Efficacy of Mesenchymal Stromal Cell Therapy for Acute Lung Injury in Preclinical Animal Models: A Systematic Review
Bibliographic record
Abstract
The Acute Respiratory Distress Syndrome (ARDS) is a devastating clinical condition that is associated with a 30-40% risk of death, and significant long term morbidity for those who survive. Mesenchymal stromal cells (MSC) have emerged as a potential novel treatment as in pre-clinical models they have been shown to modulate inflammation (a major pathophysiological hallmark of ARDS) while enhancing bacterial clearance and reducing organ injury and death. A systematic search of MEDLINE, EMBASE, BIOSIS and Web of Science was performed to identify pre-clinical studies that examined the efficacy MSCs as compared to diseased controls for the treatment of Acute Lung Injury (ALI) (the pre-clinical correlate of human ARDS) on mortality, a clinically relevant outcome. We assessed study quality and pooled results using random effect meta-analysis. A total of 54 publications met our inclusion criteria of which 17 (21 experiments) reported mortality and were included in the meta-analysis. Treatment with MSCs, as compared to controls, significantly decreased the overall odds of death in animals with ALI (Odds Ratio 0.24, 95% Confidence Interval 0.18-0.34, I2 8%). Efficacy was maintained across different types of animal models and means of ALI induction; MSC origin, source, route of administration and preparation; and the clinical relevance of the model (timing of MSC administration, administration of fluids and or antibiotics). Reporting of standard MSC characterization for experiments that used human MSCs and risks of bias was generally poor, and although not statistically significant, a funnel plot analysis for overall mortality suggested the presence of publication bias. The results from our meta-analysis support that MSCs substantially reduce the odds of death in animal models of ALI but important reporting elements were sub optimal and limit the strength of our conclusions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".