In the context of the modernization of traditional Chinese medicine （TCM）， the inheritance of the experiences of famous doctors faces significant challenges due to its complex nonlinear characteristics and dynamic evolution. There are still issues in the current inheritance system， such as the homogenization of talent cultivation models， lack of standardized mentoring practices， and monotonous evaluation method， which hinder the systematic inheritance and innovative development of famous doctors' experiences. Based on a systematic review of the current state of artificial intelligence （AI）-assisted inheritance of famous doctors' experiences， this study explores innovative pathways for deep integration of modern information technologies with famous doctors' experiences from key dimensions， including data authenticity assurance， interdisciplinary collaboration mechanisms， and the establishment of dynamic inheritance standards. It proposes a paradigm shift in the inheritance of TCM famous doctors' experiences in the AI era， aiming to build a new TCM inheritance system of "digital intelligence empowerment and cross-disciplinary innovation"， providing theoretical support and practical pathways for the inheritance of famous doctors' experiences in TCM.

The abnormal accumulation of neutrophil extracellular traps （NETs） can promote the initiation and progression of pancreatic cancer， which is considered a potential therapeutic target for this disease. The Miraculous Pivot·Inquiry About Statement （《灵枢·口问》） have recorded the concept of "defensive qi stagnation". Based on the recognition that the function of defensive qi is similar to the immune function of neutrophils， and combining traditional Chinese medicine theory with clinical practice， it is proposed that the abnormal accumulation of NETs may be a pathological product of "defensive qi stagnation"， with the spleen being the critical site of pathology. Further exploring the application strategy of cleaning spleen and restoring defensive qi method in pancreatic cancer treatment， it is proposed to employ three approaches such as dredging method to eliminate spleen stagnation and inhibit pancreatic cancer proliferation， cleaning method to remove spleen dampness and suppress the inflammatory micro-environment， and tonifying method to strengthen Weiqi and to improve the immune microenvironment， which aims to provide new insights for the clinical treatment of pancreatic cancer with traditional Chinese medicine.

Identifying drug-drug interactions (DDIs) is essential to prevent adverse effects from polypharmacy. Although deep learning has advanced DDI identification, the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits. Here, we developed a Multi-Dimensional Feature Fusion model named MDFF, which integrates one-dimensional simplified molecular input line entry system sequence features, two-dimensional molecular graph features, and three-dimensional geometric features to enhance drug representations for predicting DDIs. MDFF was trained and validated on two DDI datasets, evaluated across three distinct scenarios, and compared with advanced DDI prediction models using accuracy, precision, recall, area under the curve, and F1 score metrics. MDFF achieved state-of-the-art performance across all metrics. Ablation experiments showed that integrating multi-dimensional drug features yielded the best results. More importantly, we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs. Among 12 real-world adverse drug reaction reports, the predictions of 9 reports were supported by relevant evidence. Additionally, MDFF demonstrated the ability to explain adverse DDI mechanisms, providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.

Objective To develop a new model for predicting compound toxicity and exploring related toxicity mechanisms using transcriptomic data and gene ontology knowledge.Methods Using the TOXRIC database,two toxicity-related datasets were constructed and a Tox VNN model was established that incorporated gene ontology knowledge to evaluate compound toxicity and identify key biological processes.Results Tox VNN demonstrated good predictability.The identification of important biological processes related to CYP enzyme activity and p53 pathway stress response provided insights into the toxicity mechanisms.Conclusion The Tox VNN,which integrates data and knowledge,can not only ensure high predictability,but also effectively identify important biological processes related to toxicity.This model offers a new approach to predicting and understanding compound toxicity in drug safety evaluation.

Objective To study the dynamic regulatory role of early growth response 4(EGR4)in mouse neurodevelopment.Methods Data on single-cell chromatin accessibility(single-cell assay for transposase-accessible chromatin by sequencing,scATAC-seq)and transcriptome(single-cell RNA sequencing,scRNA-seq)from seven key stages spanning from the embryonic period to adulthood was collected and analyzed.The transcription factor binding site network was inferred,a quantitative analysis from a temporal perspective was conducted,its functional modules were parsed,and the results were visualized.Results egr4 was significantly highly expressed from the late embryonic stage to adulthood,and specifically activated during the maturation of inhibitory neurons[parvalbumin(PV)and somatostatin(SST)subtypes].Moreover,its transcription was not directly regulated by changes in chromatin accessibility.Temporal network analysis indicated that EGR4 became a regulatory hub in the late embryonic stage and drove neuron differentiation and subtype specification.Functional enrichment analysis showed that EGR4 regulated the"cell differentiation"pathway through dynamic interacting factors,and there were subtype-specific interaction modules in PV/SST neurons respectively.Conclusion EGR4 can participate in the late-stage maturation of cortical neurons through a stage-specific regulatory network.This study provides a new perspective on mechanisms underlying the temporal logic of neural development.

Identifying drug-drug interactions(DDIs)is essential to prevent adverse effects from polypharmacy.Although deep learning has advanced DDI identification,the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits.Here,we developed a Multi-Dimensional Feature Fusion model named MDFF,which integrates one-dimensional simplified molec-ular input line entry system sequence features,two-dimensional molecular graph features,and three-dimensional geometric features to enhance drug representations for predicting DDIs.MDFF was trained and validated on two DDI datasets,evaluated across three distinct scenarios,and compared with advanced DDI prediction models using accuracy,precision,recall,area under the curve,and F1 score metrics.MDFF achieved state-of-the-art performance across all metrics.Ablation experiments showed that integrating multi-dimensional drug features yielded the best results.More importantly,we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs.Among 12 real-world adverse drug reaction reports,the predictions of 9 reports were supported by relevant evidence.Additionally,MDFF demon-strated the ability to explain adverse DDI mechanisms,providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.

Ying and wei qi is derived from the essence of water and grain， and is the basic material to maintain the normal life activities of the body. Ying and wei qi has the difference of nature of yin and yang or the clear and the turbid. When the relationship between ying and wei qi is out of order and in reverse disorder， it will lead to the occurrence of various diseases. Based on the analysis of the theory of "ying-wei disorder" in The Inner Canon of Yellow Emperor （《黄帝内经》）， this paper started from the perspective of the exuberance and debilitation， circulation and function of ying qi and wei qi， focused on the theoretical understanding that the change of deficiency and excess （"fall"） and the change of circulation （"move"）， and refined the core mechanism of "ying-wei disorder"， which is the depletion of ying qi and wei qi， stagnation of ying qi and wei qi， and disharmony of ying qi and wei qi， and finally summarized the syndrome identification and treatment method of supporting ying qi and assisting wei qi， promoting ying qi and moving wei qi， regulating ying qi and wei qi， and constructed the syndrome identification and treatment system for ying-wei disorder of malignant tumor in clinic.

Objective:To explore the predictive value of peripheral blood cytokine models on organ functional impairment after chimeric antigen receptor T(CAR-T) cell therapy in children with B-lineage lymphocytic leukemia.Methods:The clinical data of 44 children with acute B-lineage lymphoblastic leukemia who received CAR-T cell therapy at Children′s Hospital of Soochow University from September 2018 to October 2020 were retrospectively analyzed.Peripheral blood cytokines, including interleukin(IL)-2, IL-4, IL-6, IL-10, tumor necrosis factor-α, interferon(IFN)-γ and IL-17A, were measured daily for 14 days after receiving CAR-T cell therapy.The trend of peripheral blood cytokine levels was analyzed at the endpoint of organ function recovery or death within 14 days after CAR-T cell treatment.Receiver operating characteristic curve was used to establish a mathematical prediction model to predict the occurrence of organ damage in the children.Results:Of the 44 children, 31 cases were boys and 13 cases were girls, with a median age of 7.96 (5.19, 11.48)years.Cytokine release syndrome(CRS) response occurred in 95.5% (42/44) children, with 88.1% (37/42) had a grade 1-3 CRS response, and 16.7% (7/42) had a severe grade 4-5 CRS response.Using IL-6>3 892.95 pg/mL as cut-off value, the area under the curve(AUC) for predicting acute respiratory failure was 0.818, with a sensitivity of 0.8 and a specificity of 0.735, while combining IFN-γ>414.4 pg/mL, IL-6>3 892.95 pg/mL and IL-2>27.05 pg/mL were the three cut-off values, with an AUC of 0.741, sensitivity of 0.6 and specificity of 0.912 for predicting acute respiratory failure. Using IFN-γ>1 699.5 pg/mL as cut-off value, the AUC for predicting shock was 0.908, with a sensitivity of 0.722 and a specificity of 1.With IL-6>4 607.3 pg/mL as cut-off value, the AUC for predicting liver injury was 0.964, with a sensitivity of 1 and a specificity of 0.906, while combining both IL-6>4 607.3 pg/mL and IFN-γ>1 446.2 pg/mL as cut-off values, the AUC for predicting liver injury was 0.977, with a sensitivity of 1 and a specificity of 0.906.Combining both IL-6>6 972.2 pg/mL and IFN-γ>3 981.5 pg/mL predicted a positive predictive value of 62.5% and a negative predictive value of 94.4% for grade 4-5 CRS response, with an AUC of 0.846, a predictive sensitivity of 0.714 and a specificity of 0.838, and all children had a combination of two or more organ function injuries.Conclusion:The combination of IL-6 and IFN-γ can effectively predict the incidence of liver injury and cytokine release syndrome.The combination of peripheral blood cytokines IFN-γ, IL-6 and IL-2 can be used to predict the incidence of acute respiratory failure after the treatment of CAR-T cells in children with acute B-lineage lymphoblastic leukaemia.IFN-γ single index can be used to predict the incidence of shock.The combination of IL-6 and IFN-γ can be used to predict the incidence of liver injury and the severity of CRS.

A transcriptional regulatory network for wild-type and ATP7B-knockout HepG2 cells exposed to copper was constructed by bioinformatics methods to explore the potential mechanism of key transcription factors in the pathogenesis of hepatolenticular degeneration. The differentially expressed genes (DEGs) for wild-type and ATP7B-knockout HepG2 cell lines without copper and exposed to copper were collected from the gene expression omnibus (GEO) database. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis were performed for DEGs induced by copper. The key functional modules and genes were identified based on the protein-protein interaction (PPI) network. Moreover, the enrichment analysis of genes in functional modules was performed. Finally, a transcriptional regulatory network was constructed to screen the core transcription factors. A total of 1 034 genes, including 509 down-regulated genes and 525 up-regulated genes, were selected as DEGs. The up-regulated and down-regulated functional modules based on PPI network included 3 785 and 3 931 genes, respectively. Genes in key functional modules were enriched in cell-substrate junction, chromosomal region, spliceosomal complex and ribosome. They were involved in mRNA processing, histone modification, RNA splicing, regulation of DNA metabolic process, protein phosphorylation and other biological processes. Moreover, they were correlated to transcriptional coregulator activity, DNA-binding transcription factor binding, ubiquitin-like protein ligase binding and other molecular functions. KEGG analysis showed that genes in key functional modules were significantly enriched in hepatitis B, MAPK signaling pathway, cellular senescence and apoptosis, neurotrophin signaling pathway and pathways of neurodegeneration-multiple diseases. The transcriptional regulatory network contained 11 differentially expressed transcription factors and 96 DEGs. Among them, U2AF1, NFRKB, FUS, MAX, SRSF1, CEBPA and RXRA were the core transcription factors, which may facilitate the study of the biological function of relevant molecules in transcriptional regulation of hepatolenticular degeneration.
Humans ; Transcriptome ; Gene Expression Profiling/methods* ; Hepatolenticular Degeneration/genetics* ; Copper ; Gene Regulatory Networks ; Computational Biology/methods* ; Transcription Factors/genetics* ; DNA ; DNA-Binding Proteins/genetics* ; Serine-Arginine Splicing Factors/genetics*

Genome-wide physical protein-protein interaction(PPI)mapping remains a major chal-lenge for current technologies.Here,we reported a high-efficiency BiFC-seq method,yeast-enhanced green fluorescent protein-based bimolecular fluorescence complementation(yEGFP-BiFC)coupled with next-generation DNA sequencing,for interactome mapping.We first applied yEGFP-BiFC method to systematically investigate an intraviral network of the Ebola virus.Two-thirds(9/14)of known interactions of EBOV were recaptured,and five novel interactions were discovered.Next,we used the BiFC-seq method to map the interactome of the tumor protein p53.We identified 97 interactors of p53,more than three-quarters of which were novel.Furthermore,in a more complex background,we screened potential interactors by pooling two BiFC libraries together and revealed a network of 229 interactions among 205 proteins.These results show that BiFC-seq is a highly sensitive,rapid,and economical method for genome-wide interactome map-ping.