Hydrazo‐CEST: Hydrazone‐Dependent Chemical Exchange Saturation Transfer Magnetic Resonance Imaging Contrast Agents
Bibliographic record
Abstract
Abstract The rapid formation of hydrazones under physiological conditions was exploited for the detection of aldehydes through chemical exchange saturation transfer magnetic resonance imaging (CEST‐MRI). A metal‐free, diamagnetic contrast agent derived from N‐amino anthranilic acid was introduced, which selectively “turned‐on” upon hydrazone formation through an effect termed Hydrazo‐CEST. While the hydrazine form of the probe produced no CEST‐MRI signal enhancement, the formation of the aryl hydrazone resulted in >20 % intensity decrease in the bulk water signal through the CEST effect, as measured by 300 MHz 1H NMR, 3 T and 7 T MRI. Both the electronic contributions of the N‐amino anthranilate and the aldehyde binding partner were shown to directly impact the exchange rate of the proton on the ring‐proximal nitrogen, and thus the imaging signal. Additionally, the presence of the carboxylic acid moiety ortho to the hydrazine was necessary not only for contrast production, but also for rapid hydrazone formation and prolonged hydrazone product stability under physiological conditions. This work provided the first example of an MRI‐based contrast agent capable of a “turn on” response upon reaction with bioactive aldehydes, and outlined both the structural and electronic requirements to expand on Hydrazo‐CEST, a novel, hydrazone‐dependent subtype of diamagnetic CEST‐MRI.
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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) | 0.001 | 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 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".