Development of the Caco-2 Model for Assessment of Iron Absorption and Utilisation at Supplemental Levels
Bibliographic record
Abstract
Caco-2 cells may be typically used as a first step to investigate the bioavailability of different dietary and fortificant forms of iron (Fe) at low levels (< 10 µM) in tissue culture medium (TCM). Whether this model is suitable with supplemental levels of Fe (ca. 200 µM in TCM) is not clear and neither, therefore, is the choice of reference iron compound under those conditions as a ‘positive control’. Here we show that with 200 ?M iron in TCM (serum-free MEM), Fe(II) sulphate precipitates and while high levels of ascorbic acid can prevent this, it is to the detriment of the Caco-2 cell monolayer and/or it adversely affects the pH of the TCM. Adjusting the pH of TCM to account for this issue again leads to Fe precipitation, which is detectable as both a true precipitate (~ 50%) and a nano-precipitate in suspension (~20%). In contrast, Fe(III) maltol which, clinically, appears less toxic to the intestinal mucosa than Fe(II) sulphate, retains solubility at supplemental levels in cell culture medium, without adversely affecting pH or the Caco-2 cell monolayer. Moreover, the iron is also well utilized by the cells as assessed through ferritin formation. Thus Caco-2 cells may also provide a model for screening iron uptake and utilisation at supplemental levels through the cellular generation of ferritin although care must be taken in ensuring (i) appropriate TCM conditions (e.g. pH and chemical speciation of the iron) (ii) monolayer integrity (i.e. the assay response is not an artefact of toxicity) and (iii) that an appropriate reference material is used (e.g. Fe:maltol at 1:5 ratio).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".