The Role of Mechanical Stress in Regulating Integrin Recruitment to Latent TGF-β1
Bibliographic record
Abstract
Fibrosis is characterized by excessive extracellular matrix (ECM) production and contraction of myofibroblasts that differentiate from fibroblasts under the action of pro-fibrotic cytokine TGF-β1 and mechanical stress. Myofibroblasts liberate active TGF-β1 by integrin-mediated pulling on the latency-associated protein pro-peptide (LAP) which is stored together with the latent TGFβ1-binding protein-1 (LTBP-1) in the ECM. I hypothesized that the binding affinity/strength of LAP binding integrins, in particular of integrin αvβ3, is regulated by the mechanical stress arising from the contractile fibroblast cytoskeleton and the stiff ECM. To test this hypothesis, different methods were employed to investigate mechanically-regulated integrin αvβ3 recruitment to LAP in the physiological context of fibroblasts. The main findings of my thesis work are that (a) fibroblasts use αvβ3 integrin to bind to LAP; (b) αvβ3 integrin is capable of transmitting considerable cell force to LAP; and (c) mechanical stress increases the recruitment of αvβ3 integrin to LAP.
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 | 0.000 | 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 | 0.001 | 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".