Screening for osteogenic activity in extracts from Irish marine organisms: The potential of Ceramium pallidum
Bibliographic record
Abstract
Extracts and compounds derived from marine organisms have reportedly shown some osteogenic potential. As such, these bioactives may aid in the treatment of musculoskeletal conditions such as osteoporosis; helping to address inefficacies with current treatment options. In this study, 72 fractions were tested for their in vitro osteogenic activity using a human foetal osteoblast (hFOB) cell line and bone marrow derived mesenchymal stem cells (MSCs), focusing on their cytotoxic, proliferative and differentiation effects. Extracts dissolved in dimethyl sulfoxide and ethanol showed no significant osteogenic potential. However, two extracts derived from powder residues (left over from original organic extractions) caused a significant promotion of MSC differentiation. Bioactivity from powder residues derived from the epiphytic red algae Ceramium pallidum is described in detail to highlight its treatment potential. In vitro, C. pallidum was shown to promote MSC differentiation and extracellular matrix mineralisation. In vivo, this extract caused a significant increase in opercular bone growth of zebrafish larvae and a significant increase in bone density of regenerated adult caudal fins. Our findings therefore show the importance of continued screening efforts, particularly of novel extract sources, and the presence of bioactive compounds in C. pallidum extract.
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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 | 0.001 | 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 | 0.000 |
| 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".