MRI-based assessment of liver perfusion and hepatocyte injury in the murine model of acute hepatitis
Bibliographic record
Abstract
Objective To assess alterations in perfusion and liver function in the concanavalin A (ConA)-induced mouse model of acute liver failure (ALF) using two magnetic resonance imaging (MRI)-based methods: dynamic contrast-enhanced MRI (DCE-MRI) with Gd-EOB-DTPA contrast agent and arterial spin labelling (ASL). Materials and methods BALB/c mice were studied using a 9.4 T MRI system. The IntraGateFLASHTM and FAIR-EPI pulse sequences were used for optimum mouse abdomen imaging. Results The average perfusion values for the liver of the control and ConA group were equal to 245 ± 20 and 200 ± 32 ml/min/100 g (p = 0.008, respectively). DCE-MRI showed that the time to the peak of the image enhancement was 6.14 ± 1.07 min and 9.72 ± 1.69 min in the control and ConA group (p < 0.001, respectively), while the rate of the contrast wash-out in the control and ConA group was 0.037 ± 0.008 and 0.021 ± 0.008 min−1 (p = 0.004, respectively). These results were consistent with hepatocyte injury in the ConA-treated mice as confirmed by histopathological staining. Conclusions Both the ASL and DCE-MRI techniques represent a reliable methodology to assess alterations in liver perfusion and hepatocyte integrity in murine hepatitis.
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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) | 0.001 | 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 | 0.000 | 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".