The Automated Screening Working Groups is a group of software engineers and biologists passionate about improving scientific manuscripts on a large scale. Our members have created tools that check for common problems in scientific manuscripts, including information needed to improve transparency and reproducibility. We have combined our tools into a single pipeline, called ScreenIT. We're currently using our tools to screen COVID preprints.

About Followers

Latest preprint reviews

  1. COVID-19 Clinical Characteristics, and Sex-Specific Risk of Mortality: Systematic Review and Meta-Analysis

    1. Mohammad Javad Nasiri
    2. Sara Haddadi
    3. Azin Tahvildari
    4. Yeganeh Farsi
    5. Mahta Arbabi
    6. Saba Hasanzadeh
    7. Parnian Jamshidi
    8. Mukunthan Murthi
    9. Mehdi Mirsaeidi

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  2. Coast-to-Coast Spread of SARS-CoV-2 during the Early Epidemic in the United States

    1. Joseph R. Fauver
    2. Mary E. Petrone
    3. Emma B. Hodcroft
    4. Kayoko Shioda
    5. Hanna Y. Ehrlich
    6. Alexander G. Watts
    7. Chantal B.F. Vogels
    8. Anderson F. Brito
    9. Tara Alpert
    10. Anthony Muyombwe
    11. Jafar Razeq
    12. Randy Downing
    13. Nagarjuna R. Cheemarla
    14. Anne L. Wyllie
    15. Chaney C. Kalinich
    16. Isabel M. Ott
    17. Joshua Quick
    18. Nicholas J. Loman
    19. Karla M. Neugebauer
    20. Alexander L. Greninger
    21. Keith R. Jerome
    22. Pavitra Roychoudhury
    23. Hong Xie
    24. Lasata Shrestha
    25. Meei-Li Huang
    26. Virginia E. Pitzer
    27. Akiko Iwasaki
    28. Saad B. Omer
    29. Kamran Khan
    30. Isaac I. Bogoch
    31. Richard A. Martinello
    32. Ellen F. Foxman
    33. Marie L. Landry
    34. Richard A. Neher
    35. Albert I. Ko
    36. Nathan D. Grubaugh

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  3. A model to estimate bed demand for COVID-19 related hospitalization

    1. Teng Zhang
    2. Kelly McFarlane
    3. Jacqueline Vallon
    4. Linying Yang
    5. Jin Xie
    6. Jose Blanchet
    7. Peter Glynn
    8. Kristan Staudenmayer
    9. Kevin Schulman
    10. David Scheinker

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  4. A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis

    1. Shuo Wang
    2. Yunfei Zha
    3. Weimin Li
    4. Qingxia Wu
    5. Xiaohu Li
    6. Meng Niu
    7. Meiyun Wang
    8. Xiaoming Qiu
    9. Hongjun Li
    10. He Yu
    11. Wei Gong
    12. Yan Bai
    13. Li Li
    14. Yongbei Zhu
    15. Liusu Wang
    16. Jie Tian

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  5. Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China

    1. Hanming Fang
    2. Long Wang
    3. Yang Yang

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  6. Risk Factors Associated With Clinical Outcomes in 323 Coronavirus Disease 2019 (COVID-19) Hospitalized Patients in Wuhan, China

    1. Ling Hu
    2. Shaoqiu Chen
    3. Yuanyuan Fu
    4. Zitong Gao
    5. Hui Long
    6. Hong-wei Ren
    7. Yi Zuo
    8. Jie Wang
    9. Huan Li
    10. Qing-bang Xu
    11. Wen-xiong Yu
    12. Jia Liu
    13. Chen Shao
    14. Jun-jie Hao
    15. Chuan-zhen Wang
    16. Yao Ma
    17. Zhanwei Wang
    18. Richard Yanagihara
    19. Youping Deng

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  7. Home collection of nasal swabs for detection of influenza in the Household Influenza Vaccine Evaluation Study

    1. Ryan E. Malosh
    2. Joshua G. Petrie
    3. Amy P. Callear
    4. Arnold S. Monto
    5. Emily T. Martin

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  8. Frontline Science: COVID-19 infection induces readily detectable morphologic and inflammation-related phenotypic changes in peripheral blood monocytes

    1. Dan Zhang
    2. Rui Guo
    3. Lei Lei
    4. Hongjuan Liu
    5. Yawen Wang
    6. Yili Wang
    7. Hongbo Qian
    8. Tongxin Dai
    9. Tianxiao Zhang
    10. Yanjun Lai
    11. Jingya Wang
    12. Zhiqiang Liu
    13. Tianyan Chen
    14. Aili He
    15. Michael O’Dwyer
    16. Jinsong Hu

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  9. Metabolic disturbances and inflammatory dysfunction predict severity of coronavirus disease 2019 (COVID-19): a retrospective study

    1. Shuke Nie
    2. Xueqing Zhao
    3. Kang Zhao
    4. Zhaohui Zhang
    5. Zhentao Zhang
    6. Zhan Zhang

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  10. Identify potent SARS-CoV-2 main protease inhibitors via accelerated free energy perturbation-based virtual screening of existing drugs

    1. Zhe Li
    2. Xin Li
    3. Yi-You Huang
    4. Yaoxing Wu
    5. Runduo Liu
    6. Lingli Zhou
    7. Yuxi Lin
    8. Deyan Wu
    9. Lei Zhang
    10. Hao Liu
    11. Ximing Xu
    12. Kunqian Yu
    13. Yuxia Zhang
    14. Jun Cui
    15. Chang-Guo Zhan
    16. Xin Wang
    17. Hai-Bin Luo

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity
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