Quantification of fibrosis in infarcted swine hearts by<i>ex vivo</i>late gadolinium-enhancement and diffusion-weighted MRI methods
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Bibliographic record
Abstract
Many have speculated that MRI signal characteristics can be used to identify regions of heterogeneous infarct associated with an arrhythmogenic substrate; however, direct evidence of this relationship is limited. The aim of this study was to demonstrate the remodelling characteristics of fibrosis by means of histology and high-resolution MR imaging. For this purpose, we performed whole-mount histology in heart samples (n = 9) collected from five swine at six weeks post-infarction and compared the extent of fibrosis in the infarcted areas delineated in these histological images with that obtained ex vivo by MRI using late gadolinium-enhancement (LGE) and diffusion-weighted imaging (DWI) methods. All MR images were obtained at a submillimetre resolution (i.e., voxel size of 0.6×0.6×1.2 mm(3)). Specifically, in the histology images, we differentiated moderate fibrosis (consisting of a mixture of viable and non-viable myocytes, known as border zone, BZ) from severe fibrosis (i.e., the dense scar). Correspondingly, tissue heterogeneities in the MR images were categorized by a Gaussian mixture model into healthy, BZ and scar. Our results showed that (a) both MRI methods were capable of qualitatively distinguishing sharp edges between dense scar and healthy tissue from regions of heterogeneous BZ; (b) the BZ and dense scar areas had intermediate-to-high increased values of signal intensity in the LGE images and of apparent diffusion coefficient in the DWI, respectively. In addition, as demonstrated by the Picrosirius Red and immunohistochemistry stains, the viable bundles in the BZ were clearly separated by thin collagen strands and had reduced expression of Cx43, whereas the core scar was composed of dense fibrosis. A quantitative analysis demonstrated that the comparison between BZ/scar extent in LGE and DWI to the corresponding areas identified in histology yielded very good correlations (i.e., for the scar identified by LGE, R(2) was 0.96 compared to R(2) = 0.93 for the scar identified in ADC maps, whereas the BZ had R(2) = 0.95 for the correlation between LGE and histology compared to R(2) = 0.91 obtained for ADC). This novel study represents an intermediate step in translating such research to the in vivo stages, as well as in establishing the best and most accurate MR method to help identify arrhythmia substrate in patients with structural heart disease.
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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 | 0.000 | 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) | 0.000 | 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 it