Bowstring Stretching and Quantitative Imaging of Single Collagen Fibrils via Atomic Force Microscopy
Bibliographic record
Abstract
Collagen is the primary structural protein in animals. Serving as nanoscale biological ropes, collagen fibrils are responsible for providing strength to a variety of connective tissues such as tendon, skin, and bone. Understanding structure-function relationships in collagenous tissues requires the ability to conduct a variety of mechanical experiments on single collagen fibrils. Though significant advances have been made, certain tests are not possible using the techniques currently available. In this report we present a new atomic force microscopy (AFM) based method for tensile manipulation and subsequent nanoscale structural assessment of single collagen fibrils. While the method documented here cannot currently capture force data during loading, it offers the great advantage of allowing structural assessment after subrupture loading. To demonstrate the utility of this technique, we describe the results of 23 tensile experiments in which collagen fibrils were loaded to varying levels of strain and subsequently imaged in both the hydrated and dehydrated states. We show that following a dehydration-rehydration cycle (necessary for sample preparation), fibrils experience an increase in height and decrease in radial modulus in response to one loading-unloading cycle to strain <5%. This change is not altered by a second cycle to strain >5%. In fibril segments that ruptured during their second loading cycle, we show that the fibril structure is affected away from the rupture site in the form of discrete permanent deformations. By comparing the severity of select damage sites in both hydrated and dehydrated conditions, we demonstrate that dehydration masks damage features, leading to an underestimate of the degree of structural disruption. Overall, the method shows promise as a powerful tool for the investigation of structure-function relationships in nanoscale fibrous materials.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".