Zebrafish as a predictive screening model to assess macrophage clearance of liposomes in vivo
Bibliographic record
Abstract
Macrophage recognition of nanoparticles is highly influenced by particle size and surface modification. Due to the lack of appropriate in vivo screening models, it is still challenging and time-consuming to characterize and optimize nanomedicines regarding this undesired clearance mechanism. Therefore, we validate zebrafish embryos as an emerging vertebrate screening tool to assess the macrophage sequestration of surface modified particulate formulations with varying particle size under realistic biological conditions. Liposomes with different PEG molecular weights (PEG350-PEG5000) at different PEG densities (3.0-10.0 mol%) and particle sizes between 60 and 120 nm were used as a well-established reference system showing various degrees of macrophage uptake. The results of in vitro experiments, zebrafish embryos, and in vivo rodent biodistribution studies were consistent, highlighting the validity of the newly introduced zebrafish macrophage clearance model. We hereby present a strategy for efficient, systematic and rapid nanomedicine optimization in order to facilitate the preclinical development of nanotherapeutics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".