High-Resolution Longitudinal Screening with Magnetic Resonance Imaging in a Murine Brain Cancer Model
Bibliographic record
Abstract
One of the main limitations of intracranial models of diseases is our present inability to monitor and evaluate the intracranial compartment noninvasively over time. Therefore, there is a growing need for imaging modalities that provide thorough neuropathological evaluations of xenograft and transgenic models of intracranial pathology. In this study, we have established protocols for multiple-mouse magnetic resonance imaging (MRI) to follow the growth and behavior of intracranial xenografts of gliomas longitudinally. We successfully obtained weekly images on 16 mice for a total of 5 weeks on a 7-T multiple-mouse MRI. T2- and T1-weighted imaging with gadolinium enhancement of vascularity was used to detect tumor margins, tumor size, and growth. These experiments, using 3D whole brain images obtained in four mice at once, demonstrate the feasibility of obtaining repeat radiological images in intracranial tumor models and suggest that MRI should be incorporated as a research modality for the investigation of intracranial pathobiology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".