2.Metabolic dysfunction in patients following DAA-induced viral cure for HCV infection: A non-negligible risk to liver-related health: Editorial on “Adverse impact of metabolic dysfunction on fibrosis regression following direct-acting antiviral therapy: A multicenter study for chronic hepatitis C”
Clinical and Molecular Hepatology 2025;31(2):658-661
4.Metabolic dysfunction in patients following DAA-induced viral cure for HCV infection: A non-negligible risk to liver-related health: Editorial on “Adverse impact of metabolic dysfunction on fibrosis regression following direct-acting antiviral therapy: A multicenter study for chronic hepatitis C”
Clinical and Molecular Hepatology 2025;31(2):658-661
6.Metabolic dysfunction in patients following DAA-induced viral cure for HCV infection: A non-negligible risk to liver-related health: Editorial on “Adverse impact of metabolic dysfunction on fibrosis regression following direct-acting antiviral therapy: A multicenter study for chronic hepatitis C”
Clinical and Molecular Hepatology 2025;31(2):658-661
7.Artificial intelligence-driven personalized teaching new paradigm for thoracic wall dissection
Quan-Cheng CHENG ; Ping LIU ; Huai-Cun LIU ; Liang WANG ; Yan ZHANG ; Li-Ju LUAN ; Chun-Hua CHEN ; Shu-Wei LIU ; Wei-Guang ZHANG
Acta Anatomica Sinica 2025;56(5):601-606
Facing of mounting resource constraints and rising demands for personalization in medical education,regional anatomy teaching urgently requires transformation.In this paper,we focus on the regional anatomy of the thoracic wall,in order to explore a novel AI-driven teaching paradigm.Anchored in the core principle of"virtual-real integration with cadaveric dissection as the cornerstone,"the paradigm redefines educational objective and constructs an intelligent,closed-loop teaching model integrating students,computers,and instructors.Leveraging the robust support of digital intelligence(e.g.,DeepSeek),this paradigm incorporates interactive method including group collaboration,branching instruction,and gamified assessments.It achieves a comprehensive intelligent transformation of the entire teaching process-from goal setting and plan customization to activity implementation,task completion,outcome exchange,multidimensional evaluation,and reflective iteration.This new paradigm centers on medical students and leverages digital intelligence to activate deep personalized learning potential.It seamlessly integrates fundamental anatomical knowledge with clinical scenarios(e.g.,key anatomy in breast cancer surgery,flap design in breast reconstruction),and significantly enhances clinical decision-making abilities,scientific research and innovative thinking,as well as medical humanistic literacy,paving a new path for intelligent medical education.
8.CURRENT DISTRIBUTION OF AEDES AEGYPTI IN LEIZHOU PENINSULA,ZHANJIANG CITY,GUANGDONG PROVINCE
Rui-Peng LU ; Jin-Hua DUAN ; Yu-Wen ZHONG ; Hui DENG ; Jun WU ; Li-Ping LIU ; Wei-Xiong YIN ; Feng XING ; Hui HUANG ; Chang-Jie FU ; Zong-Jing CHEN ; Ming-Ji CHENG ; Sheng-Jun HU ; Ya-Ting CHEN ; Wen-Ting GUO ; Li-Feng LIN
Acta Parasitologica et Medica Entomologica Sinica 2025;32(1):16-21
Objective To investigate the status of population dynamics and distribution changes of Aedes aegypti in Guangdong Province.Methods Continuous monitoring was conducted from May 2018 to July 2024 in Wushi Town and Qishui Town,Leizhou City,Zhanjiang City,Guangdong Province.Additionally,a survey of the distribution of Ae.aegypti along the Leizhou Peninsula coast was carried out.Results The density of Ae.aegypti in Zhanjiang showed a gradual decline from 2018 to 2024.The last detection of adult Ae.aegypti in Wushi Town was in September 2021,and the last larva was found in October 2023.No Ae.aegypti was detected in Qishui Town during surveys from 2021 to 2024.A survey of 18 coastal villages in the Leizhou Peninsula revealed no detections of Ae.aegypti.Conclusions This study provides a basis for understanding the distribution and population density fluctuations of Ae.aegypti,assessing its invasion risk,and scientifically conducting relevant prevention and control efforts.
9.Graph Neural Networks and Multimodal DTI Features for Schizophrenia Classification: Insights from Brain Network Analysis and Gene Expression.
Jingjing GAO ; Heping TANG ; Zhengning WANG ; Yanling LI ; Na LUO ; Ming SONG ; Sangma XIE ; Weiyang SHI ; Hao YAN ; Lin LU ; Jun YAN ; Peng LI ; Yuqing SONG ; Jun CHEN ; Yunchun CHEN ; Huaning WANG ; Wenming LIU ; Zhigang LI ; Hua GUO ; Ping WAN ; Luxian LV ; Yongfeng YANG ; Huiling WANG ; Hongxing ZHANG ; Huawang WU ; Yuping NING ; Dai ZHANG ; Tianzi JIANG
Neuroscience Bulletin 2025;41(6):933-950
Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans
;
Schizophrenia/pathology*
;
Diffusion Tensor Imaging/methods*
;
Male
;
Female
;
Adult
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Brain/metabolism*
;
Young Adult
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Middle Aged
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White Matter/pathology*
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Gene Expression
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Nerve Net/diagnostic imaging*
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Graph Neural Networks
10.Complete genomic sequence analysis of the G6P1bovine rotavirus BLL strain
Jin-hua ZHANG ; Xia-fei LIU ; Jun-jie YU ; Jia-xin FAN ; Ming-yue WANG ; Guang-ping XIONG ; Yi-peng WANG ; Dan-di LI ; Xiao-man SUN ; Li-li PANG ; Zhao-jun DUAN
Chinese Journal of Zoonoses 2025;41(1):8-14
Bovine rotavirus(BRV)is an important pathogen causing diarrhea in calves.To understand the genomic charac-teristics and genetic variations in bovine rotavirus,and to further enrich data on the biological characteristics of rotavirus,we aimed to amplify 11 gene segments of the isolated and cultured G6P[1]bovine rotavirus BLL strain,perform whole genome se-quencing,and analyze the molecular characteristics.MEGA7.0 and DNAMAN software were used for homology and typing a-nalysis,and the whole genome phylogenetic tree was constructed to analyze genetic evolution relationships.The complete geno-type of the BLL strain was G6-P[1]-I2-R2-C2-M2-A3-N2-T6-E2-H3.Phylogenetic analysis of the VP7 and VP4 genes of the BLL strain showed that the VP7 gene had the highest homology with RVA/Cow-wt/HB01/China/2021,and the VP4 gene of the BLL strain was in the same branch as RVA/Human-tc/ISR/Ro8059/1995.From the sequence alignment of VP8*amino acids,the sialic acid domain of the BLL strain was found to be similar to that in other P[1]strains,but different from those in other types of strains,except for residue 189,which was the same as that in Ro8059 but different from that in other strains.The results suggested that the BLL strain might potentially infect humans.Therefore,continued monitoring and study of the biological characteristics of this strain are necessary to provide more information and evidence supporting further research on the cross-species transmission of group A rotavirus in China.

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