<scp>R</scp>egulation of RAW264.7 macrophage polarization on smooth and rough surface topographies by galectin‐3
Bibliographic record
Abstract
Recognition of topographical features induces phenotypic changes in macrophages although the receptors and signaling pathways are not completely characterized. As integrin molecules in focal adhesions/podosomes are in intimate contact with topography and topography modulates the NFkB pathway through cholesterol enriched raft-associated adhesive signaling structures we hypothesized that a cell-surface signaling complex comprised of galectin-3 together with its ligand CD98 and integrinβ1 is important for topography-directed lineage determination. This study used polished, sand blasted and acid etched (SLA) surfaces and two novel grooved topographies (G1 and G2) produced by anisotropic etching of Si <1 1 0> to evaluate the role of galectin-3 in macrophage polarization in RAW 264.7 macrophages, as determined by gene expression and morphology. In the presence of the galectin-3 inhibitor, lactose, the M2 marker (mannose receptor) was down-regulated while the M1 marker (iNOS) was up-regulated on smooth and rough surfaces. This skewing of phenotype suggests a role for galectin-3 in macrophage polarization towards the M2 phenotype. Additionally, we evaluated the role of PI3K on polarization using PI3K inhibitor LY294002. We found that the M2 marker was down-regulated on both PO (surface polished) and G1 surfaces implicating PI3K in lineage determination. We also found that surface topography altered cell morphology; macrophages had a larger area on G2 surfaces. Lactose treatment significantly reduced the cell area on all topographies suggesting that the galectin-3 is also involved in signaling complexes triggering the rearrangement of the actin cytoskeleton. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 2499-2509, 2017.
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 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 | 0.002 | 0.002 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".