Anti-Inflammatory Properties of Eugenol in Lipopolysaccharide-Induced Macrophages and Its Role in Preventing β-Cell Dedifferentiation and Loss Induced by High Glucose-High Lipid Conditions
Bibliographic record
Abstract
Inflammation is a natural immune response to injury, infection, or tissue damage. It plays a crucial role in maintaining overall health and promoting healing. However, when inflammation becomes chronic and uncontrolled, it can contribute to the development of various inflammatory conditions, including type 2 diabetes. In type 2 diabetes, pancreatic β-cells have to overwork and the continuous impact of a high glucose, high lipid (HG-HL) diet contributes to their loss and dedifferentiation. This study aimed to investigate the anti-inflammatory effects of eugenol and its impact on the loss and dedifferentiation of β-cells. THP-1 macrophages were pretreated with eugenol for one hour and then exposed to lipopolysaccharide (LPS) for three hours to induce inflammation. Additionally, the second phase of NLRP3 inflammasome activation was induced by incubating the LPS-stimulated cells with adenosine triphosphate (ATP) for 30 min. The results showed that eugenol reduced the expression of proinflammatory genes, such as IL-1β, IL-6 and cyclooxygenase-2 (COX-2), potentially by inhibiting the activation of transcription factors NF-κB and TYK2. Eugenol also demonstrated inhibitory effects on the levels of NLRP3 mRNA and protein and Pannexin-1 (PANX-1) activation, eventually impacting the assembly of the NLRP3 inflammasome and the production of mature IL-1β. Additionally, eugenol reduced the elevated levels of adenosine deaminase acting on RNA 1 (ADAR1) transcript, suggesting its role in post-transcriptional mechanisms that regulate inflammatory responses. Furthermore, eugenol effectively decreased the loss of β-cells in response to HG-HL, likely by mitigating apoptosis. It also showed promise in suppressing HG-HL-induced β-cell dedifferentiation by restoring β-cell-specific biomarkers. Further research on eugenol and its mechanisms of action could lead to the development of therapeutic interventions for inflammatory disorders and the preservation of β-cell function in the context of type 2 diabetes.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".