ObjectiveTo investigate the effect of different exercise interventions on metabolism and liver parameters in patients with metabolic dysfunction-associated fatty liver disease （MAFLD）， and to provide evidence-based recommendations for clinical exercise rehabilitation. MethodsThis study was conducted according to the PRISMA guidelines， and the protocol was registered on the PROSPERO platform， with a registration number of CRD42025641717. PubMed， Web of Science， Scopus， Wiley Online Library， CNKI， Wanfang Data， and VIP were searched for related articles published up to September 2024. The Cochrane tool for assessing risk of bias was used to assess the quality of articles， and Stata MP 17.0 was used to perform the network meta-analysis. ResultsA total of 57 articles were included， involving 2 648 patients. The results showed that aerobic exercise combined with resistance exercise had the best effect in improving body mass index （mean difference ［WMD］=-0.97， 95% confidence interval ［CI］： -1.66 to -0.28］， P<0.05， surface under the cumulative ranking curve ［SUCRA］=85.4） and triglycerides （WMD=-29.6， 95%CI： -46.66 to 12.54， P<0.05， SUCRA=87.3）； resistance exercise was the optimal intervention method for improving total cholesterol （WMD=-15.99， 95%CI： -24.19 to -7.79， P<0.05， SUCRA=79.9） and glutamine transaminase （WMD=-8.08， 95%CI： -12.13 to -4.02， P<0.05， SUCRA=87.3）； low-intensity aerobic exercise had the best effect in improving aspartate aminotransferase （WMD=-4.3， 95%CI： -8.45 to -0.15， P<0.05， SUCRA=73.5）， gamma-glutamyl transpeptidase （GGT） （WMD=-3.26， 95%CI： -7.79 to 1.27， P>0.05， SUCRA=82.3）， and glycated hemoglobin （HbA1c） （WMD=-0.6， 95%CI： -2.02 to 0.82， P>0.05， SUCRA=78.8）； moderate-intensity aerobic exercise was the optimal intervention modality to improve Homeostasis Model Assessment of Insulin Resistance （WMD=-0.92， 95%CI： -1.51 to -0.33， P<0.05， SUCRA=69.4）. It should be noted that there were no significant differences in HbA1c and GGT across different exercise interventions （all P>0.05）， suggesting that there was currently no sufficient statistical evidence to support that exercise could improve these two indicators. ConclusionBased on the comprehensive league table and cumulative probability ranking， aerobic exercise combined with resistance exercise， resistance exercise， and low- and moderate-intensity aerobic exercise may be the best exercise modality for improving key indicators in MAFLD patients， and targeted exercise modalities should be selected for intervention against different indicators； however， due to limitations of the original studies， further studies are needed for validation and exploration.

The clinical diagnosis and treatment of kidney transplantation involve a large amount of high-dimensional, nonlinear and multimodal complex data. Traditional statistical methods make it difficult to fully identify the underlying patterns. However, artificial intelligence (AI) technology has stronger capabilities in data integration and analysis, and is particularly adept at handling complex data. This article reviews the application of AI in the field of kidney transplantation, covering the role of deep learning models in the quantitative assessment of donor kidney quality in the pre-transplantation stage, the value of virtual biopsy systems and the optimization strategies for donor-recipient matching, as well as the dynamic prediction of graft survival rate by the iBox system in the post-transplantation stage, the advantages of AI models in non-invasive monitoring of rejection and the application results of individualized dose prediction of immunosuppressive drugs. Although the clinical application of medical AI is still limited by insufficient model interpretability, questionable generalization ability, and ethical and legal risks, technologies such as federated learning and digital twins are expected to solve the problems of data privacy and simulation prediction, and further promote the precise development of kidney transplantation.

Drug-target interaction (DTI) is fundamental to novel drug research and development (R &D), playing a critical role in elucidating drug mechanisms of action and improving the cost-effectiveness of drug discovery. Although China has seen rapid accumulation of drug-target resources in recent years, the industry still faces challenges such as a shortage of original targets and excessively high target concentration. The rapid advancement of artificial intelligence (AI) has provided an efficient technological pathway for DTI prediction, establishing it as a core tool for accelerating drug discovery. This paper systematically reviews the fundamental data support framework for DTI prediction tasks, elaborating in detail on three core data types— drug characterization, target characterization, and drug-target associations-as well as the classification and application boundaries of mainstream benchmark datasets tailored to different tasks within the field. On this basis, it comprehensively surveys the technological evolution of AI-driven DTI prediction, summarizing the core paradigms and technical advances of traditional machine learning and deep learning methods in a single-modality setting, alongside cutting-edge multimodal approaches that integrate two modalities (cross-subject/within-subject fusion) and three or more modalities for multidimensional information integration. Finally, this paper analyzes the current major challenges in data quality, model generalizability and interpretability, and real-world deployment, while also discussing future trends in standardized dataset construction, generative AI, and large-scale biomedical foundation models, aiming to provide a reference for both research and industrial applications in the AI-driven DTI prediction field.

This consensus will introduce the characteristics of fillers used in the surgical cavities of domestic nasal surgery patients based on relevant literature and expert opinions. It will also provide recommendations for the selection of cavity fillers for different nasal diseases, with chronic sinusitis as a representative example.
Humans ; Nasal Cavity/surgery* ; Nasal Surgical Procedures ; China ; Consensus ; Sinusitis/surgery* ; Dermal Fillers

Objective:To prepare decellularized extracellular matrix (dECM)-gelatin methacryloyl (GelMA) composite hydrogels and to study their effects on hepatocyte proliferation.Methods:Hepatic dECM was prepared by elution, and GelMA hydrogel and 10%, 30% and 50% dECM-GelMA composite hydrogels were prepared by pepsin solubilization. The morphology of normal liver and dECM liver was observed by eyes and scanning electron microscopy using hematoxylin-eosin, Sirius red and periodate-Schiff staining, respectively. The internal structure of the dECM-GelMA composite hydrogels was observed by scanning electron microscopy, and the pore diameter was measured. Liver HL-7702 cells were co-cultured with GelMA hydrogel and 10%, 30% and 50% dECM-GelMA composite hydrogels, and the cell proliferation viability was determined by cell counting kit-8. The expression of proliferating cell nuclear antigen (PCNA), Wnt family protein 5a (Wnt5a), β-catenin, extracellular-regulated protein kinase 1/2 (ERK1/2) and phosphorylated ERK1/2 (p-ERK1/2) were detected by Western blotting. Comparisons were made using independent sample t-test or one-factor analysis of variance. Results:After decellularization, the hepatocyte morphology showed rounded depressions, and the extracellular matrix structure was intact. The GelMA hydrogel and 10%, 30% and 50% dECM-GelMA composite hydrogels showed inernally porous structures. The pore diameter increased from (3.06±1.35) μm in the GelMA hydrogel to (16.01±4.02) μm in the 50% dECM-GelMA composite hydrogel. On the 3rd, 5th and 7th day, the relative cell proliferation was higher in the 50% dECM-GelMA composite hydrogel group than that in the GelMA hydrogel group (1.89±0.04 vs 1.53±0.01, 9.36±0.04 vs 3.89±0.09, 7.15±0.27 vs 4.89±0.15, all P<0.05). The relative expression levels of PCNA, Wnt5a, β-catenin, and p-ERK1/2/ERK1/2 proteins in the 50% dECM-GelMA composite hydrogel group were higher than those in the GelMA hydrogel group (2.14±0.04 vs 1.00±0.03, 2.36±0.09 vs 1.00±0.08, 1.45±0.03 vs 1.00±0.04, 1.43±0.04 vs 1.00±0.01, all P<0.05). Conclusions:A dECM-GelMA composite hydrogel can be prepared, which may promote hepatocyte proliferation by upregulating the phosphorylation of ERK1/2 and activating Wnt/β-catenin signaling pathway.

Objective To explore the research status and hotspots of umbilical acupuncture over the past 20 years using bibliometric methods,thus to providing references for the subsequent clinical treatment of umbilical acupuncture and studies of the therapeutic mechanism.Methods Literature on umbilical acupuncture published between 2004 and 2024 was retrieved from the CNKI,Wanfang Data Knowledge Service Platform,and VIP Chinese Journal Service Platform.Duplicate check was conducted using NoteExpress 3.9.0 software,and then manual screening was performed.The included literature data were imported into VOSviewer for visualized analysis,including institutional collaboration,author collaboration,and keyword co-occurrence.Results A total of 388 articles were included,comprising 286 original research articles,23 reviews,and 79 dissertations.Annual publication analysis revealed a significant upward trend in umbilical acupuncture research output starting from 2017.Zhejiang Chinese Medical University and Hangzhou Hospital of Traditional Chinese Medicine ranked first in publication volume,and Bao Yehua was the most prolific author.Inter-institutional collaboration remained limited,and author collaborations usually occurred in small-team models.Clinical research was the most commonly-seen keyword,and insomnia and stroke were the diseases being frequently studied.Conclusion Clinical research has become the focus of umbilical acupuncture studies over the past two decades,while mechanism research is still in its early stages.Current research hotspots include the research about insomnia and stroke.

Objective To establish a classification tree model for non-alcoholic fatty liver disease(NAFLD)among aircrews,screen for influencing factors of NAFLD,so as to provide scientific basis for prevention and intervention decisions for NAFLD.Methods Aircrews who underwent recuperation at a sanatorium from January 2019 to December 2023 were selected as the research objects.Their annual physical examination data were collected and the NAFLD detection rate was calculated.Age,body mass index(BMI),blood pressure,waist circumference,blood routine,biochemistry indexes,and thyroid function were incorporated,and a NAFLD risk model was constructed using classification regression tree method.The predictive performance of the NAFLD classification tree model was evaluated through model misclassification matrix,risk statistics,and receiver operating characteristic curve.Results A total of 4088 aircrews were included in the study,and NAFLD was detected in 380 persons(380/4088,9.30%).The NAFLD model consisted of three layers,and five explanatory variables affecting the onset of NAFLD were extracted,including BMI,triglycerides(TG),high-density lipoprotein cholesterol(HDL-C),alanine aminotransferase(ALT),and total bilirubin(TBIL).BMI was located at the top of the classification tree and was the most important risk factor for NAFLD in aircrews.The area under the curve(AUC)of the model was 0.853.The predictive accuracy of NAFLD was 90.9%,indicating that the model has good accuracy and fitting effect.Conclusion In this study,the detection rate of NAFLD in aircrews was 9.30%.BMI,TG,HDL-C,ALT,and TBIL are risk factors for the onset of NAFLD.NAFLD is mainly related to weight gain and lipid metabolism disorders caused by unhealthy lifestyles.

Liver transplantation is the preferred treatment for cirrhosis, end-stage liver failure, and hepatocellular carcinoma, and is also the only effective curative method. Liver ischemia-reperfusion injury (IRI) is one of the main adverse reactions of liver transplantation. During the operation, ischemia mediates the occurrence of liver IRI, promoting the cascade activation of reactive oxygen species and pro-inflammatory signals in Kupffer cells. With continued hepatocellular death during ischemia, damage-associated molecular patterns (DAMPs) accumulate and are released into systemic circulation, triggering a cytokine and chemokine storm, resulting in poor prognosis, postoperative liver failure, systemic inflammatory response syndrome (SIRS), and multiple organ dysfunction syndrome (MODS). In liver transplantation-related IRI, PANoptosis—including apoptosis, pyroptosis, necroptosis, ferroptosis, and autophagy—participates in the process, but a comprehensive review is lacking. This article systematically elaborates on the roles of different types of cell death in liver IRI and the crosstalk among these pathways. It also discusses the protective effects of inhibiting different forms of cell death, aiming to provide direction for future basic research and offer new ideas and strategies for the clinical treatment of liver IRI.

Objective To identify key acoustic features associated with the progression of Alzheimer's disease(AD)through voice analysis combined with machine learning and feature selection techniques,thereby constructing classification models that serve as candidate tools for the early screening of AD.Methods Voice samples from AD,mild cognitive impairment(MCI)and healthy(HC)elderly individuals were obtained from the NCMMSC2021 AD voice dataset.The voice samples underwent data preprocessing,followed by feature extraction from the eGeMAPS feature set via the OpenSmile toolkit.Classification models were obtained utilizing random forest and support vector machine(SVM)algorithms.Significance testing and feature importance ranking were conducted using Python,and the further selection of the optimal features was performed through sequential forward selection(SFS).The classification performance before and after feature selection was compared and evaluated using accuracy and the area under the receiver operating characteristic curve(AUC).Results The significant acoustic features in the classification models primarily derived from spectral slope,formant,fundamental frequency,and loudness.The optimal classification performance was achieved with the SVM model following SFS feature selection,with recognition accuracies of 0.926(AUC=0.974)for AD/MCI group,0.875(AUC=0.956)for AD/HC group,and 0.879(AUC=0.904)for MCI/HC group.Conclusion SVM model performs better than random forest model,and the use of SFS for feature selection can effectively enhance model performance.Voice analysis has the potential to serve as a valuable supplementary tool for the rapid AD assessment and screening.