Evolution of Pig Fecal Microbiota Composition and Diversity in Response to Enterotoxigenic Escherichia coli Infection and Colistin Treatment in Weaned Piglets
Bibliographic record
Abstract
The intestinal microbiota plays several important roles in pig health and growth. The aim of the current study was to characterize the changes in the fecal microbiota diversity and composition of weaned piglets following an oral challenge with an ETEC: F4 strain and/or a treatment with colistin sulfate (CS). Twenty-eight piglets were used in this experiment and were divided into four groups: challenged untreated, challenged treated, unchallenged treated, and unchallenged untreated. Rectal swab samples were collected at five sampling times throughout the study. Total genomic DNA was used to assess the fecal microbiota diversity and composition using the V4 region of the 16S rRNA gene. The relative abundance, the composition, and the community structure of piglet fecal microbiota was highly affected by the ETEC: F4 challenge throughout the experiment, while the oral treatment with CS, a narrow spectrum antibiotic, resulted in a significant decrease of E. coli/Shigella populations during the treatment period only. This study was the first to identify some gut microbiota subgroups (e.g., Streptococcus, Lachnospiraceae) that are associated with healthy piglets as compared to ETEC: F4 challenged animals. These key findings might contribute to the development of alternative strategies to reduce the use of antimicrobials in the control of post-weaning diarrhea in pigs.
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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 | 0.001 | 0.001 |
| 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".