Tuning cell adhesion on titanium with osteogenic rosette nanotubes
Bibliographic record
Abstract
Self-assembled rosette nanotubes (RNTs), obtained from a twin G∧C base functionalized with lysine-arginine-serine-arginine [KRSR-(G∧C)(2)], were designed and investigated as bioactive coatings on titanium. These results were compared to RNTs derived from Lysine-G∧C (K-G∧C), Arg-Gly-Asp-G∧C (RGD-G∧C), and aminobutane-(G∧C)(2) [AB-(G∧C)(2)]. The results from this study revealed that these materials had excellent cytocompatibility properties as they enhanced osteoblast (bone forming cell) adhesion when coated on titanium. In particular, KRSR and RGD functionalized RNTs coated on titanium promoted the greatest osteoblast densities relative to untreated titanium. Furthermore, KRSR functionalized RNTs selectively improved osteoblast adhesion relative to fibroblast (soft-tissue forming cell) and endothelial (cells that line the vascular) cell adhesion. In contrast with these results, RNTs obtained from an unfunctionalized twin base [AB-(G∧C)(2)], RGD-G∧C co-assembled with K-G∧C and K-G∧C significantly enhanced endothelial cell attachment, which may find applications in the vascularization of newly formed bone tissue. In summary, these studies suggest that the surface of orthopedic implant materials (such as titanium) could be tailored to promote selective cell adhesion using biologically-inspired nanotubular structures functionalized with osteogenic compounds.
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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) | 0.000 | 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".