Neuroinflammation of the spinal cord and nerve roots in chronic radicular pain patients
Bibliographic record
Abstract
Numerous preclinical studies support the role of spinal neuroimmune activation in the pathogenesis of chronic pain, and targeting glia (eg, microglia/astrocyte)- or macrophage-mediated neuroinflammatory responses effectively prevents or reverses the establishment of persistent nocifensive behaviors in laboratory animals. However, thus far, the translation of those findings into novel treatments for clinical use has been hindered by the scarcity of data supporting the role of neuroinflammation in human pain. Here, we show that patients suffering from a common chronic pain disorder (lumbar radiculopathy), compared with healthy volunteers, exhibit elevated levels of the neuroinflammation marker 18 kDa translocator protein, in both the neuroforamina (containing dorsal root ganglion and nerve roots) and spinal cord. These elevations demonstrated a pattern of spatial specificity correlating with the patients' clinical presentation, as they were observed in the neuroforamen ipsilateral to the symptomatic leg (compared with both contralateral neuroforamen in the same patients as well as to healthy controls) and in the most caudal spinal cord segments, which are known to process sensory information from the lumbosacral nerve roots affected in these patients (compared with more superior segments). Furthermore, the neuroforaminal translocator protein signal was associated with responses to fluoroscopy-guided epidural steroid injections, supporting its role as an imaging marker of neuroinflammation, and highlighting the clinical significance of these observations. These results implicate immunoactivation at multiple levels of the nervous system as a potentially important and clinically relevant mechanism in human radicular pain, and suggest that therapies targeting immune cell activation may be beneficial for chronic pain patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".