Physicochemical Design of Nanoparticles to Interface with and Degrade Neutrophil Extracellular Traps
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Neutrophil extracellular traps (NETs) are networks of decondensed chromatin, histones, and antimicrobial proteins released by neutrophils in response to an infection. NET overproduction can cause an exacerbated hyperinflammatory response in a variety of diseases and can lead to host tissue damage without clearance of infection. Nanoparticle drug delivery is a promising avenue for creating materials that can both target NETs and deliver sustained amounts of NET-degrading drugs to alleviate hyperinflammation. Here, we study how particle physicochemical properties can influence NET interaction and leverage our findings to create NET-interfacing and NET-degrading particles. We fabricated a panel of particles of varying sizes (200 to 1000 nm) and charges (positive, neutral, negative) and found that positive charge is the main driver of NET–particle interaction, with smaller 200 nm positive particles having a 10-fold increase in binding compared to larger 1000 nm positive particles. Negative and neutral particles were mostly noninteracting, except for small negatively charged particles that exhibited very low levels of NET localization. Interaction strength of particles with NETs was quantified via shear flow assays and atomic force microscopy. This information was leveraged to create DNase-loaded particles that could adhere to NETs at varying degrees and therefore degrade NETs at different rates in vitro . Positively charged, 200 nm DNase-loaded particles showed the highest degree of interaction with NETs and therefore led to faster degradation compared with larger sizes, underscoring the importance of physicochemical design for NET-targeting drug delivery. Overall, this work provides fundamental knowledge of the drivers of particle–NET interaction and a basis for designing NET-targeting particles for various disease states.
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 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) | 0.000 | 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 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".