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.

Flexible ureteral lithotripsy (FURL) under general anesthesia (GA) is the dominant method in the treatment of renal and upper ureteral calculi，but some patients cannot tolerate GA.In recent years，there has been a growing interest in the use of local anesthesia (LA) as a safe and effective alternative.And it is also an option for patients who have calculi ≤20 mm with high fragility，lower CT value and better compatibility.Before surgery，it is important to conduct relevant examinations，evaluate the status of patients，prevent infections，and indwell ureteral stents.During surgery，lithotomy position，scissors position，prone leg position and other positions should be selected according to the specific conditions of patients.LA drugs should be used to control physiological pain and relieve psychological anxiety.Patients' breathing state should be carefully monitored，and appropriate ureteroscope and lens sheath should be selected for the success and safety of the operation.In this paper，the perioperative management of FURL under LA is briefly summarized，so as to provide reference for clinical practice.

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

Drug repurposing offers a promising alternative to traditional drug development and significantly reduces costs and timelines by identifying new therapeutic uses for existing drugs. However, the current approaches often rely on limited data sources and simplistic hypotheses, which restrict their ability to capture the multi-faceted nature of biological systems. This study introduces adaptive multi-view learning (AMVL), a novel methodology that integrates chemical-induced transcriptional profiles (CTPs), knowledge graph (KG) embeddings, and large language model (LLM) representations, to enhance drug repurposing predictions. AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning (MVL), matrix factorization, and ensemble optimization techniques to integrate heterogeneous multi-source data. Comprehensive evaluations on benchmark datasets (Fdataset, Cdataset, and Ydataset) and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art (SOTA) methods, achieving superior accuracy in predicting drug-disease associations across multiple metrics. Literature-based validation further confirmed the model's predictive capabilities, with seven out of the top ten predictions corroborated by post-2011 evidence. To promote transparency and reproducibility, all data and codes used in this study were open-sourced, providing resources for processing CTPs, KG, and LLM-based similarity calculations, along with the complete AMVL algorithm and benchmarking procedures. By unifying diverse data modalities, AMVL offers a robust and scalable solution for accelerating drug discovery, fostering advancements in translational medicine and integrating multi-omics data. We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.

Diabetic kidney disease (DKD) with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression. Therapeutic targets supported by causal genetic evidence are more likely to succeed in randomized clinical trials. In this study, we integrated large-scale plasma proteomics, genetic-driven causal inference, and experimental validation to identify prioritized targets for DKD using the UK Biobank (UKB) and FinnGen cohorts. Among 2844 diabetic patients (528 with DKD), we identified 37 targets significantly associated with incident DKD, supported by both observational and causal evidence. Of these, 22% (8/37) of the potential targets are currently under investigation for DKD or other diseases. Our prospective study confirmed that higher levels of three prioritized targets-insulin-like growth factor binding protein 4 (IGFBP4), family with sequence similarity 3 member C (FAM3C), and prostaglandin D2 synthase (PTGDS)-were associated with a 4.35, 3.51, and 3.57-fold increased likelihood of developing DKD, respectively. In addition, population-level protein-altering variants (PAVs) analysis and in vitro experiments cross-validated FAM3C and IGFBP4 as potential new target candidates for DKD, through the classic NLR family pyrin domain containing 3 (NLRP3)-caspase-1-gasdermin D (GSDMD) apoptotic axis. Our results demonstrate that integrating omics data mining with causal inference may be a promising strategy for prioritizing therapeutic targets.

Objective To investigate the technical capacity and service quality of occupational health technical service institutions (hereinafter referred to as "occupational health institutions") in Guangdong Province. Methods All occupational health institutions in Guangdong Province that had valid occupational health service qualifications and within the validity period were included for analysis. Data on basic information, employed personnel, and results of professional technical capacity assessments across occupational health institutions were obtained through the Guangdong Provincial Occupational Health Technical Quality Control Center. Results A total of 99 institutions with 2 732 technical staff were included in this study. Occupational health institutions in Guangdong Province were mainly distributed in the Pearl River Delta region, accounting for 87.9% (87/99) of the total. The number of public and private health institutions was 23 and 76, accounted for 23.2% and 76.8% respectively. In terms of technical personnel, the percentage of individuals worked in public or private health institutions was 24.1% and 75.9%, respectively. Personnel titles were predominantly intermediate level and no title, accounting for 38.7% and 26.4%, respectively. Individuals with a bachelor′s degree or above accounted for 67.4%. Engineering and other professionals accounted for 35.4% and 30.5%, respectively. Private institutions undertook 97.3% of testing and evaluation workload related to occupational hazard in the province. The number of occupational health institutes acquiring category Ⅰ and Ⅱ service license were 97 and 13. Among institutions participating in inter-laboratory comparisons, the overall pass rates for quantitative items were 95.5% in public and 70.3% in private institutions, while the pass rates for qualitative items were 100.0% and 94.5%, respectively. Conclusion Occupational health institutions in Guangdong Province face issues such as imbalanced regional distribution, uneven development, and insufficient technical competence and testing capacity of professional personnel. Health authorities at all levels should continue to strengthen supervision and quality control to solidify the technical foundation and comprehensively enhance service capacity and quality.

Objective:To compare the accuracy of 11 ultrasound parameters prediction formulas for fetal body mass,and to explore the effect of gestational weight gain(GWG)on the accuracy of ultrasound prediction of fetal body mass.Methods:A total of 502 single and full-term postpartum women who gave birth from August 2020 to December 2020 at Jinshan Hospital,Fudan University were collected.The gestational weight gain,fetal ultrasound measurement indicators within 7 days before delivery,and newborn birth weight were calculated and analyzed.The accuracy of multiple ultrasound prediction formulas were calculated and analyzed.According to the criteria for weight gain during pregnancy,the reasonable weight gain during pregnancy was 12.5-18.0 kg for singleton pregnancies with pre-pregnancy(body mass index)BMI<18.5 kg/m2,11.5-16.0 kg for those with BMI 18.5-24.9 kg/m2,7.0-11.5 kg for those with BMI 25.0-29.9 kg/m2,and 5.0-9.0 kg for those with BMI≥30.0 kg/m2.The cases were divided into the group with insufficient GWG(125 cases),the normal group(202 cases),and the group with too much GWG(175 cases)to analyze the effect of different GWG on the accuracy of ultrasound pre-diction of fetal body mass.Results:Among the 11 ultrasound parameter formulas for predicting fetal body mass,the HadlockⅢformula predicted fetal body mass with an absolute error of 186.64±149.28 g and a relative error of(5.52±4.18)%,which was the smallest error among 11 prediction formulas,with a statistically significant difference(P<0.05).The absolute and relative error compliance rates were 72.31%,86.25%,respectively,both of which were the highest,and the difference was statistically significant(P<0.05).When the HadlockⅢformula was used to predict birth weight in the insufficient GWG group,the normal group,and the group with too much GWG,the absolute errors were 190.23±136.69 g,148.12±99.39 g,228.54±189.57 g,and the relative errors were(5.95±4.25)%,(4.40±2.78)%,(6.49±5.09)%,respectively,and the differences were statistically significant(P<0.05).Conclusions:The accuracy of Hadlock Ⅲ formula in predicting fetal body mass is better than that of other formulas,but its accuracy can be affected by GWG,and it is necessary to consider multiple as-pects when estimating fetal body mass in clinical practice.

Depression is a prevalent mental illness worldwide, its multifaceted pathogenesis is still in the exploratory stage. MicroRNA (miRNA), as a crucial epigenetic regulator, plays an important role in depression. miR-124 is one of the most abundant miRNAs in the central nervous system including neurons and microglia, and involved in various biological events like neuron development and differentiation, synaptic and axonal growth, neural plasticity, inflammation and autophagy. Recent studies have reported abnormal expression of miR-124 in both depression patients and animal models. Most of the studies showed that miR-124 is upregulated in the hippocampus or prefrontal cortex in stress-induced rodent depression animal models such as CUMS, CSDS, CORT, CRS and LH but some evidence for divergence. Upregulation of miR-124 expression may be involved in depression-like behavior via CREB/BDNF/TrkB pathway, GR pathway, SIRT1 pathway, apoptosis and autophagy pathways by directly targeting these genes including Creb, Bdnf, Sirt1, Nr3c1, Ezh2 and Stat3. The downregulation of miR-124 expression in neurons is mainly involved in the neurogenesis and neuroplasticity impairments in depression by targeting the Notch signaling pathway and DDIT4/TSC1/2/mTORC1 pathway. The downregulation of miR-124 expression also was found in the activated microglia in the stress-induced models, and resulted in neuroinflammation. In summary, the abnormal expression of miR-124 in the brain of depression-related models and its related mechanisms are complex and even contradictory, and still need further research. This review provides a summary of the research progress of miR-124 in depression.