Long non-coding RNA LINC01194 promotes the inflammatory response and apoptosis of lipopolysaccharide-treated MLE-12 cells through the miR-203a-3p/MIP-2 axis
Bibliographic record
Abstract
Acute lung injury (ALI) induced by bacteria lipopolysaccharide (LPS) is characterized by the upregulation of the apoptosis rate of tissue cells and aggravation of inflammatory response. Although many studies have focused on the pathogenesis of this disease, its mechanism remains unknown. This study examined the regulatory role of long non-coding RNA (lncRNA) LINC01194 in the progression of ALI through various bioinformatics analyses and experimental work, including ELISA assay, dual-luciferase reporter assay, biotinylated RNA pull-down assay, and Western blot analysis. The result showed that the LINC01194 was overexpressed in the ALI-induced mice model. We observed a significant upregulation of LINC01194 in LPS-treated mouse lung epithelial type II cells (MLE-12 cells) after 24 h of induction. Bioinformatics analysis, ELISA assay, quantitative reverse transcription polymerase chain reaction analysis, biotinylated RNA pull-down assay, apoptosis test, and Western blot analysis demonstrated that the LINC01194 could act as a microRNA (miR) miR-203a-3p sponge to activate the inflammatory response in LPS-induced ALI model through post-transcriptional upregulation of macrophage inflammatory protein (MIP-2). We showed that LINC01194 regulates the inflammatory response and apoptosis of LPS-induced mice and MLE-12 cells via the miR-203a-3p/MIP-2 axis. LINC01194 could be a potential biomarker for early diagnosis and the treatment of ALI.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".