A sensitive PARACEST contrast agent for temperature MRI: Eu<sup>3+</sup>‐DOTAM‐glycine (Gly)‐phenylalanine (Phe)
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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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 | 0.001 | 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".