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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".