Spinal cord vascular degeneration impairs duloxetine penetration
Bibliographic record
Abstract
Introduction: Chronic pain is a prevalent physically debilitating health-related morbidity. Frontline analgesics are inadequate, providing only partial pain relief in only a proportion of the patient cohort. Here, we explore whether alterations in spinal cord vascular perfusion are a factor in reducing the analgesic capability of the noradrenaline reuptake inhibitor, duloxetine. Method: An established rodent model of spinal cord vascular degeneration was used. Endothelial-specific vascular endothelial growth factor receptor 2 knockout mouse was induced via hydroxytamoxifen administered via intrathecal injection. Duloxetine was administered via intraperitoneal injection, and nociceptive behavioural testing was performed in both WT and VEGFR2KO mice. LC-MS/MS was performed to explore the accumulation of duloxetine in the spinal cord in WT and VEGFR2KO mice. Results: Spinal cord vascular degeneration leads to heat hypersensitivity and a decline in capillary perfusion. The integrity of noradrenergic projections (dopa - hydroxylase labelled) in the dorsal horn remained unaltered in WT and VEGFR2KO mice. There was an association between dorsal horn blood flow with the abundance of accumulated duloxetine in the spinal cord and analgesic capacity. In VEGFR2KO mice, the abundance of duloxetine in the lumbar spinal cord was reduced and was correlated with reduced anti-nociceptive capability of duloxetine. Discussion: Here, we show that an impaired vascular network in the spinal cord impairs the anti-nociceptive action of duloxetine. This highlights that the spinal cord vascular network is crucial to maintaining the efficacy of analgesics to provide pain relief.
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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 | 0.000 | 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) | 0.004 | 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".