Tumor implantation model for rapid testing of lymphatic dye uptake from paw to node in small animals
Bibliographic record
Abstract
Morbidity and complexity involved in lymph node staging via surgical resection and biopsy calls for staging techniques that are less invasive. While visible blue dyes are commonly used in locating sentinel lymph nodes, since they follow tumor-draining lymphatic vessels, they do not provide a metric to evaluate presence of cancer. An area of active research is to use fluorescent dyes to assess tumor burden of sentinel and secondary lymph nodes. The goal of this work was to successfully deploy and test an intra-nodal cancer-cell injection model to enable planar fluorescence imaging of a clinically relevant blue dye, specifically methylene blue – used in the sentinel lymph node procedure – in normal and tumor-bearing animals, and subsequently segregate tumor-bearing from normal lymph nodes. This direct-injection based tumor model was employed in athymic rats (6 normal, 4 controls, 6 cancer-bearing), where luciferase-expressing breast cancer cells were injected into axillary lymph nodes. Tumor presence in nodes was confirmed by bioluminescence imaging before and after fluorescence imaging. Lymphatic uptake from the injection site (intradermal on forepaw) to lymph node was imaged at approximately 2 frames/minute. Large variability was observed within each cohort.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".