Identification and In Vitro Evaluation of Milkfish (Chanos chanos) Frame Proteins and Hydrolysates with DPP-IV Inhibitory and Antioxidant Activities
Bibliographic record
Abstract
The study presents the potential of milkfish frame, a by-product of milkfish processing, as a source of dipeptidyl peptidase IV (DPP-IV) inhibitory and antioxidant peptides with potential applications in type 2 diabetes management. Proteomic analysis identified key proteins, including 65 kDa warm temperature acclimation protein 1 and myosin heavy chain. In silico prediction (BIOPEP-UWM) guided the selection of proteases for generating DPP-IV inhibitory peptides. Enzymatic hydrolysates were produced and evaluated for bioactivity. Among the treatments, pepsin hydrolysis (2% v/v, 8 h) yielded the highest peptide content (283.64 mg/g), soluble protein (86.46%), and DPP-IV inhibitory activity (68.47%). The resulting milkfish frame pepsin hydrolysate (MFH) was further enhanced through ultrafiltration and simulated gastrointestinal digestion, which improved the DPP-IV inhibitory and antioxidant capacities. Cytotoxicity assays confirmed that MFH (0–100 μg/mL) was non-toxic to FL83B hepatocytes after 24 h. Moreover, treating TNF-α-induced FL83B cells with 10 μg/mL MFHs improved cell viability, reducing the toxicity induced by TNF-α in cells. These findings show that MFHs exhibit promising antidiabetic potential and could serve as natural alternatives to synthetic drugs for type 2 diabetes management. This also demonstrates the valorization of fish processing by-products into functional food ingredients, advancing sustainable approaches in food innovation.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".