Role of Primary Afferents in Arthritis Induced Spinal Microglial Reactivity
Bibliographic record
Abstract
both neuron-neuron and neuron-glia interactions. Microglia, resident immune cells of the central nervous system (CNS), play a crucial role in the pathogenesis of chronic pain. This study provides a framework for understanding how peripheral joint injury signals the CNS to engage spinal microglial responses. During the first week of monosodium iodoacetate (MIA)-induced knee joint injury in male rats, inflammatory and neuropathic pain were characterized by increased firing of peripheral joint afferents. This increased peripheral afferent activity was accompanied by increased Iba1 immunoreactivity within the spinal dorsal horn indicating microglial activation. Pharmacological silencing of C and A afferents with co-injections of QX-314 and bupivacaine, capsaicin, or flagellin prevented the development of mechanical allodynia and spinal microglial activity after MIA injection. Elevated levels of ATP in the cerebrospinal fluid (CSF) and increased expression of the ATP transporter vesicular nucleotide transporter (VNUT) in the ipsilateral spinal dorsal horn were also observed after MIA injections. Selective silencing of primary joint afferents subsequently inhibited ATP release into the CSF. Furthermore, increased spinal microglial reactivity, and alleviation of MIA-induced arthralgia with co-administration of QX-314 with bupivacaine were recapitulated in female rats. Our results demonstrate that early peripheral joint injury activates joint nociceptors, which triggers a central spinal microglial response. Elevation of ATP in the CSF, and spinal expression of VNUT suggest ATP signaling may modulate communication between sensory neurons and spinal microglia at 2 weeks of joint degeneration.
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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 | 0.000 |
| 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".