Inhibition of long noncoding RNA MALAT1 suppresses high glucose-induced apoptosis and inflammation in human umbilical vein endothelial cells by suppressing the NF-κB signaling pathway
Bibliographic record
Abstract
The study investigated the expression of long noncoding RNA (lncRNA) MALAT1 in high glucose (HG)-induced human vascular endothelial cells (HUVECs) and the role of MALAT1 in the apoptosis of HG-induced HUVECs. The HUVECs were cultured and induced with 25 mmol/L HG. After that, the HUVECs were transfected with MALAT1 siRNA. The expression levels of MALAT1 were detected with qPCR, whereas the expression levels of Bax, Bcl-2, cleaved-caspase-3, cleaved-caspase-9, p-65, and p-p65 were detected using Western blot. The roles of MALAT1 in cell activities, including apoptosis, were evaluated using the CCK-8 assay, TUNEL staining, and flow cytometry. The expression levels of inflammatory factors (TNF-α and IL-6) were measured using ELISA. The expression levels of MALAT1, TNF-α, and IL-6 in HUVECs were increased in the HG environment; however, when MALAT1 was silenced in the HUVECs, cell proliferation increased significantly, the expression levels of TNF-α, IL-6, Bax, cleaved-caspase-3, and cleaved-caspase-9 decreased, and the rate of apoptosis also decreased. Silencing MALAT1 inhibited the expression of p-p65 in HG-induced HUVECs. In conclusion, our study demonstrated that MALAT1 is upregulated in HG-induced HUVECs, and inhibition of MALAT1 inhibits HG-induced apoptosis and inflammation in HUVECs by suppression of the NF-κB signaling pathway.
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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".