Nuclear Microscopy: A Novel Technique for Quantitative Imaging of Gadolinium Distribution within Tissue Sections
Bibliographic record
Abstract
All clinically-approved and many novel gadolinium (Gd)-based contrast agents used to enhance signal intensity in magnetic resonance imaging (MRI) are optically silent. To verify MRI results, a "gold standard" that can map and quantify Gd down to the parts per million (ppm) levels is required. Nuclear microscopy is a relatively new technique that has this capability and is composed of a combination of three ion beam techniques: scanning transmission ion microscopy, Rutherford backscattering spectrometry, and particle induced X-ray emission used in conjunction with a high energy proton microprobe. In this proof-of-concept study, we show that in diseased aortic vessel walls obtained at 2 and 4 h after intravenous injection of the myeloperoxidase-sensitive MRI agent, bis-5-hydroxytryptamide-diethylenetriamine-pentaacetate gadolinium, there was a time-dependant Gd clearance (2 h = 18.86 ppm, 4 h = 8.65 ppm). As expected, the control animal, injected with the clinically-approved conventional agent diethylenetriamine-pentaacetate gadolinium and sacrificed 1 week after injection, revealed no significant residual Gd in the tissue. Similar to known in vivo Gd pharmacokinetics, we found that Gd concentration dropped by a factor of 2 in vessel wall tissue in 1.64 h. Further high-resolution studies revealed that Gd was relatively uniformly distributed, consistent with random agent diffusion. We conclude that nuclear microscopy is potentially very useful for validation studies involving Gd-based magnetic resonance contrast agents.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".