Regulation of hard α-keratin mechanics via control of intermediate filament hydration: matrix squeeze revisited
Bibliographic record
Abstract
Mammalian hard α-keratins are fibre-reinforced biomaterials that consist of 10 nm intermediate filaments (IFs) embedded in an elastomeric protein matrix. Recent work suggests that the mechanical properties of IFs are highly sensitive to hydration, whereas hard α-keratins such as wool, hair and nail are relatively hydration insensitive. This raises the question of how mammalian keratins remain stiff in water. The matrix squeeze hypothesis states that the IFs in hard α-keratins are stiffened during an air-drying step during keratinization, and subsequently locked into a dehydrated state via the oxidation and cross-linking of the keratin matrix around them. The result is that even when hard α-keratins are immersed in water, their constituent IFs remain essentially 'dry' and therefore stiff. This hypothesis makes several predictions about the effects of matrix abundance and function on hard α-keratin mechanics and swelling behaviour. Specifically, it predicts that high matrix keratins in water will swell less, and have a higher tensile modulus, a higher yield stress and a lower dry-to-wet modulus ratio. It also predicts that disruption of the keratin matrix in water should lead to additional swelling, and a drop in modulus and yield stress. Our results are consistent with these predictions and suggest that the keratin matrix plays a critical role in governing the mechanical properties of mammalian keratins via control of IF hydration.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".