Abstract: Provided are therapeutic agent including nucleic acid and CAR-modified immune cell and the use thereof. The therapeutic agent comprises first composition and second composition, the first composition comprises a nucleic acid having a labeling polypeptide coding sequence for being introduced into a tumor cell and/or a cancer cell; the labeling polypeptide has an extracellular antigen determining region, a spacer portion, and a transmembrane portion that are operatively linked, which can be expressed to form modification on the surface of the tumor cell and/or cancer cell; the extracellular antigen determining region comprises one or more epitope polypeptides; wherein, amino acid sequences of proteins on cell membrane or secreted proteins of mammal do not comprise the epitope polypeptide amino acid sequence in the natural state; the second composition comprises chimeric antigen receptor modified immune cell which specifically recognize and bind to the extracellular antigen determining region.

Abstract: Provided is a method of establishing a machine learning model for predicting toxicity of siRNA to certain type of cells and application thereof. The method includes A) providing n siRNAs of 19-29 bp, wherein n?2; B) obtaining input and output values for establishing the model from each siRNA, the input values being obtained by i) aligning each siRNA with genomic mRNAs and selecting complementary off-target genes having no more than 7 mismatched bases; ii) obtaining off-target weights according to mismatched bases' characteristic and mRNA's secondary structure in complementary region; iii) obtaining omic weights of the off-target genes using databases; iv) calculating omic eigenvalues as the input values, based on omic and off-target weights of all the off-target genes; the output values being obtained by conducting experiments with the siRNAs to obtain cell survival indexes; and C) calculating the input and output values of the n siRNAs through machine learning algorithm.