Receptor for Advanced Glycation End Products Facilitates Host Defense during<i>Escherichia coli</i>–Induced Abdominal Sepsis in Mice
Bibliographic record
Abstract
BACKGROUND: The receptor for advanced glycation end products (RAGE) mediates a variety of inflammatory responses. METHODS: To determine the role of RAGE in the innate immune response to abdominal sepsis caused by Escherichia coli, RAGE-deficient (RAGE(-/-)) and normal wild-type mice were intraperitoneally injected with E. coli. In a separate experiment, wild-type mice received either anti-RAGE immunoglobulin (Ig) G or control IgG. RESULTS: E. coli sepsis resulted in an up-regulation of RAGE in the liver but not in the lungs. RAGE-deficient mice demonstrated an enhanced bacterial outgrowth in their peritoneal cavity and increased dissemination of the infection, accompanied by increased hepatocellular injury and exaggerated systemic cytokine release and coagulation activation, 20 h after intraperitoneal administration of E. coli. Wild-type mice treated with anti-RAGE IgG also displayed a diminished defense against the growth and/or dissemination of E. coli. RAGE was important for the initiation of the host response, as reflected by a reduced immune and procoagulant response early after intraperitoneal injection of E. coli lipopolysaccharide. CONCLUSION: These data are the first to suggest that intact RAGE signaling contributes to an effective antibacterial defense during E. coli sepsis, thereby limiting the accompanying inflammatory and procoagulant response.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".