How do your contacts (or their absence) shape your fate?
Bibliographic record
Abstract
Tissue accumulation of contractile myofibroblasts is a key feature of a multitude of fibrotic diseases. Myofibroblast generation either from epithelial or mesenchymal precursors involves the activation of a myogenic program, hallmarked by the expression of α-smooth muscle actin (SMA). Recent research suggests that this robust phenotypic reprogramming requires two critical inputs: the fibrogenic cytokine transforming growth factor-β1 (TGFβ) and an injury (or absence) of intercellular junctions. This two-hit paradigm of epithelial-myofibroblast transition (EMyT) postulates that the injured (contact-deprived) epithelium is locally and selectively sensitive (topically susceptible) to the transforming effect of TGFβ, while the intact areas are quite resistant to the phenotype-changing effect of this cytokine. Searching for molecular mechanisms underlying the synergy between contact injury and TGFβ, we found that an interplay among three multifunctional transcriptional (co)activators, the junction component β-catenin, the TGFβ receptor target Smad3, and the actin cytoskeleton-regulated myocardin-related transcription factor (MRTF) controls the magnitude and timing of SMA expression.(1) Moreover, this regulation is realized not only at the transcriptional level. Notably, these factors form a pretranscriptional circuit, in which they impact each other's activity and stability. Based on this recent paper we ponder about the mechanisms of cellular plasticity in the context of EMyT. We propose that topical susceptibility to TGFβ, triggered by cell contact-modulated pretranscriptional and transcriptional control is realized through the crosstalk of a few master regulators, whose coordinated action tailors SMA expression and contributes to the major decision of whether injury leads to healing or fibrosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".