Airway mucus penetration and anti-inflammatory effects of charge-tunable nanocarriers in an asthma model
Bibliographic record
Abstract
In asthma, thick airway mucus blocks drug movement and lowers treatment efficiency. In this study, lipid nanoparticles (LNPs) with different surface charges (+20 mV, 0 mV, and −20 mV) were made to test how charge affects mucus transport and anti-inflammatory action. The LNPs had an average size of 160 ± 12 nm and were studied in mice with ovalbumin (OVA)-induced asthma. Imaging results showed that neutral LNPs spread more evenly in the lungs and stayed for up to 8 hours, while positively and negatively charged ones cleared faster. Measurement of inflammation markers showed that the neutral group lowered IL-4 and IL-13 levels by about 65% (P < 0.001) compared with the positive group. These findings show that neutral charge helps nanoparticles pass through mucus, remain longer in the lungs, and produce stronger anti-inflammatory effects. This work provides a simple way to improve inhaled nanoparticle treatments for asthma and other airway diseases with mucus buildup.
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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 | 0.001 |
| 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".