Targeting Neutrophilic Inflammation Using Polymersome-Mediated Cellular Delivery
Bibliographic record
Abstract
Neutrophils are key effector cells in inflammation and play an important role in neutralizing invading pathogens. During inflammation resolution, neutrophils undergo apoptosis before they are removed by macrophages, but if apoptosis is delayed, neutrophils can cause extensive tissue damage and chronic disease. Promotion of neutrophil apoptosis is a potential therapeutic approach for treating persistent inflammation, yet neutrophils have proven difficult cells to manipulate experimentally. In this study, we deliver therapeutic compounds to neutrophils using biocompatible, nanometer-sized synthetic vesicles, or polymersomes, which are internalized by binding to scavenger receptors and subsequently escape the early endosome through a pH-triggered disassembly mechanism. This allows polymersomes to deliver molecules into the cell cytosol of neutrophils without causing cellular activation. After optimizing polymersome size, we show that polymersomes can deliver the cyclin-dependent kinase inhibitor (R)-roscovitine into human neutrophils to promote apoptosis in vitro. Finally, using a transgenic zebrafish model, we show that encapsulated (R)-roscovitine can speed up inflammation resolution in vivo more efficiently than the free drug. These results show that polymersomes are effective intracellular carriers for drug delivery into neutrophils. This has important consequences for the study of neutrophil biology and the development of neutrophil-targeted therapeutics.
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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".