Convolutional neural network can recognize drug resistance of single cancer cells

It is known that single or isolated tumor cells enter cancer patients' circulatory systems. These circulating tumor cells (CTCs) are thought to be an effective tool for diagnosing cancer malignancy. However, handling CTC samples and evaluating CTC sequence analysis results are challenging. Recently, the convolutional neural network (CNN) model, a type of deep learning model, has been increasingly adopted for medical image analyses. However, it is controversial whether cell characteristics can be identified at the single-cell level by using machine learning methods. This study intends to verify whether an AI system could classify the sensitivity of anticancer drugs, based on cell morphology during culture. We constructed a CNN based on the VGG16 model that could predict the efficiency of antitumor drugs at the single-cell level. The machine learning revealed that our model could identify the effects of antitumor drugs with ~0.80 accuracies. Our results show that, in the future, realizing precision medicine to identify effective antitumor drugs for individual patients may be possible by extracting CTCs from blood and performing classification by using an AI system

Computational healthcare: present and future perspectives (Review)

Artificial intelligence (AI) has been developed through repeated new discoveries since around 1960. The use of AI is now becoming widespread within society and our daily lives. AI is also being introduced into healthcare, such as medicine and drug development; however, it is currently biased towards specific domains. The present review traces the history of the development of various AI-based applications in healthcare and compares AI-based healthcare with conventional healthcare to show the future prospects for this type of care. Knowledge of the past and present development of AI-based applications would be useful for the future utilization of novel AI approaches in healthcare

Drug discovery of anticancer drugs targeting methylenetetrahydrofolate dehydrogenase 2

Many anticancer drugs have serious adverse effects; therefore, it is necessary to target features specific to cancer cells to minimize the effects on healthy cells. Methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) was reported to be specifically enhanced in cancer. We confirmed the validity of MTHFD2 as a drug discovery target using clinical data. In addition, we performed in silico screening to design an anticancer drug specifically targeting MTHFD2. Analysis of the clinical data indicated that MTHFD2 was enhanced in most cancers compared with normal tissues, and affected the prognosis in cancer patients. Candidate compounds for MTHFD2 inhibitors were identified using in silico drug discovery techniques, and the important interactions for MTHFD2 binding were determined. In addition, these candidate compounds decreased levels of MTHFD2 metabolites in cancer cells. The findings of the present study may help to develop anticancer drugs targeting MTHFD2, with a view to minimizing the adverse effects of anticancer drugs

Hereditary Pancreatitis Model by Blastocyst Complementation in Mouse

The application of pluripotent stem cells is expected to contribute to the elucidation of unknown mechanism of human diseases. However, in vitro induction of organ-specific cells, such as pancreas and liver, is still difficult and the reproduction of their disorders in a model has been unfeasible. To study the mechanism of human hereditary pancreatitis (HP), we here performed the blastocyst complementation (BC) method. In the BC method, mouse embryonic stem (ES) cells harboring CRISPR/CAS9-mediated mutations in the Prss1 gene were injected into blastocysts with deficient Pdx1 gene, which is a critical transcription factor in the development of pancreas. The results showed that trypsin was activated extremely in Prss1-mutant mice. This implied that the mouse phenotype mimics that of human HP and that the BC method was useful for the reproduction and study of pancreatic disorders. The present study opens the possibility of investigating uncharacterized human diseases by utilizing the BC method