Commensal microbiome effects on mucosal immune system development in the ruminant gastrointestinal tract
Bibliographic record
Abstract
Commensal microflora play many roles within the mammalian gastrointestinal tract (GIT) that benefit host physiology by way of direct or indirect interactions with mucosal surfaces. Commensal flora comprises members across all microbial phyla, although predominantly bacterial, with population dynamics varying with host species, genotype, and environmental factors. Little is known, however, about the complex mechanisms regulating host-commensal interactions that underlie this mutually beneficial relationship and how alterations in the microbiome may influence host development and susceptibility to infection. Research into the gut microbiome has intensified as it becomes increasingly evident that symbiont-host interactions have a significant impact on mucosal immunity and health. Furthermore, evidence that microbial populations vary significantly throughout the GIT suggest that regional differences in the microbiome may also influence immune function within distinct compartments of the GIT. Postpartum colonization of the GIT has been shown to have a direct effect on mucosal immune system development, but information is limited regarding regional effects of the microbiome on the development, activation, and maturation of the mucosal immune system. This review discusses factors influencing the colonization and establishment of the microbiome throughout the GIT of newborn calves and the evidence that regional differences in the microbiome influence mucosal immune system development and maturation. The implications of this complex interaction are also discussed in terms of possible effects on responses to enteric pathogens and vaccines.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".