Role of IFN-γ and IL-2 in rat lung epithelial cell migration and apoptosis after oxidant injury
Bibliographic record
Abstract
In the present study, IFN-gamma exposure to primary cultures of rat type II epithelial cells (TIIP) upregulated membrane expression of the common gamma-chain of the IL-2 receptor (approximately 2.5- to 4-fold increase) and redistributed receptor affinity in TIIP, as assessed by Western blot, cell, and tissue histochemistry and Scatchard analysis. As for restitution processes of the lung epithelium, functionality of IL-2R on TIIP was conditional to IFN-gamma exposure: 1) IFN-gamma priming promoted a fivefold increase of IL-2-driven TIIP locomotion (P < 0.05 vs. control at 100 U/ml) and 2) IFN-gamma coincubation with IL-2 reduced bleomycin-induced TIIP apoptosis in vitro by 25% (caspase-3 activity) and by approximately 70% (TdT-mediated dUTP nick end labeling/4',6'-diamidino-2-phenylindole assay) as well as in vivo by approximately 90% (caspase-3 activity; P < 0.05 vs. control). Sustained p42/44 extracellular signal-regulated kinase activity played a protective role in this process, whereas specific inhibition by PD-98059 (50 microM) significantly reversed bleomycin-induced TIIP apoptosis (P < 0.05 vs. control). From these in vitro and in vivo data, it is proposed that combinations of IFN-gamma and IL-2 can drive repair activity of TIIP by stimulating migration and preventing programmed cell death, both of which are speculated to be very fast restitution events after oxidant-induced acute lung injury.
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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) | 0.001 | 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) | 0.001 | 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".