3,4 dihydroxyphenyl ethanol reduces secretion of angiogenin in human retinal pigment epithelial cells
Bibliographic record
Abstract
BACKGROUND: Age-related macular degeneration (AMD) is currently the leading cause of blindness in developed countries. Bevacizumab is a widely used anti-VEGF agent that is a commonly applied therapy for neovascular AMD; however, a consequence of bevacizumab therapy may be the activation of compensatory angiogenic signalling. Combination of bevacizumab with 3,4 dihydroxyphenyl ethanol (DPE) may attenuate this compensatory signalling. The goal of the study was to investigate this therapeutic option in a human retinal pigment epithelial cell line (ARPE-19). METHODS: ARPE-19 cells were incubated under both normoxic and hypoxic conditions. The cells were treated as follows: control, 100 µM DPE, 0.25 mg/ml bevacizumab, the combination of DPE and bevacizumab. Media was harvested after 24 h for sandwich ELISA-based angiogenesis assays. The secretion of the following 10 pro-angiogenic cytokines was measured: angiogenin, ANG2, EGF, bFGF, HB-EGF, PDGF-BB, Leptin, PIGF, HGF, and VEGF-A. RESULTS: Treatment of ARPE-19 cells with bevacizumab significantly increased the secretion of angiogenin. Secretion of angiogenin and VEGF-A were significantly reduced following treatment with DPE under both normoxia and hypoxia. In addition, angiogenin secretion was significantly reduced following treatment with the combination of DPE and bevacizumab compared to bevacizumab alone. CONCLUSIONS: Compensatory angiogenic signalling may occur in neovascular AMD following treatment with bevacizumab. Here we show that DPE, both alone and in combination with bevacizumab, can reduce the secretion of angiogenin, a cytokine that has been upregulated following treatment with bevacizumab in RPE cells. Therefore, DPE may represent a possible therapeutic agent to be used in combination with bevacizumab for the treatment of neovascular AMD.
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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) | 0.001 | 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) | 0.002 | 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".