Picroside Ⅱ attenuates neuropathic pain by regulating inflammation and spinal excitatory synaptic transmission
Bibliographic record
Abstract
Nerve injury induced microglia activation, which released inflammatory mediators and developed neuropathic pain. Picroside Ⅱ (PⅡ) attenuated neuropathic pain by inhibiting the neuroinflammation of the spinal dorsal horn; however, how it engaged in the cross talk between microglia and neurons remained ambiguous. This study aimed to investigate PⅡ in the modulation of spinal synaptic transmission mechanisms on pain hypersensitivity in neuropathic rats. We investigated the analgesia of PⅡ in mechanical and thermal hyperalgesia using the spinal nerve ligation (SNL)-induced neuropathic pain model and formalin-induced tonic pain model, respectively. RNA sequencing and network pharmacology were employed to screen core targets and signaling pathways. Immunofluorescence staining and qPCR were performed to explore the expression level of microglia and inflammatory mediator mRNA. The whole-cell patch-clamp recordings were utilized to record miniature excitatory postsynaptic currents in excitatory synaptic transmission. Our results demonstrated that the analgesic of PⅡ was significant in both pain models, and the underlying mechanism may involve inflammatory signaling pathways. PⅡ reversed the SNL-induced overexpression of microglia and inflammatory factors. Moreover, PⅡ dose dependently inhibited excessive glutamate transmission. Thus, this study suggested that PⅡ attenuated neuropathic pain by inhibiting excitatory glutamate transmission of spinal synapses, induced by an inflammatory response on microglia.
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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) | 0.001 | 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 | 0.000 | 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".