Mesenchymal stromal cells may improve outcomes in severe COVID-19 related ARDS by normalizing SARS-CoV2-related lymphopenia
Bibliographic record
Abstract
Background: SARS-CoV2 virus resulted in severe acute respiratory distress syndrome (ARDS) which was the main cause of morbidity and mortality. Mesenchymal stromal cells (MSCs) exert beneficial immunomodulatory effects in preclinical models of ARDS. Objective: To determine safety and efficacy of freshly cultured umbilical cord (UC)-MSCs for treatment of severe ARDS in COVID-19 patients. Methods: CIRCA-19 was a multi-site, placebo-controlled, randomized (2:1) clinical trial with a primary outcome of number of days free of oxygen by noninvasive ventilation, high flow nasal cannula or mechanical ventilation within 28 days. Results: 22 patients were enrolled: 14 received 90×106 UC-MSCs over 3 consecutive days (cumulative dose: 270×106 MSCs) and 8 received placebo. Although not adequately powered for statistical analysis, MSCs resulted in an ~40% reduction in median oxygen-free days and median ICU-free days at 28-days compared to the placebo group, with a decrease in mortality at day 28 (14% vs. 25%, respectively) and 1 year (21% vs. 40%, respectively). Biomarker analyses suggested a reduction in IL-6 and IL-8 with MSCs, associated with an increase in IFN gamma. Importantly, at baseline both groups demonstrated lymphopenia, which was selectively and significantly reversed by MSC treatment (p<0.01). Conclusion: Freshly cultured UC-derived MSCs resulted in a non-statistically significant improvement in oxygen free-days, ICU-free days and survival, associated with a significant increase in lymphocyte count. These findings suggest that normalization of lymphopenia may play a key role in the beneficial immunomodulatory effects of MSCs in severe COVID-19 ARDS.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".