Preparation and characterization of Sipunculus nudus peptide-calcium chelate: Structural insights and osteogenic bioactivity assessment
Bibliographic record
Abstract
• Calcium chelate of Squarrosus nudus peptide (SNP-Ca) was successfully prepared. • The chelating conditions was optimized by single factor test. • SNP-Ca could significantly improve calcium uptake in Caco-2 cells. • SNP-Ca could significantly increase the osteogenic capacity of MC3T3-E1 cells. • SNP-Ca could significantly enhance the bone formation of zebrafish larvae. In order to prepare calcium supplements for efficient absorption and utilization in the human body, this study focused on synthesizing SNP-Ca by chelating Squarrosus nudus peptide (SNP) with calcium. We characterized its structure, stability, and calcium uptake properties in Caco-2 cells and its impact on osteogenic activity in vitro. Optimal preparation conditions were determined: a peptide-calcium mass ratio of 5:1, a 30-min reaction time, a temperature of 60 °C, and a pH of 7.0. Under these conditions, a calcium chelating rate of 68.32 % was achieved. Calcium binds to the peptide primarily via carboxyl oxygen and amino nitrogen atoms, and the morphology of SNP-Ca was similar to porous nanoflowers. Our cellular experiments revealed that SNP-Ca significantly increases calcium uptake in Caco-2 cells, stimulating proliferation, differentiation, and mineralization in MC3T3-E1 cells. Additionally, zebrafish larvae models showed enhanced bone formation following SNP-Ca administration. SNP-Ca has the potential of a novel calcium supplement.
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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) | 0.000 | 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) | 0.001 | 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".