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.

The structure of intestinal tissue is complex. In vitro simulation of intestinal structure and function is important for studying intestinal development and diseases. Recently, organoids have been successfully constructed and they have come to play an important role in biomedical research. Organoids are miniaturized three-dimensional (3D) organs, derived from stem cells, which mimic the structure, cell types, and physiological functions of an organ, making them robust models for biomedical research. Intestinal organoids are 3D micro-organs derived from intestinal stem cells or pluripotent stem cells that can successfully simulate the complex structure and function of the intestine, thereby providing a valuable platform for intestinal development and disease research. In this article, we review the latest progress in the construction and application of intestinal organoids.
Organoids/cytology* ; Intestines/physiology* ; Humans ; Animals ; Pluripotent Stem Cells

Objective To gain insights into the care service preferences and willingness of patients with severe mental disorders in Beijing,analyze the main factors affecting their participation in care services,and provide data support and decision-making reference for the optimal design of care services for patients with severe mental disorders and the improvement of relevant policies.Methods In July 2022,a questionnaire survey was conducted for a part of Beijing community patients with severe mental disorders selected by multi-stage stratified sampling,including the basic personal information and the preferences of discrete choice experiment.A mixed Logit model was used to perform regression analysis on the care service preferences,and the trade off between general and monetary attributes was quantified by willingness to pay(WTP).Results A total of 242 questionnaires were distributed,and 181 valid questionnaires were collected,with a response rate of 74.79%.The regression coefficients for the four attributes-service type,service content,service frequency,and service cost-all showed statistical significance(all P<0.05).Patients' most preferred attribute level was a service frequency covering about 90% of the time per month/year( β=1.059),while the least preferred was full-time residential care( β=-1.025).Increasing the service frequency from 30% to 90% corresponded to a WTP of 492.5 yuan,while changing the service type from home-based care to full-time residential care resulted in a WTP of -476.6 yuan.Moreover,there were differences in care service preferences and WTP among patient groups with different characteristics(all P<0.05).Conclusions Service type,service content,service frequency,and service cost all significantly affect the care service preferences of patients with severe mental disorders.There is heterogeneity in care service preferences among patient groups with different characteristics.
Humans ; Mental Disorders/therapy* ; Patient Preference ; Beijing ; Surveys and Questionnaires ; Male ; Female ; Choice Behavior ; Adult ; Middle Aged

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.

Diabetic encephalopathy(DE)is a complication of the central nervous system induced by diabetes,characterized by insidious onset and complex pathological mechanisms.Recent research has revealed that the pathological mechanisms of DE are closely associated with the imbalance of intercellular communica-tion across multiple organs.Exosomes,serving as vital media-tors of intercellular communication,are involved in the patholog-ical progression of DE and hold promise as diagnostic biomarkers and therapeutic targets for DE.Traditional Chinese medicine(TCM)can improve communication between brain cells and be-tween peripheral and brain tissues by regulating exosomes and their contents,thereby preventing and treating DE.Meanwhile,exosomes,as targeted delivery carriers of active molecules,can carry TCM monomers more easily across the blood-brain barrier to prevent and treat DE.In light of this,this review summarizes the regulatory role of exosomes in the pathological progression of DE and explores the enormous potential of TCM in the preven-tion and treatment of DE by regulating exosomes and using exo-somes as traditional Chinese medicine carriers,aiming to pro-vide new evidence for the prevention and treatment of DE with TCM.

Aim To investigate the effect of long-chain non-coding RNA(lncRNA)GS1-124K5.4 targeting regulation of PRDX6 on proliferation,migration and in-vasion of lung squamous carcinoma(LUSC)cells and the underlying mechanism.Methods The expression level of lncRNA GS1-124K5.4 in lung cancer tissues and adjacent tissues of 60 patients with LUSC were de-termined by fluorescence in situ hybridization.The ex-pression level of lncRNA GS1-124K5.4 in human nor-mal lung cells and LUSC cells were determined by qRT-PCR.Two kinds of LUSC cells(NCI-H 1703,SK-MES-1)with highest expression level of lncRNA GS1-124K5.4 were selected for subsequent experi-ments.The distribution of lncRNA GS1-124K5.4 in cells was studied by fluorescence in situ hybridization and prokaryotic separation.The effect of knockdown of lncRNA GS1-124K5.4 on proliferation of NCI-H1703 and SK-MES-1 cells was studied by CCK-8 experiment and cell clone formation experiment;the effect of knockdown of lncRNA GS1-124K5.4 on migration of NCI-H1703 and SK-MES-1 cells was studied by cell scratch experiment and Transwell cell migration experi-ment;and the effect of knockdown of lncRNA GS1-124K5.4 on invasion of NCI-H1703 and SK-MES-1 cells was studied by Transwell invasion experiment.The protein to be bound by lncRNA GS1-124K5.4 was detected by RNA pull-down combined with mass spec-trometry and immune-precipitation.The effect of knockdown of lncRNA GS1-124K5.4 targeting PRDX6 on proliferation,migration and invasion of NCI-H1703 and SK-MES-1 cells was studied.Results(1)The fluorescence intensity of lncRNA GS1-124K5.4 in lung squamous cell carcinoma increased compared with that in adjacent tissues(P＜0.05),and the expression of lncRNA GS1-124K5.4 was related with lymph node metastasis and clinical stage(P＜0.05).(2)The ex-pression level of lncRNA GS1-124K5.4 in NCI-H1703,NCI-H520 and SK-MES-1 cells significantly increased(P＜0.05).(3)The result of fluorescence in situ hybridization experiment and nucleoplasm sepa-ration experiment showed that lncRNA GS1-124K5.4 was mainly distributed in cell nucleus.(4)The prolif-eration,migration and invasion ability of NCI-H1703 and SK-MES-1 cells with knockdown of lncRNA GS1-124K5.4 significantly decreased(P＜0.05).(5)PRDX6 protein to be bound to LncRNA GS1-124K5.4 was determined by RNA pull-down combined with mass spectrometry and immunoprecipitation.(6)The prolif-eration,migration and invasion ability of NCI-H1703 and SK-MES-1 cells with overexpression of lncRNA GS1-124K5.4 significantly increased(P＜0.05);the proliferation,migration and invasion ability of NCI-H1703 and SK-MES-1 cells with knockdown of PRDX6 significantly decreased(P＜0.05);the proliferation,migration and invasion ability of NCI-H1703 and SK-MES-1 cells with overexpression of lncRNAGS1-124K5.4 and knockdown of PRDX6 showed no signifi-cant change(P＞0.05).Conclusions LncRNA GS1-124K5.4 is highly expressed in lung squamous cell carcinoma,and it may promote the proliferation,migration and invasion of lung squamous carcinoma cells by targeting the expression of PRDX6 protein.

Objective To propose a dynamic early warning model based on machine learning methods and validate its predi-ctive efficacy so as to achieve precise assessment and early warning of mortality risk in patients with traumatic hypothermia.Methods Firstly,a total of 480 patients who met inclusion criteria were retrospectively selected from the eICU database and randomly divided into training and test sets at an 8∶2 ratio.Secondly,physiological parameters were extracted from these patients,and five machine learning algorithms including XGBoost,AdaBoost,LightGBM,logistic regression(LR)and random forest(RF)were employed respectively to develop dynamic mortality risk warning models for traumatic hypothermia patients,utilizing a 1-hour observation window.Thirdly,receiver operating characteristic curves(ROC)were plotted using the test set data and the effects of different warning windows on the model performance were analyzed by calculating the AUC.Finally,the interpretability of the models was analyzed using the SHapley Additive exPlanations(SHAP)algorithm to elucidate the contribution of each feature to predictive performance.Results The optimal warning window for the dynamic warning model constructed using the eICU database was 12 hours,and in case of 12-hour warning window the logistic regression model achieved the highest AUC of 0.935 and showed optimal predictive performance.The results of the interpretability analysis by the SHAP algorithm showed that body temperature was the feature that had the greatest impact on the model results,and its reduction was positively correlated with the increased risk of death.Conclusion The machine learning-based dynamic warning model for mortality risk in traumatic hypothermia patients enables real-time dynamic risk assessment,providing robust support for clinicians to identify the patient's condition changes at an early stage and references for the adjustment of clinical treatment programs.[Chinese Medical Equipment Journal,2025,46(3):9-14]