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

Based on the α7 nicotinic acetylcholine receptor(α7nAChR), this study examined how tetrahydropalmatine(THP) affected BV2 microglia exposed to lipopolysaccharide(LPS), aiming to clarify the possible mechanism underlying the anti-depression effect of THP from the perspectives of preventing inflammation and regulating polarization. First, after molecular docking and determination of the content of Corydalis saxicola Bunting total alkaloids, THP was initially identified as a possible anti-depression component. The BV2 microglia model of inflammation was established with LPS. BV2 microglia were allocated into a normal group, a model group, low-and high-dose(20 and 40 μmol·L~(-1), respectively) THP groups, and a THP(20 μmol·L~(-1))+α7nAChR-specific antagonist MLA(1 μmol·L~(-1)) group. The CCK-8 assay was used to screen the safe concentration of THP. A light microscope was used to examine the morphology of the cells. Western blot and immunofluorescence were used to determine the expression of α7nAChR. qRT-PCR was performed to determine the mRNA levels of inducible nitric oxide synthase(iNOS), cluster of differentiation 86(CD86), suppressor of cytokine signaling 3(SOCS3), arginase-1(Arg-1), cluster of differentiation 206(CD206), tumor necrosis factor(TNF)-α, interleukin(IL)-6, and IL-1β. Enzyme-linked immunosorbent assay(ELISA) was employed to measure the levels of TNF-α, IL-6, and IL-1β in the cell supernatant. The experimental results showed that THP at concentrations of 40 μmol·L~(-1) and below had no effect on BV2 microglia. THP improved the morphology of BV2 microglia, significantly up-regulated the protein level of α7nAChR, significantly down-regulated the mRNA levels of iNOS, CD86, SOCS3, TNF-α, IL-6, and IL-1β, significantly up-regulated the mRNA levels of Arg-1 and CD206, and dramatically lowered the levels of TNF-α, IL-6, and IL-1β in the cell supernatant. However, the antagonist MLA abolished the above-mentioned ameliorative effects of THP on LPS-treated BV2 microglia. As demonstrated by the aforementioned findings, THP protected LPS-treated BV2 microglia by regulating the M1/M2 polarization and preventing inflammation, which might be connected to the regulation of α7nAChR on BV2 microglia.
Berberine Alkaloids/chemistry* ; alpha7 Nicotinic Acetylcholine Receptor/chemistry* ; Microglia/metabolism* ; Mice ; Animals ; Cell Line ; Corydalis/chemistry* ; Humans ; Molecular Docking Simulation ; Inflammation/drug therapy* ; Nitric Oxide Synthase Type II/immunology* ; Tumor Necrosis Factor-alpha/immunology*

ObjectiveIn recent years, with the intensification of environmental issues and the depletion of ozone layer, incidence of skin tumors has also significantly increased, becoming one of the major threats to people’s lives and health. However, due to factors such as high concealment in the early stage of skin tumors, unclear symptoms, and large human skin area, most cases are detected in the middle to late stage. Early detection plays a crucial role in postoperative survival of skin tumors, which can significantly improve the treatment and survival rates of patients. We proposed a rapid non-invasive electrical impedance detection method for early screening of skin tumors based on bioimpedance spectroscopy (BIS) technology. MethodsFirstly, we have established a complete skin stratification model, including stratum corneum, epidermis, dermis, and subcutaneous tissue. And the numerical analysis method was used to investigate the effect of dehydrated and dry skin stratum corneum on contact impedance in BIS measurement. Secondly, differentiation effect of different diameter skin tumor tissues was studied using a skin model after removing the stratum corneum. Then, in order to demonstrate that BIS technology can be used for detecting the microinvasion stage of skin tumors, we conducted a simulation study on the differentiation effect of skin tumors under different infiltration depths. Finally, in order to verify that the designed BIS detection system can distinguish between tumor microinvasion periods, we conducted tumor invasion experiments using hydrogel treated pig skin tissue. ResultsThe simulation results show that a dry and high impedance stratum corneum will bring about huge contact impedance, which will lead to larger measurement errors and affect the accuracy of measurement results. We extracted the core evaluation parameter of relaxed imaginary impedance (Z_imag-relax) from the simulation results of the skin tumor model. When the tumor radius (R_tumor) and invasion depth (h)>1.5 mm, the designed BIS detection system can distinguish between tumor tissue and normal tissue. At the same time, in order to evaluate the degree of canceration in skin tissue, the degree of tissue lesion (ε_worse) is defined by the relaxed imaginary impedance (Z_imag-relax) of normal and tumor tissue (ε_worse is the percentage change in virtual impedance of tumor tissue relative to that of normal tissue), and we fitted a Depth-Z_imag-relax curve using relaxation imaginary impedance data at different infiltration depths, which can be applied to quickly determine the infiltration depth of skin tumors after being supplemented with a large amount of clinical data in the future. The experimental results proved that when ε_worse=0.492 0, BIS could identify microinvasive tumor tissue, and the fitting curve correction coefficient of determination was 0.946 8, with good fitting effect. The simulation using pig skin tissue correlated the results of real human skin simulation with the experimental results of pig skin tissue, proving the reliability of this study, and laying the foundation for further clinical research in the future. ConclusionOur proposed BIS method has the advantages of fast, real-time, and non-invasive detection, as well as high sensitivity to skin tumors, which can be identified during the stage of tumor microinvasion.

Temporomandibular joint (TMJ) diseases are common clinical conditions. The number of patients with TMJ diseases is large, and the etiology, epidemiology, disease spectrum, and treatment of the disease remain controversial and unknown. To understand and master the current situation of the occurrence, development and prevention of TMJ diseases, as well as to identify the patterns in etiology, incidence, drug sensitivity, and prognosis is crucial for alleviating patients′suffering.This will facilitate in-depth medical research, effective disease prevention measures, and the formulation of corresponding health policies. Cohort construction and research has an irreplaceable role in precise disease prevention and significant improvement in diagnosis and treatment levels. Large-scale cohort studies are needed to explore the relationship between potential risk factors and outcomes of TMJ diseases, and to observe disease prognoses through long-term follw-ups. The consensus aims to establish a standard conceptual frame work for a cohort study on patients with TMJ disease while providing ideas for cohort data standards to this condition. TMJ disease cohort data consists of both common data standards applicable to all specific disease cohorts as well as disease-specific data standards. Common data were available for each specific disease cohort. By integrating different cohort research resources, standard problems or study variables can be unified. Long-term follow-up can be performed using consistent definitions and criteria across different projects for better core data collection. It is hoped that this consensus will be facilitate the development cohort studies of TMJ diseases.

Objective To investigate the Gram-positive coccus resistance in nationwide's tertiary hospitals and understand the trend of antimicrobial resistance.Methods All the clinical isolates were collected from 19 hospitals and the minimal inhibitory concentrations(MICs)were tested using agar/broth dilution method recommended.Results A total of 1 974 pathogenic Gram-positive coccus from 19 tertiary hospitals in 19 cities nationwide over the period from July 2021 to June 2022 were studied.Based on the MIC results,the prevalence of methicillin resistant Stapylococcus aureus(MRSA)and methicillin resistant Stapylococcus epidermidis(MRSE)were 36.4％and 79.9％respectively.No vancomycin insensitivity Staphylococcus was detected.Staphylococcus aureus were 100％susceptibility to linezolid and teicoplanin.Antibiotic resistance rate of Enterococcus faecalis and Enterococcus faecium to ampicillin were 3.1％and 92.9％.The detectation rate of vancomycin resistant Enterococcus(VRE)was 1.6％.Nonsusceptibility rate of Enterococcus faecalis to linezolid was 32.2％,two consecutive monitoring rises and nonsusceptibility rate of Enterococcus faecium(12.5％)was also significantly increased.The prevalence of penicillin non-susceptible Streptococcus pneumoniae(PNSSP)was 0.8％based on non-meningitis and parenteral administration criterion,decrease of nearly 30 percentage points from the previous surveillance.While for cases of oral penicillin,the rate was 71.8％,showing similar to last time.The results indicated that the number of strains with higher MIC value of penicillin(MIC ≥4 mg·L-1)decreased significantly.There were no significant differences of resistance rates of Stapylococcus aureus,Stapylococcus epidermidis,Enterococcus faecalis,Enterococcus faecium and Streptococcus pneumoniae among various groups such as different department,age,or specimen source.Conclusion VRE detection ratio stablized at a relatively low level.The number of Streptococcus pneumoniae with higher MIC value of penicillin decreased significantly compared with the previous monitoring.The increase of linezolidin-insensitive Enterococcus was noteworthy.