Fermentation Improves Calcium Bioavailability in <i>Moringa oleifera</i> leaves and Prevents Bone Loss in Calcium‐deficient Rats
Bibliographic record
Abstract
Abstract Nowadays, there is an increasing demand of healthier plant calcium supplements. Moringa oleifera leaves (MOL) are rich in calcium and thus are promising candidates for developing efficient calcium supplements. Here, using fermentation‐based approaches, we developed a Moringa oleifera leaf ferment (MOLF), which contents higher levels of calcium. The therapeutic potential of the MOLF was also examined both in vitro and in vivo. Nine lactic acid bacteria and four yeasts were tested for better fermentation of MOL. Calcium‐deficient rats were used for evaluating the therapeutic effects of MOLF. The results of liquid fermentation showed that the mixture of Lactobacillus reuteri, Lactobacillus acidophilus, and Candida utilis elevated the content of MOL calcium most strikingly, with the content of calcium increased nearly 2.4‐fold (from 2.08% to 4.90%). The resulting MOLF was then subjected to cell experiments and animal experiments. The results showed that calcium absorption in Caco‐2 cells in MOLF group was higher than that in CaCl2 group significantly. Interestingly, in calcium‐deficient rats, MOLF treatment significantly increased the thickness of cortical bone, rat body weight, wet weight of the femur, and the femur bone density, whereas it decreased osteoclast numbers. These results indicate that microbial fermentation increased calcium bioavailability of MOL, promote the growth and development of calcium‐deficient rats, bone calcium deposition, and bone growth; enhance bone strength; reduce bone resorption; and prevent calcium deficiency.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".