ReviewInterdisciplinary perspectives on infectious diseases2022
SARS-CoV-2: Molecular Structure, Pathogenesis, Potential Therapeutic Targets, and Immune Response of the Infected Subject.
Review in Interdisciplinary perspectives on infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The pathogenic mechanisms and immune response of COVID-19 are far from clear. Through a documentary review of literature, the authors describe virological and molecular aspects of SARS-CoV-2, the intimate mechanisms of cell infection, and potential therapeutic targets. They also analyze the characteristics of immune response of the infected subject. Objectives: Objectives of this study are to describe the state of knowledge on virological data, molecular and physiopathogenic mechanisms of SARS-CoV-2, with a view to a better understanding of the therapeutic targets, as well as the immune response of the infected subject. Conclusion: Reported data could contribute to a better understanding of molecular mechanisms of cellular infection by SARS-CoV-2 as well as to a more easy explanation of the action of pharmacological agents used for the treatment, while elucidating intimate mechanisms of the immunity of infected subject.
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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.