ArticlebioRxiv : the preprint server for biology2021
Modeling SARS-CoV-2 and Influenza Infections and Antiviral Treatments in Human Lung Epithelial Tissue Equivalents.
Article in bioRxiv : the preprint server for biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed, 12 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
18 authors at 4 institutions in 2 countries.
Funding
Abstract
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the third coronavirus in less than 20 years to spillover from an animal reservoir and cause severe disease in humans. High impact respiratory viruses such as pathogenic beta-coronaviruses and influenza viruses, as well as other emerging respiratory viruses, pose an ongoing global health threat to humans. There is a critical need for physiologically relevant, robust and ready to use,
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.