MRI monitoring of monocytes to detect immune stimulating treatment response in brain tumor
Bibliographic record
Abstract
Background: Glioblastoma (GBM) is an aggressive brain cancer with a poor prognosis. The use of immune therapies to treat GBM has become a promising avenue of research. It was shown that amphotericin B (Amp B) can stimulate the innate immune system and suppress the growth of brain tumor initiating cells (BTICs). However, it is not feasible to use histopathology to determine immune activation in patients. We developed an MRI technique that can rapidly detect a therapeutic response in animals treated with drugs that stimulate innate immunity. Ultra-small iron oxide nanoparticles (USPIOs) are MRI contrast agents that have been widely used for cell tracking. We hypothesized that the increased monocyte infiltration into brain tumors due to Amp B can be detected using USPIO-MRI, providing an indicator of early drug response. Methods: We implanted human BTICs into severe combined immunodeficient mice and allowed the tumor to establish before treating the animals with either Amp B or vehicle and then imaged them using MRI with USPIO (ferumoxytol) contrast. Results: After 7 days of treatment, there was a significantly decreased T2* in the tumor of Amp B but not vehicle animals, suggesting that USPIO is carried into the tumor by monocytes. We validated our MRI results with histopathology and confirmed that Amp B-treated animals had significantly higher levels of macrophage/microglia that were colocalized with iron staining in their brain tumor compared with vehicle mice. Conclusion: USPIO-MRI is a promising method of rapidly assessing the efficacy of anticancer drugs that stimulate innate immunity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".