Transglutaminases in fibrosis—overview and recent advances
Bibliographic record
Abstract
Transglutaminases (TGs) are a family of protein cross-linking enzymes that are capable of stiffening and insolubilizing proteins and creating protein networks, and thereby altering biological functions of proteins. Their role in fibrosis progression has been widely investigated with a focus on kidney, lung, liver, and heart where activity is triggered by various stimuli including hypoxia, inflammation, and hyperglycemia. TG2 has been considered one of the key enzymes in the pathogenesis of fibrosis mainly through transforming growth factor beta (TGF-beta) signaling and matrix cross-linking mechanisms. Although TG2 has been most widely studied in this context, the involvement of other TGs, TG1 and Factor XIII-A (FXIII-A), is beginning to emerge. This mini-review highlights the major steps taken in the TG and fibrosis research and summarizes the most recent advances and contributions of TG2, TG1, and FXIII-A to the progression of fibrosis in various animal models. Also, their mechanisms of action as well as therapeutic prospects are discussed.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".