Development of glioblastoma cell lines expressing red fluorescence for non-invasive live imaging of intracranial tumors.
Bibliographic record
Abstract
UNLABELLED: Glioblastoma (GBM) cell lines expressing red fluorescent proteins were evaluated as a tool for non-invasive imaging of orthotopic tumors. MATERIALS AND METHODS: mKate2- and mCherry-transduced U251MG GBM lines were sorted by flow cytometry. The growth rates and drug sensitivity of the resulting cell lines were compared to those of the parental line. Following orthotopic implantation, mKate2-expressing cells were detected using multispectral imaging. RESULTS: Flow cytometry-sorted fluorescent populations exhibiting growth curves that were comparable to those of the parental line were selected. mKate2-expressing cells were inoculated orthotopically and formed tumors which were visualized non-invasively, allowing monitoring of tumor growth over time and the assessment of tumor response to temozolomide drug treatment. CONCLUSION: The strategy reported here led to the successful development of GBM models expressing mKate2 or mCherry. The fluorescence signal intensity measured in the brain of live animals correlates with tumor size, thus providing a method to assess tumor progression and response to treatment.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".