Does tissue fixation change the mechanical properties of dry ovine bone extracellular matrix?
Bibliographic record
Abstract
Tissue fixation is a prevalent method for bone conservation. Bone biopsies are typically fixed in formalin, dehydrated in ethanol, and infiltrated with polymethyl methacrylate (PMMA) Since some experiments can only be performed on fixed bone samples, it is essential to understand how fixation affects the measured material properties. The aim of this study was to quantify the influence of tissue fixation on the mechanical properties of cortical ovine bone at the extracellular matrix (ECM) level with state-of-the-art micromechanical techniques. A small section from the middle of the diaphysis of two ovine tibias (3.5 and 5.5 years old) was cut in the middle and polished on each side, resulting in a pair of mirrored surfaces. For each pair, one specimen underwent a fixation protocol involving immersion in formalin, dehydration with ethanol, and infiltration with PMMA. The other specimen (mirrored) was air-dried. Six osteons were selected in both pairs, which could be identified in both specimens. The influence of fixation on the mechanical properties was first analyzed using micropillar compression tests and nanoindentation in dry condition. Additionally, changes in the degree of mineralization were evaluated with Raman spectroscopy in both fixed and native bone ECM. Finally, micro tensile experiments were conducted in the 3.5-year fixed ovine bone ECM and compared to reported properties of unfixed dry ovine bone ECM. Interestingly, we found that tissue fixation does not alter the mechanical properties of ovine cortical bone ECM compared to experiments in dry state. However, animal age increases the degree of mineralization (p = 0.0159) and compressive yield stress (p = 0.041). Tissue fixation appears therefore as a valid conservation technique for investigating the mechanical properties of dehydrated bone ECM.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".