Mesoscale characterization of osseointegration around an additively manufactured genistein-coated implant
Bibliographic record
Abstract
Given the hierarchical nature of bone and bone interfaces, osseointegration, namely the formation of a direct bone-implant contact, is best evaluated using a multiscale approach. However, a trade-off exists between field of view and spatial resolution, making it challenging to image large volumes with high resolution. In this study, we combine established electron microscopy techniques to probe bone-implant interfaces at the microscale and nanoscale with plasma focused ion beam-scanning electron microscopy (PFIB-SEM) tomography to evaluate osseointegration at the mesoscale. This characterization workflow is demonstrated for bone response to an additively manufactured Ti-6Al-4V implant which combines engineered porosity to facilitate bone ingrowth and surface functionalization via genistein, a phytoestrogen, to counteract bone loss in osteoporosis. SEM demonstrated new bone formation at the implant site, including in the internal implant pores. At the nanoscale, scanning transmission electron microscopy and energy-dispersive X-ray spectroscopy confirmed the gradual nature of the bone-implant interface. By leveraging mesoscale analysis with PFIB-SEM tomography that captures large volumes of bone-implant interface with nearly nanoscale resolution, the presence of mineral ellipsoids varying in size and orientation was revealed. In addition, a well-developed lacuno-canalicular network and mineralization fronts directed both towards the implant and away from it were highlighted.
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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".