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Record W4295837316 · doi:10.1093/cdn/nzac143

Fecal Iron Measurement in Studies of the Human Intestinal Microbiome

2022· review· en· W4295837316 on OpenAlexaff
Afreen Z. Khan, Sayema Badar, Karen M O’Callaghan, Stanley Zlotkin, Daniel Roth

Bibliographic record

VenueCurrent Developments in Nutrition · 2022
Typereview
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsHospital for Sick ChildrenSimon Fraser UniversitySickKids FoundationPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsIron deficiencyMicrobiomeMicronutrientFecesGut floraBioavailabilityBiologyGut microbiomeMicrobiologyImmunologyMedicineInternal medicineBioinformaticsPathologyAnemia

Abstract

fetched live from OpenAlex

Iron is an essential micronutrient for humans and their intestinal microbiota. Host intestinal cells and iron-dependent bacteria compete for intraluminal iron, so the composition and functions of the gut microbiota may influence iron availability. Studies of the effects of the microbiota or probiotic interventions on host iron absorption may be particularly relevant to settings with high burdens of both iron deficiency (ID) and gastrointestinal infections, since inflammation reduces iron bioavailability and unabsorbed intraluminal iron may modify the composition of the microbiota. The quantification of stool iron content may serve as an indicator of the amount of intraluminal iron to which the intestinal microbiota is exposed, which is particularly relevant for studies of the effect of iron on the intestinal microbiome, where fecal samples collected for purposes of microbiome characterization can be leveraged for stool iron analysis. However, few studies are available to guide researchers in the selection and implementation of stool iron assays. In this review, we describe stool iron quantification methods and highlight their potential application in studies of iron-microbiome relationships, with a specific focus on pediatric research. Mass-spectrometry-based methods offer high sensitivity and precision, but the need for expensive equipment and the high per-sample and maintenance costs may limit their widespread use. Conversely, colorimetric assays offer lower cost, ease of use and rapid turn-around times but have thus far been optimized primarily for blood-derived matrices rather than stool. Further research efforts are needed to validate and standardize methods for stool iron assessment, and to determine if the incorporation of such analyses in human microbiome studies yields insights into the interactions between intestinal microbiota and iron and contributes to the development of interventions that mitigate iron deficiency and promote a healthy microbiome.

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)0.008
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.246
GPT teacher head0.421
Teacher spread0.175 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations8
Published2022
Admission routes1
Has abstractyes

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