Interleukin-4– and Interleukin-13–Enhanced Transforming Growth Factor- β 2 Production in Cultured Human Bronchial Epithelial Cells Is Attenuated by Interferon- γ
Bibliographic record
Abstract
Cytokines derived from lymphocytes are believed to play key roles in a variety of diseases, including airway diseases such as asthma. The current study was designed to evaluate the hypothesis that cytokines derived from Th2 cells, interleukin (IL)-4 and IL-13, might contribute to tissue remodeling by modulating the production of transforming growth factor (TGF)-beta. In addition, the ability of interferon (IFN)-gamma, a cytokine derived from Th1 cells that can antagonize many effects of IL-4 and IL-13, was also assessed for its effects on TGF-beta production. IL-4 and IL-13 both stimulated production of TGF-beta2 release from human bronchial epithelial cells in a time- and concentration-dependent manner. Both with and without acidification, TGF-beta2 were detected. Neither TGF-beta1 nor TGF-beta3 was released. In contrast to the stimulatory effect on human bronchial epithelial cells, neither IL-4 nor IL-13 stimulated release of any TGF-beta isoform from human lung fibroblasts. IFN-gamma reduced both basal, IL-4-, and IL-13-stimulated release of TGF-beta2 in human bronchial epithelial cells. The stimulatory effects of IL-4 and IL-13 and the inhibitory effect of IFN-gamma on TGF-beta2 release were paralleled by mRNA levels, as assessed by real-time reverse transcriptase-polymerase chain reaction (RT-PCR). In summary, the Th2-derived cytokines, IL-4 and IL-13, can stimulate production of TGF-beta from airway epithelial cells but not from lung fibroblasts. IFN-gamma, in contrast, can inhibit TGF-beta2 release both under basal conditions and following IL-4 or IL-13 stimulation. The ability of these cytokines to modulate TGF-beta release may contribute to both normal airway repair and to the development of subepithelial fibrosis 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 | 0.000 | 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 | 0.001 |
| 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".