The gastrointestinal antibiotic resistome in pediatric leukemia and lymphoma patients
Bibliographic record
Abstract
Introduction: Most children with leukemia and lymphoma experience febrile neutropenia. These are treated with empiric antibiotics that include β-lactams and/or vancomycin. These are often administered for extended periods, and the effect on the resistome is unknown. Methods: We examined the impact of repeated courses and duration of antibiotic use on the resistome of 39 pediatric leukemia and lymphoma patients. Shotgun metagenome sequences from 127 stool samples of pediatric oncology patients were examined for abundance of antibiotic resistance genes (ARGs) in each sample. Abundances were grouped by repeated courses (no antibiotics, 1-2 courses, 3+ courses) and duration (no use, short duration, long and/or mixed durationg) of β-lactams, vancomycin and "any antibiotic" use. We assessed changes in both taxonomic composition and prevalence of ARGs among these groups. Results: We found that Bacteroidetes taxa and β-lactam resistance genes decreased, while opportunistic Firmicutes and Proteobacteria taxa, along with multidrug resistance genes, increased with repeated courses and/or duration of antibiotics. Efflux pump related genes predominated (92%) among the increased multidrug genes. While we found β-lactam ARGs present in the resistome, the taxa that appear to contain them were kept in check by antibiotic treatment. Multidrug ARGs, mostly efflux pumps or regulators of efflux pump genes, were associated with opportunistic pathogens, and both increased in the resistome with repeated antibiotic use and/or increased duration. Conclusions: Given the strong association between opportunistic pathogens and multidrug-related efflux pumps, we suggest that drug efflux capacity might allow the opportunistic pathogens to persist or increase despite repeated courses and/or duration of antibiotics. While drug efflux is the most direct explanation, other mechanisms that enhance the ability of opportunistic pathogens to handle environmental stress, or other aspects of the treatment environment, could also contribute to their ability to flourish within the gut during treatment. Persistence of opportunistic pathogens in an already dysbiotic and weakened gastrointestinal tract could increase the likelihood of life-threatening blood borne infections. Of the 39 patients, 59% experienced at least one gastrointestinal or blood infection and 60% of bacteremia's were bacteria found in stool samples. Antimicrobial stewardship and appropriate use and duration of antibiotics could help reduce morbidity and mortality in this vulnerable population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".