Central Delivery of Dicer-substrate siRNA: A Direct Application for Pain Research
Bibliographic record
Abstract
RNA interference (RNAi) is gaining acceptance as a potential therapeutic strategy against peripheral disease, and several clinical trials are already underway with 21-mer small-interfering RNA (siRNA) as the active pharmaceutical agent. However, for central affliction like pain, such innovating therapies are limited but nevertheless crucial to improve pain research and management. We demonstrate here the proof-of-concept of the use of 27-mer Dicer-substrate siRNA (DsiRNA) for silencing targets related to CNS disorders such as pain states. Indeed, low dose DsiRNA (0.005 mg/kg) was highly efficient in reducing the expression of the neurotensin receptor-2 (NTS2, a G-protein-coupled receptor (GPCR) involved in ascending nociception) in rat spinal cord through intrathecal (IT) administration formulated with the cationic lipid i-Fect. Along with specific decrease in NTS2 mRNA and protein, our results show a significant alteration in the analgesic effect of a selective-NTS2 agonist, reaching 93% inhibition up to 3-4 days after administration of DsiRNA. In order to ensure that these findings were not biased by unsuspected off-target effects (OTEs), we also demonstrated that treatment with a second NTS2-specific DsiRNA also reversed NTS2-induced antinociception, and that NTS2-specific 27-mer duplexes did not alter signaling through NTS1, a closely related receptor. Altogether, DsiRNAi represents a potent tool for dissecting nociceptive pathways and could further lead to a new class of central active drugs.
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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 | 0.000 |
| 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 | 0.001 | 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".