The influence of ceftriaxone, ceftazidime-avibactam, and piperacillin-tazobactam on the gut microbiota
Bibliographic record
Abstract
Abstract Purpose The administration of antibiotics can induce dysbiosis of the gut microbiota, leading to diseases, e.g., irritable bowel syndrome, metabolic syndrome, and Clostridium difficile infection. However, the specific effects of different β-lactam antibiotics on the dysbiosis of the gut microbiota remain poorly understood, particularly in human studies. Methods This study assessed the impacts of ceftriaxone, ceftazidime-avibactam, and piperacillin-tazobactam on the diversity, composition, and bacterial interactions within the gut microbiota at days 1, 6, and 37 after administration. Results All three antibiotics significantly altered beta diversity of the gut microbiota by day 6, with ceftriaxone showing the most prolonged effects. Changes in the composition of the gut microbiota were more similar between the ceftazidime-avibactam and piperacillin-tazobactam groups and differed markedly from those in the ceftriaxone group. Consistent with beta diversity changes, bacterial interaction networks showed greater and longer-lasting disruptions of bacterial interaction in the gut microbiota in the ceftriaxone group compared to the ceftazidime-avibactam and piperacillin-tazobactam groups. Conclusion These findings highlight distinct patterns of microbiota disruption following ceftriaxone, ceftazidime-avibactam, and piperacillin-tazobactam treatments and provide insights for mitigating dysbiosis of the gut microbiota during β-lactam therapy.
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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".