Efficacy and Safety of Umbilical Cord-Derived Mesenchymal Stromal Cell Therapy in Preclinical Models of Sepsis: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: In preclinical studies, mesenchymal stromal cells (MSCs), including umbilical cord-derived MSCs (UC-MSCs), demonstrate the ability to modulate numerous pathophysiological processes related to sepsis; however, a systematic synthesis of the literature is needed to assess the efficacy of UC-MSCs for treating sepsis. OBJECTIVE: To examine the effects of UC-MSCs on overall mortality (primary outcome) as well as on organ dysfunction, coagulopathy, endothelial permeability, pathogen clearance, and systemic inflammation (secondary outcomes) at prespecified time intervals in preclinical models of sepsis. METHODS: A systematic search was conducted on Embase, Ovid MEDLINE, and Web of Science up to June 20, 2023. Preclinical controlled studies using in vivo sepsis models with systemic UC-MSC administration were included. Meta-analyses were conducted and expressed as odds ratios (OR) and ratios of the weighted means with 95% CI for categorical and continuous data, respectively. Risk of bias was assessed with the SYRCLE tool. RESULTS: Twenty-six studies (34 experiments, n = 1258 animals) were included in this review. Overall mortality was significantly reduced with UC-MSC treatment as compared to controls (OR: 0.26, 95% CI: 0.18-0.36). At various prespecified time intervals, UC-MSCs reduced surrogate measures of organ dysfunction related to the kidney, liver, and lung; reduced coagulopathy and endothelial permeability; and enhanced pathogen clearance from multiple sites. UC-MSCs also modulated systemic inflammatory mediators. No studies were rated as low risk across all SYCLE domains. CONCLUSIONS: These results demonstrate the efficacy of UC-MSC treatment in preclinical sepsis models and highlight their potential as a therapeutic intervention for septic shock.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.017 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".