• Reproducibility of CRISPR-Cas9 Methods for generation of conditional mouse alleles (Genome Biology, correspondence, 2021, in press)

    In collaboration with the international consortium, the correspondence was published regarding the efficiency and conditions of genetic engineering technology in animal model.

  • Ribonucleotide base enzymes and diseases (Elsevier book, 2021, in press)

    A practical textbook for industry-academia collaboration will be published in English by Elsevier. As a result of our research, Dr. Konno et al. updated about ribonucleotide biochemistry.

  • Epitranscriptomics and diseases (Springer book, Editors, Stefan Jurga and Jan Barciszewski, 2021, in press)

    In this book chapter, Konno et al. lectured RNA modification (epitranscriptomics) and discussed its significance in human diseases. This is timely and critical, given that RNA encodes and decodes information essential to organismal survival, and RNA is neither a plain molecule, nor is it only an information-carrying factor, but also a catalyst with complex structures.

  • Targeting cancer stem cells in refractory cancer (Regenerative Therapy, 2021, in press)

    In collaboration with Osaka University, Osaka International Cancer Institute, and Kyushu University, Dr. Miyoshi et al. updated that the innovative approaches for cancer stem cells, but also for immune cells and other mesenchymal cells. This novel approach in diagnosis and therapy will open an avenue to cancer precision medicine.

  • A four-dimensional organoid system to visualize cancer cell vascular invasion (Biology, 2020, in press)
    In collaboration with Osaka University Graduate School of Medicine, Department of Gastroenterology (Profs Yuichiro Doki, Hidetoshi Eguchi, Masaki Mori) and Graduate School of Engineering, Department of Applied Chemistry (Prof Michiya Matsusaki), Yanagisawa et al. studied a four-dimensional (4D) organoid system to visualize cancer cell vascular invasion. Using this model, it was possible to observe and evaluate the cell dynamics and histological positional relationship of invasive cancer cells in four dimensions. Thus, drug discovery using this vascular invasion mimic model will be advanced and many applications are expected. 
  • immuno-surgical management of pancreatic cancer with analysis of cancer exosomes (Cells, 2020, in press).   
    In the international collaboration with Hirotsu Bio Science Inc., Kinshu-kai Medical Corporation, IDEA Consultants Inc., Kyowa-kai Medical Corporation, Unitech Co. Ltd., as well as department of gastroenterology (Profs Yuichiro Doki and Hidetoshi Eguchi), Takeda et al. updated the state-of-art technology of immuno-surgical strategy of monitoring pancreatic cancer for its early diagnosis and therapy.
  • Application of C. elegans to cancer screening test (Book Chapter, Prime Archives in Cancer Research)
    In collaboration with Hirotsu Bio Science Inc. as well as department of gastroenterology (Profs Yuichiro Doki and Hidetoshi Eguchi), the industry-academia collaborative research updated the application of C. elegans to cancer precision medicine.
  • State-of-the-Art Technology of Model Organisms for Current Human (Diagnostics, 2020, in press)
    In the international collaboration with Hirotsu Bio Science Inc., Kinshu-kai Medical Corporation, IDEA Consultants Inc., Kyowa-kai Medical Corporation, Unitech Co. Ltd., as well as department of gastroenterology (Profs Yuichiro Doki and Hidetoshi Eguchi), Konno et al. reviewerd the state-of-art technology for the diagnosis of human diseases by utilizing various organisms as tools
  • Genome-wide association meta-analysis identifies risk variants for pancreatic cancer (Nature Communications, 2020, in press)
    In inter-institutional collaboration with the Aichi Cancer Center (Professor Keitaro Matsuo), the genome characteristics in pancreatic cancer were studied and elucidated for further improvement of precision medicine in Japan.
  • Convolutional neural network can recognize drug resistance of single cancer cells (Int. J. Mol. Sci., 2020, in press).   

    In the international collaboration with University of Rome, as well as department of gastroenterology (Profs Yuichiro Doki and Hidetoshi Eguchi), Yanagisawa et al. performed the analysis of drug resistance of single cancer cells by the convolutional neural network (CNN) model, which demonstrated that a CNN based on the VGG16 model could predict the efficiency of anti-tumor drugs at single-cell level, suggesting that precision medicine for individual patients may be possible by extracting circulating tumor cells from blood by using an AI system.