<p>Drugs for Multiple Sclerosis Activate Natural Killer Cells: Do They Protect Against COVID-19 Infection?</p>
Bibliographic record
Abstract
Abstract: COVID-19 infection caused by the newly discovered coronavirus severe acute respiratory distress syndrome virus-19 (SARS-CoV-2) has become a pandemic issue across the globe. There are currently many investigations taking place to look for specific, safe and potent anti-viral agents. Upon transmission and entry into the human body, SARS-CoV-2 triggers multiple immune players to be involved in the fight against the viral infection. Amongst these immune cells are NK cells that possess robust antiviral activity, and which do not require prior sensitization. However, NK cell count and activity were found to be impaired in COVID-19 patients and hence, could become a potential therapeutic target for COVID-19. Several drugs, including glatiramer acetate (GA), vitamin D 3 , dimethyl fumarate (DMF), monomethyl fumarate (MMF), natalizumab, ocrelizumab, and IFN-β, among others have been previously described to increase the biological activities of NK cells especially their cytolytic potential as reported by upregulation of CD107a, and the release of perforin and granzymes. In this review, we propose that such drugs could potentially restore NK cell activity allowing individuals to be more protective against COVID-19 infection and its complications. Keywords: NK cells, multiple sclerosis, COVID-19
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".