Feeding hempseed cake alters the bovine gut, respiratory and reproductive microbiota
Bibliographic record
Abstract
Abstract A growing number of studies have investigated the feasibility of utilizing hemp by-products as livestock feedstuffs; however, their impact on livestock microbiomes remains unexplored. Here, we evaluated the effects of feeding hempseed cake on the gastrointestinal, respiratory, and reproductive microbiota in beef heifers. Angus-crossbred heifers (19-months old, initial body weight = 494 ± 10 kg [SE]) were fed a corn-based finishing diet containing 20% hempseed cake as a substitute for 20% corn dried distillers’ grains with solubles (DM basis; Control; n = 16/group) for 111 days until slaughter. Ruminal fluid and deep nasopharyngeal swabs (days 0, 7, 42, 70 and 98), and vaginal and uterine swabs (at slaughter) were collected, and the microbiota assessed using 16S rRNA gene sequencing. Diet affected the community structure of the ruminal (d 7−98; 0.06 ≤ R 2 ≤ 0.12; P < 0.05), nasopharyngeal (d 98; R 2 = 0.18; P < 0.001), and vaginal (R 2 = 0.06; P < 0.01) microbiota. Heifers fed hempseed cake had increased microbial diversity in the rumen, reduced microbial richness in the vagina, and greater microbial diversity and richness in the uterus. In addition to the distinct microbial communities in the rumen, nasopharynx, vagina and uterus, we identified 28 core taxa that were shared (≥ 60% of all samples) across these sampling locations. Feeding hempseed cake appeared to alter the bovine gut, respiratory and reproductive microbiota. Our results suggest that future research aiming to evaluate the use of hemp by-products in livestock diet should consider their impact on animal microbiome and microbiome mediated animal health and reproductive efficiency. Our findings also highlight the need for research evaluating the impact of hemp-associated food and personal care products on the human microbiome.
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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 | 0.000 | 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) | 0.001 | 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".