Expression of Fat and Cholesterol Biomarkers in Meat Goats
Bibliographic record
Abstract
Consumption of high amounts of saturated fatty acids in meat has been implicated in the onset of cardiovascular disease. Chevon (goat meat) is higher in mono-unsaturated and poly-unsaturated fatty acids than beef and lamb. Limited data is available on the expression of fat and cholesterol biomarkers in meat goats. The objective of this experiment was to determine expression of Acetyl-CoA Carboxylase (ACC1), Apoplipoproteins, A (ApoA1), and B (ApoB) in different breeds of meat goats. Protein sequence alignments were generated to determine conservation for antibody selection. The motif (SMS79pGL) was conserved in the goat, human, mouse, rat and bovine ACC1 proteins. The ApoA1 and ApoB protein alignments (human, bovine and rabbit) revealed high protein sequence homology. The Enzyme-Linked Immunosorbent Assay (ELISA) was used to determine serum ACC1, ApoA1 and ApoB in Spanish and Myotonic goats. Spanish goats had higher (P<0.05) ACC1 than Myotonic goats. There was a gender effect (P<0.05) where females expressed more ACC1 than males. Breed and gender differences were detected in Spanish and Myotonic goats, with Spanish goats showing 37% more (P<0.05) ApoA1 expression in the blood than Myotonic goats and female goats with 47% higher expression of ApoA1 than males. Inversely, Myotonic goats expressed 35% higher (P<0.05) levels of ApoB than Spanish goats and males had 46% higher (P<0.05) ApoBexpression than females. These data demonstrate that inherent differences exist in lipid metabolism of meat goats and can lead to lipid biomarker assisted breeding programs to produce a heart, healthy red meat for human consumption.
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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 | 0.001 | 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) | 0.001 | 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".