Visualization of Collagen–Mineral Arrangement Using Atom Probe Tomography
Bibliographic record
Abstract
Bone is a functional material comprised of mainly two phases: an organic collagenous phase and an inorganic mineral phase. Collagen-mineral arrangement has implications for bone function, aging, and disease. However, theories on collagen-mineral arrangement have been confined to studies with low spatial and/or compositional resolution resulting in an extensive debate over the location of mineral with respect to collagen. Herein, a strategy is developed to extract a single mineralized collagen fibril from bone and analyze its composition and structure atom-by-atom with 3D sub-nanometer accuracy and compositional clarity using atom probe tomography (APT). It is shown for the first time a method to probe fibril-level mineralization and collagen-mineral arrangement from an in vivo system with both the spatial and compositional precision required to comment on nanoscale collagen-mineral arrangement. APT of leporine bone shows distinct and helical collagen fibrils with mineralized deposits both encapsulating and incorporated into the collagenous structures. This study demonstrates a novel fibril-level detection method that can be used to probe the composition of bone and contribute new insights to the structure and organization of mineralized materials such as bones and teeth.
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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 | 0.000 | 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) | 0.002 | 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".