Calcium from LactoCalcium<sup>TM</sup>Milk Mineral after Digestion with Pepsin Stimulates Mineralized Bone Nodule Formation in Human Osteoblast-Like SaOS-2 Cells<i>in Vitro</i>and May Be Rendered Bioavailable<i>in Vivo</i>
Bibliographic record
Abstract
Many individuals cannot obtain the optimum calcium requirement from food for a variety of reasons. Therefore, calcium supplements are important sources of dietary calcium. One of the calcium sources commercially available is LactoCalcium (milk minerals) that has 28% calcium, and a 2:1 ratio of calcium to phosphorus. The objectives of this study were (a) to examine whether calcium can be released from LactoCalcium by using digestive enzymes and (b) to determine its biological activity by examining its ability to stimulate bone formation. LactoCalcium was treated in vitro by using simulated gastric and intestinal fluids or porcine gastric, pancreatic and intestinal extracts. Our results indicate the role of enzymes or bile extract in the digestion of the product. We show that, by increasing the concentration of pepsin at a fixed concentration of LactoCalcium (substrate), the percentage of released calcium increased in a dose-dependent manner, showing that, at the right enzyme concentration, as much as 100% of the calcium present in LactoCalcium can be made available. The biological activity of the digested calcium was demonstrated by the stimulation of mineralized bone nodules in SaOS-2 cells in a dose-dependent manner. Thus, 1 mM and 3 mM calcium released from LactoCalcium increased the nodule area by 23.17 mm(2) (p<0.0001) and 77.78 mm(2) (p<0.0001), respectively, as compared to a value of 0.99 mm(2) at 0.5 mM calcium from LactoCalcium. These results demonstrate the in vitro bioavailability and bioactivity of calcium from LactoCalcium and serve as a basis for carrying out in vivo analyses to determine the suitability of using LactoCalcium as a source of calcium for individuals at risk of developing osteoporosis.
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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) | 0.001 | 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 | 0.001 |
| 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".