<i>In vitro</i> biocompatibility of titanium alloy discs made using direct metal fabrication
Bibliographic record
Abstract
Custom orthopedic implants may be generated using free-form fabrication methods (FFF) such as electron beam melting (EBM). EBM FFF may be used to make solid metal implants whose surface is often polished using CNC machining and porous scaffolds that are usually left unpolished. We assessed the in vitro biocompatibility of EBM titanium-6 aluminum-4 vanadium (Ti6Al4V) structures by comparing the cellular response to solid polished, solid unpolished, and porous EBM discs to the cellular response to discs made of commercially produced Ti6Al4V. The discs were seeded with 20,000 human adipose-derived adult stem cells (hASCs) and assessed for cell viability, proliferation, and release of the proinflammatory cytokines interleukin-6 (IL-6) and interleukin-8 (IL-8). Cell viability was assessed with Live/Dead staining 8 days after seeding. Cell proliferation was assessed using alamarBlue assays at days 0, 1, 2, 3, and 7. The hASCs were alive on all discs after 8 days. Cellular proliferation on porous EBM discs was increased at days 2, 3, and 7 compared to discs made of commercial Ti6Al4V. Cellular proliferation on porous EBM discs was also increased compared to solid polished and unpolished EBM discs. IL-6 and IL-8 releases at day 7 were lower for porous EBM discs than for other discs. Solid polished, unpolished, and porous EBM Ti6Al4V discs exhibited an acceptable biocompatibility profile compared to solid Ti6Al4V discs from a commercial source. EBM FFF may be considered as an option for the fabrication of custom orthopedic implants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".