Revisiting cattle respiratory health: key roles of the gut-lung axis in the dynamics of respiratory tract pathobiome
Bibliographic record
Abstract
SUMMARYDespite the increasing preventative efforts (vaccines, hygiene, pre-conditioning), respiratory tract (RT) infections pose a significant challenge across mammalian species. Recently, there has been a greater emphasis on promoting healthy microbiome colonization to mitigate respiratory infection in humans and livestock species. In livestock animals, especially in cattle, RT microbiome research has mainly focused on characterizing the respiratory tract microbial community in healthy and sick animals, aiming to identify microbiota linked to disease or health status. However, this approach overlooked the dynamics of RT microbiome that comprises commensal opportunistic pathogens (an element of the pathobiome) contributing to the infection and disease pathogenesis. Moreover, there is a lack of attempts to evaluate the interactions among host immunity-microbiome-pathobiome during pathogenesis for the development of successful microbiome-based interventions to improve cattle respiratory health. Recent research has revealed new insights into the gut-lung axis (GLA) and the regulatory role of the gut microbiota in determining host susceptibility or resilience to respiratory infections. Therefore, this review aims to critically discuss the roles of RT microbiome (including pathobiome) and GLA in respiratory health, while elucidating the mechanisms driving the dynamic transition from a commensal state to pathogenic state during microbiome dysbiosis and immune dysregulation, and identifying microbiome targets for RT health improvement.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".