Millet Bran Bound Phenolic Compounds Suppresses LPS‐Induced Inflammatory Response in Macrophages and Liver Injury Mice via TLR4/NF‐κB Signaling Pathway
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
ABSTRACT Millet bran, rich in bioactive phenolic compounds, holds potential for both nutritional and therapeutic applications. In this study, bound phenolic compounds were isolated from millet bran, yielding a potent fraction named BPS‐2. UPLC‐MS/MS detected 16 major phenolic compounds in BPS‐2. In vitro assays revealed that BPS‐2 exerted a significant anti‐inflammatory activity in lipopolysaccharide (LPS)‐induced RAW 264.7 macrophage, as manifested by reduced production of inflammatory mediators (IL‐1β, IL‐6, and TNF‐α) and downregulation of the expression levels of the pro‐inflammatory enzymes Cyclooxygenase‐2 (COX‐2) and nitric oxide synthase (iNOS). Network pharmacological analysis identified the suppression of the TLR4/NF‐κB pathway as the primary mechanism mediating the anti‐inflammatory activity of BPS‐2, which was validated using the LPS‐induced RAW 264.7 macrophage model and liver injury mice model. Western blot analysis revealed that BPS‐2 significantly decreased the phosphorylation of IκBα and p65 to regulate the TLR4/NF‐κB signaling pathway, thereby exerting anti‐inflammatory activity. Molecular docking studies revealed strong interactions between the active compounds of BPS‐2 and TLR4 through key amino acid residues, including Pro116, Thr114, and Arg105. These results underscore the potential application of millet bran bound phenolic compounds as naturally occurring anti‐inflammatory substances.
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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 | 0.000 |
| 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 it