Evaluation of brain tumor vessels specific contrast agents for glioblastoma imaging
Bibliographic record
Abstract
A mouse model of glioblastoma multiforme was used to determine the accumulation of a targeted contrast agent in tumor vessels. The contrast agent, consisting of superparamagnetic iron oxide coated with dextran, was functionalized with an anti-insulin-like-growth-factor binding protein 7 (anti-IGFBP7) single domain antibody. The near infrared marker, Cy5.5, was also attached for an in vivo fluorescence study. A 9.4T magnetic resonance imaging (MRI) system was used for in vivo studies on days 10 and 11 following tumor inoculation. T(2) relaxation time was used to measure the accumulation of the contrast agent in the tumor. Changes in tumor to brain contrast because of active targeting were compared with a nontargeted contrast agent. Effective targeting was confirmed with near infrared measurements and fluorescent microscopic analysis. The results showed that there was a statistically significant (P < .01) difference in normalized T(2) between healthy brain and tumor tissue 10 min, 1 h, and 2 h point postinjection of the anti-IGFBP7 single domain antibody targeted and nontargeted iron oxide nanoparticles. A statistical difference remained in animals treated with targeted nanoparticles 24 h postinjection only. The MRI, near infrared imaging, and fluorescent microscopy studies showed corresponding spatial and temporal changes. We concluded that the developed anti-IGFBP7-iron oxide single domain antibody-targeted MRI contrast agent selectively binds to abnormal vessels within a glioblastoma. T(2)-weighted MRI and near infrared imaging are able to detect the targeting effects in brain tumors.
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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) | 0.000 | 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 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".