Mesenchymal stem/stromal cell-based therapies for severe viral pneumonia: therapeutic potential and challenges
Bibliographic record
Abstract
Severe viral pneumonia is a significant cause of morbidity and mortality globally, whether due to outbreaks of endemic viruses, periodic viral epidemics, or the rarer but devastating global viral pandemics. While limited anti-viral therapies exist, there is a paucity of direct therapies to directly attenuate viral pneumonia-induced lung injury, and management therefore remains largely supportive. Mesenchymal stromal/stem cells (MSCs) are receiving considerable attention as a cytotherapeutic for viral pneumonia. Several properties of MSCs position them as a promising therapeutic strategy for viral pneumonia-induced lung injury as demonstrated in pre-clinical studies in relevant models. More recently, early phase clinical studies have demonstrated a reassuring safety profile of these cells. These investigations have taken on an added importance and urgency during the COVID-19 pandemic, with multiple trials in progress across the globe. In parallel with clinical translation, strategies are being investigated to enhance the therapeutic potential of these cells in vivo, with different MSC tissue sources, specific cellular products including cell-free options, and strategies to 'licence' or 'pre-activate' these cells, all being explored. This review will assess the therapeutic potential of MSC-based therapies for severe viral pneumonia. It will describe the aetiology and epidemiology of severe viral pneumonia, describe current therapeutic approaches, and examine the data suggesting therapeutic potential of MSCs for severe viral pneumonia in pre-clinical and clinical studies. The challenges and opportunities for MSC-based therapies will then be considered.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".