Microbial interventions to improve health in neonatal ruminants: discoveries to applications
Bibliographic record
Abstract
There is an increasing interest in using early-life microbial interventions in neonatal ruminants to improve health and create long-lasting effects. Early life provides a window of opportunity to alter microbial community composition (gut and respiratory tract) through interventions and thereby modulate the developing immune system. Studies in neonatal ruminants, especially calves, have revealed that microbiome (composition, diversity and functions) are markers of animals’ health status and colonization of beneficial gut bacterial groups ( Bifidobacterium, Lactobacillus, Faecalibacterium) can promote healthy growth. Microbial interventions, such as probiotics, prebiotics, and fecal microbial transplantation have shown positive effects by mitigating calf diarrhea, improving weight gain, and altering microbial community composition. However, there is a huge variability among studies in terms of the intervention method, dosage, timing of the intervention, and calf age. Moreover, these studies overlook the impact of interventions on the host, especially the impact of microbial interventions on the priming of immune responses and creating immune memory. While a majority of studies are discovery-based, the lack of knowledge on microbiome, host-microbe interactions, and long-term causal effects prevents translating these discoveries into applications. Neonatal ruminant microbiome research heavily relies on limited small-scale trials that focus only on a few selected calf-raring practices. Additionally, the lack of uniformity (especially in profiling microbiome) prevents combining studies into meta-analyses. A thorough understanding of the impact of microbial interventions on the microbiome and immune system, followed by proof-of-concept studies to validate their impact is vital in developing successful microbial interventions to improve neonatal ruminant health.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".