Compensatory Growth And Blood Pressure Using Non‐Invasive And Telemetry Techniques In Yucatan Mini‐Pigs
Bibliographic record
Abstract
In epidemiological studies, low birth‐weight followed by catch‐up growth is associated with an increased risk of hypertension and cardiovascular disease in adulthood. Runt Yucatan miniature pigs (<800g) were paired with the largest same sex littermate (>1000g) and fed milk replacer ad libitum from 3 to 31 days of age; thereafter they were fed standard chow ad libitum. During the first 4 wk, runts demonstrated catch‐up growth and by 8 months of age their body weights were not different than their littermates. In order to monitor hypertension, we used the noninvasive blood pressure (NIBP) technique using a foot cuff at 8 months of age. We subsequently measured BP using telemetry. With NIBP, systolic, diastolic and mean arterial pressure (MAP) (runt: 93 ± 12, littermate: 92± 10 mmHg) were not different between groups. However, for all pigs, none of these data correlated with respective telemetry data. NIBP heart rate tended to correlate with peak heart rate using telemetry (P=0.08) but blood pressure estimates tended to be negatively correlated (P=0.11). Using telemetry data for all pigs, unimodal distributions over 24 h were observed for MAP (117± 7 mmHg), heart rate (87± 5 bpm), systolic (140± 7 mmHg), diastolic (97± 7 mmHg) and pulse (43 ± 6 mmHg) pressures. Although blood pressure parameters were not different between runts and littermates, relative size of left ventricles in runts were larger than littermates. BP telemetry data during a salt challenge will also be presented. We have established a pig model of compensatory growth which can be used to further investigate early origins of cardiovascular disease (Supported by CIHR).
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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".