Minimal Impact on the Resistome of Children in Botswana After Azithromycin Treatment for Acute Severe Diarrheal Disease
Bibliographic record
Abstract
BACKGROUND: Macrolide antibiotics, including azithromycin, can reduce under-five mortality rates and treat various infections in children in sub-Saharan Africa. These exposures, however, can select for antibiotic-resistant bacteria in the gut microbiota. METHODS: Our previous randomized controlled trial (RCT) of a rapid-test-and-treat strategy for severe acute diarrhoeal disease in children in Botswana included an intervention (three-day azithromycin dose) group and a control group that received supportive treatment. In this prospective matched cohort study using stools collected at baseline and 60 days after treatment from RCT participants, the collection of antibiotic resistance genes or resistome was compared between groups. RESULTS: Certain macrolide resistance genes increased in prevalence by 13% to 55% at 60 days, without differences in gene presence between the intervention and control groups. These genes were linked to tetracycline resistance genes and mobile genetic elements. CONCLUSIONS: Azithromycin treatment for bacterial diarrhoea for young children in Botswana resulted in similar effects on the gut resistome as the supportive treatment and did not provide additional selective pressure for macrolide resistance gene maintenance. The gut microbiota of these children contains diverse macrolide resistance genes that may be transferred within the gut upon repeated exposures to azithromycin or co-selected by other antibiotics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".