Molecular Magnetic Resonance Imaging Visualizes Nitric Oxide Dynamics in a Mouse Model of Acute Myocardial Inflammation
Bibliographic record
Abstract
Nitric oxide (NO) is an essential signaling molecule that appears early in inflammation and persists in chronic diseases, making it a promising biomarker for early detection and treatment monitoring. Although transient NO production supports immune defense, sustained elevations lead to nitrosative stress, tissue injury, and accelerated disease progression. However, NO’s short half-life and deep-tissue localization make noninvasive detection challenging. Current diagnostic methods lack combined sensitivity, specificity, and spatial resolution needed to effectively image NO in vivo . To address these challenges, we developed a manganese porphyrin-based magnetic resonance imaging (MRI) contrast agent, MnTPPS 3 Diaminotoluene, that selectively detects NO in vivo . Acute, reversible myocardial inflammation was induced in mice using isoproterenol (ISO), and the contrast agent was administered at various time points following injury. Cardiac MRI was conducted and validated through biochemical assays of tissue NO levels, immunoblotting, histopathology, and echocardiography. ISO-treated mice exhibited significantly greater T 1 reduction (i.e., greater contrast) compared to sham mice for up to 10 days following MnTPPS 3 Diaminotoluene administration. Importantly, MRI detected a peak myocardial NO level within 3 days postinjury, returning to baseline by day 7. This temporal pattern matched immune cell infiltration and was corroborated by an elevated level of myocardial nitrotyrosine, indicating NO-derived peroxynitrite and nitrosative stress. Mild interstitial fibrosis developed, peaking at day 7, while cardiomyocyte hypertrophy and elevated ejection fraction normalized by day 21. Together, these findings demonstrate that this NO-activatable MRI contrast agent enables direct imaging of NO overproduction in vivo, providing a tool for studying inflammation and advancing anti-inflammatory therapeutic development.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".