Impact of Hydrolyzed Collagen from Defatted Sea Bass Skin on Proliferation and Differentiation of Preosteoblast MC3T3-E1 Cells
Bibliographic record
Abstract
Osteoporosis is a serious problem affecting health of the elderly. Drugs (bisphosphonates) applied for treatment are often accompanied by adverse side effects. Thus, fish byproduct-derived peptides, particularly hydrolyzed collagen (HC) from defatted sea bass skin, could be a safe source of anti-osteoporosis agents. This study aimed to examine the effects of HC on proliferation and differentiation of preosteoblast cells. HC prepared using papain before Alcalase hydrolysis was determined for molecular weight (MW) distribution. Thereafter, the resulting HC (50–800 µg/mL) was added to the cell. Proliferation, alkaline phosphatase activity (AP-A) and mineralization of cells were investigated. Moreover, the expression of runt-related transcription factor 2 (RUNX2) and the p-Akt/Akt pathway were also determined using Western blot. The results showed that HC had an MW < 3 kDa. HC (50–200 µg/mL) could promote cell proliferation. Nevertheless, HC at 100 µg/mL (HC-100) had enhanced AP-A and increased mineralization during the first 7 days of culture. Moreover, HC-treated cells had higher calcium depositions than the control (p < 0.05). Additionally, cells treated with HC-100 had higher levels of RUNX2 and p-Akt expressions than control (p < 0.05). Therefore, HC could be a promising functional ingredient to promote osteoblast proliferation and differentiation, which could enhance bone strength.
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.
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".