The runt miniature pig is a good model to study the early origins of adult diseases
Bibliographic record
Abstract
Recently, small birth weight and rapid compensatory growth have been linked to chronic diseases, such as type 2 diabetes, hypertension and cardiovascular disease. We sought to develop a Yucatan miniature pig model to study how neonatal changes in metabolism can lead to the development of chronic disease. Runt piglets (<800g) (N=6) were paired with the largest same sex littermate (>1100g) (N=6) and fed milk replacer ad lib from 3 to 31 d of age; thereafter pigs were fed chow ad lib. At 8 mo, pigs were fitted with venous catheters and an arterial blood pressure (BP) telemeter to measure BP continuously in unrestrained pigs. During formula feeding, runts demonstrated more efficient compensatory growth and by 8 mo, runts (62.6±6.6 kg) caught up in body weight to littermates (67.5±4.7) (P>0.05), but with more back fat (P=0.03). At 8 mo, runts had 2x higher plasma triglycerides (P<0.001), 45% slower clearance of triglycerides after a fat tolerance test (P=0.045), and higher (P<0.05) mean arterial pressure (MAP) (115.6 vs 110.7 mmHg) and systolic pressure (SP) (140.8 vs 134.3 mmHg), averaged over 24 h. Birth weight was negatively correlated to MAP (P=0.02) and SP (P=0.05). Glucose tolerance and insulin sensitivity tests were not different. Using the miniature pig, we have successfully established a model for compensatory growth that can be used to investigate the mechanisms of early origins of adult 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".