High Frequency Ultrasound in Monitoring Suitability for Transplantation
Bibliographic record
Abstract
Currently there are no validated clinical methods to assess liver preservation injury. In this work we use high frequency ultrasound integrated backscatter (HFUIB) to assess liver damage in different experimental models of liver ischemia. The ultimate goal of this work is to provide a non-invasive tool to assess organ suitability for transplantation. To examine the effects of liver ischemia at different temperatures, livers from Wistar rats are surgically excised, immersed in phosphate buffer saline (PBS) and stored at 4 and 20°C for 24h. To mimic organ preservation, livers are excised, flushed with University of Wisconsin (UW) solution and stored at 4°C for 24h. Preservation injury is simulated by not flushing livers with UW solution. Ultrasound images and corresponding radio frequency data are collected over the ischemic periods. No significant increase in HFUIB is measured for the livers prepared using standard preservation conditions. For all other ischemia models, the HFUIB increases by 4-9 dBr demonstrating kinetics dependent on storage conditions. HFUIB increase is associated with liver tissue injury. 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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".