A sensitive PARACEST contrast agent for temperature MRI: Eu<sup>3+</sup>‐DOTAM‐glycine (Gly)‐phenylalanine (Phe)
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Bibliographic record
Abstract
Tissue temperature is a fundamental physiological parameter that can provide insight into pathological processes. The purpose of this study was to develop and characterize a novel paramagnetic chemical exchange saturation transfer (CEST) agent suitable for in vivo temperature mapping at 9.4T. The CEST properties of the europium (Eu(3+)) complex of the DOTAM-Glycine (Gly)-Phenylalanine (Phe) ligand were studied in vitro at 9.4T as a function of temperature, pH, and agent concentration. The transfer of magnetization (CEST effect) from the bound water to bulk water pools was approximately 75% greater for Eu(3+)-DOTAM-Gly-Phe compared to Eu(3+)-DOTAM-Gly at physiologic temperature (38 degrees C) and pH (7.0 pH units) when using power level sufficiently low for in vivo imaging. Unlike Eu(3+)-DOTAM-Gly, whose CEST effect decreased with increasing temperature in the physiologic range, the CEST effect of Eu(3+)-DOTAM-Gly-Phe was optimal at body temperature. A strong linear dependence of the chemical shift of the bound water pool on temperature was observed (0.3 ppm/ degrees C), which was insensitive to pH and agent concentration. Temperature maps with SDs < 1 degrees C were acquired at 9.4T in phantoms containing: 1) phantom A, an aqueous solution of 10 mM Eu(3+)-DOTAM-Gly-Phe; 2) phantom B, 5% bovine serum albumin (BSA) with 15 mM Eu(3+)-DOTAM-Gly-Phe; and 3) phantom C, mouse brain tissue with 4 mM Eu(3+)-DOTAM-Gly-Phe. The temperature sensitivity combined with the high CEST effect observed at low concentration using low saturation power (B(1)) suggests this compound may be a good choice for in vivo temperature mapping at 9.4T.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it