The effect of surface roughness on RAW 264.7 macrophage phenotype
Bibliographic record
Abstract
Monocyte-derived cells, including macrophages and foreign body giant cells, can determine the performance of implanted devices. Upon contact with biomaterials, macrophages can be activated into a classic inflammatory (M1) or wound-healing (M2) phenotype. Previously, we showed that high macrophage density on rough SLA implants was associated with early bone formation. This study examined a possible mechanism, namely, surface roughness activation of macrophages to the M2 phenotype to enhance bone formation on the SLA surface. RAW 264.7 macrophages were seeded on SLA or smooth (Po) epoxy substrates and the expression of the M1 and M2 specific markers, NOS2 and Arg-1 measured by qPCR on days 1, 3, and 5. Additionally, secretion of inflammation-associated cytokines and chemokines was studied by antibody arrays and ELISAs. Controls included RAW 264.7 macrophages primed into the M1 or M2 phenotypes by LPS/IFN-γ and IL-4, respectively. Rough SLA surfaces did not activate Arg-1 and NOS2 expression, but relative to Po surfaces MCP-1 and MIP-1α were upregulated after 5 days, whereas the secretion of the M1-associated chemokine IP-10 was lowered. RAW 264.7 macrophages on the SLA surface thus adopted elements of an M2-like phenotype, suggesting that when implanted the SLA surfaces may enhance wound repair.
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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".