Selective In Vivo Visualization of Immune-Cell Infiltration in a Mouse Model of Autoimmune Myocarditis by Fluorine-19 Cardiac Magnetic Resonance
Bibliographic record
Abstract
BACKGROUND: The goal of this study was to characterize the performance of fluorine-19 ((19)F) cardiac magnetic resonance (CMR) for the specific detection of inflammatory cells in a mouse model of myocarditis. Intravenously administered perfluorocarbons are taken up by infiltrating inflammatory cells and can be detected by (19)F-CMR. (19)F-labeled cells should, therefore, generate an exclusive signal at the inflamed regions within the myocardium. METHODS AND RESULTS: Experimental autoimmune myocarditis was induced in BALB/c mice. After intravenous injection of 2×200 µL of a perfluorocarbon on day 19 and 20 (n=9) after immunization, in vivo (19)F-CMR was performed at the peak of myocardial inflammation (day 21). In 5 additional animals, perfluorocarbon combined with FITC (fluorescein isothiocyanate) was administered for postmortem immunofluorescence and flow-cytometry analyses. Control experiments were performed in 9 animals. In vivo (19)F-CMR detected myocardial inflammation in all experimental autoimmune myocarditis-positive animals. Its resolution was sufficient to identify even small inflammatory foci, that is, at the surface of the right ventricle. Postmortem immunohistochemistry and flow cytometry confirmed the presence of perfluorocarbon in macrophages, dendritic cells, and granulocytes, but not in lymphocytes. The myocardial volume of elevated (19)F signal (rs=0.96; P<0.001), the (19)F signal-to-noise ratio (rs=0.92; P<0.001), and the (19)F signal integral (rs=0.96; P<0.001) at day 21 correlated with the histological myocarditis severity score. CONCLUSIONS: In vivo (19)F-CMR was successfully used to visualize the inflammation specifically and robustly in experimental autoimmune myocarditis, and thus allowed for an unprecedented insight into the involvement of inflammatory cells in the disease process.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".