorvastatin monotherapy, with a good overall safety profile.
Male ; Humans ; Middle Aged ; Atorvastatin/therapeutic use* ; Hydroxymethylglutaryl-CoA Reductase Inhibitors/therapeutic use* ; Hypercholesterolemia/drug therapy* ; Cholesterol, LDL/therapeutic use* ; Anticholesteremic Agents/therapeutic use* ; Treatment Outcome ; Triglycerides ; Apolipoproteins B/therapeutic use* ; Double-Blind Method ; Pyrroles/therapeutic use*

OBJECTIVE: To investigate the correlation between plasmacytoid dendritic cell (pDC) dose in grafts and the occurrence of cytomegalovirus (CMV) infection after allogeneic hematopoietic stem cell transplantation (allo-HSCT). METHODS: The clinical data of 80 children who received allo-HSCT in Children's Hospital of Soochow University from August 20, 2020 to June 11, 2021 were retrospectively analyzed. Proportions of DC subsets and T-cell subsets in grafts were detected by flow cytometry in order to calculate infused cell dose of each cell. Weekly monitoring of CMV-DNA copies in peripheral blood for each child were performed after transplantation. The last follow-up date was December 31, 2021. RESULTS: All the children gained hematopoietic reconstitution. CMV infection was observed in 51 children (63.8%±5.4%) within the first 100 days after transplantation, including 2 cases developing CMV disease. Univariate analysis indicated that infused doses of DC and pDC were significantly associated with CMV infection within 100 days after allo-HSCT (P <0.05). Multivariate analysis indicated that a high dose infusion of pDC was an independent protective factor for CMV infection within 100 days after allo-HSCT (P <0.05). By the end of follow-up, 7 children died of transplantation-related complications, including 2 deaths from CMV disease, 2 deaths from extensive chronic graft-versus-host disease, and 3 deaths from capillary leak syndrome. The overall survival rate was 91.2%. CONCLUSION The pDC in grafts may be associated with early infection of CMV after allo-HSCT, while a high infused pDC dose may serve as a protective factor for CMV infection after transplantation.
Child ; Humans ; Retrospective Studies ; Graft vs Host Disease/complications* ; Cytomegalovirus Infections ; Hematopoietic Stem Cell Transplantation/adverse effects* ; Dendritic Cells

OBJECTIVE: To investigate the optimal duration of dual antiplatelet therapy (DAPT) in patients with diabetes mellitus (DM) requiring complex percutaneous coronary intervention (PCI). METHODS: A total of 2403 patients with DM who underwent complex PCI from January to December 2013 were consecutively enrolled in this observational cohort study and divided according to DAPT duration into a standard group (11-13 months, n = 689) and two prolonged groups (13-24 months, n = 1133; > 24 months, n = 581). RESULTS: Baseline characteristics, angiographic findings, and complexity of PCI were comparable regardless of DAPT duration. The incidence of major adverse cardiac and cerebrovascular event was lower when DAPT was 13-24 months than when it was 11-13 months or > 24 months (4.6% vs. 8.1% vs. 6.0%, P = 0.008), as was the incidence of all-cause death (1.9% vs. 4.6% vs. 2.2%, P = 0.002) and cardiac death (1.0% vs. 3.0% vs. 1.2%, P = 0.002). After adjustment for confounders, DAPT for 13-24 months was associated with a lower risk of major adverse cardiac and cerebrovascular event [hazard ratio (HR) = 0.544, 95% CI: 0.373-0.795] and all-cause death (HR = 0.605, 95% CI: 0.387-0.944). DAPT for > 24 months was associated with a lower risk of all-cause death (HR = 0.681, 95% CI: 0.493-0.942) and cardiac death (HR = 0.620, 95% CI: 0.403-0.952). The risk of major bleeding was not increased by prolonging DAPT to 13-24 months (HR = 1.356, 95% CI: 0.766-2.401) or > 24 months (HR = 0.967, 95% CI: 0.682-1.371). CONCLUSIONS For patients with DM undergoing complex PCI, prolonging DAPT might improve the long-term prognosis by reducing the risk of adverse ischemic events without increasing the bleeding risk.