Direct comparison of different therapeutic cell types susceptibility to inflammatory cytokines associated with COVID-19 acute lung injury
Bibliographic record
Abstract
Abstract Background Although 90% of infections with the novel coronavirus 2 (COVID-19) are mild, many patients progress to acute respiratory distress syndrome (ARDS) which carries a high risk of mortality. Given that this dysregulated immune response plays a key role in the pathology of COVID-19, several clinical trials are underway to evaluate the effect of immunomodulatory cell therapy on disease progression. However, little is known about the effect of ARDS associated pro-inflammatory mediators on transplanted stem cell function and survival, and any deleterious effects could undermine therapeutic efficacy. As such, we assessed the impact of inflammatory cytokines on the viability, and paracrine profile (extracellular vesicles) of bone marrow-derived mesenchymal stromal cells, heart-derived cells, and umbilical cord-derived mesenchymal stromal cells. Methods All cell products were manufactured and characterized to established clinical release standards by an accredited clinical cell manufacturing facility. Cytokines and Extracellular vesicles in the cell conditioned media were profiled using proteomic array and nanoparticle tracking analysis. Using a survey of the clinical literature, 6 cytotoxic cytokines implicated in the progression of COVID-19 ARDS. Flow cytometry was employed to determine receptor expression of these 6 cytokines in three cell products. Based on clinical survey and flow cytometry data, a cytokine cocktail that mimics cytokine storm seen in COVID-19 ARDS patients was designed and the impact on cytokine cocktail on viability and paracrine secretory ability of cell products were assessed using cell viability and nanoparticle tracking analysis. Results Flow cytometry revealed the presence of receptors for all cytokines but IL-6, which was subsequently excluded from further experimentation. Despite this widespread expression, exposure of each cell type to individual cytokines at doses tenfold greater than observed clinically or in combination at doses associated with severe ARDS did not alter cell viability or extracellular vesicle character/production in any of the 3 cell products. Conclusions The paracrine production and viability of the three leading cell products under clinical evaluation for the treatment of severe COVID-19 ARDS are not altered by inflammatory mediators implicated in disease progression.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".