Bone formation on rough, but not polished, subcutaneously implanted Ti surfaces is preceded by macrophage accumulation
Bibliographic record
Abstract
Implanted rough surfaces have long been associated with the accumulation of macrophages and other cells of the monocytic lineage such as foreign body giant cells and osteoclasts. As cells of the moncytic lineage are part of the immune system, the response of this cell family to biomaterials has attracted wide concern. This study compared events at the interface of implant surface topographies with varied roughness in a rat subcutaneous model. Titanium-coated epoxy replicas of machined, etched, blasted, titanium-plasma-sprayed (TPS), sandblasted-and-etched (SLA), micromachined, and polished surfaces were implanted for up to 11 weeks, and processed for light or electron microscopy or immunohistochemistry for ED1, a marker for recruited macrophages. Initially, healing appeared similar among all surfaces, the frequency of mineralization followed the order of SLA, micromachined, TPS, machined, etched, blasted, and polished surfaces. On the SLA surface macrophages, as identified by both ultrastructural morphology and immunohistochemistry were the predominant cell type at 1 week and persisted until mineralization occurred as early as 2 weeks. On smoother surfaces collagenous matrix predominated at 2 weeks and subsequently increased with time. There, thus, appears to be two routes to bone-like tissue formation on Ti implants in this rat subcutaneous model; macrophage-mediated and macrophage-independent dense collagenous-matrix-associated.
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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".