Inhibition of the RAGE products increases survival in experimental models of severe sepsis and systemic infection
Bibliographic record
Abstract
INTRODUCTION: The receptor for advanced glycation end products (RAGE), a multi-ligand member of the immunoglobulin superfamily, contributes to acute and chronic disease processes, including sepsis. METHODS: We studied the possible therapeutic role of RAGE inhibition in the cecal ligation and puncture (CLP) model of polymicrobial sepsis and a model of systemic listeriosis using mice genetically deficient in RAGE expression or mice injected with a rat anti-murine RAGE monoclonal antibody. RESULTS: The 7-day survival rates after CLP were 80% for RAGE-/- mice (n = 15) (P < 0.01 versus wild-type), 69% for RAGE+/- mice (n = 23), and 37% for wild-type mice (n = 27). Survival benefits were evident in BALB/c mice given anti-RAGE antibody (n = 15 per group) over serum-treated control animals (P < 0.05). Moreover, delayed treatment with anti-RAGE antibody up to 24 hours after CLP resulted in a significant survival benefit compared with control mice. There was no significant increase in tissue colony counts from enteric Gram-negative or Gram-positive bacteria in animals treated with anti-RAGE antibody. RAGE-/-, RAGE+/-, and anti-RAGE antibody-treated animals were resistant to lethality from Listeria monocytogenes by almost two orders of magnitude compared with wild-type mice. CONCLUSION: Further studies are warranted to determine the clinical utility of anti-RAGE antibody as a novel treatment for sepsis.
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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 | 0.000 | 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) | 0.002 | 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".