3D Characterization of Human Nano-osseointegration by On-Axis Electron Tomography without the Missing Wedge
Bibliographic record
Abstract
Three-dimensional (3D) visualization of bone-implant interfaces via electron tomography (ET) has contributed to the novel perspective of nano-osseointegration and offers evidential support for nanoscaled biomaterial surface modification. Conventional single-axis ET provides a relatively large field of view of the human bone to titanium implant interface showing bone structure arrangement near the interface. However, the "missing wedge" associated with conventional single-axis ET leads to artifacts and elongation in the reconstruction, limiting the resolution and fidelity of reconstructions, as well as the ability to extract quantitative information from nanostructured interfaces. On-axis ET, performed by 180° rotation of a needle-shaped sample, is a promising method to solve this problem. In this work, we present the first application of on-axis ET for investigation of human bone and laser-modified titanium implant interfaces without the missing wedge. This work demonstrates a near artifact-free 3D visualization of the nanotopographies of the implant surface oxide layer and bone growth into these features. Complementary electron energy-loss spectroscopy (EELS) mapping was used to illustrate the gradual intermixing of carbon and calcium (characteristic elements of bone) with the nanoscaled oxide layer of the implant surface. Ultimately, this approach serves as direct evidence of nano-osseointegration and as a potential platform to evaluate differently structured implant surfaces.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".