The Topography of Silica Films Modulates Primary Macrophage Morphology and Function
Bibliographic record
Abstract
Abstract Macrophages are major contributors to the rejection of foreign materials introduced to living tissues. Given that cell‐surface interactions can have important effects on phagocytic capacity and cytokine production, changes in macrophage morphology have been reported for different materials and surface patterns. However, the details of how surface topography impacts morphology and function remain unclear. This study investigates whether changes in the surface topography of glassy substrates alter macrophage shape and modulate phagocytic function and the secretion of pro‐inflammatory cytokine IL‐6. The morphology of murine bone marrow–derived macrophages cultured on micro‐ and nanostructured SiO2 films is quantified through fractal analysis. It is observed that membrane protrusions increase on nanostructured surfaces and macrophages adopt unique star‐shaped morphologies on microstructures. Macrophages on both micro‐ and nanostructured surfaces display greater phagocytic capacity, compared to those on flat controls. In contrast, the secretion of pro‐inflammatory cytokine IL‐6 is not increased when cells are cultured on the structured surfaces. The diffusion of a transmembrane receptor is also measured, which reveals no impact of structuring or plasma treatment on receptor diffusion. Altogether, these data indicate that surface topography does not increase IL‐6 production or alter membrane mobility but can significantly impact phagocytosis.
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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".