Investigating the interplay between environmental conditioning and nanotopographical cueing on the response of human MG63 osteoblastic cells to titanium nanotubes
Bibliographic record
Abstract
Titanium nanotubular surfaces have been extensively studied for their potential use in biomedical implants due to their ability to promote relevant phenomena associated with osseointegration, among other functions. However, despite the large body of literature on the subject, potential synergistic/antagonistic effects resulting from the combined influence of environmental variables and nanotopographical cues remain poorly investigated. Specifically, it is still unclear whether the nanotube-induced variations in cellular activity are preserved across different biochemical contexts. To bridge this gap, this study systematically evaluates the combined influence of nanotopographical cues and environmental factors on human MG63 osteoblastic cells. To this end, we capitalized on a triphasic anodization protocol to create nanostructured surfaces characterized by an average nanotube inner diameter of 25 nm (NT1) and 82 nm (NT2), as well as a two-tiered honeycomb (HC) architecture. A variable glucose content was chosen as the environmental modifier due to its well-known ability to affect specific functions of MG63 cells. Alkaline phosphatase (ALP), viability/metabolic activity and proliferation were quantified to identify the suitable preconditioning window required for dictating a change in behaviour without significantly damaging cells. Successively, a combination of immunofluorescence, colorimetric assays, live cell imaging and western blots quantified viability/metabolic activity and cell proliferation, migration and differentiation as a function of the combined effects exerted by the nanostructured substrates and the glucose content. To achieve a thorough understanding of MG63 cell adaptation and response, a comparative analysis table that includes and systematically cross-analyzes all variables from this study was used for interpretation and discussion of the results. Taken together, we have demonstrated that all surfaces mitigate the negative effects of high glucose. However, nanotubular topographies, particularly NT2, elicit a more beneficial outcome in high glucose in respect to untreated titanium. In addition, while NT1 surfaces are associated with the most stable cellular response across varying glucose levels, the NT2 and HC substrates exhibit the strongest enhancement of cell migration, viability/metabolism and differentiation. Moreover, shorter-term processes such as adhesion and proliferation are favored on untreated titanium, while anodized samples support later-term events. Lastly, the role of anodized surfaces is dominant over the effects of environmental glucose, underscoring the importance of carefully considering nanoscale surface features in the design and development of cell-instructive titanium surfaces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 0.002 |
| 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 teacher head, 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".