Copolymer functional groups modulate extracellular trap accumulation and inflammatory markers in HL60 and murine neutrophils
Bibliographic record
Abstract
Abstract Undesirable host responses to implants commonly lead to impaired device function. As the first immune cell to respond to inflammation, activated neutrophils release antimicrobials and neutrophil extracellular traps (NETs) that prime microenvironments for macrophages and other infiltrating cells. This research aims to understand how functional groups in copolymers of isodecyl acrylate (IDA) that are known to modulate healing in vivo, modulate neutrophil cells. Phorbol myristate acetate-activated HL60 cells and bone marrow-derived murine neutrophils (BMDN) were incubated with coatings of IDA copolymerized with, methacrylic acid (MAA films), methyl methacrylate (MM films), or MM functionalized with hexamethylenediamine (HMD films). Cells incubated on HMD films resulted in increased accumulation of NETs at the film’s surface in comparison to other copolymers because of increased adhesion of HL60 onto HMD films or increased rates of NETosis from BMDN. Overall, lower inflammation was observed with cells on MAA films. HL60 cells had no increase in classical inflammatory markers such as tumor necrosis factor alpha and intracellular adhesion molecule-1, whereas HL60 on HMD films had increases in these same markers. Taken together, these studies give important insights into how neutrophils interact differently with functionalized copolymers and the proteins that adsorb to them, with MAA (carboxyl groups) leading to behavior associated with lower inflammation and HMD (amine groups) with higher inflammation and accumulation of NETs.
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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".