Deficiency of decorin induces expression of <scp>F</scp>oxp3 in <scp>CD</scp>4<sup>+</sup><scp>CD</scp>25<sup>+</sup><scp>T</scp> cells in a murine model of allergic asthma
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Decorin (Dcn), an extracellular matrix proteoglycan, has several important biological functions, and its deposition is altered in the airway wall of humans with asthma and animal models of asthma. Due to its high affinity for transforming growth factor beta (TGF)-β, Dcn can function as part of a negative feedback mechanism, resulting in the regulation of this factor's bioavailability. Dcn deficient (Dcn(-/-) ) mice develop reduced airway inflammation, hyperresponsiveness and remodeling in response to repeated allergen challenge; we investigated whether regulatory T cells play a role in the diminished airway response of Dcn(-/-) mice. METHODS: Dcn(-/-) and Dcn(+/+) mice (C57Bl/6) were sensitized with ovalbumin (OVA) and challenged intra-nasally 3 days/week × 3 weeks. After allergen challenge, bronchoalveolar lavage was collected to quantify total and differential cell counts and cytokine levels. Inflammatory cell number and cytokine messenger ribonucleic acid (mRNA) production were assessed in lung tissues. Cells from lung and spleen were extracted to evaluate regulatory T cells. RESULTS: Tissue inflammation and interleukin (IL)-13 mRNA expression were significantly increased in OVA-challenged Dcn(+/+) mice, only. The increased expression of Foxp3 in CD4(+) CD25(+) T cells found in lung of OVA-challenged Dcn(-/-) mice was accompanied by an increase in IL-10 mRNA. CONCLUSIONS: Our data demonstrated that a diminished lung inflammation in OVA challenged Dcn(-/-) mice was accompanied by a higher expression of regulatory T cells and IL-10 mRNA levels. These results reinforce the importance of Dcn in biological processes, particularly in an allergic model of asthma.
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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 | 0.001 |
| 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 | 0.001 |
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".