Patterns of pain: Meta-analysis of microarray studies of pain
Bibliographic record
Abstract
Existing microarray gene expression profiling studies of tonic/chronic pain were subjected to meta-analysis to identify genes found to be regulated by these pain states in multiple, independent experiments. Twenty studies published from 2002 to 2008 were identified, describing the statistically significant regulation of 2254 genes. Of those, a total of 79 genes were found to be statistically significant "hits" in 4 or more independent microarray experiments, corresponding to a conservative P<0.01 overall. Gene ontology-based functional annotation clustering analyses revealed strong evidence for regulation of immune-related genes in pain states. A multi-gene quantitative real-time polymerase chain reaction experiment was run on dorsal root ganglion (DRG) and spinal cord tissue from rats and mice given nerve (sciatic chronic constriction; CCI) or inflammatory (complete Freund's adjuvant) injury. We independently confirmed the regulation of 43 of these genes in the rat-CCI-DRG condition; the genetic correlates in all other conditions were largely and, in some cases, strikingly, independent. However, a handful of genes were identified whose regulation bridged etiology, anatomical locus, and/or species. Most notable among these were Reg3b (regenerating islet-derived 3 beta; pancreatitis-associated protein) and Ccl2 (chemokine [C-C motif] ligand 2), which were significantly upregulated in every condition in the rat.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".