Adaptation of a mouse Doppler echocardiograph system for assessing cardiac function and thermal performance in a juvenile salmonid
Bibliographic record
Abstract
Abstract Measures of cardiac performance are pertinent to the study of thermal physiology and exercise in teleosts, particularly as they pertain to migration success. Increased heart rate, stroke volume and cardiac output have previously been linked to improved swimming performance and increased upper thermal tolerance in anadromous salmonids. To assess thermal performance in fishes, it has become commonplace to measure the response of maximum heart rate to warming using electrocardiograms. However, electrocardiograms do not provide insight into the hemodynamic characteristics of heart function that can impact whole-animal performance. Doppler echocardiography is a popular tool used to examine live animal processes, including real-time cardiac function. This method allows for nonsurgical measurements of blood flow velocity through the heart and has been used to detect abnormalities in cardiovascular function, particularly in mammals. Here, we show how a mouse Doppler echocardiograph system can be adapted for use in a juvenile salmonid over a range of temperatures and timeframes. Using this compact, noninvasive system, we measured maximum heart rate, atrioventricular (AV) blood flow velocity, the early flow-atrial flow ratio and stroke distance in juvenile Atlantic salmon (Salmo salar) during acute warming. Using histologically determined measures of AV valve area, we show how stroke distance measurements obtained with this system can be used to calculate ventricular inflow volume and approximate cardiac output. Further, we show how this Doppler system can be used to determine cardiorespiratory thresholds for thermal performance, which are increasingly being used to predict the consequences that warming water temperatures will have on migratory fishes.
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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 | 0.000 | 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".