Maximizing T<sub>2</sub>‐exchange in Dy<sup>3+</sup>DOTA‐(amide)<sub>X</sub> chelates: Fine‐tuning the water molecule exchange rate for enhanced T<sub>2</sub> contrast in MRI
Bibliographic record
Abstract
PURPOSE: The water molecule exchange rates in a series of DyDOTA-(amide)X chelates were fine-tuned to maximize the effects of T2-exchange line broadening and improve T2 contrast. METHODS: Four DyDOTA-(amide)X chelates having a variable number of glycinate side-arms were prepared and characterized as T2-exchange agents. The nonexchanging DyTETA chelate was also used to measure the bulk water T2 reduction due solely to T2*. The total transverse relaxivity (r2tot) at 22, 37, and 52°C for each chelate was measured in vitro at 9.4 Tesla (400 MHz) by fitting plots of total T2 (-1) versus concentration. The water molecule exchange rates for each complex were measured by fitting (17)O line-width versus temperature data taken at 9.4 Tesla (54.3 MHz). RESULTS: The measured transverse relaxivities due to water molecule exchange (r2ex) and bound water lifetimes (τM) were in excellent agreement with Swift-Connick theory, with DyDOTA-(gly)3 giving the largest r2ex = 11.8 s(-1) mM(-1) at 37°C. CONCLUSION: By fine-tuning the water molecule exchange rate at 37°C, the transverse relaxivity has been increased by 2 to 30 times compared with previously studied Dy(3+)-based chelates. Polymerization or dendrimerization of the optimal chelate could yield a highly sensitive, molecule-sized T2 contrast agent for improved molecular imaging applications.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".