Autophagy in phenoconversion of differentiated and undifferentiated fibroblasts
Bibliographic record
Abstract
Cardiac fibroblasts phenoconvert to hypersecretory myofibroblasts and deposit matrix resulting in cardiac fibrosis and autophagy that is associated to fibroproliferative events. Autophagy inhibition was studied in both primary (P0) rat cardiac fibroblasts (PF) and mouse embryonic fibroblasts (MEF). Autophagy was inhibited in PF using Baf‐A1 or 3‐MA while MEF ATG3 and ATG5 KO were also used. Western blot analysis, Immunofluorescence, and transmission electron microscopy were used to investigate autophagy and fibroblast to myofibroblast phenoconversion. α‐SMA and ED‐A fibronectin were assessed as phenoconversion markers. Inhibition of autophagy was revealed by punctate LC3‐II staining and double membrane autophagosomes in combination with decreased myofibroblast markers in PF vs. controls. Inhibition of autolysosome formation and accumulation of autophagosomes were found in Baf‐A1 treated cells. Conversely, ATG3 and ATG5 KO MEF cells showed increased myofibroblast markers vs. WT controls. Increased α‐SMA was seen in MEF ATG3 and ATG5 KO vs. controls while inhibition of autophagy in PFs showed diminished myofibroblastic phenotype vs. controls. A link between fibroblast phenoconversion and autophagy is established. This effect is dependent on cell type eg, PFs vs. MEFs. This finding illustrates that autophagy functions in a variable manner in differentiated vs. non‐differentiated fibroblasts.
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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 | 0.000 |
| 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".