Nonionophore antibiotics do not affect the trans-18:1 and conjugated linoleic acid composition in beef adipose tissue1
Bibliographic record
Abstract
The common practice in North American feedlot industries is to add antibiotics to the diet to prevent disease and improve both BW gain and feed efficiency. In this study, 240 crossbred steer calves were backgrounded on a 54% silage diet for 80 d and fed a finishing diet consisting of 81% barley grain, 10% barley silage, and 7.5% supplement (DM basis) with and without in-feed antibiotics for approximately 120 d. Calves were assigned to 1 of 5 treatments: a control with no antibiotics, 11 mg/kg of chlortetracycline, 44 mg/kg of chlortetracycline, 44 mg/kg of chlortetracycline plus 44 mg/kg of sulfamethazine, and 11 mg/ kg of tylosin phosphate. A combination of GLC and silver-ion HPLC methods was used to analyze the fatty acid composition of brisket adipose tissue, with emphasis on trans-18:1 and CLA isomers. The inclusion of nonionophore antibiotics in the diet had little effect on the fatty acid composition, except that feeding either 44 mg/kg of chlortetracycline or 11 mg/kg of tylosin caused small increases in 9c-14:1 and 16:0 relative to the control (0.26 and 0.9 g/100 g of total fatty acids, respectively). Likewise, profiles of trans-18:1 and CLA isomers were unchanged by antibiotics, but across treatments the predominant trans-18:1 isomer was 10t-18:1 (where t = trans; 3.22%) at 3 times the concentration of the second most abundant isomer (11t-18:1; vaccenic acid, 1.05%). Rumenic acid (9c,11t-18:2, where c = cis) was the major CLA isomer at 61% of total CLA, followed by 7t,9c-18:2 at 9%. Because no other effects on fatty acid composition were evident, data for trans-18:1 and CLA were pooled across treatments to investigate possible relationships among rumen PUFA metabolites. The total trans-18:1 content in brisket adipose tissue was positively correlated with 10t-18:1, but not with 11t-18:1, whereas the total CLA was positively correlated with 9c,11t-18:2, but not with 7t,9c-18:2. The 7t,9c-18:2 was, however, positively correlated with 10t-18:1 and 6t/7t/8t-18:1 but was negatively correlated with rumenic acid. These metabolic interrelationships suggest the presence of bacterial populations with distinct pathways for PUFA biohydrogenation in which either 10t-18:1 or 11t-18:1 predominate. Overall, the nonionophore antibiotics tested did not appreciably change adipose tissue composition and consequently could not be used to improve the trans-18:1 or CLA profile (i.e., increase vaccenic and rumenic acids at the expense of 10t-18:1).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".