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.

OBJECTIVES: To determine the pharmacological impact of hesperidin, the main component of Citri Reticulatae Pericarpium, on depressive behavior and elucidate the mechanism by which hesperidin treats depression, focusing on the gut-brain axis. METHODS: Fifty-four Sprague Dawley male rats were randomly allocated to 6 groups using a random number table, including control, model, hesperidin, probiotics, fluoxetine, and Citri Reticulatae Pericarpium groups. Except for the control group, rats in the remaining 5 groups were challenged with chronic unpredictable mild stress (CUMS) for 21 days and housed in single cages. The sucrose preference test (SPT), immobility time in the forced swim test (FST), and number in the open field test (OFT) were performed to measure the behavioral changes in the rats. Enzyme-linked immunosorbent assay was used to determine the levels of 5-hydroxytryptamine (5-HT) and brain-derived neurotrophic factor (BDNF) in brain tissue, and the histopathology was performed to evaluate the changes of colon tissue, together with sequencing of the V3-V4 regions of 16S rRNA gene on feces to explore the changes of intestinal flora in the rats. RESULTS: Compared to the control group, the rats in the model group showed notable reductions in body weight, SPF, and number in OFT (P<0.01). Hesperidin was found to ameliorate depression induced by CUMS, as seen by improvements in body weight, SPT, immobility time in FST, and number in OFT (P<0.05 or P<0.01). Regarding neurotransmitters, it was found that at a dose of 50 mg/kg hesperidin treatment upregulated the levels of 5-HT and BDNF in depressed rats (P<0.05). Compared to the control group, the colon tissue of the model group exhibited greater inflammatory cell infiltration, with markedly reduced numbers of goblet cells and crypts and were significantly improved following treatment with hesperidin. Simultaneously, the administration of hesperidin demonstrated a positive impact on the gut microbiome of rats treated with CUMS, such as Shannon index increased and Simpson index decreased (P<0.01), while the abundance of Pseudomonadota and Bacteroidota increased in the hesperidin-treated group (P<0.05). CONCLUSION The mechanism responsible for the beneficial effects of hesperidin on depressive behavior in rats may be related to inhibition of the expressions of BDNF and 5-HT and preservation of the gut microbiota.
Animals ; Hesperidin/therapeutic use* ; Rats, Sprague-Dawley ; Depression/drug therapy* ; Male ; Stress, Psychological/drug therapy* ; Brain/metabolism* ; Brain-Derived Neurotrophic Factor/metabolism* ; Serotonin/metabolism* ; Gastrointestinal Microbiome/drug effects* ; Behavior, Animal/drug effects* ; Rats ; Brain-Gut Axis/drug effects* ; Chronic Disease ; Colon/drug effects*

OBJECTIVE: The relationship between fish consumption and stroke is inconsistent, and it is uncertain whether this association varies across predicted stroke risks. METHODS: A cohort study comprising 95,800 participants from the Prediction for Atherosclerotic Cardiovascular Disease Risk in China project was conducted. A standardized questionnaire was used to collect data on fish consumption. Participants were stratified into low- and moderate-to-high-risk categories based on their 10-year stroke risk prediction scores. Hazard ratios ( HRs) and 95% confidence intervals ( CIs) were estimated using Cox proportional hazard models and additive interaction by relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI). RESULTS: During 703,869 person-years of follow-up, 2,773 incident stroke events were identified. Higher fish consumption was associated with a lower risk of stroke, particularly among moderate-to-high-risk individuals ( HR = 0.53, 95% CI: 0.47-0.60) than among low-risk individuals ( HR = 0.64, 95% CI: 0.49-0.85). A significant additive interaction between fish consumption and predicted stroke risk was observed (RERI = 4.08, 95% CI: 2.80-5.36; SI = 1.64, 95% CI: 1.42-1.89; AP = 0.36, 95% CI: 0.28-0.43). CONCLUSION Higher fish consumption was associated with a lower risk of stroke, and this beneficial association was more pronounced in individuals with moderate-to-high stroke risk.
Humans ; China/epidemiology* ; Male ; Female ; Stroke/etiology* ; Middle Aged ; Prospective Studies ; Incidence ; Aged ; Animals ; Fishes ; Risk Factors ; Diet ; Seafood ; Adult ; Cohort Studies

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.

Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large language models, large visual models, and multimodal large models, offer unprecedented capabilities in processing and interpreting complex medical data across various imaging modalities. By leveraging self-supervised pretraining on vast unlabeled datasets, cross-modal representation learning, and domain-specific medical knowledge adaptation through fine-tuning, large models can achieve higher diagnostic accuracy and more efficient workflows for key clinical tasks. This review summarizes the concepts, methods, and progress of large models in medical imaging, highlighting their potential in precision medicine. The article first outlines the integration of multimodal data under large model technologies, approaches for training large models with medical datasets, and the need for robust evaluation metrics. It then explores how large models can revolutionize applications in critical tasks such as image segmentation, disease diagnosis, personalized treatment strategies, and real-time interactive systems, thus pushing the boundaries of traditional imaging analysis. Despite their potential, the practical implementation of large models in medical imaging faces notable challenges, including the scarcity of high-quality medical data, the need for optimized perception of imaging phenotypes, safety considerations, and seamless integration with existing clinical workflows and equipment. As research progresses, the development of more efficient, interpretable, and generalizable models will be critical to ensuring their reliable deployment across diverse clinical environments. This review aims to provide insights into the current state of the field and provide directions for future research to facilitate the broader adoption of large models in clinical practice.
Humans ; Diagnostic Imaging/methods* ; Precision Medicine/methods* ; Image Processing, Computer-Assisted/methods*

This study developed a core outcome set(COS) for traditional Chinese medicine(TCM) interventions in diabetic peripheral neuropathy(DPN), standardizing evaluation metrics for TCM efficacy and providing a new framework for DPN treatment and management. A systematic search was conducted across databases, including CNKI, Wanfang, and PubMed, targeting clinical trial literature published between January 1, 2013, and January 1, 2023. The search focused on extracting outcome indicators and measurement tools used in TCM treatments for DPN. Retrospective data collection was performed from January 2018 to June 2023, involving 200 DPN patients hospitalized at the Department of Endocrinology of the First Affiliated Hospital of Anhui University of Chinese Medicine. Additionally, semi-structured interviews were conducted with inpatients, outpatients, their families, and nursing staff to further refine and enhance the list of outcome indicators. After two rounds of Delphi questionnaire survey and consensus meeting, a consensus was reached. The study initially retrieved 3 421 publications, of which 170 met the inclusion criteria after review. These publications, combined with retrospective analysis and semi-structured interviews, supplemented the list of indicators. After two rounds of Delphi surveys, experts agreed on 24 indicators and 6 measurement tools. The final COS determined by expert consensus meeting included 5 domains and 13 outcome indicators: neurological function signs, quality of life, TCM syndrome score, nerve conduction velocity, current perception threshold test, fasting blood glucose, 2 h postprandial blood glucose, glycated hemoglobin, complete blood count, urinalysis, liver function test, kidney function test, and electrocardiogram.
Humans ; Diabetic Neuropathies/drug therapy* ; Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal/therapeutic use* ; Retrospective Studies ; Treatment Outcome ; Male ; Female

Objective To propose a high-precision deep learning-based image assessment method of osteosarcoma chemotherapy efficacy for clinical treatment,as existing methos have low accuracy of osteosarcoma assessment.Methods The low incidence of osteosarcoma led to the small scale of its imaging data and the problem of imbalance in data categories.This study combined deep learning with clinical medical information,combined the bone sarcoma generation module of BoneGAN and the scale lesion information capture module,and proposed OMLA-Net,a deep learning assessment network for chemotherapy effect of bone sarcoma based on multi-scale lesion attention network,which achieved computer-aided bone tumor assessment with integrated data augmentation and focused lesion information through pre-training and generalized loss training.Results In this study,40 cases of osteosarcoma MRI data were used as the basis for the comparison test on the generated dataset,and the OMLA-Net assessment outperformed the SOTA method Conv-LSTM-GAN in terms of the assessment effects such as accuracy and F1 scores,and the difference was statistically significant(Bootstrap statistical method P＜0.05);the subsequent K-fold cross-validation ablation experiments further demonstrated the effectiveness of each module proposed by OMLA-Net.Conclusion OMLA-Net can effectively perform the impact assessment of chemotherapy effect on osteosarcoma,which provides a new idea for subsequent clinical application.

Objective:To investigate protective effect and mechanism of Tim-3 on sepsis-induced acute lung injury(ALI)by pro-moting mitophagy of alveolar macrophages and inhibiting activation of NLRP3 inflammasome.Methods:LPS-stimulated mouse alveo-lar macrophage(MH-S)model and sepsis-induced ALI mouse model were constructed.Tim-3 siRNA interference technique was used to knock down Tim-3 expression in MH-S cells,and anti-Tim-3 antibody mice were injected intraperitoneally to block Tim-3 function.Western blot was used to detect protein expressions of NLRP3,ASC,cleaved-caspase-1 and mitophagy-related proteins(LC3B,P62,PINK1 and Parkin)in MH-S cells and lung tissue of mice with sepsis-induced ALI.Laser confocal fluorescence staining was used to measure ROS level and mitochondrial membrane potential of MH-S cells.Pathological examination of lung tissue was performed in mice with sepsis-induced ALI in each group,and degree of lung tissue injury was evaluated by Smith scoring system.Bronchoalveolar lavage fluid(BALF)and lung tissue were collected from mice with ALI induced by sepsis in each group.BCA protein quantification method was used to determine protein concentration in BALF.MPO activity in lung tissue was detected by colorimetry.MDA content in lung tissue was detected by TBA method.LC3B protein expression in lung tissue was detected by immunohistochemistry.Results:In mouse alveolar macrophages,Tim-3 knockdown could promote expressions of NLRP3,ASC,cleaved-caspase-1 and P62 proteins,increase ROS release,inhibit PINK1/Parkin pathway activation and LC3B protein expression,and reduce mitochondrial membrane potential.In mice with sepsis-induced ALI,Tim-3 functional blockade could promote expressions of NLRP3,ASC,cleaved-caspase-1 and P62 proteins in lung tissue,aggravate lung pathological injury and pulmonary edema,increase MPO activity and MDA content in lung tissue,and reduce positive rate of LC3B protein.Conclusion:Tim-3 plays a protective role in sepsis-induced ALI by promoting mitophagy in alveolar macrophages and inhibiting NLRP3 inflammasome activation via PINK1/Parkin.