The role of amino acids in hydroxyapatite mineralization
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Polar and charged amino acids (AAs) are heavily expressed in non-collagenous proteins (NCPs), and are involved in hydroxyapatite (HA) mineralization in bone. Here, we review what is known on the effect of single AAs on HA precipitation. Negatively charged AAs, such as aspartic acid, glutamic acid (Glu) and phosphoserine are largely expressed in NCPs and play a critical role in controlling HA nucleation and growth. Positively charged ones such as arginine (Arg) or lysine (Lys) are heavily involved in HA nucleation within extracellular matrix proteins such as collagen. Glu, Arg and Lys intake can also increase bone mineral density by stimulating growth hormone production. In vitro studies suggest that the role of AAs in controlling HA precipitation is affected by their mobility. While dissolved AAs are able to inhibit HA precipitation and growth by chelating Ca2+ and PO43− ions or binding to nuclei of calcium phosphate and preventing their further growth, AAs bound to surfaces can promote HA precipitation by attracting Ca2+ and PO43− ions and increasing the local supersaturation. Overall, the effect of AAs on HA precipitation is worth being investigated more, especially under conditions closer to the physiological ones, where the presence of other factors such as collagen, mineralization inhibitors, and cells heavily influences HA precipitation. A deeper understanding of the role of AAs in HA mineralization will increase our fundamental knowledge related to bone formation, and could lead to new therapies to improve bone regeneration in damaged tissues or cure pathological diseases caused by excessive mineralization in tissues such as cartilage, blood vessels and cardiac valves.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 it