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Record W4288033816 · doi:10.3389/fcimb.2022.924707

Antibiotic and antifungal use in pediatric leukemia and lymphoma patients are associated with increasing opportunistic pathogens and decreasing bacteria responsible for activities that enhance colonic defense

2022· article· en· W4288033816 on OpenAlexafffund
Katherine A. Dunn, Tamara MacDonald, Gloria J. Rodrigues, Zara Forbrigger, Joseph P. Bielawski, Morgan G. I. Langille, Johan Van Limbergen, Ketan Kulkarni

Bibliographic record

VenueFrontiers in Cellular and Infection Microbiology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
FundersIWK Health CentreResearch Nova ScotiaW. Garfield Weston FoundationNova Scotia Health Research FoundationBeatrice Hunter Cancer Research Institute
KeywordsAntibioticsMicrobiomeBiologyGut floraImmunologyPediatric cancerPopulationMicrobiologyCancerMedicineBioinformaticsGeneticsEnvironmental health

Abstract

fetched live from OpenAlex

Due to decreased immunity, both antibiotics and antifungals are regularly used in pediatric hematologic-cancer patients as a means to prevent severe infections and febrile neutropenia. The general effect of antibiotics on the human gut microbiome is profound, yielding decreased diversity and changes in community structure. However, the specific effect on pediatric oncology patients is not well-studied. The effect of antifungal use is even less understood, having been studied only in mouse models. Because the composition of the gut microbiome is associated with regulation of hematopoiesis, immune function and gastrointestinal integrity, changes within the patient gut can have implications for the clinical management of hematologic malignancies. The pediatric population is particularly challenging because the composition of the microbiome is age dependent, with some of the most pronounced changes occurring in the first three years of life. We investigated how antibiotic and antifungal use shapes the taxonomic composition of the stool microbiome in pediatric patients with leukemia and lymphoma, as inferred from both 16S rRNA and metagenome data. Associations with age, antibiotic use and antifungal use were investigated using multiple analysis methods. In addition, multivariable differential abundance was used to identify and assess specific taxa that were associated with multiple variables. Both antibiotics and antifungals were linked to a general decline in diversity in stool samples, which included a decrease in relative abundance in butyrate producers that play a critical role in host gut physiology (e.g.,Faecalibacterium,Anaerostipes, Dorea, Blautia),. Furthermore, antifungal use was associated with a significant increase in relative abundance of opportunistic pathogens. Collectively, these findings have important implications for the treatment of leukemia and lymphoma patients. Butyrate is important for gastrointestinal integrity; it inhibits inflammation, reinforces colonic defense, mucosal immunity. and decreases oxidative stress. The routine use of broad-spectrum anti-infectives in pediatric oncology patients could simultaneously contribute to a decline in gastrointestinal integrity and colonic defense while promoting increases in opportunistic pathogens within the patient gut. Because the gut microbiome has been linked to both short-term clinical outcomes, and longer-lasting health effects, systematic characterization of the gut microbiome in pediatric patients during, and beyond, treatment is warranted.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.010
GPT teacher head0.213
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2022
Admission routes2
Has abstractyes

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