TWIRLS , a knowledge‐mining technology, suggests a possible mechanism for the pathological changes in the human host after coronavirus infection via ACE2

  1. Xiaoyang Ji
  2. Wenting Tan
  3. Chunming Zhang
  4. Yubo Zhai
  5. Yiching Hsueh
  6. Zhonghai Zhang
  7. Chunli Zhang
  8. Yanqiu Lu
  9. Bo Duan
  10. Guangming Tan
  11. Renhua Na
  12. Guohong Deng
  13. Gang Niu

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Abstract

Faced with the current large‐scale public health emergency, collecting, sorting, and analyzing biomedical information related to the “SARS‐CoV‐2” should be done as quickly as possible to gain a global perspective, which is a basic requirement for strengthening epidemic control capacity. However, for human researchers studying viruses and hosts, the vast amount of information available cannot be processed effectively and in a timely manner, particularly if our scientific understanding is also limited, which further lowers the information processing efficiency. We present TWIRLS ( T opic‐ w ise i nfe r ence engine of massive biomedical l iterature s ), a method that can deal with various scientific problems, such as liver cancer, acute myeloid leukemia, and so forth, which can automatically acquire, organize, and classify information. Additionally, this information can be combined with independent functional data sources to build an inference system via a machine‐based approach, which can provide relevant knowledge to help human researchers quickly establish subject cognition and to make more effective decisions. Using TWIRLS, we automatically analyzed more than three million words in more than 14,000 literature articles in only 4 hr. We found that an important regulatory factor angiotensin‐converting enzyme 2 (ACE2) may be involved in host pathological changes on binding to the coronavirus after infection. On triggering functional changes in ACE2/AT2R, the cytokine homeostasis regulation axis becomes imbalanced via the Renin‐Angiotensin System and IP‐10, leading to a cytokine storm. Through a preliminary analysis of blood indices of COVID‐19 patients with a history of hypertension, we found that non‐ARB (Angiotensin II receptor blockers) users had more symptoms of severe illness than ARB users. This suggests ARBs could potentially be used to treat acute lung injury caused by coronavirus infection.

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  1. Version published to 10.1002/ddr.21717
  2. ScreenIT

    SciScore for 10.1101/2020.02.24.20025437: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    First, PubMed was searched for articles including titles, abstracts, author and affiliation information containing the subject keyword “coronavirus”.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Programming language and efficiency: Part of the algorithm was developed using the MatLab programming environment and Python language.
    MatLab
    suggested: (MATLAB, RRID:SCR_001622)
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2020.02.24.20025437v1 on medRxiv