Utility of Hepatic Phosphorus-31 Magnetic Resonance Spectroscopy in a Rat Model of Acute Liver Failure
Bibliographic record
Abstract
The ability to document the extent of hepatic injury and predict the outcome of fulminant hepatic failure would be helpful in identifying those patients who might benefit from liver transplantation. The aim of the present study was to determine whether in vivo phosphorus-31 magnetic resonance spectroscopy (31P MRS) accurately assesses the severity of liver damage and is of prognostic value in a D-galactosamine (D-galN)-induced model of acute liver failure. Adult male Sprague-Dawley rats (n = 36) received an intraperitoneal dose of D-galN (1.0 g/kg), and MRS examinations were performed at peak (48 hours) and in subsequent experiments, just prior to peak (30 hours) hepatic injury. Rats not exposed to D-galN served as controls. The concentration of hepatic phosphorylated metabolites decreased in proportion to the severity of liver injury at 48 hours. Significant correlations were detected between hepatic adenosine triphosphate (ATP) and serum aspartate aminotransferase, bilirubin, and percentage of hepatocyte necrosis identified histologically (r = -.91, -.74, and -.92, respectively; p < .001). Prior to peak hepatic injury (30 hours), 31P MRS was able to predict with 100% accuracy those rats that would survive (ATP > 2.3 mM) and those that would not (ATP < 1.5 mM). When an intermediate cutoff value of 2.0 mM was selected, ATP levels were able to correctly predict survival and death with 80% and 60% accuracy, respectively. These findings indicate that hepatic ATP levels as measured by 31P MRS provide a noninvasive indication of the severity of liver damage and serve as a useful prognostic indicator of outcome in this model of acute liver failure.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".