Comprehensive clinical imaging, histopathological analysis and liquid biopsy-based surveillance of human uveal melanoma in a prolonged rabbit xenograft model
Bibliographic record
Abstract
Uveal melanoma is the most common intraocular tumor in adults. Our group has previously developed a human uveal melanoma animal model; however, adverse effects caused by the immunosuppressive agent, cyclosporine A, prevented animals from surviving more than 12 weeks. In this study, we tested multiple cyclosporine A doses over an extended disease course up to 20 weeks, providing complete clinical imaging of intraocular tumors, histopathological analysis and liquid biopsy biomarker analysis. Twenty albino rabbits were divided into four groups with different daily cyclosporine A schedules (0-10 mg/kg) and inoculated with human uveal melanoma cell lines, 92.1 or MP41, into the suprachoroidal space. Rabbits were monitored with fundoscopy, ultrasound and optical coherence tomography. Intraocular tumors (macroscopic or microscopic) were detected in all study animals. Tumor size and growth were correlated to cyclosporine A dose, with tumors regressing when cyclosporine A was arrested. All tumors expressed HMB-45 and MelanA; however, tumor size, pigmentation and cell morphology differed in 92.1 vs. MP41 tumors. Finally, across all groups, circulating tumor DNA from plasma and aqueous humor was detected earlier than tumor detection by imaging and correlated to tumor growth. In conclusion, using three clinically relevant imaging modalities (fundoscopy, ultrasonography and optical coherence tomography) and liquid biopsy, we were successfully able to monitor tumor progression in our rabbit xenograft model of human uveal melanoma.
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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".