An Optimized Methyl Methacrylate Embedding Protocol for Undecalcified Bone Histology Applications in Skeletal Biology
Bibliographic record
Abstract
Methyl methacrylate (MMA) impregnation and embedding procedures have been extensively employed for the examination of bone tissue to visualize microstructural and cellular details for quantifiable histological analyses. Current detailed MMA embedding protocols may require expensive instruments or limit use with certain tissue preparations (e.g., fresh). These techniques often require excessive heat, various chemicals for fixation and dehydrating, long fixation times, or decalcification. The introduction of such variables may result in damage to fragile and invaluable bone samples. Our newly developed protocol introduces a time-efficient MMA embedding technique allowing for replicable results for bone sections as thin as 50-100 μm from samples from diverse conditions (e.g., fresh, embalmed, diagenetic) and various animals (e.g., human, cervids, swine, lagomorphs). The presented technique limits heat and chemical exposure, does not require decalcification, reduces the amount of bone required, and significantly decreases embedding time. Over 300 trials were performed to optimize the procedure to ensure replicability. Our embedding protocol is currently being employed for the histological preparation of bone specimens from a large-scale modern human skeletal collection, the Andronowski Skeletal Collection for Histological and Imaging Research. The embedding procedure presented here will further extend the long-term fixation and preservation of samples for microscopy and imaging applications beyond traditional epoxy resin and hardener mounting systems.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".