Noninvasive Monitoring of the Fate of <sup>111</sup>In-Labeled Block Copolymer Micelles by High Resolution and High Sensitivity MicroSPECT/CT Imaging
Bibliographic record
Abstract
The validation of high sensitivity and high resolution microSPECT/CT imaging for tracking the in vivo pathway and fate of an 111Indium-labeled (111In) amphiphilic diblock copolymer micelle formulation is presented. Heterobifunctional poly(ethylene glycol) was used to initiate cationic ring opening polymerization of epsilon-caprolactone, which was then conjugated to 2-(4-isothiocyanatobenzyl)-diethylenetriaminepentaacetic acid (p-SCN-Bn-DTPA) for chelation with 111In. The micelles were characterized in terms of their physicochemical properties including size, size distribution, zeta-potential, and radiochemical purity. Elimination kinetics and tissue deposition were evaluated in healthy mice following administration of 111In-micelles, 111In-DTPA-b-PCL unimers (i.e., administered under the critical micelle concentration) or 111In-Bn-DTPA. Healthy and MDA-MB-231 tumor-bearing mice were imaged using microSPECT/CT following iv administration of 111In-micelles or 111In-Bn-DTPA. Overall, incorporation of 111In onto the surface of thermodynamically stable micelles results in long plasma residence times for the radionuclide and preferential localization within the spleen (22 +/- 5% i.d/g), liver (13 +/- 3% i.d./g), and tumor (9 +/- 2% i.d./g). MicroSPECT/CT imaging provided noninvasive longitudinal visualization of circulation dynamics and tissue deposition. A strong correlation between image-based region of interest (ROI) analysis and biodistribution data was found, implying that nuclear imaging can be used as a noninvasive tool to accurately quantify tissue distribution. As well, the image-based assessment provided unique insight into the intratumoral distribution of the micelles in vivo.
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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 | 0.000 |
| 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 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".