Human bone mesoscale 3D structure revisited by plasma focused ion beam serial sectioning
Bibliographic record
Abstract
Abstract Visualizing bone mineralization and collagen microfibril organization at intermediate scales between the nanometer and the 100s of microns range, the mesoscale, is still an important challenge. Similarly, visualizing cellular components which locally affect the tissue structure requires a precision of a few tens of nanometers at maximum while spanning several tens of micrometers. To address this issue, we employed a plasma focused ion beam (PFIB) equipped with a scanning electron microscope (SEM) to sequentially section nanometer-scale layers of demineralized and mineralized human femoral lamellar bone over volumes of approximately 46 × 40 × 9 μm 3 , and 29 × 26 × 9 μm 3 , respectively. This large scale view retained high enough resolution to visualize the collagen microfibrils while partly visualizing the lacuno-canalicular network (LCN) in three-dimensions (3D). We showed that serial sectioning can be performed on mineralized sections, and does not require demineralization. Moreover, this method revealed ellipsoidal mineral clusters, noted by others in high resolution studies, as a ubiquitous motif in lamellar bone over tens of microns, suggesting a heterogeneous and yet regular pattern of mineral deposition past the single collagen fibril level. These findings are strong evidence for the need to revisit bone mineralization over multi-length scales. Graphical Abstract
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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 | 0.001 | 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) | 0.004 | 0.001 |
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".