Mast Cell Stabilization Improves Survival by Preventing Apoptosis in Sepsis
Bibliographic record
Abstract
Inhibiting single cytokines produced modest effects in clinical trials, in part because the cytokines were not specific for sepsis, and sepsis may require cellular strategies. Previous studies reported that mast cells (MCs) fight infections in early sepsis. In this study, we report that MC stabilizers restrain serum TNF levels and improve survival in wild-type but not in MC-deficient mice. Yet, MC depletion in knockout mice attenuates serum TNF but does not improve survival in sepsis. Serum HMGB1 was the only factor correlating with survival. MC stabilizers inhibit systemic HMGB1 levels and rescue mice from established peritonitis. MC stabilizers fail to inhibit HMGB1 secretion from macrophages, but they prevent apoptosis and caspase-3 activation in sepsis. These results suggest that MC stabilization provides therapeutic benefits in sepsis by inhibiting extracellular release of HMGB1 from apoptotic cells. Our study provides the first evidence that MCs have major immunological implications regulating cell death in sepsis and represent a pharmacological target for infectious disorders in a clinically realistic time frame.
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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".