Detectability of fluorescent gold nanoparticles under micro-CT and optical projection tomography imaging
Bibliographic record
Abstract
Purpose: Preclinical studies often compare micro-computed tomography (micro-CT) imaging with histology using optical microscopy of fluorescently labeled slides. However, correlating the images is difficult because the tissues appear differently in the two modalities. It would be valuable to have a single contrast medium visible on both radiographic and optical imaging. Approach: We have explored the detectability of fluorescently labeled gold nanoparticles under micro-CT and optical projection tomography (OPT) in agarose phantoms and a murine melanoma tumor model. Murine melanoma cells were used to induce tumor growth in the right hind legs of 12 C57Bl6 mice, with the maximal tumor size of 1 cm3. We injected Cy3 fluorescently coated gold nanorods directly into the tumors. The mice were scanned with in vivo micro-CT (for pre- and post-contrast scans). Once euthanized, the hind leg was dissected and scanned with a higher resolution specimen micro-CT and OPT. Results: The distribution of the gold nanoparticles appeared to be contained and isolated to the tumor. Alignment of micro-CT specimen scans with the OPT scans was possible, although there was also autofluorescence of the surrounding muscle tissue. Conclusions: This study highlights the potential use of fluorescently labeled gold nanoparticles for imaging murine melanoma tumors using micro-CT and OPT.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".