Assessing tissue mechanical properties: Development of a custom-made tensile device and application on rodents sciatic nerves
Bibliographic record
Abstract
The development of biomaterials such as synthetic scaffolds for peripheral nerve regeneration requires a precise knowledge of the mechanical properties of the nerve in physiological-like conditions. Mechanical properties (Young's modulus, maximum stress and strain at break) for peripheral nerves are scarce and large discrepancies are observed in between reports. This is due in part to the absence of a robust testing device for nerves. To overcome this limitation, a custom-made tensile device (CMTD) has been built. To evaluate its reproducibility and accuracy, the imposed speed and distance over measured speed and distance was performed, followed by a validation using poly(dimethylsiloxane) (PDMS), a commercial polymer with established mechanical properties. Finally, the mechanical characterization of rodents (mice and rats) sciatic nerves using the CMTD was performed. Mouse and rat sciatic nerves Young's modulus were 4.57 ± 2.04 and 19.2 ± 0.86 MPa respectively. Maximum stress was 1.26 ± 0.56 MPa for mice and 3.81 ± 1.84 MPa for rats. Strain at break was 53 ± 17% for mice and 32 ± 12% for rats. The number of axons per sciatic nerve was found to be twice higher for rats. Statistical analysis of the measured mechanical properties revealed no sex-related trends, for both mice and rats (except for mouse maximum stress with p=0.03). Histological evaluation of rat sciatic nerve corroborated these findings. By developing a robust CMTD to establish the key mechanical properties (Young's modulus, maximum stress and strain at break) values for rodents sciatic nerves, our work represent an essential step toward the development of better synthetic scaffolds for peripheral nerve regeneration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".