Engineering the NET-biomaterial interface to treat disease
Bibliographic record
Abstract
Neutrophil extracellular traps (NETs) are matrices composed of DNA and antimicrobial proteins that are released from neutrophils to entrap and degrade pathogens. Overproduction of these biological networks can induce hyperinflammation in infectious diseases and autoimmune disorders and exacerbate cancer metastasis formation. Systemic administration of immunosuppressive therapeutics and NET-degrading drugs can have adverse side effects, underscoring the importance of creating controlled release formulations to target NETs. In this review, we discuss the NET-biomaterial interface for drug delivery to address infection, inflammation, and cancer. First, we examine how drug delivery platforms can be engineered for localized delivery of NET-modulating or NET-degrading drugs. Then, we consider a class of NET-inspired materials that can replicate NET function and pathogen degradation without triggering downstream hyperinflammation. Finally, we discuss current challenges in the field and how biomaterials can be further developed to elucidate fundamental insights on NET biology and target NET dysregulation in various disease states.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".