Bidirectional control of the inner dynamics of focal adhesions promotes cell migration
Bibliographic record
Abstract
Focal adhesions (FA) are bidirectional mechanical biosensors that allow cells to integrate intracellular and extracellular cues. Their function is tightly regulated by changes in molecular composition and also by variation in the spatio-temporal dynamics of FA components within this structure. A closely regulated turnover of FA proteins within FA sites allows cells to respond appropriately to their environment, thereby impacting on cell shape and function. FA protein dynamics are linked to FA maturation and rates of assembly and disassembly, and have a significant influence on tumor cell migration. Using the FRAP technique to investigate the hidden internal dynamics of FA, we identified two new regulators of FA dynamics and cell migration: the Mgat5/galectin-3 lattice and tyrosine phosphorylated caveolin-1 (pY14Cav1). In this short review we first introduce FA and their complex dynamic behavior. We then present the Mgat5/galectin-3 lattice and caveolin-1 and discuss their concerted role in FA dynamics, which defines previously unknown, interdependent roles in tumor cell migration. We conclude with a discussion of interesting unexplored avenues that might lead to a better understanding of the complex mechanism of FA dynamics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".