CD44 Deficiency Is Associated with Enhanced<i>Escherichia coli</i>-Induced Proinflammatory Cytokine and Chemokine Release by Peritoneal Macrophages
Bibliographic record
Abstract
CD44 is involved in several immune responses, such as cellular adhesion, migration, proliferation, and activation. Peritonitis is an important cause of sepsis, and Escherichia coli is one of the major pathogens involved therein. We sought to determine the role of CD44 in the host response to E. coli-induced abdominal sepsis and to assess the function of CD44 in the activation of primary peritoneal macrophages by E. coli or lipopolysaccharide (LPS) purified from this bacterium by using wild-type (WT) and CD44 knockout (KO) mice. CD44 KO mice already demonstrated enhanced CXC chemokine levels in peritoneal lavage fluid at 6 h after infection, whereas tumor necrosis factor alpha (TNF-alpha) and interleukin-6 levels were elevated at 20 h postinfection. In line with this, CD44 KO mouse peritoneal macrophages released more TNF-alpha and macrophage inflammatory protein 2 (MIP-2) than did WT cells upon stimulation with E. coli or LPS in the presence of autologous serum. In contrast, plasma TNF-alpha levels were lower in CD44 KO mice and CD44 KO blood leukocytes secreted similar amounts of TNF-alpha and MIP-2 upon ex vivo incubation with E. coli or LPS. The proinflammatory phenotype of CD44 KO macrophages was not associated with an altered expression of inhibitors of Toll-like receptor signaling, whereas it could be partially reversed by addition of WT serum. CD44 deficiency did not impact on leukocyte recruitment into the peritoneal cavity or organ failure. These data suggest that CD44 differentially influences cytokine and chemokine release by different leukocyte subsets.
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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 | 0.001 | 0.001 |
| 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".