In vivo detection of MRI‐PARACEST agents in mouse brain tumors at 9.4 T
Bibliographic record
Abstract
Paramagnetic chemical exchange saturation transfer (PARACEST) contrast agents are under development for biological target identification by magnetic resonance imaging. Image contrast associated with PARACEST agents can be generated by radiofrequency irradiation of the chemically shifted protons bound to a PARACEST contrast agent molecule or by direct irradiation of the on-resonance bulk water protons. The observed signal change in a magnetic resonance image after the administration of a PARACEST contrast agent is due to both altered relaxation time constants and the CEST effect. Despite high sensitivity in vitro, PARACEST agents have had limited success in vivo where sensitivity is reduced by the magnetization transfer effect from endogenous macromolecules. The purpose of this study was to demonstrate the in vivo detection of a PARACEST contrast agent using the on-resonance paramagnetic chemical exchange effect (OPARACHEE) in a mouse glioblastoma multiforme tumor model and to isolate the OPARACHEE effect from the changes in relaxation induced by the PARACEST agent. Three mice with tumors were imaged on a 9.4 T MRI scanner following tail vein injection of 150 μL 50 mM Tm(3+)-DOTAM-glycine-lysine. A fast low angle shot pulse sequence with a low power radiofrequency pulse train (WALTZ-16) as the preparation pulse was used to generate OPARACHEE contrast. To study the dynamics of agent uptake, reference images (without the preparation pulse) and OPARACHEE images were acquired continuously in an alternating fashion before, during and after agent injection. Signal intensity decreased by more than 10% in tumor in the control images after agent administration. Despite these changes, a clear OPARACHEE contrast of 1-5% was also observed in brain tumors after contrast agent injection and maintained in the hour following injection. This result is the first in vivo observation of OPARACHEE contrast in brain tumors with correction of T(1) and T(2) relaxation effects.
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 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 | 0.000 |
| 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) | 0.003 | 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".