Positive feedback loops as potential contributors to Interleukin-17A-driven synergistic potentiation of inflammatory response and corticosteroid insensitivity
Bibliographic record
Abstract
Exacerbations of respiratory diseases are linked to overproduction of Interleukin (IL) 17 family members (e.g., IL-17A, IL-17C). These cytokines exert their own inflammatory effects, amplify pre-existing airway inflammation, and may contribute to corticosteroid insensitivity. Effective therapies are not available yet. Objective To study the effects of IL-17A, alone or in combination with other stimuli or corticosteroids, on bronchial epithelial cells. Methods Exposure to IL-17A (1 – 24 hrs), with or without Tumor Necrosis Factor (TNF)-α (both: 10–100 ng/mL), with or without corticosteroid budesonide (1–1000 nM), was studied in BEAS-2B cells, submerged and differentiated primary human bronchial epithelial cells, and differentiated murine tracheal epithelial cells by singleplex / multiplex ELISAs (cytokines), qPCR (mRNA), Western blot and reporter gene assay (respectively, transcription factor translocation and transactivation). Results IL-17A is a mild stimulus (<10-fold upregulation of IL-8). In co-stimulation with TNF-α, IL-17A synergistically and potently upregulates production of IL-8, IL-6, and Granulocyte Colony-Stimulating Factor, and Nuclear Factor-κB transactivation. Synergistically upregulated IL-8 is suppressed less efficiently by budesonide. The co-stimulation does not inhibit nuclear translocation of Glucocorticoid Receptor or expression of the corticosteroid-induced gene GILZ, but upregulates expression of IL17C, suggesting a positive feedback loop. Conclusion Positive feedback loops may contribute to IL-17A driven synergistic potentiation of inflammation and corticosteroid insensitivity. LL and ND: co-senior authors
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".