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Record W4308059907 · doi:10.1128/mbio.02558-22

Inhalation of Low Molecular Weight Heparins as Prophylaxis against SARS-CoV-2

2022· article· en· W4308059907 on OpenAlexfundno aff
Julia Eder, Marta Bermejo‐Jambrina, Killian E. Vlaming, Tanja M. Kaptein, Viktoria Zaderer, E. Marleen Kemper, Doris Wilflingseder, Sietze Reitsma, Godelieve J. de Bree, Danny M. Cohn, Teunis B. H. Geijtenbeek

Bibliographic record

VenuemBio · 2022
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsnot available
FundersInstitute of Infection and ImmunityEuropean Research CouncilNederlandse Organisatie voor Wetenschappelijk OnderzoekZonMw
KeywordsInhalationIn vivoMedicineIn vitroNasal administrationPharmacologyEx vivoImmunologyPlaceboNebulizerAnesthesiaBiologyPathology

Abstract

fetched live from OpenAlex

New SARS-CoV-2 variants of concern and waning immunity demonstrate the need for a quick and simple agent to prevent infection. Low molecular weight heparins (LMWH) have been shown to inhibit SARS-CoV-2 in experimental settings. The airways are a major route for SARS-CoV-2 infection and inhaled LMWH could be a prophylactic treatment. We investigated the efficacy of inhalation of the LMWH enoxaparin in humans to prevent SARS-CoV-2 attachment because this is a prerequisite for infection. Volunteers received enoxaparin in the right and a placebo in the left nostril using a nebulizer. Subsequently, nasal epithelial cells were retrieved with a brush and exposed to SARS-CoV-2. LMWH inhalation significantly reduced the binding of SARS-Cov-2 to human nasal cells. Cell phenotyping revealed no differences between placebo and treatment groups and no adverse events were observed in the participants. Our data indicated that LMWH can be used to block SARS-CoV-2 attachment to nasal cells. LMWH was ubiquitously available, affordable, and easily applicable, making them excellent candidates for prophylactic treatment against SARS-CoV-2.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.381
Teacher spread0.351 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations14
Published2022
Admission routes1
Has abstractyes

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