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. Disentangling primer interactions improves SARS-CoV-2 genome sequencing by multiplex tiling PCR

    1. Kentaro Itokawa
    2. Tsuyoshi Sekizuka
    3. Masanori Hashino
    4. Rina Tanaka
    5. Makoto Kuroda

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  2. Aerodynamic Characteristics and RNA Concentration of SARS-CoV-2 Aerosol in Wuhan Hospitals during COVID-19 Outbreak

    1. Yuan Liu
    2. Zhi Ning
    3. Yu Chen
    4. Ming Guo
    5. Yingle Liu
    6. Nirmal Kumar Gali
    7. Li Sun
    8. Yusen Duan
    9. Jing Cai
    10. Dane Westerdahl
    11. Xinjin Liu
    12. Kin-fai Ho
    13. Haidong Kan
    14. Qingyan Fu
    15. Ke Lan

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  3. Data‐driven discovery of a clinical route for severity detection of COVID‐19 paediatric cases

    1. Hui Yu
    2. Yuqi Guo
    3. Yun Xiang
    4. Chuan Sun
    5. Tao Yang

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  4. Diagnosis of Acute Respiratory Syndrome Coronavirus 2 Infection by Detection of Nucleocapsid Protein

    1. Bo Diao
    2. Kun Wen
    3. Jian Chen
    4. Yueping Liu
    5. Zilin Yuan
    6. Chao Han
    7. Jiahui Chen
    8. Yuxian Pan
    9. Li Chen
    10. Yunjie Dan
    11. Jing Wang
    12. Yongwen Chen
    13. Guohong Deng
    14. Hongwei Zhou
    15. Yuzhang Wu

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  5. A deterministic epidemic model for the emergence of COVID-19 in China

    1. Meng Wang
    2. Jingtao Qi

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  6. Prognostic value of NT-proBNP in patients with severe COVID-19

    1. Lei Gao
    2. Dan Jiang
    3. Xue-song Wen
    4. Xiao-cheng Cheng
    5. Min Sun
    6. Bin He
    7. Lin-na You
    8. Peng Lei
    9. Xiao-wei Tan
    10. Shu Qin
    11. Guo-qiang Cai
    12. Dong-ying Zhang

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  7. Severe Acute Respiratory Syndrome Coronavirus 2 Transmission Potential, Iran, 2020

    1. Kamalich Muniz-Rodriguez
    2. Isaac Chun-Hai Fung
    3. Shayesteh R. Ferdosi
    4. Sylvia K. Ofori
    5. Yiseul Lee
    6. Amna Tariq
    7. Gerardo Chowell

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  8. Type I Interferon Susceptibility Distinguishes SARS-CoV-2 from SARS-CoV

    1. Kumari G. Lokugamage
    2. Adam Hage
    3. Maren de Vries
    4. Ana M. Valero-Jimenez
    5. Craig Schindewolf
    6. Meike Dittmann
    7. Ricardo Rajsbaum
    8. Vineet D. Menachery

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  9. Sensitive one-step isothermal detection of pathogen-derived RNAs

    1. Chang Ha Woo
    2. Sungho Jang
    3. Giyoung Shin
    4. Gyoo Yeol Jung
    5. Jeong Wook Lee

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  10. Comparison of severe and non-severe COVID-19 pneumonia: review and meta-analysis

    1. Weiping Ji
    2. Jing Zhang
    3. Gautam Bishnu
    4. Xudong Du
    5. Xinxin Chen
    6. Hui Xu
    7. Xiaoling Guo
    8. Zhenzhai Cai
    9. Xian Shen

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity
Newer Page 420 of 2361 Older