Evaluating the Feasibility and Efficacy of a Dual-Modality Nanoparticle Contrast Agent (Nanotrast-CF800) for Image-Guided Sentinel Lymph Node Mapping in the Oral Cavity of Healthy Dogs
Bibliographic record
Abstract
A combination of pre and intraoperative sentinel lymph node (SLN) mapping techniques have been suggested to optimize SLN detection. A novel liposomal nanoparticle, Nanotrast-CF800 (CF800), utilizes computed tomography lymphography (CTL) and near infrared fluorescence imaging (NIRF) for image-guided surgery and SLN mapping. This novel tracer agent has not been evaluated in companion animals. The objective of this study was to evaluate the feasibility and efficacy of CF800 for SLN mapping in the oral cavity of healthy dogs and to report any local adverse effects. Six healthy adult purpose-bred research dogs randomly received either 1 mL (group 1) or 2 mL (group 2) of CF800 injected into the submucosa at the level of the right canine maxillary tooth. CTL and percutaneous NIRF were performed at 1, 3, and 10 min, then 1, 2, 4, 7, and 10 days post-injection (p.i). Overall, both CTL and NIRF identified SLNs in all dogs. The overall peak mean contrast enhancement of the SLNs was 73.98 HU (range 63.45-86.27 HU) at 2 days p.i. Peak fluorescence of the SLN occurred at 1 day p.i. The agent was retained within the SLN for at least 7 days for CTL and 4 days for percutaneous NIRF. No adverse effects were observed. Local administration of CF800 was simple and feasible for the detection of SLNs using CTL+NIRF in the head and neck of healthy dogs and was not associated with significant local adverse events.
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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 | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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 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".