Suppression of MALAT1 ameliorates chronic constriction injury‐induced neuropathic pain in rats via modulating miR‐206 and ZEB2
Bibliographic record
Abstract
Long noncoding RNAs (lncRNAs) are involved in multiple nervous system diseases, including neuropathic pain. Previous studies have demonstrated that lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) has been identified as a diagnostic biomarker in many diseases. Nevertheless, the function of MALAT1 in neuropathic pain progression is still unclear. Here, we established a chronic constriction injury (CCI) rat model. We found that MALAT1 was remarkably upregulated in CCI rats. In addition, neuropathic pain behaviors such as mechanical and thermal hyperalgesia were reduced by the inhibition of MALAT1. Meanwhile, the loss of MALAT1 was able to depress the neuroinflammation process via the inhibition of COX-2, interleukin-1β, and interleukin-6. A previous study has indicated that miR-206 upregulation can restrain the CCI-induced neuropathic pain. Furthermore, we exhibited that miR-206 was significantly downregulated and silence of MALAT1 restrained its expression in CCI rats. For another, ZEB2 was a target of miR-206 and it was shown that ZEB2 was elevated in CCI rats in a time-dependent manner. Overexpression of miR-206 obviously suppressed ZEB2 levels in rat microglial cells. Subsequently, it was demonstrated that upregulation of miR-206 rescued the neuropathic pain triggered by ZEB2 overexpression in vivo through neuroinflammation inhibition. Overall, we indicated that suppression of MALAT1 ameliorated neuropathic pain progression via miR-206/ZEB2 axis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".