High-frequency ultrasound for monitoring changes in liver tissue during preservation
Bibliographic record
Abstract
Currently the only method to assess liver preservation injury is based on liver appearance and donor medical history. Previous work has shown that high-frequency ultrasound could detect ischemic cell death due to changes in cell morphology. In this study, we use high-frequency ultrasound integrated backscatter to assess liver damage in experimental models of liver ischemia. Ultimately, our goal is to predict organ suitability for transplantation using high-frequency imaging and spectral analysis techniques. To examine the effects of liver ischemia at different temperatures, livers from Wistar rats were surgically excised, immersed in phosphate buffer saline and stored at 4 and 20 degrees C for 24 h. To mimic organ preservation, livers were excised, flushed with University of Wisconsin (UW) solution and stored at 4 degrees C for 24 h. Preservation injury was simulated by either not flushing livers with UW solution or, before scanning, allowing livers to reach room temperature. Ultrasound images and corresponding radiofrequency data were collected over the ischemic period. No significant increase in integrated backscatter (approximately 2.5 dBr) was measured for the livers prepared using standard preservation conditions. For all other ischemia models, the integrated backscatter increased by 4-9 dBr demonstrating kinetics dependent on storage conditions. The results provide a possible framework for using high-frequency imaging to non-invasively assess liver preservation injury.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".