Dynamic manganese‐enhanced magnetic resonance imaging can detect chronic cryoinjury‐induced infarction in pig hearts <i>in vivo</i>
Bibliographic record
Abstract
The purpose was to investigate whether MnCl(2) can serve as an MRI contrast agent to detect chronic cryoinjury infarction in pigs in vivo and whether MnCl(2) causes significant hemodynamic disturbances. Hearts were subjected to a topical 2 min cryothermia to establish myocardial infarction (MI). Thereafter GdDTPA-enhanced MRI was performed at 0, 1, 2 and 3 weeks using a 3 T scanner. Four weeks post-cryoinjury the pigs underwent in vivo Mn-enhanced magnetic resonance imaging (MEMRI). MnCl(2) (70 μmol/kg, 14 min) was infused i.v. intermittently (n = 4) or continuously (n = 5) and T(1)-weighted images were acquired every 2 min simultaneously recording heart rate and arterial blood pressure. Either infusion scheme led to an immediate increment in MR signal intensity (SI) within the left ventricular (LV) blood pool and LV normal and cryoinjured myocardium, which reached a maximum at the end of infusion. No significant difference was observed between the normal and cryoinjured myocardium. After infusion termination, SI decreased faster within the LV blood pool and the MI, as compared with the normal myocardium in either group, resulting in significant contrast between the MI and normal tissue (intermittent: 18 ± 7 vs 49 ± 13%, p = 0.002; continuous: 19 ± 8 vs 36 ± 9%, p = 0.004). Infarction sizes were similar in Mn(2+)- and GdDTPA-enhanced images at 4 and 3 weeks post injury, respectively. Thus, in vivo MEMRI differentiated infarcted from normal myocardium in pig hearts subjected to 4-week cryoinjury. Compared with intermittent infusion, continuous infusion minimized hemodynamic fluctuations.
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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 | 0.000 | 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".