Sinensetin Inhibits Interleukin-6 in Human Mast Cell - 1 Via Signal Transducers and Activators of the Transcription 3 (STAT3) and Nuclear Factor Kappa B (NF-κB) Pathways
Bibliographic record
Abstract
Sinensetin, a pentamethoxyflavone, is known to exert various pharmacological activities including anti-angiogenesis, anti-diabetic and anti-inflammatory activities. However, its effects on the human mast cell - 1 (HMC-1) mediated inflammatory mechanism remain unknown. To explore the mediator and cellular inflammatory response of sinensetin, we examined its influence on phorbol 12-myristate 13-acetate (PMA) plus A23187 induced inflammatory mediator production in a human mast cell line. In this study, interleukin (IL)-6 production was measured using the enzyme-linked immunosorbent assay and reverse transcription polymerase chain reaction. Sinensetin inhibited PMA plus A23187 induced IL-6 production in a dose-dependent manner as well as IL-4, IL-5 and IL-8 mRNA expression. Furthermore, sinensetin inhibited signal transducer and activator of transcription 3 (STAT3) phosphorylation, suggesting that sinensetin inhibits the production of inflammatory mediators by blocking STAT3 phosphorylation. Moreover, sinensetin was found to inhibit nuclear factor kappa B activation. These findings suggest that sinensetin may be involved in the regulation of mast cell-mediated inflammatory responses.
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".