Ultrastructural viscoelastic behavior of fibrillar collagen identified by AFM Nano-Rheometry and direct indentation
Bibliographic record
Abstract
Abstract Soft tissues exhibit predominantly time-dependent mechanical behavior critical for their biological function in organs like the lungs and aorta, as they can deform and stretch at varying rates depending on their function. Collagen type I serves as the primary structural component in these tissues. The viscoelastic characteristics of such tissues, stemming from diverse energy dissipation mechanisms across various length scales, remains poorly characterized at the nanoscale. Prior experimental investigations have predominantly centered on analyzing tissue responses largely attributed to interactions between cells and fibers. Despite many studies on tissue viscoelasticity from scaffolds to single collagen fibrils, the time-dependent mechanics of collagen fibrils at the sub-fibrillar level remain poorly understood. This pioneering study employs atomic force microscopy (AFM) nano-rheometry and indentation testing to examine the viscoelastic characteristics of individual collagen type I fibrils at the ultrastructural level within distinct topographical zones, specifically focusing on gap and overlap regions. Our investigation has unveiled that collagen fibrils display a viscoelastic response that replicates the mechanical behavior of the tissue at the macroscale. Further, our findings suggest a distinct viscoelastic behavior between the gap and overlap regions, likely stemming from variances in molecular organization and cross-linking modalities within these specific sites. The results of our investigation provide unequivocal proof of the temporal dependence of mechanical properties and provides unique data to be compared to atomistic models, laying a foundation for refining the precision of macroscale models that strive to capture tissue viscoelasticity across varying length scales.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".