Cell-based therapies in preclinical models of necrotizing enterocolitis: a systematic review and meta-analysis
Bibliographic record
Abstract
Necrotizing enterocolitis (NEC) remains an incurable gut complication of prematurity with significant morbidity and mortality. Cell therapies, including mesenchymal stromal cells (MSCs), may be a promising treatment given their anti-inflammatory and regenerative potential. We assessed the effect of MSCs and other cell therapies (not classified as MSCs) on incidence, severity, and mortality in preclinical models of NEC. Bibliographic and gray literature searches yielded 17 371 records with 107 full-text articles assessed and ultimately 16 studies were included. These studies featured only rodents NEC models via combination of hyperosmolar feeds, hypoxia, hypothermia, or lipopolysaccharides. Ten studies used interventions with MSCs. Only 2 met the minimal criteria to define MSCs proposed by the International Society for Cell & Gene Therapy (ISCT). The overall risk of bias was assessed as high partly due to paucity of data with important gaps in reporting, reinforcing the importance of rigorous research framework, appropriate cell-therapy and outcome reporting in preclinical research. A reduction in the incidence of NEC (odds ratio [OR] 0.32, 95% CI [0.17, 0.62]), severe NEC (OR 0.30, 95% CI [0.18, 0.50]), and mortality (OR 0.30, 95% CI [0.16, 0.55]) was noted with MSCs treatment, seemingly more pronounced for ISCT-defined (ISCT+) MSCs. Amniotic fluid stem cells, neural stem cells, and placenta stem cells also showed a reduction in these measures. Given their accessibility (ie, umbilical cord) and proven safety profile in extremely preterm infants, our analysis provides a foundation for considering MSCs as promising candidate that requires further evaluation for the treatment of NEC.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".