JoVE Video Dataset
Bibliographic record
Abstract
Assessment of cardiac function is essential to conduct cardiovascular and pulmonary-vascular preclinical research. Pressure-volume loops (PV loops) generated by recording both pressure and volume during cardiac catheterization are vital when assessing both systolic and diastolic cardiac function. Left and right heart function are closely related, reflected in ventricular interdependence. Thus, recording biventricular function in the same animal is important to get a complete assessment of cardiac function. In this protocol, a closed chest approach to cardiac catheterization consistent with the way catheterization is performed in patients is adopted in mice. While challenging, the closed chest strategy is a more physiological approach, because opening the chest results in major changes in preload and afterload that create artifacts, most notably a fall in systemic blood pressure. While high-resolution echocardiography is used to assess rodents, cardiac catheterization is invaluable, particularly when assessing diastolic pressures in both ventricles. Described here is a procedure to perform invasive, closed chest, sequential left and right ventricular pressure-volume (PV) loops in the same animal. PV loops are acquired using admittance technology with a mouse pressure-volume catheter and pressure-volume system acquisition. The procedure is described, beginning with the neck dissection, which is required to access the right jugular vein and the right carotid artery, to the insertion and positioning of the catheter, and finally the data acquisition. Then, the criteria required to ensure the acquisition of high-quality PV loops are discussed. Finally, the analysis of the left and right ventricular PV loops and the different hemodynamic parameters available to quantify systolic and diastolic ventricular function are briefly described.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.600 | 0.376 |
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".