The Topography of Silica Films Modulates Primary Macrophage Morphology and Function
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 SiO 2 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it