Low birth weight and reduced postnatal nutrition lead to cardiac dysfunction in piglets
Bibliographic record
Abstract
Heart disease is the leading cause of death in humans and evidence suggests early life growth-restriction increases heart disease risk in adulthood. Therefore, this study sought to investigate the effects of low birth weight (LBW) and postnatal restricted nutrition (RN) on cardiac function in neonatal pigs. We hypothesized that LBW and RN would reduce cardiac function in pigs but this effect would be reversed with refeeding. To investigate this hypothesis, pigs born weighing <1.5 kg were assigned LBW, and pigs born >1.5 kg were assigned normal birth weight (NBW). Half the LBW and NBW pigs underwent ~25% total nutrient restriction via intermittent suckling (assigned RN) for the first 4 wk post-farrowing. The other half of piglets were allowed unrestricted suckling access to the sow (assigned NN). At 28 d of age (weaning), pigs were weaned and provided ad libitum access to a standard diet. Echocardiographic, vascular ultrasound, and blood pressure (BP) measurements were performed on day 28 and again on day 56 to assess cardiovascular structure and function. A full factorial three-way ANOVA (NN vs. RN, LBW vs. NBW, male vs. female) was performed. Key findings include reduced diastolic BP (P = 0.0401) and passive ventricular filling (P = 0.0062) in RN pigs at 28 d but this was reversed after refeeding. LBW piglets have reduced cardiac output index (P = 0.0037) and diastolic and systolic wall thickness (P = 0.0293 and P = 0.0472) at 56 d. Therefore, cardiac dysfunction from RN is recovered with adequate refeeding while LBW programs irreversible cardiac dysfunction despite proper refeeding in neonatal pigs.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".