Exploring Passive Clearing for 3D Optical Imaging of Nanoparticles in Intact Tissues
Bibliographic record
Abstract
The three-dimensional (3D) optical imaging of nanoparticle distribution within cells and tissues can provide insights into barriers to nanoparticle transport in vivo. However, this approach requires the preparation of optically transparent samples using harsh chemical and physical methods, which can lead to a significant loss of nanoparticles and decreased sensitivity of subsequent analyses. Here, we investigate the influence of electrophoresis and clearing time on nanoparticle retention within intact tissues and the impact of these factors on the final 3D image quality. Our findings reveal that longer clearing times lead to a loss of nanoparticles but improved transparency of tissues. We discovered that passive clearing improved nanoparticle retention 2-fold compared to results from electrophoretic clearing. Using the passive clearing approach, we were able to observe a small population of nanoparticles undergoing hepatobiliary clearance, which could not be observed in liver tissues that were prepared by electrophoretic clearing. This strategy enables researchers to visualize the interface between nanomaterials and their surrounding biological environment with high sensitivity, which enables quantitative and unbiased analysis for guiding the next generation of nanomedicine designs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".