Insulin-Like Growth Factor Binding Protein (IGFBP-6) as a Novel Regulator of Inflammatory Response in Cystic Fibrosis Airway Cells
Bibliographic record
Abstract
infections have been linked to chronic inflammation in the CF lung, whose hallmarks are increased levels of cytokines (i.e., TNF-α, IL-1β, IL-6) and neutrophil attraction by chemokines, like IL-8. Recently, insulin-like growth factor binding protein 6 (IGFBP-6) has been shown to play a putative role in the immune system and was found at higher levels in the sera and synovial tissue of rheumatoid arthritis patients. Moreover, it has been demonstrated that IGFBP-6 has chemoattractant properties towards cells of the innate (neutrophils, monocytes) and adaptive (T cells) immunity. However, it is not known whether IGFBP-6 expression is dysregulated in airway epithelial cells under infection/inflammatory conditions. Therefore, we first measured the basal IGFBP-6 mRNA and protein levels in bronchial epithelial cells lines (Wt and F508del-CFTR CFBE), finding they both are upregulated in F508del-CFTR CFBE cells. Interestingly, LPS and IL-1β+TNFα treatments increased the IGFBP-6 mRNA level, that was reduced after treatment with an anti-inflammatory (Dimethyl Fumarate) in CFBE cell line and in patient-derived nasal epithelial cultures. Lastly, we demonstrated that IGFBP-6 reduced the level of pro-inflammatory cytokines in both CFBE and primary nasal epithelial cells, without affecting rescued CFTR expression and function. The addition of a neutralizing antibody to IGFBP-6 increased pro-inflammatory cytokines expression under challenge with LPS. Together, these data suggest that IGFBP-6 may play a direct role in the CF-associated inflammation.
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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 | 0.001 |
| 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) | 0.001 | 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".