Mouse models to study the role of CD34 in allergy and inflammatory diseases
Bibliographic record
Abstract
The CD34 antigen is widely used as a stem cell marker. However, the exact role of CD34 expression is still misunderstood. Our lab is currently using mouse models of allergy and inflammation induced in CD34‐null or chimeric mice in order to elucidate the role of CD34 expression in inflammation. Asthma, hypersentitivity pneumonitis (HP), experimental animal encephalomyetis (EAE) or arthritis was induced in CD34‐null and wild type mice. These pathologies are characterized by activation of various CD34‐expressing cells. Disease progression and cell recruitment and function were compared between wild type and CD34‐deficient mice. In lung inflammation, CD34‐null mice are protected against development of disease and show various cell trafficking defects. In EAE, mast cells accumulate in the CNS of CD34‐null mice, possibly through an emigration defect. Finally, in arthritis, CD34‐null mice show higher susceptibility to disease due to lack of CD34 on vascular endothelia. These results suggest a role for CD34 in hematopoietic cell trafficking, either through CD34 expression on inflammatory cells or vascular endothelia. Also, CD34 expression on vascular endothelia is involved in progression of arthritis. The use of these mouse models in combination with CD34‐null mice has allowed us to better understand the role of CD34 expression in development of inflammation and allergy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".