Regulation of nociceptive neurons by nerve growth factor and glial cell line derived neurotrophic factor
Bibliographic record
Abstract
Nociceptive dorsal root ganglion (DRG) cells can be divided into three main populations, namely (1) small diameter non-peptide-expressing cells, (2) small-diameter peptide-expressing (calcitonin gene related peptide (CGRP), substance P) cells, and (3) medium-diameter peptide-expressing (CGRP) cells. The properties of these cell populations will be reviewed, with a special emphasis on the expression of the vanilloid (capsaicin) receptor VR1 and its regulation by growth factors. Cells in populations 1 and 2 express VR1, a nonselective channel that transduces certain nociceptive stimuli and that is crucial to the functioning of polymodal nociceptors. Cells in population 1 can be regulated by glial cell line derived neurotrophic factor (GDNF) and those in populations 2 and 3 by nerve growth factor (NGF). In vivo, DRG cells express a range of levels of VR1 expression and VR1 is downregulated after axotomy. However, treatment with NGF or GDNF can prevent this downregulation. In vitro, DRG cells also show a range of VR1 expression levels that is NGF and (or) GDNF dependent. Functional studies indicate that freshly dissociated cells also show differences in sensitivity to capsaicin. The significance of this is not known but may indicate a difference in the physiological role of cells in populations 1 and 2.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".