Human Umbilical Cord Mesenchymal Stromal Cells Improve Survival and Bacterial Clearance in Neonatal Sepsis in Rats
Bibliographic record
Abstract
Sepsis is the main cause of morbidity and mortality in neonates. Mesenchymal stromal cells (MSCs) are potent immune-modulatory cells. Their effect in neonatal sepsis has never been explored. We hypothesized that human umbilical cord-derived MSCs (hUC-MSCs) improve survival in experimental neonatal sepsis. Sepsis was induced in 3-day-old rats by intravenous injection of Escherichia coli (5 × 105/rat). One hour after infection, rats were treated intravenously with normal saline, hUC-MSCs, or with interferon-γ preconditioned hUC-MSCs (107 cells/kg). Eighteen hours after infection, survival, bacterial counts, lung neutrophil and macrophage influx, phagocytosis and apoptosis of splenocytes plasma, and LL-37 concentration were evaluated. Animals were observed for survival for 72 h after E. coli injection. Treatment with either hUC-MSCs or preconditioned hUC-MSCs significantly increased survival (hUC-MSCs, 81%; preconditioned hUC-MSCs, 89%; saline, 51%; P < 0.05). Both hUC-MSCs and preconditioned hUC-MSCs enhanced bacterial clearance. Lung neutrophil influx was decreased with preconditioned hUC-MSCs. The number of activated macrophages (CD206+) in the spleen was increased with hUC-MSCs and preconditioned hUC-MSCs; preconditioned hUC-MSCs increased the phagocytic activity of CD206+ macrophages. hUC-MSCs and preconditioned hUC-MSCs decreased splenocyte apoptosis in E. coli infected rats. Finally, LL-37 plasma levels were elevated in neonatal rats treated with hUC-MSCs or preconditioned hUC-MSCs. hUC-MSCs enhance survival and bacterial clearance in experimental neonatal sepsis. hUC-MSCs may be an effective adjunct therapy to reduce neonatal sepsis-related morbidity and mortality.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".