A Nitric Oxide-Sensing <i>T</i><sub>1</sub> Contrast Agent for In Vivo Molecular MR Imaging of Inflammatory Disease
Bibliographic record
Abstract
Nitric oxide (NO) is a signaling molecule that not only appears in the very early stage of inflammatory disease but also persists in chronic conditions. Its detection in vivo can, therefore, potentially enable early disease detection and treatment monitoring. Due to its transient nature and low abundance, however, noninvasive and deep-tissue imaging of NO dynamics is challenging. In this study, we present a magnetic resonance imaging (MRI) contrast agent based on a manganese porphyrin for specific imaging of NO. This agent is activated by NO, binds to tissue protein, accumulates so long as NO is actively produced, and confers a substantial bright contrast on T 1 -weighted MRI. In vitro tests confirm the specificity of activation by NO over other reactive oxygen or nitrogen species, absence of inflammation induced by the contrast agent, and sensitivity to NO levels in the tens of micromolar. In vivo demonstration in a mouse model of stress-induced acute myocardial inflammation revealed an over 2.2-times increase in T 1 reduction in the inflamed heart compared to a healthy heart. This new NO-activatable T 1 contrast agent holds the potential to provide early diagnosis of inflammatory disease, characterize different stages of inflammation, and ultimately guide the design of novel anti-inflammation therapeutics.
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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 | 0.001 | 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".