Role of the TSLP–DC–OX40L pathway in asthma pathogenesis and airway inflammation in mice
Post-publication record
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Bibliographic record
Abstract
This study aimed to explore the effect of the TSLP–DC–OX40L pathway in asthma pathogenesis and airway inflammation in mice. For this, 65 male BALF/c mice were distributed among the control, asthma, immunoglobulin G (IgG) + asthma (IgG, 500 μg/500 μL, intratracheal injection of 50 μL each time), LY294002 (OX40L inhibitor) + asthma (intratracheal injection of 2 mg/kg LY294002), and anti-TSLP + asthma (intratracheal injection of 500 μg/500 μL TSLP antibody, 50 μL each time) groups. ELISA was applied to measure the serum levels of immunoglobulin E (IgE), ovalbumin (OVA)-sIgE, interleukin-4 (IL-4), IL-5, IL-13, and interferon-γ (IFN-γ); flow cytometry was employed to detect Treg cells and dendritic cell (DC) and lymphopoiesis. RT–qPCR and Western blot assays were used to measure the levels of TSLP, OX40L, T-bet, GATA-3, NF-κB, p38, and ERK. Treatment with LY294002 and anti-TSLP resulted in increases in the numbers of total cells, eosinophils, neutrophils, and lymphocytes in the bronchoalveolar lavage fluid; total serum levels of IgE, OVA-sIgE, IL-4, IL-5, and IL-13; levels of DC cells; lymphopoiesis; and levels of TSLP, OX40L, GATA-3, NF-κB, p38, and ERK, whereas there were decreases in the levels of IFN-γ and CD4 + CD25 + Treg cells; CD4 + Foxp3 + Treg cells; and T-bet. The TSLP–DC–OX40L pathway may contribute to asthma pathogenesis and airway inflammation by modulating the levels of CD4 + CD25 + Treg cells and inflammatory cytokines.
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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 | 0.002 |
| 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".