Modulation of the allergic asthma transcriptome following resiquimod treatment
Bibliographic record
Abstract
Resiquimod is a compound belonging to the imidazoquinoline family of compounds known to signal through Toll-like receptor 7. Resiquimod treatment has been demonstrated to inhibit the development of allergen induced asthma in experimental models. The aim of the present study was to elucidate the molecular processes that were altered following resiquimod treatment and allergen challenge in a mouse model of allergic asthma. Employing microarray analysis, we have characterized the "asthmatic" transcriptome of the lungs of A/J and C57BL/6 mice and determined that it includes genes involved in the control of cell cycle progression, the complement and coagulation cascades, and chemokine signaling. Our results demonstrated that resiquimod treatment resulted in the normalization of the expression of genes involved with airway remodeling, and generally, chemokine signaling. Resiquimod treatment also altered the expression of cell adhesion molecules, and molecules involved in natural killer (NK) cell-mediated cytotoxicity. Furthermore, we have demonstrated that systemic resiquimod administration resulted in the recruitment of NK cells to the lungs and livers of the mice, although no causal relationship between NK cell recruitment and treatment efficacy was found. Overall, our findings identified several genes, important in the development of asthma pathology, that were normalized following resiquimod treatment, thus improving our understanding of the molecular consequences of resiquimod treatment in the lung milieu. The recruitment of NK cells to the lungs may also have application in the treatment of virally induced asthma exacerbations.
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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".