Elimination Pathways of Nanoparticles
Bibliographic record
Abstract
Understanding how nanoparticles are eliminated from the body is required for their successful clinical translation. Many promising nanoparticle formulations for in vivo medical applications are large (>5.5 nm) and nonbiodegradable, so they cannot be eliminated renally. A proposed pathway for these nanoparticles is hepatobiliary elimination, but their transport has not been well-studied. Here, we explored the barriers that determined the elimination of nanoparticles through the hepatobiliary route. The route of hepatobiliary elimination is usually through the following pathway: (1) liver sinusoid, (2) space of Disse, (3) hepatocytes, (4) bile ducts, (5) intestines, and (6) out of the body. We discovered that the interaction of nanoparticles with liver nonparenchymal cells ( e. g., Kupffer cells and liver sinusoidal endothelial cells) determines the elimination fate. Each step in the route contains cells that can sequester and chemically or physically alter the nanoparticles, which influences their fecal elimination. We showed that the removal of Kupffer cells increased fecal elimination by >10 times. Combining our results with those of prior studies, we can start to build a systematic view of nanoparticle elimination pathways as it relates to particle size and other design parameters. This is critical to engineering medically useful and translatable nanotechnologies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".