Reactive Astrocytes: Critical Players in the Development of Chronic Pain
Bibliographic record
Abstract
Chronic pain is associated with long term plasticity of nociceptive pathways in the central nervous system. Astrocytes can profoundly affect synaptic function and increasing evidence has highlighted how altered astrocyte activity may contribute to the pathogenesis of chronic pain. In response to injury, astrocytes undergo a shift in form and function known as reactive astrogliosis, which affects their release of cytokines and gliotransmitters. These neuromodulatory substances have been implicated in driving the persistent changes in central nociceptive activity. Astrocytes also release lactate which neurons can use to produce energy during synaptic plasticity. Furthermore, recent research has provided insight into lactate's emerging role as a signaling molecule in the central nervous system, which may be involved in directly modulating neuronal and astrocytic activity. In this review, we present evidence for the involvement of astrocyte-derived tumor necrosis factor alpha in pain-associated plasticity, in addition to research suggesting the potential involvement of gliotransmitters D-serine and adenosine-5'-triphosphate. We also discuss work implicating astrocyte-neuron metabolic coupling, and the possible role of lactate, which has been sparsely studied in the context of chronic pain, in supporting pathological changes in central nociceptive activity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".