New Mechanisms for Regulation of Intracellular Collagen Degradation in Fibrotic Lesions of Periodontal Tissues
Bibliographic record
Abstract
ABSTRACT Fibrosis is involved in the pathogenesis of 45% of deaths in developed countries. Thus, defining the signaling systems that promote fibrotic lesions is important for developing therapies that can preserve human health. Fibrosis affects several organs and is also commonly manifest in the gingival connective tissue of tooth‐supporting tissues, which exhibit very rapid turnover of collagen. Gingiva, therefore, provides an instructive model system for determining how the signals that regulate the loss of the balance of collagen synthesis and degradation are dysregulated in gingival tissue enlargement, a common fibrotic lesion that is associated with considerable morbidity. Notably, these fibrotic lesions affect a high proportion of patients who are treated with drugs that affect Ca 2+ signaling and trafficking of collagen through vacuolar compartments. As Ca 2+ signaling is functionally related to the control of vacuolar trafficking by the small G‐proteins known as Rabs, here we discuss dysregulation of collagen degradation in the gingiva with a particular focus on signaling through Rabs and on the role of vacuolar ATPases in lysosomal acidification to optimize collagen degradation. We consider how exploration of Rabs‐mediated regulation of the intracellular degradation pathway may inform new approaches for the clinical management of fibrotic lesions.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".