0496 Nurturing healthy gut microbiome: route to increased disease resistance in ruminants
Bibliographic record
Abstract
Perturbations in gut microbiota colonization during early life have been shown to present long lasting influences to the host immune responses, health and metabolism as well as subsequent microbial succession. These perturbations can be caused by nutritional imbalances, differing feeding methods, nutritional regimes and antibiotic treatments during the early life. Management of ruminants can vary significantly due to different feeding practices and it relies heavily on antibiotics prevention/treatments for diarrhea and pneumonia in pre-ruminants. However, our understanding on the influence of such management practices on gut microbiome as well as ruminant health and metabolism is very limited. Our recent study has revealed that feeding of heat-treated colostrum soon after birth enhances the colonization of beneficial bacterium, bifidobacteria, which is well-studied using mouse models and has been shown to have greater impact on mucosal immune system development as well as weight gain in children. Besides, the colonization of bifidobacteria in calf gut has been suggested as a preventive mechanism of pathogenic E. coli that causes neonatal calf diarrhea. The use of next generation sequencing approaches to study calf gut microbiome and linking the early microbial composition with that of calf phenotypes have reported that higher abundance of Fecalibacterium is associated with decreased diarrhea incidences and increased body weight, suggesting linkages between gut microbiome, calf health and growth. Moreover, diet-driven changes in rumen microbiome are related to the development of subacute ruminal acidosis, a prevalent metabolic disorder in adult cattle. Thus, understanding on gut microbiome and their link to gut/rumen development and metabolism will provide means to improve health in ruminants via microbial manipulation. Such manipulation methods toward nurturing a healthy gut microbiome not only improve the disease resistance in ruminants, but may also decrease the heavy antibiotic usage that is in practice in the industry.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".