Abstracts from the Conjoint Annual Meeting of L’Association des Pneumologues de la Province de Québec and le Réseau en santé Respiratoire du FRSQ
Bibliographic record
Abstract
Background: In asthma, CD4 + T cells are selectively recruited into the bronchial mucosa.CD4 + T cells consist of different subsets that express lineage-specific transcription factors and play different roles either in initiating and supporting the development of immune response, but also in orchestrating and regulating them.oBjectives: The aim of our study was to evaluate the effect of T cellsbronchial fibroblasts interaction on CD4 + T cell phenotype.Methods: Human bronchial fibroblasts were isolated from mild steroid naïve asthmatics and nonatopic healthy controls.CD4 + T cells were purified from the peripheral blood of healthy and asthmatic subjects.Co-culture of confluent healthy (HF) or asthmatic bronchial fibroblasts (AF) with T cells were performed.CD4 + T cell total RNA was purified and GATA-3, Foxp3 and RORc expression was detected by quantitative PCR.Th17 (IL-17,IL-22) lineage-specific cytokines profile were also evaluated.results: Co-culture of T cells with bronchial fibroblasts significantly stimulated RORc in asthmatic T cells only, whereas Foxp3 and GATA-3 were not affected in both asthmatic and healthy T cells.IL-6 and IL-23 expression either by AF and HF were also significantly increased by the coculture when, TGF-β expression was not affected.In CD4 + T cells, IL-17 and IL-22, Th17 lineage-specific cytokines were significantly increased by the coculture with AF. conclusion: Interaction between bronchial fibroblasts and T cells seems to specifically promote Th17 cells profile in asthma.These results suggest that cellular interactions particularly between T cells and fibroblasts may play a pivotal role in the regulation of the inflammatory response in 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.287 | 0.050 |
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".