QUANTIFYING NANOPARTICLE TRANSPORT<i>IN VIVO</i>USING HYPERSPECTRAL IMAGING WITH A DORSAL SKINFOLD WINDOW CHAMBER
Bibliographic record
Abstract
We have developed a noninvasive imaging method to quantify in vivo drug delivery pharmacokinetics without the need for blood or tissue collection to determine drug concentration. By combining the techniques of hyperspectral imaging and a dorsal skinfold window chamber, this method enabled the real-time monitoring of vascular transport and tissue deposition of nanoparticles labeled with near-infrared (NIR) dye. Using this imaging method, we quantified the delivery pharmacokinetics of the native high-density lipoprotein (HDL) and epidermal growth factor receptor (EGFR)-targeted HDL nanoparticles and demonstrated these HDLs had long circulation time in blood stream (half-life >12 h). These HDL nanoparticles could efficiently carry cargo DiR-BOA to extravasate from blood vessels, diffuse through extracellular matrix, and penetrate and be retained in the tumor site. The EGFR targeting specificity of EGFR-targeted HDL (EGFR-specific peptide conjugated HDL) was also visualized in vivo by competitive inhibition with excess EGFR-specific peptide. In summary, this imaging technology may help point the way toward the development of novel imaging-based pharmacokinetic assays for preclinical drugs and evaluation of drug delivery efficiency, providing a dynamic window into the development and application of novel drug delivery systems.
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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 | 0.001 |
| 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".