Comparing early liver graft function from heart beating and living‐donors: A pilot study aiming to identify new biomarkers of liver injury
Bibliographic record
Abstract
Abstract The liver and kidney functions of recipients of liver transplantation (LT) surgery with heart beating (HBD, n = 13) or living donors (LD, n = 9) with different cold ischemia times were examined during the neohepatic phase for the elimination of rocuronium bromide (ROC, cleared by liver and kidney) and tranexamic acid (TXA, cleared by kidney). Solid phase micro‐extraction and LC–MS/MS was applied to determine the plasma concentrations of ROC and TXA, and creatinine was determined by standard laboratory methods. Metabolomics and the relative expressions of miR‐122, miR‐148a and γ‐glutamyltranspeptidase (GGT), liver injury biomarkers, were also measured. The ROC clearance for HBD was significantly lower than that for LD (0.147 ± 0.052 vs. 0.265 ± 0.148 ml·min−1·g−1 liver) after intravenous injection (0.6 mg·kg−1). The clearance of TXA, a compound cleared by glomerular filtration, given as a 1 g bolus followed by infusion (10 mg·kg−1·h−1), was similar between HBD and LD groups (~ 1 ml·min−1·kg−1). The TXA clearance in both groups was lower than the GFR, showing a small extent of hepatorenal coupling. The miR‐122 and miR‐148a expressions were similar for the HBD and LD groups, whereas GGT expression was significantly increased for HBD. The lower ROC clearance and the higher GGT levels in the HBD group of longer cold ischemia times performed worse than the LD group during the neophase. Metabololmics further showed clusters of bile acids, phospholipids and lipid ω‐oxidation products for the LD and HBD groups. In conclusion, ROC CL and GGT expression, and metabolomics could serve as sensitive indices of early graft function. Copyright © 2017 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".