72 Young Scholar Presentation: Use of medium chain fatty acids as mitigation or prevention strategies against pathogens in swine feed
Bibliographic record
Abstract
Abstract Four experiments were conducted to evaluate: 1) medium chain fatty acids (MCFA) application to swine feed pre- or post-viral contamination with porcine epidemic diarrhea virus (PEDV) measured by quantitative reverse transcription polymerase chain reaction (qRT-PCR), 2) MCFA levels and combinations measured by qRT-PCR, and 3) selected MCFA in bioassay. In Exp. 1, treatments were a 2x2 + 1 factorial with the main effects of chemical treatment (0.3% commercial formaldehyde (CF), Sal CURB [Kemin Industries, Des Moines, IA] or 1% MCFA blend (Blend) of 1:1:1 C6:C8:C10 [PMI, Arden Hills, MN]) and timing of application pre- or post-inoculation with PEDV; plus a positive control (PC; feed inoculated with PEDV and no chemical treatment). All combinations of treatment and timing decreased detectable PEDV compared to PC (P< 0.05). Pre-inoculation had decreased PEDV detection compared to post-inoculation (P=0.009). Commercial formaldehyde decreased PEDV detection compared to MCFA (P< 0.001). In Exp. 2 and 3, pre-inoculation treatments consisted of: 1) PC, 2) 0.3% CF, and varying levels (0.125-0.66%) and combinations of MCFA (C5:0, C6:0, C8:0, or C10:0). In Exp. 2, treating feed with 0.33% C8:0 decreased (P< 0.05) PEDV detection compared to all levels of MCFA and PC. In Exp. 3, treating feed with CF, 0.5-1% Blend, all levels of C6:0+C8:0, 0.25% C6:0+C10:0, 0.33% C6:0+C10:0, 0.25% C8:0+C10:0, or 0.33% C8:0 + 0.33% C10:0 resulted in decreased PEDV detection compared to PC (P< 0.05). In Exp. 4, feed was treated pre-inoculation with either 1) no treatment (PC), 2) 0.3% CF, 3) 0.5% Blend, or 4) 0.3% C8:0 and analyzed via qRT-PCR and bioassay. Adding 0.5% Blend or 0.3% C8:0 resulted in decreased PEDV detection compared to PC. All chemical treatments resulted in no evidence of infectivity in the bioassay while the positive control did produce evidence of infectivity. In conclusion, lower levels of MCFA than previously evaluated may provide in-feed protection against PEDV.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.213 | 0.032 |
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".