Fecal Iron Measurement in Studies of the Human Intestinal Microbiome
Bibliographic record
Abstract
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.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".