Contribution of the glycosyltransferase FucT-VII to allergic skin inflammation
Bibliographic record
Abstract
Abstract The development of E and P selectin ligands on the surface of immune cells requires α1,3 fucosyltransferase FucT-VII, an enzyme encoded by the Fut7 gene. T cell activation and cytokine environments regulate glycosyltransferase expression, eventual glycan formation and cell migration. The T helper 2 (Th2) subset are mediators of allergic inflammation, but the requirement for FucT-VII-dependent migration in allergic inflammation is less clear. In this study we determined if Fut7 expression was required for allergic inflammation in the constitutively active STAT6VT model in which mice are predisposed to a spontaneous inflammatory phenotype similar to that of atopic dermatitis. STAT6VT mice were crossed with Fut7−/− animals and compared for the development of disease and inflammation. Decreased incidence of disease was observed in Fut7−/− STAT6VT mice, compared to STAT6VT mice. Interestingly, if disease did occur in Fut7−/− animals, the inflammation was not attenuated and in some cases was more severe. One factor that impacted disease development was related to offspring who inherited the STAT6VT allele from their maternal parent. The dams would have experienced inflammation throughout pregnancy and might have promoted a mechanism of migration independent of Fut7 gene expression. Together, these data indicate that Fut7 promotes, but is not strictly required for, allergic skin inflammation.
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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) | 0.001 | 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".