Dynamic responses of energy metabolism in ewes during late pregnancy: a meta-regression
Bibliographic record
Abstract
Insufficient energy intake throughout late gestation in ewes can induce a negative energy balance, leading to hypoglycemia and hyperketonemia, and increasing the risk of metabolic diseases. Previous studies have investigated the changes in key metabolites related to energy metabolism during late gestation in sheep, focusing on glucose, non-esterified fatty acids ( NEFA ), and beta-hydroxybutyrate ( BHB ). The current meta-analysis was undertaken to gather available data on the kinetic of energy metabolites throughout late gestation to investigate the metabolic status of ewes in relation to dietary energy level and litter size. For the construction of the database, dietary energy concentration of each experiment was classified into two categories: diets covering between 60 and 100% ( E60 ) or covering more than 100% ( E100 ) of the metabolizable energy requirement for twin-bearing ewes at 133 days of gestation according to the National Research Council ( N RC , 2007). Treatment groups were also categorized according to average litter size: single ([1.0 – 1.5[), twins ([1.5 – 2.5[), triplets ([2.5 – 3.5[), and quadruplets + ([3.5 – 5.0]). The analysis of the meta-design revealed a quadratic effect of day to lambing for circulating glucose. For both dietary energy levels, glucose concentration decreased from day –42 to reach a nadir between days –28 and –14, depending on litter size group, and then increased until day –1. A greater decrease in glucose concentration was observed with larger litter sizes and was more pronounced for E60 than E100 groups. For each litter size group, when comparing similar days in gestation, E100 groups showed greater glucose concentrations than E60. Circulating NEFA increased linearly throughout late gestation, and a significant interaction was observed between dietary energy level and litter size group. For a similar litter size, NEFA concentrations were higher for E60 compared with E100, and the increase in NEFA concentrations with litter size was more pronounced for E60 than E100 groups, during the last 42 days in gestation. Circulating BHB increased with gestation. The intercept was higher and the slope steeper for E60 compared with E100, as well as for groups of ewes bearing larger litters. The meta-regression developed demonstrate the impact of litter size on energy requirements of ewes in late gestation. The meta-design also highlighted that data on the energy demands of ewes bearing three or more lambs are scarce. Of all the dietary treatments gathered in this meta-analysis, according to NRC (2007), only three provided an adequate amount of energy for triplet-bearing and none for quadruplet + -bearing ewes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".