Nanotubular Gradients on Titanium: High-Throughput Screening of Nanoscale Architectures of Variable Topographical Complexity
Bibliographic record
Abstract
Advancements in cell-instructive biomaterials hinge on the precise design of their nanoscale topography, a critical factor in controlling cell–surface interactions. Nanofabrication techniques such as e-beam and nanoimprint lithography enable accurate nanopatterning on a wide range of materials. However, their limited applicability and scalability to medically relevant metals such as titanium, hinder the creation and modulation of precisely designed nanotopographies on metallic substrates to investigate structure–function relationships and clinical translation of nanotopographical surfaces for biomedical implants. In this context, anodization is a cost-effective, scalable method to nanopattern titanium and its alloys, producing arrays of TiO 2 nanotubes with precisely controlled diameters. Despite the significant advances in the understanding of how cells sense and respond to nanotubular surfaces, traditional diameter-focused research reliant on single-sized nanostructures restricts analysis to a narrow set of geometrical parameters and often overlook the spatial arrangement of nanotubes. To address these limitations, this study capitalizes on anodization to create scalable nanotubular gradients on titanium, introducing a high-throughput platform to explore the cellular response to a wide range of nanotopographical configurations within a single sample. Utilizing spatial metrics such as lacunarity, entropy, and fractal dimension, we characterized the structural complexity of the nanotubular surfaces, emphasizing geometrical considerations beyond the nanotube diameter in evaluating cellular response. In vitro assays with human MG63 osteoblastic cells revealed that more disordered, high-entropy regions significantly enhance cellular spreading and proliferation while promoting early osteogenic differentiation, evidenced by elevated RUNX2 and osteocalcin (OCN) expression. In contrast, mitochondrial activation and longer-term mineral deposition are elicited by more ordered nanotubular arrays. By streamlining the screening of nanotopographical features and enabling reproduction of user-selected designs as homogeneous surfaces, this gradient-based approach deepens mechanistic insights into structure–function relationships governing MG63 cell response to anodized titanium and offers a translatable framework for designing and evaluating nanotubular surfaces, shortening the gap between in vitro research and clinical applications.
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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 | 0.001 | 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".