Development of Coronavirus Treatments Using Neutralizing Antibodies
Bibliographic record
Abstract
The Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus SARS-CoV-2, was first reported in December 2019 in Wuhan, Hubei province, China. This virus has led to 61.8 million cases worldwide being reported as of December 1st, 2020. Currently, there are no definite approved therapies endorsed by the World Health Organization for COVID-19, focusing only on supportive care. Treatment centers around symptom management, including oxygen therapy or invasive mechanical ventilation. Immunotherapy has the potential to play a role in the treatment of SARS-CoV-2. Monoclonal antibodies (mAbs), in particular, is a relatively new approach in the world of infectious diseases and has the benefit of overcoming challenges with serum therapy and intravenous immunoglobulins preparations. Here, we reviewed the articles published in PubMed with the purpose of summarizing the currently available evidence for the use of neutralizing antibodies as a potential treatment for coronaviruses. Studies reporting in vivo results were summarized and analyzed. Despite promising data from some studies, none of them progressed to clinical trials. It is expected that neutralizing antibodies might offer an alternative for COVID-19 treatment. Thus, there is a need for randomized trials to understand the potential use of this treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".