Dietary Factors Modulate Iron Uptake in Caco-2 Cells from an Iron Ingot Used as a Home Fortificant to Prevent Iron Deficiency
Bibliographic record
Abstract
Iron deficiency is a major public health concern and nutritional approaches are required to reduce its prevalence. The aim of this study was to examine the iron bioavailability of a novel home fortificant, the "Lucky Iron Fish™" (LIF) (www.luckyironfish.com/shop, Guelph, Canada) and the impact of dietary factors and a food matrix on iron uptake from LIF in Caco-2 cells. LIF released a substantial quantity of iron (about 1.2 mM) at pH 2 but this iron was only slightly soluble at pH 7 and not taken up by cells. The addition of ascorbic acid (AA) maintained the solubility of iron released from LIF (LIF-iron) at pH 7 and facilitated iron uptake by the cells in a concentration-dependent manner. In vitro digestion of LIF-iron in the presence of peas increased iron uptake 10-fold. However, the addition of tannic acid to the digestion reduced the cellular iron uptake 7.5-fold. Additionally, LIF-iron induced an overproduction of reactive oxygen species (ROS), similar to ferrous sulfate, but this effect was counteracted by the addition of AA. Overall, our data illustrate the major influence of dietary factors on iron solubility and bioavailability from LIF, and demonstrate that the addition of AA enhances iron uptake and reduces ROS in the intestinal lumen.
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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 | 0.000 | 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) | 0.001 | 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 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".