Efficacy of COVID-HIGIV in animal models of SARS-CoV-2 infection
Bibliographic record
Abstract
In late 2019 the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus emerged in China and quickly spread into a worldwide pandemic. It has caused millions of hospitalizations and deaths, despite the use of COVID-19 vaccines. Convalescent plasma and monoclonal antibodies emerged as major therapeutic options for treatment of COVID-19. We have developed an anti-SARS-CoV-2 immunoglobulin intravenous (Human) (COVID-HIGIV), a potential improvement from using convalescent plasma. In this report the efficacy of COVID-HIGIV was evaluated in hamster and mouse models of SARS-CoV-2 infection. COVID-HIGIV treatment in both mice and hamsters significantly reduced the viral load in the lungs. Among COVID-HIGIV treated animals, infection-related body weight loss was reduced and the animals regained their baseline body weight faster than the PBS controls. In hamsters, COVID-HIGIV treatment reduced infection-associated lung pathology including lung inflammation, and pneumocyte hypertrophy in the lungs. These results support ongoing trials for outpatient treatment with COVID-HIGIV for safety and efficacy evaluation (NCT04910269, NCT04546581).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".