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 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) | 0.000 | 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 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".