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Record W3154166937 · doi:10.21203/rs.3.rs-30934/v2

COVID-19: Famotidine, Histamine, Mast Cells, and Mechanisms

2020· preprint· en· W3154166937 on OpenAlexaff
Robert W. Malone, Philip Tisdall, Philip Fremont‐Smith, Yongfeng Liu, Xi‐Ping Huang, Kris M. White, Lisa Miorin, Assaf Alon, Elise Delaforge, Christopher Hennecker, Guanyu Wang, Joshua Pottel, Robert Bona, Nora Smith, Julie M. Hall, Gideon Shapiro, Howard Clark, Anthony Mittermaier, Andrew C. Kruse, Adolfo García‐Sastre, Bryan L. Roth, Jill Glasspool‐Malone, Victor P. Francone, Norbert Hertzog, Maurice Fremont‐Smith, Darrell Ricke

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldImmunology and Microbiology
TopicMast cells and histamine
Canadian institutionsMcGill University
FundersNational Institute of Allergy and Infectious DiseasesU.S. Air ForceDefense Advanced Research Projects AgencyDefense Threat Reduction AgencyAdvanced Research Projects AgencyU.S. Department of Defense
KeywordsFamotidineHistamineMast cellCoronavirus disease 2019 (COVID-19)Dysfunctional familyPandemicMechanism (biology)ImmunologyMedicineDiseasePharmacologyInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

<title>Abstract</title> SARS-CoV-2 infection is required for COVID-19, but many signs and symptoms of COVID-19 differ from common acute viral diseases. Currently, there are no pre- or post-exposure prophylactic COVID-19 medical countermeasures. Clinical data suggest that famotidine may mitigate COVID-19 disease, but both mechanism of action and rationale for dose selection remain obscure. We explore several plausible avenues of activity including antiviral and host-mediated actions. We propose that the principal famotidine mechanism of action for COVID-19 involves on-target histamine receptor H<sub>2</sub> activity, and that development of clinical COVID-19 involves dysfunctional mast cell activation and histamine release.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.757
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.237
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations27
Published2020
Admission routes1
Has abstractyes

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