Cholesterol metabolism and neuroinflammatory changes in a non-human primate spinal nerve ligation model
Bibliographic record
Abstract
Neuropathic pain remains one of the major neurological conditions with high unmet medical needs. Poor translation from preclinical studies using rodent models to clinical trials is one of the major obstacles to the development of new pharmacological medications to treat neuropathic pain. The aims of this study were to establish a behavioral test to evaluate spontaneous pain in a spinal nerve ligation (SNL) model using cynomolgus monkey as a non-human primate (NHP) model. After right unilateral L7 SNL surgery in cynomolgus monkeys, the percentage of weight-bearing on ipsilateral hindlimb significantly decreased, which was well-associated with an analytical score of electroencephalography (EEG). Transcriptomic analysis of RNA-seq results from the dorsal part of the spinal cord identified pathways matching those in equivalent rodent models, along with NHP-specific pathways, suggesting that neuroinflammation and cholesterol transportation/metabolism were the main pathways altered in this NHP model. Additionally, several upregulated genes observed here were previously reported uniquely in clinical studies, but not in rodent models. This study provides a potentially useful model that can aid our understanding of pathophysiological mechanism of neuropathic pain and the development of pain relief therapies by inducing a robust behavioral phenotype and changes in gene expression resembling those in patients.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".