Noninvasive Immunometabolic Cardiac Inflammation Imaging Using Hyperpolarized Magnetic Resonance
Bibliographic record
Abstract
Rationale: Current cardiovascular clinical imaging techniques offer only limited assessment of innate immune cell-driven inflammation, which is a potential therapeutic target in myocardial infarction (MI) and other diseases. Hyperpolarized magnetic resonance (MR) is an emerging imaging technology that generates contrast agents with 10- to 20 000-fold improvements in MR signal, enabling cardiac metabolite mapping. Objective: To determine whether hyperpolarized MR using [1- 13 C]pyruvate can assess the local cardiac inflammatory response after MI. Methods and Results: We performed hyperpolarized [1- 13 C]pyruvate MR studies in small and large animal models of MI and in macrophage-like cell lines and measured the resulting [1- 13 C]lactate signals. MI caused intense [1- 13 C]lactate signal in healing myocardial segments at both day 3 and 7 after rodent MI, which was normalized at both time points after monocyte/macrophage depletion. A near-identical [1- 13 C]lactate signature was also seen at day 7 after experimental MI in pigs. Hyperpolarized [1- 13 C]pyruvate MR spectroscopy in macrophage-like cell suspensions demonstrated that macrophage activation and polarization with lipopolysaccharide almost doubled hyperpolarized lactate label flux rates in vitro; blockade of glycolysis with 2-deoxyglucose in activated cells normalized lactate label flux rates and markedly inhibited the production of key proinflammatory cytokines. Systemic administration of 2-deoxyglucose after rodent MI normalized the hyperpolarized [1- 13 C]lactate signal in healing myocardial segments at day 3 and also caused dose-dependent improvement in IL (interleukin)-1β expression in infarct tissue without impairing the production of key reparative cytokines. Cine MRI demonstrated improvements in systolic function in 2-DG (2-deoxyglucose)–treated rats at 3 months. Conclusions: Hyperpolarized MR using [1- 13 C]pyruvate provides a novel method for the assessment of innate immune cell-driven inflammation in the heart after MI, with broad potential applicability across other cardiovascular disease states and suitability for early clinical translation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".