Contrast-enhanced ultrasound imaging for assessing organ perfusion in rainbow trout (Oncorhynchus mykiss)
Bibliographic record
Abstract
Contrast-enhanced ultrasound (CEUS) imaging has great potential as a non-lethal, inexpensive monitoring tool in aquatic toxicology. It is a well-established clinical imaging approach that combines real-time, quantitative assessment of organ blood flow, with morphological data. In humans, it has been extensively used to measure changes in blood flow that can be attributed to cancer, inflammation, and other biological abnormalities. However, it has yet to be explored as a tool for fish physiology or environmental toxicology. In this study, our goal was to determine if CEUS could be used to visualize and measure blood flow in the liver of a rainbow trout. All rainbow trout received two injections of an ultrasound contrast agent, microbubbles. A subset received a third injection after administration of propranolol, a non-specific beta1 & 2-blocker, to determine if changes in blood flow could be detected. Ultrasound contrast time-intensity curves (TIC) were obtained, fit to a lognormal model, and different perfusion parameters were calculated. Contrast enhancement was observed in all rainbow trout livers, with high percentage between repeated measurements, including blood flow (80.6 ± 27.3%), area under the curve (73.2 ± 14%), blood volume (84 ± 14.2%) and peak enhancement (86.7 ± 7.5%). After administration of propranolol, we detected a non-significant (p > 0.05) increase in area under the curve (102.6 ± 44.2%), peak enhancement (77.3 ± 106.4), blood volume (48.2 ± 74.5%), and decrease in hepatic blood flow (-17.3 ± 37.1%). These data suggest that CEUS imaging is suitable to measure organ blood flow in fish, and demonstrates tremendous potential for exploring different organs, fish species, and effects of chemical contaminants in future studies.
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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) | 0.000 | 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".