Gut Bacterial Microbiota and its Resistome Rapidly Recover to Basal State Levels after Short-term Amoxicillin-Clavulanic Acid Treatment in Healthy Adults
Bibliographic record
Abstract
Clinical effects of antimicrobials and probiotics in combination have been reported, however, little is known about their impact on gut microbiota and its resistome. In this study 16S rRNA gene amplicon, shotgun metagenomics sequencing and antibiotic resistance (ABR) microarray were used on fecal samples of 70 healthy participants, taken at four time points in probiotic (Lactobacillus rhamnosus R0011 and Lactobacillus helveticus R0052) and placebo groups to profile the gut bacterial microbiota and its resistome following administration of amoxicillin-clavulanic acid for one week. Significant shifts in microbiota family composition caused by the antimicrobial in both groups that included decreases in the proportion of Lachnospiraceae, Coriobacteriaceae and unidentified Clostridiales; and notable increases for the proportion of Enterobacteriaceae, Bacteroidaceae and Porphyromonadaceae compared to baseline levels. Resistome showed a corresponding enrichment of ABR genes compared to baseline from such classes as aminoglycosides and beta-lactams that were linked, by in silico inference, to the enrichment of the family Enterobacteriaceae. Despite perturbations caused by short-term antibiotic treatment, both gut microbiota and resistome showed prompt recovery to baseline levels one week after cessation of the antimicrobial. This rapid recovery may be explained by the hypothesis of community resilience.
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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 | 0.001 |
| 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".