Imaging of murine melanoma tumors using fluorescent gold nanoparticles
Bibliographic record
Abstract
Contrast agents are required to view and differentiate soft tissue structures in computed tomography (CT) yet in research, histology is still considered to be the gold standard. Preclinical contrast agents used for radiographic imaging are not visible when viewed histologically, nor are histological stains visible radiographically. By identifying a single agent that is visible in both x-ray and optical imaging, we can ensure that the target tissues can be easily identified and correlated in both images, without the need of additional staining techniques. Here we present an approach to allow for the correlation of imaging murine melanoma tumours using micro-computed tomography (micro-CT) and optical projection tomography (OPT), using fluorescently-labelled gold nanoparticles without additional tissue staining. B16F10 cells (murine melanoma cell line) were used to induce tumour growth in the right hind legs of twelve C57Bl6 mice. Tumor growth times varied between 2-3 weeks, with maximal tumor size of 1 cm3. We injected Cy3 fluorescently coated gold nanorods directly into the tumours. The mice were scanned with in vivo micro-CT (for pre- and post-contrast scans at a resolution of 50 microns) and once euthanized and hind leg dissected, further scanned with a specimen micro-CT at a higher resolution of 10-17.2 microns. Results showed that the distribution of the gold nanoparticles appeared to be contained and isolated to the murine melanoma tumour, allowing for contrast and visualization. Correlation of micro-CT specimen scans with optical projection tomography (OPT) imaging was possible, although there was also auto-fluorescence of the surrounding muscle tissue and melanoma cells. This study highlights the potential use of fluorescently-labelled gold nanoparticles as a dual-contrast agent for radiographic imaging of murine melanoma tumours using micro-CT and optical imaging using 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 | 0.000 | 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) | 0.001 | 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".