Poly(I:C) enhances mesenchymal stem cell control of myeloid cells from COVID-19 patients
Bibliographic record
Abstract
Mesenchymal stem cells (MSCs) are being studied for the treatment of COVID-19-associated critical illness, due to their immunomodulatory properties. Here, we hypothesized that viral mimic-priming improves MSCs' abilities to rebalance the dysregulated immune responses in COVID-19. Transcriptome analysis of poly(I:C)-primed MSCs (pIC-MSCs) showed upregulation of pathways in antiviral and immunomodulatory responses. Together with increased expression of antiviral proteins such as MX1, IFITM3, and OAS1, these changes translated to greater effector functions in regulating monocytes and granulocytes while further enhancing MSCs' ability to block SARS-CoV-2 pseudovirus entry into epithelial cells. Most importantly, the addition of pIC-MSCs to COVID-19 patient whole blood significantly reduced inflammatory neutrophils and increased M2 monocytes while enhancing their phagocytic effector function. We reveal for the first time that MSCs can be primed by Toll-like receptor 3 agonist to improve their ability to rebalance the dysregulated immune responses seen in severe SARS-CoV-2 infection.
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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 | 0.000 | 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".