Adhesion response of filopodia to an AFM lateral detachment force and functional changes after centrifugation of cells grown on nanoporous titanium
Bibliographic record
Abstract
Cells sense and respond to mechanical cues from the surrounding substrate through filopodia. Regulation of cellular biomechanics operates at the nanoscale. Therefore, a better understanding of the relationship between filopodia and nanoscale surface features is highly relevant for the rational design of implant surfaces. The objective of this work was to determine the biomechanical contribution of filopodia and their nanoprotrusions to the adhesive interaction of cells with nanostructured surfaces. We have also analyzed the functional changes of entire cells subjected to an external force. MC3T3-E1 osteogenic cells were cultured on polished (Ti-Control) and nanotextured titanium discs (Ti-Nano). An AFM approach was used to measure the lateral detachment force of filopodia. Filopodia on Ti-Nano exhibited higher resistance to a lateral detachment force, which indicates that they adhere to the surface with more strength. SEM analysis revealed a restructuration of the cell membrane in response to centrifugation, being more evident on Ti-Nano. Fluorescence labeling also highlighted a difference in the mitochondrial footprint, a cellular compartment that provides energy for cellular processes. Together, these results show for the first time that surface topography can change the adhesive interaction of a subcellular structure that is fundamental in sensing physico-chemical surfaces features.
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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".