Chronic myeloid leukemia (CML) is a slowly progressing hematopoietic cell disorder. Sphingosine kinase 1 (SPHK1) plays established roles in tumor initiation, progression, and chemotherapy resistance in a wide range of cancers, including leukemia.However, small-molecule inhibitors targeting SPHK1 in CML still need to be developed. This study revealed the role of SPHK1 in CML and investigated the potential anti-leukemic activity of hirsuteine (HST), an indole alkaloid obtained from the oriental plant Uncaria rhynchophylla, in CML cells. These results suggest that SPHK1 is highly expressed in CML cells and that overexpression of SPHK1 represents poor clinical outcomes in CML patients. HST exposure led to G2/M phase arrest, cellular apoptosis, and downregulation of Cyclin B1 and CDC2 and cleavage of Caspase 3 and PARP in CML cells. HST shifted sphingolipid rheostat from sphingosine 1-phosphate (S1P) towards the ceramide coupled with a marked inhibition of SPHK1. Mechanistically, HST significantly blocked SPHK1/S1P/S1PR1 and BCR-ABL/PI3K/Akt pathways. In addition, HST can be docked with residues of SPHK1 and shifts the SPHK1 melting curve, indicating the potential protein-ligand interactions between SPHK1 and HST in both CML cells. SPHK1 overexpression impaired apoptosis and proliferation of CML cells induced by HST alone. These results suggest that HST, which may serve as a novel and specific SPHK1 inhibitor, exerts anti-leukemic activity by inhibiting the SPHK1/S1P/ S1PR1 and BCR-ABL/PI3K/Akt pathways in CML cells, thus conferring HST as a promising anti-leukemic drug for CML therapy in the future.

Data analysis models may assist the transmission of traditional Chinese medicine （TCM） experience and clinical diagnosis and treatment， and the possibility of constructing a “data-knowledge” dual-drive model was explored by taking gastric precancerous state as an example. Data-driven is to make clinical decisions around data analysis， and its syndrome-differentiation decision-making research relies on hidden structural models and partially observable Markov decision-making processes to identify the etiology of diseases， syndrome elements， evolution of pathogenesis， and syndrome differentiation protocols； knowledge-driven is to make use of data and information to promote decision-making and action processes， and its syndrome-differentiation decision-making research relies on convolutional neural networks to improve the accuracy of local disease identification and syndrome differentiation. The “data-knowledge” dual-driven model can make up for the shortcomings of single-drive numerical simulation accuracy， and achieve a balance between local disease identification and macroscopic syndrome differentiation. On the basis of previous research， we explored the construction method of diagnostic assisted decision-making platform for gastric precancerous state， and believed that the diagnostic and decision-making ability of doctors can be extended through the assistance of machines and algorithms. Meanwhile， the related research methods were integrated and the core features of gastric precancerous state based on TCM syndrome differentiation and endoscopic pathology diagnosis and prediction were obtained， and the elements of endoscopic pathology recognition based on TCM syndrome differentiation were explored， so as to provide ideas for the in-depth research and innovative application of cutting-edge data analysis technology in the field of intelligent TCM syndrome differentiation.