An eosinophil immune response characterizes the inflammatory skin disease observed in Tie-2 transgenic mice
Bibliographic record
Abstract
Although mouse models of inflammatory skin diseases such as psoriasis and atopic dermatitis fail to completely phenocopy disease in humans, they provide invaluable tools to examine the molecular and cellular mechanisms responsible for the epidermal hyperplasia, inflammation, and excess angiogenesis observed in human disease. We have previously characterized a tyrosine kinase with immunoglobin-like and epidermal growth factor-like domain-2 (Tie-2) transgenic mouse model of an inflammatory skin disease exhibiting these features. More specifically, we demonstrated that the inflammatory component consisted of increased infiltration of CD3-positive T lymphocytes and mast cells in the skin. Here, we further characterize the inflammatory component in the blood and skin of Tie-2 transgenic mice at cellular and molecular levels. We observed increased numbers of CD3-positive T lymphocytes in the blood and increased infiltration of eosinophils in the skin. Furthermore, we characterized cytokine protein and gene expression in the blood and skin, respectively, and observed the deregulated expression of cytokines associated with Th1 and eosinophil immune responses. Interestingly, treatment of Tie-2 transgenic mice with anti-CD4 antibody appeared to resolve aspects of inflammation but did not resolve epidermal hyperplasia, suggesting an important role for eosinophils in mediating the inflammatory skin disease observed in Tie-2 transgenic mice.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".