Specificity of CD200/CD200R pathway in LPS-induced lung inflammation
Bibliographic record
Abstract
Introduction: At lung mucosal surfaces, immune cells must initiate inflammatory response against pathogen without inducing tissue damage. Loss of this equilibrium can lead to acute respiratory distress syndrome (ARDS), a severe lung inflammatory disease characterized by excessive inflammation and dysregulation of anti-inflammatory pathways. Methods: To investigate the role of anti-inflammatory pathway CD200/CD200R in lung inflammatory response, we administered LPS intratracheally in CD200 KO and wild type (WT) rats. Inflammation was evaluated using bronchoalveolar lavage (BAL) cellularity. Lung injury was measured by total protein level in BAL fluid, and levels of proinflammatory cytokines (TNF, IL-6) and chemokines (CXCL2, CCL2) were determined in BAL supernatants. In a second series of experiments, recombinant CD200Fc was administered to KO rats to restore the anti-inflammatory response. Results: At baseline, CD200 KO rats did not show sign of inflammation, however KO rats had lower number of alveolar macrophages. In addition, LPS administration induced greater pulmonary edema in CD200 KO rats. This was accompanied with a higher recruitment of neutrophils as well as levels of TNF, IL-6, CXCL2, and CCL2 in BAL compared to WT rats. CD200Fc administration in KO rats reduced neutrophil accumulation and TNF and CXCL2 levels in BAL. Interestingly, the increased inflammatory response of CD200 KO rats could be attributed to greater activation potential of alveolar macrophages with higher levels of ERK and P-ERK MAPK. Conclusion: This study shows that lung inflammatory response is exacerbated in absence of CD200 in an experimental model of ARDS in rats. In addition, CD200/CD200R pathway shows selective regulation of acute lung inflammation and cannot completely abrogate the complex LPS-induced inflammatory response. However, addition of CD200 agonist in a multi-target therapy strategy could have beneficial impacts.
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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 | 0.000 |
| 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".