Mesenchymal Stem Cells Reduce Inflammation while Enhancing Bacterial Clearance and Improving Survival in Sepsis
Bibliographic record
Abstract
RATIONALE: Sepsis refers to the clinical syndrome of severe systemic inflammation precipitated by infection. Despite appropriate antimicrobial therapy, sepsis-related morbidity and mortality remain intractable problems in critically ill patients. Moreover, there is no specific treatment strategy for the syndrome of sepsis-induced multiple organ dysfunction. OBJECTIVES: We hypothesized that mesenchymal stem cells (MSCs), which have been shown to have immunomodulatory properties, would reduce sepsis-induced inflammation and improve survival in a polymicrobial model of sepsis. METHODS: Sepsis was induced in C57Bl/6J mice by cecal ligation and puncture (CLP), followed 6 hours later by an intravenous injection of MSCs or saline. Twenty-eight hours after CLP, plasma, bronchoalveolar lavage fluid and tissues were collected for analyses. Longer-term studies were performed with antibiotic coadministration to assess the effect of MSCs on survival. MEASUREMENTS AND MAIN RESULTS: MSC treatment significantly reduced mortality in septic mice receiving appropriate antimicrobial therapy. MSCs alone reduced systemic and pulmonary cytokine levels in mice with CLP-induced sepsis, preventing acute lung injury and organ dysfunction, despite the low levels of cell persistence. Microarray data highlighted an overall down-regulation of inflammation and inflammation-related genes (such as IL-10, IL-6) and a shift toward up-regulation of genes involved in promoting phagocytosis and bacterial killing. Finally, bacterial clearance was significantly greater in MSC-treated mice, in part due to enhanced phagocytotic activity of the host immune cells. CONCLUSIONS: These data demonstrate that MSCs have beneficial effects on experimental sepsis, possibly by paracrine mechanisms, and suggest that immunomodulatory cell therapy may be an effective adjunctive treatment to reduce 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".