An innovative green approach to optimize the extraction of functional ingredients from Ulva lactuca and Ascophyllum nodosum: Safety studies
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
Research to isolate seaweed bioproducts and identify their health-promoting effects is gaining increasing attention due to their potent antimicrobial, anti-inflammatory and antioxidant properties. This study describes the positive effect of particle size reduction, validated by different seaweed examples, in boosting the extraction yields of bioactive compounds, when utilized in conjunction with innovative, short-duration, green heating protocols (i. e subcritical water extraction (SBWE). In these validation protocols, we optimized the recovery of antibacterial, antioxidant and anti-inflammatory bioactivities from 2 different macroalgal species; brown algae ( Phaeophyceae ex: Ascophyllum nodosum ) and green algae ( Chlorophyta ex: Ulva lactuca ). Different hot extraction protocols were applied to extract different particle sizes (ranging from fine to coarse: <25; 25-53; 53-106; 106-355; >355 μm) of powdered seaweed samples. The obtained results demonstrated that the highest extraction yields of total carbohydrates, glucuronic acid, phenolics and flavonoids, as well as antioxidant, antimicrobial and anti-inflammatory activities were obtained with microwave protocols for a defined time using the particle sizes of 25-53 μm. Furthermore, we confirmed the safety profile of seaweed extracts on RAW 264.7 cells and verified by measuring the Transepithelial Electrical Resistance (TEER) of Caco-2 monolayer cultures. In conclusion, our study confirmed the efficacy of the optimized green extraction approach in the preparation of safe and valuable bioactive compounds that could be used in the development of seaweed-based functional foods and nutraceuticals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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