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. Snotwatch COVID-toes: An ecological study of chilblains and COVID-19 diagnoses in Victoria, Australia

    1. Rana Sawires
    2. Christopher Pearce
    3. Michael Fahey
    4. Hazel Clothier
    5. Karina Gardner
    6. Jim Buttery

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  2. Regional Replacement of SARS-CoV-2 Variant Omicron BA.1 with BA.2 as Observed through Wastewater Surveillance

    1. Alexandria B. Boehm
    2. Bridgette Hughes
    3. Marlene K. Wolfe
    4. Bradley J. White
    5. Dorothea Duong
    6. Vikram Chan-Herur

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  3. Effectiveness of the neutralizing antibody sotrovimab among high-risk patients with mild-to-moderate SARS-CoV-2 in Qatar

    1. Ahmed Zaqout
    2. Muna A. Almaslamani
    3. Hiam Chemaitelly
    4. Samar A. Hashim
    5. Ajithkumar Ittaman
    6. Abeir Alimam
    7. Fatma Rustom
    8. Joanne Daghfal
    9. Mohammed Abukhattab
    10. Sawsan AlMukdad
    11. Anvar Hassan Kaleeckal
    12. Ali Nizar Latif
    13. Adeel A. Butt
    14. Roberto Bertollini
    15. Abdullatif Al-Khal
    16. Ali S. Omrani
    17. Laith J. Abu-Raddad

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  4. Usage and Awareness of Antiviral Medications for Coronavirus Disease 2019 (COVID-19) Among Individuals at Risk for Severe COVID-19, March 2021 to 1 August 2022

    1. Noah Kojima
    2. Jeffrey D Klausner

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  5. Impaired immune response drives age-dependent severity of COVID-19

    1. Julius Beer
    2. Stefania Crotta
    3. Angele Breithaupt
    4. Annette Ohnemus
    5. Jan Becker
    6. Benedikt Sachs
    7. Lisa Kern
    8. Miriam Llorian
    9. Nadine Ebert
    10. Fabien Labroussaa
    11. Tran Thi Nhu Thao
    12. Bettina Salome Trueeb
    13. Joerg Jores
    14. Volker Thiel
    15. Martin Beer
    16. Jonas Fuchs
    17. Georg Kochs
    18. Andreas Wack
    19. Martin Schwemmle
    20. Daniel Schnepf

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  6. Antibody Responses In Non-Severe SARS-CoV-2 Infections Are Driven By CD4+ T cells and Age

    1. Amelie E. Murrell
    2. Ewono Eyoh
    3. Jeffrey G. Shaffer
    4. Monika L. Dietrich
    5. Ivy V. Trinh
    6. Thomas J. Yockachonis
    7. Shuangyi Bai
    8. Crystal Y. Zheng
    9. Celia V. Mayne
    10. Sofia E. Cabrera
    11. Anyssa Aviles-Amaro
    12. Addison E. Stone
    13. Saraswatie Rambaran
    14. Sruti Chandra
    15. Debra H. Elliott
    16. Ashley R. Smira
    17. Sara N. Harris
    18. Katharine E. Olson
    19. Samantha J. Bilton
    20. Medea J. Gabriel
    21. Nicole D. Falgout
    22. Emily J. Engel
    23. Alisha D. Prystowsky
    24. Bo Ning
    25. Tony Hu
    26. Jay K. Kolls
    27. Samuel J. Landry
    28. Stacy S. Drury
    29. John S. Schieffelin
    30. Kevin J. Zwezdaryk
    31. James E. Robinson
    32. Bronwyn M. Gunn
    33. Elizabeth B. Norton

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  7. Risks and challenges in COVID-19 infection prevention and control in a hospital setting: Perspectives of healthcare workers in Thailand

    1. Monnaphat Jongdeepaisal
    2. Puri Chunekamrai
    3. Rapeephan Rattanawongnara Maude
    4. Richard James Maude

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  8. Predicting past and future SARS-CoV-2-related sick leave using discrete time Markov modelling

    1. Jiayao Lei
    2. Mark Clements
    3. Miriam Elfström
    4. Kalle Conneryd Lundgren
    5. Joakim Dillner

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  9. Epidemiological impact and cost-effectiveness analysis of COVID-19 vaccination in Kenya

    1. Stacey Orangi
    2. John Ojal
    3. Samuel PC Brand
    4. Cameline Orlendo
    5. Angela Kairu
    6. Rabia Aziza
    7. Morris Ogero
    8. Ambrose Agweyu
    9. George M Warimwe
    10. Sophie Uyoga
    11. Edward Otieno
    12. Lynette I Ochola-Oyier
    13. Charles N Agoti
    14. Kadondi Kasera
    15. Patrick Amoth
    16. Mercy Mwangangi
    17. Rashid Aman
    18. Wangari Ng'ang'a
    19. Ifedayo MO Adetifa
    20. J Anthony G Scott
    21. Philip Bejon
    22. Matt J Keeling
    23. Stefan Flasche
    24. D James Nokes
    25. Edwine Barasa

    Reviewed by Rapid Reviews Infectious Diseases, ScreenIT

    4 evaluationsAppears in 2 listsLatest version Latest activity

  10. SARS-CoV-2 spike variants differ in their allosteric response to linoleic acid

    1. A. Sofia F. Oliveira
    2. Deborah K. Shoemark
    3. Andrew D. Davidson
    4. Imre Berger
    5. Christiane Schaffitzel
    6. Adrian J. Mulholland

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity
Newer Page 506 of 2361 Older