Sprouty-related proteins with EVH1 domain (SPRED2) prevents high-glucose induced endothelial–mesenchymal transition and endothelial injury by suppressing MAPK activation
Bibliographic record
Abstract
Diabetic retinopathy (DR) is a common complication of diabetes, and the leading cause of blindness in adults. Sprouty-related proteins with EVH1 domain (SPRED2) play an important role in diabetes and are closely related to the lens and eye morphogenesis. This study attempted to investigate the role and related mechanism of SPRED2 in DR. DR rat model was established by administration streptozocin. Human retinal endothelial cells (HRECs) were treated with high glucose (HG) to mimic DR. The results showed that SPRED2 expression was decreased in the retinal tissues of DR rats and HG-treated HRECs. MTT assay and flow cytometry data showed that SPRED2 overexpression reduced cell viability of HG-treated HRECs. SPRED2 overexpression enhanced Caspase-3 activity and promoted apoptosis of HG-treated HRECs. Furthermore, the expressions of endothelial cell markers CD31 and E-cad were down-regulated, whereas the expressions of mesenchymal cell markers FSP1, SM22, and α-SMA were up-regulated in the HG-treated HRECs. SPRED2 overexpression reversed HG-induced endothelial–mesenchymal transition in HRECs. The expressions of tight junction components claudin 3, occludin, and ZO-1 were increased in HG-treated HRECs following SPRED2 up-regulation. In addition, SPRED2 overexpression downregulated the expression of p-ERK1/2, p-p38, and p-JNK in the HG-treated HRECs. In conclusion, this study demonstrated that SPRED2 overexpression repressed endothelial–mesenchymal transition and endothelial injury in HG-treated HRECs by suppressing MAPK signaling pathway. These findings suggested that SPRED2 may be a novel potential therapeutic target implicated in DR progression.
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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) | 0.000 | 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 | 0.001 |
| 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".