Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

This study investigated the impact of metabolic syndrome (MetS) and its components on prostate volume (PV) in the general Chinese population. In total, 43 455 participants in The First Medical Center of the Chinese PLA General Hospital (Beijing, China) from January 1, 2012, to December 31, 2022, undergoing health examinations were included in the study. Participants were categorized into four groups according to PV quartiles: Q1 (PV ≤24.94 ml), Q2 (PV >24.94 ml and ≤28.78 ml), Q3 (PV >28.78 ml and ≤34.07 ml), and Q4 (PV >34.07 ml), with Q1 serving as the reference group. Logistic regression analyses were used to examine the association between MetS and PV, with subgroup analyses conducted by age. Among the participants, 18 787 (43.2%) were diagnosed with MetS. In the multivariate analysis model, a significant correlation between MetS and PV was observed, with odds ratios (ORs) increasing as PV increased (Q2, OR = 1.203, 95% confidence interval [CI]: 1.139-1.271; Q3, OR = 1.300, 95% CI: 1.230-1.373; and Q4, OR = 1.556, 95% CI: 1.469-1.648). Analysis of MetS components revealed that all components were positively associated with PV, with abdominal obesity showing the most significant effect. The number of MetS components was identified as a dose-dependent risk factor for elevated PV. The impact of MetS, its components, and component count on PV exhibited a decreasing trend with advancing age. Overall, the influence of MetS, its components, and component count on PV was predominantly observed in the age groups of 40-49 years and 50-59 years. Early intervention targeting MetS can significantly alleviate the increase in PV, particularly benefiting individuals aged 40-59 years who have abdominal obesity.
Humans ; Male ; Metabolic Syndrome/complications* ; Middle Aged ; Cross-Sectional Studies ; Aged ; Prostate/diagnostic imaging* ; Adult ; Age Factors ; Organ Size ; China/epidemiology* ; Obesity, Abdominal ; Risk Factors

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

The biological clock(also known as the circadian rhythm)is the fundamental reliance for all organisms on Earth to adapt and survive in the Earth's rotation environment.Circadian rhythm is the most basic regulatory mechanism of life activities,and plays a key role in maintaining normal physiological and biochemical homeostasis,disease occurrence and treatment.Recent studies have shown that the biologi-cal clock plays an important role in the development of oral tissues and in the occurrence and treatment of oral diseases.Since there is cur-rently no guiding literature on the research methods of biological clock in stomatology,researchers mainly conduct research based on pub-lished references,which has led to controversy about the research methods of biological clock in stomatology,and there are many confusions about how to rationally apply the research methods of circadia rhythms.In view of this,this expert consensus summarizes the characteristics of the biological clock and analyzes the shortcomings of the current biological clock research in stomatology,and organizes relevant experts to summarize and recommend 10 principles as a reference for the rational implementation of the biological clock in stomatology research.

Objective To establish a HS-GC test method for the determination of enflurane,isoflurane,diflurane,sevoflurane and its metabolite hexafluoroisopropanol(HFIP)in blood,and to establish a LC-MS/MS method for the determination of trifluoroacetic acid(TFA),the co-metabolite of enflurane,isoflurane,and deflurane in blood.Methods Place 0.5 mL blood sample in a 10 mL headspace bottle,add 1.0 mL ultrapure water to mix,then add 0.5 mL n-butanol internal standard solution,sealed and heated at 70℃for 20 min,take the upper layer of gas for HS-GC analysis,qualitatived by dual-column retention time and quantified by the internal standard curve;Blood sample was acetonitrile precipitated protein,separated by liquid chromatography,scanned with electrospray ionization(ESI),negative ion mode,and examined in multiple reaction monitoring mode(MRM),qualitatived by retention time and characteristic ions,quantified by standard curve.Results The detection limits(LOD)of enflurane,isoflurane,desflurane,heptaflurane,HFIP and TFA are 0.05,0.2,0.5,0.05,0.5 μg/mL and 0.5 ng/mL,and linear range as 1～100 μg/mL(TFA:1～100 ng/mL),with correlation coefficients greater than 0.999.The extraction recovery rate is between 30%and 80%,and the intra-day and inter-day precision are less than 5%.The accuracy is between 85%and 115%.Conclusion This method is quick and simple,and can be used for qualitative and quantitative analysis of enflurane,isoflurane,desflurane,sevoflurane and their metabolites in blood.

Background/Aims: Ubiquitination is widely involved in the progression of hepatocellular carcinoma (HCC) by regulating various cellular processes. However, systematic strategies for screening core ubiquitin-related genes, clarifying their functions and mechanisms, and ultimately developing potential therapeutics for patients with HCC are still lacking. Methods: Cox and LASSO regression analyses were performed to construct a ubiquitin-related gene prediction model for HCC. Loss- and gain-of-function studies, transcriptomic and metabolomics analysis were used to explore the function and mechanism of UBE2S on HCC cell glycolysis and growth. Results: Based on 1,423 ubiquitin-related genes, a four-gene signature was successfully constructed to evaluate the prognosis of patients with HCC. UBE2S was identified in this signature with the potential to predict the survival of patients with HCC. E2F2 transcriptionally upregulated UBE2S expression by directly binding to its promoter. UBE2S positively regulated glycolysis in a HIF-1α-dependent manner, thus promoting the proliferation of HCC cells. Mechanistically, UBE2S enhanced K11-linkage polyubiquitination at lysine residues 171 and 196 of VHL independent of E3 ligase, thereby indirectly stabilizing HIF-1α protein levels by mediating the degradation of VHL by the proteasome. In particular, the combination of cephalomannine, a small molecule compound that inhibits the expression of UBE2S, and PX-478, an inhibitor of HIF-1α, significantly improved the anti-tumor efficacy. Conclusions UBE2S is identified as a key biomarker in HCC among the thousands of ubiquitin-related genes and promotes glycolysis by E3 enzyme-independent ubiquitination, thus serving as a therapeutic target for the treatment of HCC.

Chromodomain-helicase-DNA-binding protein 1 (CHD1) deletion is among the most common mutations in prostate cancer (PCa), but its role remains unclear. In this study, RNA sequencing was conducted in PCa cells after clustered regularly interspaced palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9)-based CHD1 knockout. Gene set enrichment analysis (GSEA) indicated upregulation of hypoxia-related pathways. A subsequent study confirmed that CHD1 deletion significantly upregulated hypoxia-inducible factor 1α (HIF1α) expression. Mechanistic investigation revealed that CHD1 deletion upregulated HIF1α by transcriptionally downregulating prolyl hydroxylase domain protein 2 (PHD2), a prolyl hydroxylase catalyzing the hydroxylation of HIF1α and thus promoting its degradation by the E3 ligase von Hippel-Lindau tumor suppressor (VHL). Functional analysis showed that CHD1 deletion promoted angiogenesis and glycolysis, possibly through HIF1α target genes. Taken together, these findings indicate that CHD1 deletion enhances HIF1α expression through PHD2 downregulation and therefore promotes angiogenesis and metabolic reprogramming in PCa.
Male ; Humans ; Von Hippel-Lindau Tumor Suppressor Protein/metabolism* ; DNA-Binding Proteins/metabolism* ; Prolyl Hydroxylases/metabolism* ; Hypoxia ; Prostatic Neoplasms/pathology* ; Glycolysis ; Hypoxia-Inducible Factor 1, alpha Subunit/metabolism* ; Cell Line, Tumor ; DNA Helicases/metabolism*