Inhalation of Low Molecular Weight Heparins as Prophylaxis against SARS-CoV-2
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".