Indocyanine green fluorescence quantification during normothermic ex situ perfusion for the assessment of porcine liver grafts after circulatory death
Bibliographic record
Abstract
Current graft evaluation during normothermic ex situ liver perfusion lacks real-time parameters for predicting posttransplant hepatocyte and biliary function. Indocyanine green (ICG) imaging has been widely used in liver surgery, enabling the visualization of hepatic uptake and excretion through bile using near-infrared light. In this research, porcine livers under various ischemic conditions were examined during a 5-hour normothermic ex situ liver perfusion procedure, introducing ICG at 1 hour through the hepatic artery. These conditions included livers from heart-beating donors, donation after circulatory death (DCD) with warm ischemic durations of 60 minutes (DCD60) and 120 minutes (DCD120), as well as interventions utilizing tissue plasminogen activator in DCD120 cases (each n = 5). Distinct hepatic fluorescence patterns correlated with different degrees of ischemic injury ( p = 0.01). Low ICG uptake in the parenchyma (less than 40% of maximum intensity) was more prevalent in DCD120 (21.4%) compared to heart-beating donors (6.2%, p = 0.06) and DCD60 (3.0%, p = 0.02). Moreover, ICG clearance from 60 minutes to 240 minutes was significantly higher in heart-beating donors (69.3%) than in DCD60 (17.5%, p < 0.001) and DCD120 (32.1%, p = 0.01). Furthermore, thrombolytic intervention using tissue plasminogen activator in DCD120 resulted in noteworthy outcomes, including significantly reduced ALP levels ( p = 0.04) and improved ICG clearance ( p = 0.02) with a trend toward mitigating fibrin deposition similar to DCD60, as well as enhancements in bile production ( p = 0.09). In conclusion, ICG fluorescence imaging during normothermic ex situ liver perfusion provides real-time classification of hepatic vascular and biliary injuries, offering valuable insights for the more accurate selection and postintervention evaluation of marginal livers in transplantation.
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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".