Development and <i>in vitro</i> characterization of a novel prenatal multi‐micronutrient powder incorporating differentially microencapsulated calcium carbonate and ferrous fumarate to overcome intra‐intestinal calcium‐iron interactions
Bibliographic record
Abstract
To overcome calcium(Ca)‐iron(Fe) interactions in a prenatal multi‐micronutrient supplement for low‐income settings, we developed a Ca‐Fe‐folic acid (FA) powder for home fortification incorporating enteric coatings sensitive to low pH (for gastric Fe release) and high pH (for mid‐duodenal Ca release). Ca carbonate and ferrous fumarate granules were generated by wet granulation. A fluid‐bed coating process was used to apply subcoat to the Ca granules to improve sphericity and to apply a pH‐sensitive enteric coat. Fe granules were encapsulated with a time‐dependent coating, including FA in the matrix. Fe and Ca dissolution profiles were assessed during sequential exposure to acidic (pH 1.2, 120 min) and basic (pH 5.8, 100 min) media using a modified USP methodology. At pH 1.2, Ca release was 15% after 120 min; Fe release was 90% and 100% at 20 and 35 min, respectively. After 100 min at pH 5.8, 85% of total Ca was released. These dissolution profiles indicated that the differential release of Ca and Fe was achieved within targeted acid‐base environments. Clinical testing to document fractional Ca absorption during pregnancy is underway. Research support was provided by the Saving Lives at Birth partners. Grant Funding Source : Saving Lives at Birth
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 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) | 0.000 | 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".