IFN- <b>β</b> signalling regulates RAW 264.7 macrophage activation, cytokine production, and killing activity
Bibliographic record
Abstract
Type I IFN holds a critical role in host defence, providing protection against pathogenic organisms through coordinating a pro-inflammatory response. Type I IFN provides additional protection through mitigating this inflammatory response, preventing immunopathology. Within the context of viral infections, type I IFN signalling commonly results in successful viral clearance. Conversely, during bacterial infections, the role of type I IFN is less predictable, leading to either detrimental or beneficial outcomes. The factors responsible for the variability in the role of type I IFN remain unclear. Here, we aimed to elucidate differences in the effect of type I IFN signalling on macrophage functioning in the context of TLR activation. Using RAW 264.7 macrophages, we observed the influence of type I IFN to be dependent on the type of TLR ligand, length of TLR exposure and the timing of IFN-β signalling. However, in all conditions, IFN-β increased the production of the anti-inflammatory cytokine IL-10. Examination of RAW 264.7 macrophage function showed type I IFN to induce an activated phenotype by up-regulating MHC II expression and enhancing killing activity. Our results support a context-dependent role for type I IFN in regulating RAW 264.7 macrophage activity.
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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) | 0.001 | 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".