The effect of surface topography on cell shape and early ERK1/2 signaling in macrophages; linkage with FAK and Src
Bibliographic record
Abstract
Implant surface topography can modulate macrophage behavior during wound healing by the production of proinflammatory cytokines. This study investigated the activation of FAK, Src, and ERK1/2 signaling intermediates of the proinflammatory ERK1/2 pathway in RAW 264.7 macrophages in response to polished (P), coarse-grit-blasted (B), acid etched (E), and grit-blasted and etched (SLA) surface topographies. In addition, the effects of these topographies on cell spreading, vinculin organization, and viability were determined. Macrophages on the SLA surface changed from predominantly well-spread cells to ones with a more spherical morphology over time. In contrast, macrophages on the P surface changed from being predominantly spherical cells to well spread. The morphological changes were associated with changes in the distribution of vinculin. The overall patterns of the pFAK, pSrc, pERK1/2 levels as well as pERK1/2 nuclear translocation associated with cell shape with greater activation being seen with a more spread morphology. These results suggest that surface topography differentially activates signaling pathways that affect cell function and raise the possibility that topographies can be designed to optimize desired cell responses.
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.
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".