CD200 is required to control LPS-induced lung inflammation.
Bibliographic record
Abstract
Abstract INTRODUCTION Acute respiratory distress syndrome (ARDS) is a severe lung inflammatory disease caused by a variety of precipitants, including SARS-CoV-2 (COVID-19). In addition to excessive inflammation, ARDS is characterised by the dysregulation of anti-inflammatory pathways, including CD200/CD200R pathway. OBJECTIVE To investigate the role of CD200/CD200R pathway in lung ARDS inflammatory response. METHODS LPS was administered intratracheally to induce ARDS in Sprague-Dawley 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 experiment, recombinant CD200Fc was administered to KO rats to restore the anti-inflammatory response. RESULTS Although there was no difference in total BAL cell counts at 3 h, cell recruitment was greater in CD200 KO rats given the low cell number in naïve KO rats. BAL of KO rats had higher levels of TNF, IL-6, CXCL2, and CCL2 compared to WT rats. Total protein level in BAL was higher in CD200 KO rats, implying more pronounced pulmonary edema. CD200Fc administration in KO rats significantly decreased levels of TNF and CCL2 in BAL, suggesting an attenuation of the inflammatory response. CONCLUSION This study shows that ARDS inflammatory response is exacerbated in absence of CD200 in an experimental model of ARDS in rats and that CD200 supplementation alleviates this phenotype. Further analyses will be needed to better understand the contribution of different cell types expressing CD200 to control lung inflammatory response resulting from ARDS. Supported by grant by CIHR and IUCPQ fondation.
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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) | 0.004 | 0.002 |
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".