Microbial sensing in the neonatal pig gut: effect of diet-independent and diet-dependent factors<sup>1</sup>
Bibliographic record
Abstract
There is considerable agreement that the gastrointestinal microbiota contributes to the performance and health of the neonate, and this relationship includes an ability of the host animal to “sense” changes in the microbial community. Identifying the mechanisms used by the host to sense microbiota is one approach to developing methods to manipulate the microbiota to improve pig health and performance. Diet-independent microbial products are molecules unique to the microbial community and sensed by host pattern recognition receptors stimulating inflammation. Common among all members of the microbial community, their presence is unaffected by diet, but the nature of the response does depends on factors affecting the microenvironment in which the molecule is detected. Diet-dependent microbial products arise as products of fermentation of dietary components and include short-chain fatty acids, ammonia, phenols, hydrogen sulfide, amines, and many other compounds. A plethora of sensing mechanisms exists that include enzymatic metabolism as well as membrane receptors that have evolved to respond to microbial products (e.g., short-chain fatty acid receptors), or simply cross-react with microbial products. This review focuses on host mechanisms used to sense the intestinal microbiota and attempts to establish practical considerations for neonatal gut health based on current understanding.
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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".