Cellular imaging and texture analysis distinguish differences in cellular dynamics in mouse brain tumors
Bibliographic record
Abstract
PURPOSE: The heterogeneous tumor cell population and dynamic microenvironment within a tumor lead to regional variations in cell proliferation, migration, and differentiation. In this work, MRI and optical projection tomography were used to examine and compare the redistribution of a cellular label in two mouse glioma models. METHODS: GL261 and 4C8 glioma cells labeled with iron oxide particles or with a fluorescent probe were injected into the brains of syngeneic mice and allowed to develop into ∼10-mm(3) tumors. Texture analysis was used to quantitatively describe and compare the label distribution patterns in the two tumor types. RESULTS: The label was seen to remain predominantly in the tumor core in GL261 tumors, but become more randomly distributed throughout the tumor volume in 4C8 tumors. Histologically, GL261 tumors displayed a more invasive, aggressive phenotype, although the distribution of mitotic cells in the two tumors was similar. CONCLUSION: The redistribution of a cellular label during tumor growth is characteristic of a tumor model. The label distribution map reflects more than simple differences in cell proliferation and is likely influenced by differences in the tumor microenvironment.
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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 | 0.001 | 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".