Preparation and characterization of Sipunculus nudus peptide-calcium chelate: Structural insights and osteogenic bioactivity assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it