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Record W3211848272 · doi:10.1038/s41467-021-27010-3

Micronutrient supplements can promote disruptive protozoan and fungal communities in the developing infant gut

2021· article· en· W3211848272 on OpenAlexafffund
Ana Popovic, Céline Bourdon, Pauline W. Wang, David S. Guttman, Sajid Soofi, Zulfiqar A Bhutta, Robert Bandsma, John Parkinson, Lisa G. Pell

Bibliographic record

VenueNature Communications · 2021
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health ResearchHospital for Sick ChildrenUniversity of TorontoCompute CanadaCanada Foundation for InnovationGovernment of CanadaGovernment of OntarioHSBC Bank USA
KeywordsMicronutrientEnvironmental healthBiologyMedicinePathology

Abstract

fetched live from OpenAlex

Supplementation with micronutrients, including vitamins, iron and zinc, is a key strategy to alleviate child malnutrition. However, association of gastrointestinal disorders with iron has led to ongoing debate over their administration. To better understand their impact on gut microbiota, we analyse the bacterial, protozoal, fungal and helminth communities of stool samples collected from a subset of 80 children at 12 and 24 months of age, previously enrolled into a large cluster randomized controlled trial of micronutrient supplementation in Pakistan (ClinicalTrials.gov identifier NCT00705445). We show that while bacterial diversity is reduced in supplemented children, vitamins and iron (as well as residence in a rural setting) may promote colonization with distinct protozoa and mucormycetes, whereas the addition of zinc appears to ameliorate this effect. We suggest that the risks and benefits of micronutrient interventions may depend on eukaryotic communities, potentially exacerbated by exposure to a rural setting. Larger studies are needed to evaluate the clinical significance of these findings and their impact on health outcomes.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.802
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

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

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 head0.038
GPT teacher head0.358
Teacher spread0.321 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations43
Published2021
Admission routes2
Has abstractyes

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