Preparation of Rat Tail Tendons for Biomechanical and Mechanobiological Studies
Bibliographic record
Abstract
Rat tail tendons (RTTs) are a common biological model used in experimental in vitro studies in the fields of tendon physiology and tendinopathy. Working with those tissues is challenging because they are very fragile, and until now there was no rigorously detailed protocol for their isolation. Faced with these challenges, we have developed methods and instruments to facilitate manipulation of RTTs and control tissue viability, sterility and integrity. This article describes the experimental procedures used to prepare RTTs for biomechanical and mechanobiological studies. Our work is divided into four main steps: extraction, cross-sectional area measurement, rinsing and loading into the bioreactor chamber. At each step, all procedures, materials and manipulations are presented in detail so that they can be easily reproduced. Moreover, the specific instruments developed are presented: a manipulation plate used to segregate RTTs, an optic micrometer to position the tissue during the cross-sectional area measurement and an anchoring system to attach the RTTs onto a bioreactor. Finally, we describe the results obtained after multiple tests to validate our methods. The viability, sterility and integrity evaluations demonstrate that our procedures are sufficiently rigorous for manipulations of fragile tissues such as rat tail tendons.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".