The inflammatory speech of fibroblasts
Bibliographic record
Abstract
Activation of fibroblasts is a key event during normal tissue repair after injury and the dysregulated repair processes that result in organ fibrosis. To most researchers, fibroblasts are rather unremarkable spindle-shaped cells embedded in the fibrous collagen matrix of connective tissues and/or deemed useful to perform mechanistic studies with adherent cells in culture. For more than a century, fibroblasts escaped thorough classification due to the lack of specific markers and were treated as the leftovers after all other cells have been identified from a tissue sample. With novel cell lineage tracing and single cell transcriptomics tools, bona fide fibroblasts emerge as only one heterogeneous sub-population of a much larger group of partly overlapping cell types, including mesenchymal stromal cells, fibro-adipogenic progenitor cells, pericytes, and/or perivascular cells. All these cells are activated to contribute to tissue repair after injury and/or chronic inflammation. "Activation" can entail various functions, such as enhanced proliferation, migration, instruction of inflammatory cells, secretion of extracellular matrix proteins and organizing enzymes, and acquisition of a contractile myofibroblast phenotype. We provide our view on the fibroblastic cell types and activation states playing a role during physiological and pathological repair and their crosstalk with inflammatory macrophages. Inflammation and fibrosis of the articular synovium during rheumatoid arthritis and osteoarthritis are used as specific examples to discuss inflammatory fibroblast phenotypes. Ultimately, delineating the precursors and functional roles of activated fibroblastic cells will contribute to better and more specific intervention strategies to treat fibroproliferative and fibrocontractive disorders.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".