ArticleFrontiers in immunology
The Binary Model of Chronic Diseases Applied to COVID-19.
Article in Frontiers in immunology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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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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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Impaired glucose regulation, SARS-CoV-2 infections and adverse COVID-19 outcomes.Translational research : the journal of laboratory and clinical medicine · 2022Pooled it
- Review
- TGF-β, IL-1β, IL-6 levels and TGF-β/Smad pathway reactivity regulate the link between allergic diseases, cancer risk, and metabolic dysregulations.Frontiers in immunology · 2024Article
- SARS-CoV-2 Omicron (B.1.1.529) Variant: Corticosteroids Treatment/Respiratory Coinfection.Frontiers in immunology · 2022Article
- Cancer and Autoimmune Diseases: A Tale of Two Immunological Opposites?Frontiers in immunology · 2022Article
- The Contrasting Seasonality Patterns of Some Cancer-Types and Herpes Zoster Can Be Explained by a Binary Classification of Immunological Reactions.Journal of inflammation research · 2022Article
- Review
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A binary model for the classification of chronic diseases has formerly been proposed. The model classifies chronic diseases as "high Treg" or "low Treg" diseases according to the extent of regulatory T cells (Treg) activity (frequency or function) observed. The present paper applies this model to severe acute respiratory syndrome coronavirus 2 (SARS
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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.