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Background: Chimeric antigen receptors (CARs)-engineered T cells (CAR-T cells) therapy is a promising immunotherapy for leukemia. Encouraged by the world's first adult patient who underwent the clinical trial of CAR-T cells therapy achieved great success, more and more institutions and pharmaceutical companies devoted to the CAR-T R&D. This study aimed at exploring the status and trends of current researches on CAR-T cell therapy through bibliometric analysis. Methods: Original publications on CAR-T cell therapy were collected from the Web of Science Core Collection between 2007 and 2019.11.14. Data were analyzed in terms of publication outputs, journals, countries, institutions, authors, co-authorship, co-citation, research hotspots and evolution trends through VOSviewer and CiteSpace. Results: A total of 961 publications were identified in the period of 2007 to 2019. Molecular Therapy published the highest number of publications, followed by followed by Blood, Clinical Cancer Research and Cancer Immunology Research. The USA, which devoted the largest number of articles and the most extensive cooperation with other countries, was the most leading country in this field. The University of Pennsylvania published the largest number of articles were the most influential institution. June CH was the most productive authors in this field. Maude SL had the most cited article published on New Engl J Med in 2014. The hotspots of CAR-T cell therapy research were "CAR", "immunotherapy", "lymphocyte", "antitumor activity" and "leukemia, whereas the major frontier was chronic lymphocytic leukemia. Conclusion: This study gives investigators the landscape of CAR-T cell therapy research from the perspective of bibliometrics.

The effect of peak noise and white Gaussian noise on moving cut data-permutation entropy method is discussed by constructing the nonlinear ideal time series. The results show that the moving cut data-permutation entropy method can accurately detect the location of the mutational point, even if the time series with high noise level, which showed that the effect of peak noise and white Gaussian noise on the result of this method is small. The moving cut data-permutation entropy has strong anti-noise ability, which is beneficial to the application of this method in actual observation data.