Innate Immune Cytokines, Fibroblast Phenotypes, and Regulation of Extracellular Matrix in Lung
Bibliographic record
Abstract
Chronic inflammation can be caused by adaptive immune responses in autoimmune and allergic conditions, driven by a T lymphocyte subset balance (TH1, TH2, Th17, Th22, and/or Treg) and skewed cellular profiles in an antigen-specific manner. However, several chronic inflammatory diseases have no clearly defined adaptive immune mechanisms that drive chronicity. These conditions include those that affect the lung such as nonatopic asthma or idiopathic pulmonary fibrosis comprising significant health problems. The remodeling of extracellular matrix (ECM) causes organ dysfunction, and it is largely generated by fibroblasts as the major cell controlling net ECM. As such, these are potential targets of treatment approaches in the context of ECM pathology. Fibroblast phenotypes contribute to ECM and inflammatory cell accumulation, and they are integrated into chronic disease mechanisms including cancer. Evidence suggests that innate cytokine responses may be critical in nonallergic/nonautoimmune disease, and they enable environmental agent exposure mechanisms that are independent of adaptive immunity. Innate immune cytokines derived from macrophage subsets (M1/M2) and innate lymphoid cell (ILC) subsets can directly regulate fibroblast function. We also suggest that STAT3-activating gp130 cytokines can sensitize fibroblasts to the innate cytokine milieu to drive phenotypes and exacerbate existing adaptive responses. Here, we review evidence exploring innate cytokine regulation of fibroblast behavior.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".