Gelatin‐Hyaluronan Click‐Crosslinked Cryogels Elucidate Human Macrophage Invasion Behavior
Bibliographic record
Abstract
Abstract Hydrogel models of metastasis traditionally focus on the invasion of cancer cells; however, other cells in the tumor microenvironment that are associated with metastasis also have the ability to migrate. Macrophage phenotype plays a key role in the tumor microenvironment, yet understanding their migration within tunable 3D in vitro models has been limited. To gain a greater understanding of macrophage invasive behavior, stable and transparent oxime‐crosslinked cryogels comprised of click‐crosslinked gelatin‐oxyamine and hyaluronan‐aldehyde (GELox‐HAa) are synthesized. Fibronectin‐derived, oxyamine‐modified PHSRN‐RGDSP peptides are incorporated to further mimic the tumor extracellular matrix without impacting cryogel mechanics. It is found that primary human macrophages migrate to a greater depth in cryogels with greater porosity and pore size. To better understand the mechanism of migration, cells are treated with either inhibitors of matrix metalloproteinases (MMPs) or rho‐associated protein kinase (ROCK) and a predominantly MMP‐mediated mechanism of invasion is found. Macrophage polarization studies reveal that anti‐inflammatory, interleukin‐4/13 (IL4/IL13)‐treated macrophages migrate through cryogels to a greater extent than pro‐inflammatory, interferon‐gamma/lipopolysaccharide (IFNγ/LPS)‐treated cells. Interestingly, polarized macrophages move through cryogels using a combination of amoeboid and mesenchymal migration. These findings of macrophage invasion in this cryogel platform set the stage for their further study in a biomimetic tumor microenvironment.
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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".