CD8 positive T cells express IL-17 in patients with chronic obstructive pulmonary disease
Bibliographic record
Abstract
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a progressive and irreversible chronic inflammatory disease of the lung. The nature of the immune reaction in COPD raises the possibility that IL-17 and related cytokines may contribute to this disorder. This study analyzed the expression of IL-17A and IL-17F as well as the phenotype of cells producing them in bronchial biopsies from COPD patients. METHODS: Bronchoscopic biopsies of the airway were obtained from 16 COPD subjects (GOLD stage 1-4) and 15 control subjects. Paraffin sections were used for the investigation of IL-17A and IL-17F expression in the airways by immunohistochemistry, and frozen sections were used for the immunofluorescence double staining of IL-17A or IL-17F paired with CD4 or CD8. In order to confirm the expression of IL-17A and IL-17F at the mRNA level, a quantitative RT-PCR was performed on the total mRNA extracted from entire section or CD8 positive cells selected by laser capture microdissection. RESULTS: IL-17F immunoreactivity was significantly higher in the bronchial biopsies of COPD patients compared to control subjects (P < 0.0001). In the submucosa, the absolute number of both IL-17A and IL-17F positive cells was higher in COPD patients (P < 0.0001). After adjusting for the total number of cells in the submucosa, we still found that more cells were positive for both IL-17A (P < 0.0001) and IL-17F (P < 0.0001) in COPD patients compared to controls. The mRNA expression of IL-17A and IL-17F in airways of COPD patients was confirmed by RT-PCR. The expression of IL-17A and IL-17F was co-localized with not only CD4 but also CD8, which was further confirmed by RT-PCR on laser capture microdissection selected CD8 positive cells. CONCLUSION: These findings support the notion that Th17 cytokines could play important roles in the pathogenesis of COPD, raising the possibility of using this mechanism as the basis for novel therapeutic approaches.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".