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.

Objective To construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. Methods A total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. Results There were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.

Objective To systematically review the effectiveness of perioperative oral probiotics in hepatic resection patients,and provide evidence-based clinical evidence.Methods PubMed,Cochrane Library,Web of Science,EMbase,SinoMed,WanFang Data,CNKI and VIP databases were electronically searched to collect randomized controlled trials(RCTs)on perioperative oral probiotics in hepatectomized patients from inception to June 30,2023.Two reviewers independently screened literature,extracted data and assessed the risk of bias of the included studies.Meta-analysis was performed by RevMan 5.4 software.Results A total of 10 RCTs were included,including 715 patients.The Meta-analysis showed that compared to placebo or blank controls,the incidence of postoperative infections in oral probiotic patients(RR=0.60,95％CI 0.48 to 0.74,P＜0.001),serum endotoxin levels(MD=-0.88,95％CI-1.53 to-0.22,P=0.009),cumulative antibiotic use(MD=-1.48,95％CI-2.17 to-0.78,P＜0.001),AST levels(MD=-9.68,95％CI-11.36 to-8.01,P＜0.001),ALT levels(MD=-21.24,95％CI-34.81 to-7.68,P=0.002),TBiL levels(SMD=-0.70,95％CI-0.95 to-0.45,P＜0.001),CRP levels(SMD=-0.52,95％CI-0.91 to-0.13,P=0.009),procalcitonin levels(MD=-0.19,95％CI-0.32 to-0.05,P=0.006),IL-6 levels(MD=-7.30,95％CI-14.26 to-0.33,P=0.04),and the first flatus time(MD=-1.16,95％CI-1.51 to-0.82,P＜0.001),hospital stay(MD=-0.62,95％CI-0.83 to-0.41,P＜0.001),hospitalisation costs(SMD=-0.65,95％CI-0.95 to-0.34,P＜0.001)were lower.Conclusion Current evidence shows that perioperative oral probiotics can significantly reduce the postoperative infection rate and decrease the release of inflammatory factors in liver resection patients,promote the recovery of postoperative hepatic and gastrointestinal functions,and shorten the length of hospital stay and costs.Due to the limited quality and quantity of the included studies,more high quality studies are needed to verify the above conclusion.

Chimeric antigen receptor (CAR)-T cell therapy has achieved remarkable success in the treatment of hematological malignancies. Based on the immunomodulatory capability of CAR-T cells, efforts have turned toward exploring their potential in treating autoimmune diseases. Bibliometric analysis of 210 records from 128 academic journals published by 372 institutions in 40 countries/regions indicates a growing number of publications on CAR-T therapy for autoimmune diseases, covering a range of subtypes such as systemic lupus erythematosus, multiple sclerosis, among others. CAR-T therapy holds promise in mitigating several shortcomings, including the indiscriminate suppression of the immune system by traditional immunosuppressants, and non-sustaining therapeutic levels of monoclonal antibodies due to inherent pharmacokinetic constraints. By persisting and proliferating in vivo, CAR-T cells can offer a tailored and precise therapeutics. This paper reviewed preclinical experiments and clinical trials involving CAR-T and CAR-related therapies in various autoimmune diseases, incorporating innovations well-studied in the field of hematological tumors, aiming to explore a safe and effective therapeutic option for relapsed/refractory autoimmune diseases.

Objective To establish an animal model of lung adenoma in BALB/c mice based on dynamic characterization by micro-computed tomography(CT).Methods Eighty female SPF-grade BALB/c mice were divided randomly into four groups:model low dose group(1 mg/g urethane,iP,once),model medium dose group(1 mg/g urethane,ip,once a week,followed by 2 weeks),model high dose group(1 mg/g urethane,ip,once a week,followed by 4 weeks),and blank group(equal volume of saline).Growth of lung nodules in the mice was monitored regularly using Micro-CT.Three-dimensional images of the lungs were drawn using the Analyze 12.0 system,and lung tissues were taken for histopathological examination(hematoxylin and eosin).Results Lung nodules with round high-density shadows were observed at week 11 in all model groups compared with the findings in the blank group.The rate of nodule formation increased with increasing modeling weeks,with rates of nodule formation in the model high,medium,and low dose groups of 93.8%,93.8%,and 87.5%,respectively,at week 21.Most mice had two to four,followed by one,and one to two nodules,respectively.The average maximum diameter of the lung nodules in the low dose group was significantly higher than the diameters in the medium-and high-dose groups(P＜0.05),but there was no significant difference in lung nodule volume among the three groups.Regarding pathological type,hematoxylin and eosin staining revealed that the tumors in all the model groups were lung adenomas.Conclusions Lung adenomas were successfully induced in all urethane dose groups of mice and growth of the lung nodules could be characterized by micro-CT.The rate of nodule formation was highest in the medium dose group,which developed a moderate number of lung adenomas and provided a stable model,and was thus considered the most suitable model for the study of lung adenomas in mice.