Mice have been widely used in the study of primary liver cancer owing to the close similarity of its genome to that of humans,its strong reproductive ability,the low cost of model construction,and the ease of genetic manipulation,including molecular mechanisms of pathogenesis and potential drug targets.Traditional animal models are increasingly falling short of meeting the needs of precision medicine research because of their inability to reproduce tumor microenvironment interactions and control the specificity of molecular subtypes.This study systematically compared the technical advantages of tail vein high-pressure injection,combined with transposon system(HTVI-TS),with traditional models in liver cancer research,and focused on the application value of the HTVI-TS model in the mechanism study of tumorigenesis and development,immunotherapy response prediction,and individualized evaluation of targeted drugs.This report presents a new research platform for precise diagnosis and treatment of primary liver cancer by simulating the heterogeneous evolution process of the cancer.The findings provide a theoretical basis for optimizing the selection of preclinical research models for liver cancer;the expansion potential of this technology in liver cancer research is outlined.

Personalized drug response prediction from molecular data is an important challenge in precision medicine for treating cancer.Computational methods have been widely explored and have become increasingly accurate in recent years.However,the clinical application of prediction methods is still in its infancy due to large discrepancies between preclinial models and patients.We present a novel disentangled synthesis transfer network(DiSyn)for drug response prediction specifically designed for transfer learning from preclinical models to clinical patients.DiSyn uses a domain separation network(DSN)to disentangle drug response related features,employs data synthesis technology to increase the sample size and iteratively trains for better feature disentanglement.DiSyn is pretrained on large-scale unlabeled cancer samples and validated by three datasets,The Cancer Genome Atlas(TCGA),Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging And moLecular Analysis 2(I-SPY2)and Novartis Institutes for Biomedical Research Patient-Derived Xenograft Encyclopedia(NIBR PDXE),achieving competitive performance with the state-of-the-art methods on cancer patients and mice.Furthermore,the application of DiSyn to thousands of breast cancer patients show the heterogeneity in drug responses and demonstrate its potential value in biomarker discovery and drug combination prediction.

Personalized drug response prediction from molecular data is an important challenge in precision medicine for treating cancer. Computational methods have been widely explored and have become increasingly accurate in recent years. However, the clinical application of prediction methods is still in its infancy due to large discrepancies between preclinial models and patients. We present a novel disentangled synthesis transfer network (DiSyn) for drug response prediction specifically designed for transfer learning from preclinical models to clinical patients. DiSyn uses a domain separation network (DSN) to disentangle drug response related features, employs data synthesis technology to increase the sample size and iteratively trains for better feature disentanglement. DiSyn is pretrained on large-scale unlabeled cancer samples and validated by three datasets, The Cancer Genome Atlas (TCGA), Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging And moLecular Analysis 2 (I-SPY2) and Novartis Institutes for Biomedical Research Patient-Derived Xenograft Encyclopedia (NIBR PDXE), achieving competitive performance with the state-of-the-art methods on cancer patients and mice. Furthermore, the application of DiSyn to thousands of breast cancer patients show the heterogeneity in drug responses and demonstrate its potential value in biomarker discovery and drug combination prediction.

Mice have been widely used in the study of primary liver cancer owing to the close similarity of its genome to that of humans,its strong reproductive ability,the low cost of model construction,and the ease of genetic manipulation,including molecular mechanisms of pathogenesis and potential drug targets.Traditional animal models are increasingly falling short of meeting the needs of precision medicine research because of their inability to reproduce tumor microenvironment interactions and control the specificity of molecular subtypes.This study systematically compared the technical advantages of tail vein high-pressure injection,combined with transposon system(HTVI-TS),with traditional models in liver cancer research,and focused on the application value of the HTVI-TS model in the mechanism study of tumorigenesis and development,immunotherapy response prediction,and individualized evaluation of targeted drugs.This report presents a new research platform for precise diagnosis and treatment of primary liver cancer by simulating the heterogeneous evolution process of the cancer.The findings provide a theoretical basis for optimizing the selection of preclinical research models for liver cancer;the expansion potential of this technology in liver cancer research is outlined.

Aim To investigate the effects of emodin on the contraction of gallbladder smooth muscle(GBSM)and the L-type calcium current in GBSM cells.Methods Gallbladder muscle strips were obtained from adult guinea pigs and the resting tension was recorded.Gallbladder smooth muscle cells were isolated by enzymatic digestion,and calcium current was recorded by the whole-cell patch clamp method.Results Emodin-induced contraction of GBSM was significantly attenuated by pretreatment with nifedipine.Emodin increased the L-type calcium current in a dose-dependent manner.When 10 ?mol?L-1 emodin was applied to GBSM cells,the amplitude of L-type calcium current at +10 mV was enhanced by(45.2?2.26)%.In the presence of PKC inhibitor,staurosporine,emodin did not significantly affect the calcium current.Conclusion Emodin enhances L-type calcium current via PKC-dependent pathway and promotes gallbladder contraction.