Temporal changes of spinal microglia in murine models of neuropathic pain: a scoping review
Bibliographic record
Abstract
Neuropathic pain (NP) is an ineffectively treated, debilitating chronic pain disorder that is associated with maladaptive changes in the central nervous system, particularly in the spinal cord. Murine models of NP looking at the mechanisms underlying these changes suggest an important role of microglia, the resident immune cells of the central nervous system, in various stages of disease progression. However, given the number of different NP models and the resource limitations that come with tracking longitudinal changes in NP animals, many studies fail to truly recapitulate the patterns that exist between pain conditions and temporal microglial changes. This review integrates how NP studies are being carried out in murine models and how microglia changes over time can affect pain behavior in order to inform better study design and highlight knowledge gaps in the field. 258 peer-reviewed, primary source articles looking at spinal microglia in murine models of NP were selected using Covidence. Trends in the type of mice, statistical tests, pain models, interventions, microglial markers and temporal pain behavior and microglia changes were recorded and analyzed. Studies were primarily conducted in inbred, young adult, male mice having peripheral nerve injury which highlights the lack of generalizability in the data currently being collected. Changes in microglia and pain behavior, which were both increased, were tested most commonly up to 2 weeks after pain initiation despite aberrant microglia activity also being recorded at later time points in NP conditions. Studies using treatments that decrease microglia show decreased pain behavior primarily at the 1- and 2-week time point with many studies not recording pain behavior despite the involvement of spinal microglia dysfunction in their development. These results show the need for not only studying spinal microglia dynamics in a variety of NP conditions at longer time points but also for better clinically relevant study design considerations.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".