How Plasma Membrane and Cytoskeletal Dynamics Influence Single-Cell Wound Healing: Mechanotransduction, Tension and Tensegrity
Bibliographic record
Abstract
Organisms are able to recover from injuries by replacing damaged tissues, which recover by replacing damaged cells and extracellular structures. Similarly, a cell recovers from injuries by replacing damaged components of its structural integrity: its plasma membrane and cytoskeletal structures. Cells can be thought of as tensegral structures, their structural integrity relying on the interplay between tensile forces generated within and without the cell, and the compressive elements that counteracts them. As such, direct or indirect insults to the plasma membrane or cytoskeleton of a cell may not only result in the temporary loss of structural integrity, but also directly impact its ability to respond to its environment. This chapter will focus on the various aspects linking tensile forces and single-cell wound healing: where and how are they generated, how does the cell counteract them and how does the cell return to its previous tensegrity state? These questions will be explored using ubiquitous and cell-type specific examples of single-cell repair processes. Special attention will be given to changes in plasma membrane composition and area to cytoskeletal dynamics, and how these factor each other to influence and effect single-cell repair.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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".