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.

Objective: To investigate the current status of institutional review board construction in blood collection and supply institutions in China, and to provide evidence for standardized management of transfusion medicine ethics. Methods: A questionnaire survey was conducted to assess the development of institutional review boards in blood collection and supply institutions in 31 provinces, municipalities and autonomous regions across the country, excluding hong kong, macao and taiwan. Data were statistically analyzed using the chi-square test and Mann-Whitney U test. Results: Among 83 blood collection and supply institutions, the establishment rate of institutional review boards was 66.27% (55/83), with a higher rate in blood centers than in central blood stations. Of the 55 ethics review committees, 83.64% (46/55) uploaded information and completed registration in the National Medical Research Registration and Filing Information System. The registration rate was higher in blood centers than in central blood stations. On average, each institutional review board conducted 3 initial reviews and approved 2 research projects. The number of initial review items and the number of approved research projects were higher in blood centers than in central blood stations. Common problems in both blood centers and central blood stations included the appointment of the main leader as the director or deputy director, insufficient allocation of ethics professionals, and the general absence of an ethical review system for emergencies. Most committees lacked independent venues, and continuous training coverage was incomplete. Meanwhile, blood collection and supply institutions generally showed high level of ethical recognition of existing blood donation incentive measures. Conclusion: Progress has been made in the construction of transfusion medicine ethics in China; however, shortcomings remain in independence, professional composition of committee members, continuous training, and emergency response systems, requiring targeted improvements.

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

To investigate the role of the DPP4-nestin axis in liver fibrosis induced by alveolar cyst infection,a murine model was established using C57BL/6 mice via hepatic portal vein injection.Liver histopathological changes were assessed using HE staining,while immunohistochemistry and immunofluorescence were employed to evaluate the expression levels of nestin and DPP4 in infected mouse livers.In vitro,J S1 cell line was stimulated with recombinant DPP4 protein to es-tablish a cellular model,and qPCR,Western blot,and shRNA lentivirus interference techniques were utilized to examine the involvement of the DPP4-nestin axis in hepatic stellate cell activation.The findings demonstrated that compared to the Sham group,liver tissue structure disruption and collagen deposition were evident along with significantly increased expressions of nestin and DPP4(P＜0.050 0),which colocalized with nesin and α-SMA.Furthermore,stimulation with recombi-nant DPP4 protein significantly enhanced JS1 cell activation(P＜0.050 0)as well as upregulated nestin expression(P＜0.050 0)when compared to control group cells.Notably,shRNA lentivirus-mediated inhibition of nestin expression effectively suppressed the activating effects exerted by re-combinant DPP4 protein on JS1 cells(P＜0.050 0).Collectively,these results highlight the crucial regulatory role played by the DPP4-nestin axis in hepatic stellate cell activation triggered by alveo-lar infection;thus,targeting this axis may represent a novel therapeutic strategy for treating alveo-lar infection-induced liver fibrosis.

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.

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.