Gut microbiota-mediated protection against diarrheal infections
Bibliographic record
Abstract
BACKGROUND: The mammalian gut microbiota is a highly abundant and diverse microbial community that resides in the gastrointestinal tract. One major benefit that the gut microbiota provides to its host is colonization resistance-the ability to prevent colonization by foreign microbes, including diarrheal pathogens such as Clostridium difficile , Salmonella enterica serovar Typhimurium and diarrheagenic Escherichia coli . METHODS: We conducted a literature review of the effects of the gut microbiota on infection by diarrheal pathogens. We used PubMed to search for relevant articles published before July 2016, as well as incorporated data from our laboratory. RESULTS: The gut microbiota provides protection from diarrheal infections both by direct inhibition of pathogens and by indirect effects on host functions. Direct effects of the microbiota on diarrheal pathogens include competing for nutrients and producing metabolites that inhibit pathogen growth or virulence. Indirect effects of the gut microbiota include promoting maintenance of the gut mucosal barrier and stimulating innate and adaptive immunity. CONCLUSIONS: Human epidemiological studies and experimental infections of laboratory animals both demonstrate that antibiotic treatment can alter the gut microbial community and thereby reduce colonization resistance against diarrheal pathogens. Further research might lead to the development of next-generation probiotics that could be used to bolster colonization resistance and thus prevent travellers' diarrheal.
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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 | 0.001 |
| 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 | 0.001 |
| 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".