Expression of miR-210-3p in the aqueous humor of patients with age-related cataracts and its effect on human lens epithelial cell injury induced by hydrogen peroxide
Bibliographic record
Abstract
ABSTRACT Purpose: The regulatory effect of microRNA on diseases has been confirmed. This study aimed to evaluate the expression of microRNA-210-3p in age-related cataracts and assess the effect of abnormal miR-210-3p expressions on H2O2-induced SAR01/04 cells. Methods: Reverse-transcription quantitative polymerase chain reaction method was performed to assess the levels of miR-210-3p in aqueous humor samples. Receiver operating characteristic analysis was employed to assess the discrimination ability of miR-210-3p between patients with age-related cataracts and healthy people, and Pearson correlation analysis was used to identify the correlation between miR-210-3p and oxidative stress indices such as superoxide dismutase, glutathione peroxidase, malonaldehyde. Cell counting kit-8 assay and Transwell assay were used to estimate the biological function of H2O2-induced age-related cataract cell model. The levels of oxidative stress indices such as superoxide dismutase, glutathione peroxidase, and malonaldehyde were measured to evaluate the degree of oxidative stress damage in the age-related cataract cell model. The relationship between miR-210-3p and its target gene was verified by luciferase reporter gene analysis. Results: The miR-210-3p expression was elevated in the aqueous humor of patients with age-related cataracts. A high miR-210-3p expression showed a high diagnostic value for age-related cataracts and was significantly associated with the level of oxidative stress markers in patients with age-related cataracts. The inhibition of miR-210-3p can reverse oxidative stress stimulation and adverse effects on H2O2-induced cell function. Conclusions: The results suggested that miR-210-3p could promote cell viability, cell migration, and oxidative stress by targeting autophagy-related gene 7 in in vitro age-related cataract cell model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".