Treatment of COVID-19 Pneumonia: the Case for Placenta-derived Cell Therapy
Bibliographic record
Abstract
Nearly 500'000 fatalities due to COVID-19 have been reported globally and the death toll is still rising. Most deaths are due to acute respiratory distress syndrome (ARDS), as a result of an excessive immune response and a cytokine storm elicited by severe SARS-CoV-2 lung infection, rather than by a direct cytopathic effect of the virus. In the most severe forms of the disease therapies should aim primarily at dampening the uncontrolled inflammatory/immune response responsible for most fatalities. Pharmacological agents - antiviral and anti-inflammatory molecules - have not been able so far to achieve compelling results for the control of severe COVID-19 pneumonia. Cells derived from the placenta and/or fetal membranes, in particular amniotic epithelial cells (AEC) and decidual stromal cells (DSC), have established, well-characterized, potent anti-inflammatory and immune-modulatory properties that make them attractive candidates for a cell-based therapy of COVID19 pneumonia. Placenta-derived cells are easy to procure from a perennial source and pose minimal ethical issues for their utilization. In view of the existing clinical evidence for the innocuousness and efficiency of systemic administration of DSCs or AECs in similar conditions, we advocate for the initiation of clinical trials using this strategy in the treatment of severe COVID-19 disease.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".