Novel Flaxseed Gum Nanocomposites Are Slow Release Iron Supplements
Bibliographic record
Abstract
Nanocomposites, based on iron salts and soluble flaxseed gum (FG), were prepared as potential treatments of iron deficiency anemia (IDA). FG was extracted, characterized, and formulated into iron-loading nanocomposites via ion-exchange against FeCl3, Fe2(SO4)3, FeCl2, and FeSO4·7H2O. FG-iron nanocomposites preparation condition was optimized, and physicochemical properties of the nanocomposites were investigated. In vitro release kinetics of iron in simulated gastric fluid (SGF) was also evaluated. FG heteropolysaccharide, consisting of rhamnose (33.73%), arabinose (24.35%), xylose (14.23%), glucose (4.54%), and galactose (23.15%) monosaccharides, linked together via varieties of glycosidic bonds, was a good recipient for both ferric and ferrous irons under screened conditions (i.e., 80 °C, 2 h, I/G = 1:2). Iron loaded contents in the nanocomposites prepared from FG-FeCl3, FG-Fe2(SO4)3, FG-FeCl2, and FG-FeSO4·7H2O were 25.51%, 10.36%, 5.83%, and 22.83%, respectively. Iron in these nanocomposites was mostly in a bound state, especially in FG-FeCl3, due to chelation forming bonds between iron and polysaccharide hydroxyl or carboxyl groups and formed stable polysaccharide-iron crystal network structures. Free iron ions were effectively removed by ethanol treatments. Because of chelation, the nanocomposites delayed iron release in SGF and the release kinetics were consistent with Korsmeyer-Peppas model. This indicates that such complexes might reduce side effects of free iron in human stomach. Altogether, this study indicates that these synthetic FG-iron nanocomposites might be developed as novel iron supplements for iron deficiency, in which FG-FeCl3 is considered as the best option.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".