Delta relaxation enhanced MR: Improving activation‐specificity of molecular probes through <i>R</i><sub><i>1</i></sub> dispersion imaging
Bibliographic record
Abstract
MR molecular imaging enables high-resolution, in vivo study of molecular processes frequently utilizing gadolinium-based probes that specifically bind to a particular biological molecule or tissue. While some MR probes are inactive when unbound and produce enhancement only after binding, the majority are less specific and cause enhancement in either state. Accumulation processes are then required to increase probe concentration in regions of the target molecule/tissue. Herein, a method is described for creating specificity for traditionally nonspecific probes. This method utilizes MR field-cycling methods to produce MRI contrast related to the dependence of R(1) upon magnetic field. It is shown that the partial derivative of R(1) with respect to magnetic field strength, R(1)', can be used as an unambiguous measure of probe binding. T(1)-weighted images and R(1)' images were produced for samples of albumin and buffer both enhanced with the albumin-binding agent Vasovist. For T(1) images, samples with low concentrations of Vasovist in an albumin solution could not be differentiated from samples with higher concentrations of Vasovist in buffer. Conversely, the R(1)' images showed high specificity to albumin. Albumin samples with a 10-microM concentration of Vasovist were enhanced over buffer samples containing up to 16 times more Vasovist.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".