Harnessing Synthetic Milk Peptides as Dual Modulators of Lipid and Immune Homeostasis
Bibliographic record
Abstract
Background: Natural milk peptides are known for their bioactivity in regulating lipid metabolism and immune responses, but their therapeutic application is limited due to instability and low bioavailability. Synthetic milk peptide analogues (SMPAs) offer enhanced stability and targeted action. Objectives: This study aimed to evaluate the dual modulatory potential of SMPAs on lipid and immune homeostasis. Methods: SMPAs were synthesized via solid-phase peptide synthesis, purified by HPLC, and characterized by mass spectrometry. Human hepatocyte (HepG2) and macrophage (THP-1) cell lines were treated with SMPAs (1–50 µM). Lipid accumulation, PPARα, ABCA1 gene expression, and cytokine levels (TNF-α, IL-6, IL-10) were measured using Oil Red O assay, qPCR, and ELISA. Findings: SMPAs significantly reduced intracellular lipid accumulation, enhanced PPARα and ABCA1 expression, and modulated cytokines by decreasing TNF-α and IL-6 while increasing IL-10 levels, indicating dual lipid-lowering and immunoregulatory effects. Main Conclusions: SMPAs act as dual modulators of lipid metabolism and immune function, suggesting their potential as bioinspired therapeutic candidates for dyslipidemia and inflammation-related disorders.
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".