Anti-inflammatory effects of resolvin-D1 on human corneal epithelial cells: in vitro study
Bibliographic record
Abstract
BACKGROUND: This study evaluated the anti-inflammatory effects of Resolvin-D1 (RV-D1) and its mechanism of action in human corneal epithelial (HCE) cells. METHODS: HCE cells were incubated with different concentrations of RV-D1 for different time periods. Oleic acid (OA) and Dexamethasone (DM) served as negative and positive controls, respectively. Cells were stimulated with polyriboinosinic:polyribocytidylic acids (poly I:C). The protein contents and mRNA expression levels of Tumor necrosis factor-α (TNF-α), Interleukin (IL)-6, IL-1β and IL-8 were evaluated with multiplex fluorescent bead immunoassay (FBI) and real time-PCR, respectively. In addition, the expression of inhibitory factor-κBα (I-κBα) was evaluated with real time-PCR. RESULTS: The protein level of pro-inflammatory cytokines TNF-α, IL-6, IL-1β and IL-8 significantly increased after stimulation with Poly I:C. RV-D1 treatment at concentration of 1 μM decreased the protein level of TNF-α to 20.76 ± 9.3% (P < 0.05), IL-6 to 43.54 ± 14.16% (P < 0.001), IL-1β to 46.73 ± 15.93% (P > 0.05) and IL-8 to 51.15 ± 13.01% (P < 0.05) compared with cells stimulated with poly I:C alone. Similarly, the mRNA levels of TNF-α, IL-6, IL-1β and IL-8 were significantly reduced after treatment with RV-D1. A highly significant dose response curve was demonstrated for RV-D1 treated HCE cells for TNF-α and IL-1β.DM treatment decreased the protein content for all of the pro-inflammatory cytokines, similar results were demonstrated at the mRNA level. The anti-inflammatory effects of RV-D1 were similar to those of DM for TNF-α, IL-6 and IL-8. CONCLUSIONS: RV-D1 may serve as a potent anti-inflammatory agent in ocular surface inflammation, as evaluated in cultured HCE cells. The anti-inflammatory effects of RV-D1 were comparable to those of DM, and were mediated through nuclear factor kappa B (NF-κB) signal transduction.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".