LncRNA Gm4419 induces cell apoptosis in hepatic ischemia–reperfusion injury via regulating the miR-455–SOX6 axis
Bibliographic record
Abstract
Long non-coding RNA (lncRNA) is known to be involved in a variety of diseases. However, the role of Gm4419 in hepatic ischemia-reperfusion (I/R) injury remains unknown. To study this, we first established a rat model of hepatic I/R, and a BRL-3A cell model of hypoxia-reoxygenation (H/R) for in vivo and in vitro studies. Staining with hematoxylin and eosin and hepatic injury scores were used to evaluate the degree of hepatic I/R injury. Cell apoptosis was assessed via staining with Edu, and with annexin V-FITC-propidium iodide assays. The interactions between Gm4419 and miR-455, as well as miR-455 and SOX6 were evaluated via luciferase reporter activity assays and RNA immunoprecipitation assays. In vivo, we found that Gm4419 was up-regulated in the rats subjected to I/R. Moreover, knockdown of Gm4419 alleviated the I/R-induced liver damage in the rats. In vitro, knockdown of Gm4419 alleviated H/R-induced apoptosis in BRL-3A cells. Interestingly, we found that miR-455 is a target of Gm4419, and Gm4419 regulates the expression of miR-455 via sponging. Furthermore, SOX6 was proven to be the target of miR-455. Finally, rescue experiments confirmed that knockdown of Gm4419 inhibits apoptosis by regulating miR-455 and SOX6 in H/R-treated BRL-3A cells. Therefore, our findings show that the lncRNA Gm4419 accelerates hepatic I/R injury by targeting the miR-455-SOX6 axis, which suggests a novel therapeutic target for hepatic I/R injury.
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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) | 0.002 | 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 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".