In vivo GFP imaging of dormant liver metastasis in a human uveal melanoma mouse model
Bibliographic record
Abstract
Abstract Purpose: We previouly demonstrated the feasibility of a human uveal melanoma GFP model in nude mice. We were able to monitor the preferential colonization of the liver by malignant cells during a 20‐day period. In this study we extended the observation period in order to observe disease progression and metastatic growth. Methods: Five hundred thousand, GFP transfected, human primary uveal melanoma cells (92.1) were injected into the tail vein of 30 nude mice at time zero. Starting at day one, five mouse per week was imaged, using an abdominal incision to expose the liver, for up to 60 minutes before sacrifice. The liver was imaged in vivo and post mortem, while the lungs, spleen, and kidney were all examined post‐mortem for GFP expression. Histopathological examination of all organs was performed to ensure that the GFP signal was arising from malignant cells. Results: In vivo imaging of the liver revealed positive GFP signal in all mice throughout the experiment. Malignant cells seeded the liver after injection, but there was no apparent tumor growth upon completion of the experiment. The micrometastatic foci remained viable without changes in size or intensity of the signal. No tumors were seen in any other organ at the end of the experiment. Histopathology confirmed that the cells emitting GFP were malignant cells. Conclusions: The results of this uveal melanoma model show great promise for understanding the previously unobservable interactions between single human uveal melanoma cells and native liver tissue. The fact that our cells successfully colonize the liver yet remain dormant for six weeks should actually mirror the disease in humans; metastases in patients takes roughly five to ten years to develop.
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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".