Soybean-Derived Tripeptide Leu–Ser–Trp (LSW) Protects Human Vascular Endothelial Cells from TNFα-Induced Oxidative Stress and Inflammation via Modulating TNFα Receptors and SIRT1
Bibliographic record
Abstract
Soybean is a rich source of high-quality proteins and an excellent food source of bioactive peptides. A tripeptide, Leu−Ser−Trp (LSW), was previously identified from soybean as an angiotensin-converting enzyme inhibitory peptide. In the present work, we further studied its antioxidant and anti-inflammatory activities in human vascular endothelial cells (EA.hy926) and elucidated the mechanisms underlying these biological activities. In tumor necrosis factor alpha (TNFα)-stimulated EA.hy926 cells, LSW significantly inhibited oxidative stress, both reduced superoxide and malondialdehyde levels (p < 0.001), owing to its free-radical-scavenging ability. LSW treatment also mitigated the elevated protein expression of vascular adhesion molecule-1 (p < 0.001) and cyclooxygenase 2 (p < 0.01) via inhibition of NF-κB and p38/JNK signaling, respectively. Additionally, LSW also inhibited the endogenous formation of TNFα and attenuated the expression of its two receptors in EA.hy926 cells. Furthermore, LSW upregulated sirtuin-1 level, which partially contributed to its anti-inflammatory activity. These results demonstrate the multiple roles of LSW in ameliorating vascular endothelial oxidative stress and inflammatory responses, which support its uses as a nutraceutical or functional food ingredient for combating endothelial dysfunction and cardiovascular diseases.
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".