In Vitro Antioxidant and Enzyme Inhibitory Activity of Oat Bran-Derived Protein Hydrolysates and Peptides and their effect on the Fat Accumulation in 3T3-L1 Cells
Bibliographic record
Abstract
In recent decades, there has been a surge in interest in the production, identification, and application of bioactive protein hydrolysates and peptides derived from dietary plant and animal sources.They can be antioxidants, antihypertensives, immunomodulators, hypoglycemics, antibacterial agents, antidiabetics, anti-obesity agents.Their activity is affected by the amino acid sequence, hydrophobicity, and peptide length.To promote health, these bioactive substances can quench free radicals, modify enzyme activity, and modulate gene/protein expression in biological pathways.In many situations, these peptides are multifunctional; however, only a limited number of studies have focused on assessing the multifunctionality of these bioactive molecules.Obesity, on the other hand, develops as a complex multifactorial disease because of excessive fat accumulation in adipocytes, which significantly alters gene and protein expression, and low-grade inflammation because of increased oxidative stress, leading to other complications such as cardiovascular disease or type 2 diabetes.Treatment with multifunctional bioactive peptides may minimize lipid accumulation and oxidative stress while regulating the expression of adipogenic-relevant genes and proteins.The study hypothesis was that enzymatically digested oat bran produces protein hydrolysates and bioactive peptides with multifunctional properties such as antioxidant activity, lipase and α-amylase inhibition, and reduction of fat accumulation in differentiated 3T3-L1 preadipocytes.Enzymatically digested oat bran generated a protein hydrolysate sample mixture (VPI-Pa) using viscozyme and papain to generate the hydrolysates that showed the maximum activity in peroxyl radical scavenging (866.9 ± 10.6 µM TE/g) and metal chelating activity (75.0 ± 0.4%).Although all the selected My first and foremost thanks go to my supervisors, Dr. Apollinaire Tsopmo and Dr. William Willmore.I am thankful for all the help, advice, motivating words, and patience you have given me.Next, I wish to
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".