Atomic scale chemical tomography of human bone
Bibliographic record
Abstract
Human bone is a complex hierarchical material. Understanding bone structure and its corresponding composition at the nanometer scale is critical for elucidating mechanisms of biomineralization under healthy and pathological states. However, the three-dimensional structure and chemical nature of bone remains largely unexplored at the nanometer scale due to the challenges associated with characterizing both the structural and chemical integrity of bone simultaneously. Here, we use correlative transmission electron microscopy and atom probe tomography for the first time, to our knowledge, to reveal structures in human bone at the atomic level. This approach provides an overlaying chemical map of the organic and inorganic constituents of bone on its structure. This first use of atom probe tomography on human bone reveals local gradients, trace element detection of Mg, and the co-localization of Na with the inorganic-organic interface of bone mineral and collagen fibrils, suggesting the important role of Na-rich organics in the structural connection between mineral and collagen. Our findings provide the first insights into the hierarchical organization and chemical heterogeneity in human bone in three-dimensions at its smallest length scale - the atomic level. We demonstrate that atom probe tomography shows potential for new insights in biomineralization research on bone.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".