Mesenchymal stem cells and COVID-19: What they do and what they can do
Bibliographic record
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or coronavirus disease 2019 (COVID-19) pandemic has exhausted the health systems in many countries with thousands cases diagnosed daily. The currently used treatment guideline is to manage the common symptoms like fever and cough, but doesn't target the virus itself or halts serious complications arising from this viral infection. Currently, SARS-CoV-2 exhibits many genetic modulations which have been associated with the appearance of highly contagious strains. The number of critical cases of COVID-19 increases markedly, and many of the infected people die as a result of respiratory failure and multiple organ dysfunction. The regenerative potential of mesenchymal stem cells (MSCs) has been extensively studied and confirmed. The impressive immunomodulation and anti-inflammatory activity of MSCs have been recognized as a golden opportunity for the treatment of COVID-19 and its associated complications. Moreover, MSCs regenerative and repairing abilities have been corroborated by many studies with positive outcomes and high recovery rates. Based on that, MSCs infusion could be an effective mechanism in managing and stemming the serious complications and multiple organ failure associated with COVID-19. In the present review, we discuss the commonly reported complications of COVID-19 viral infection and the established and anticipated role of MSCs in managing these complications.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".