Estrogens influence female itch sensitivity via the spinal gastrin-releasing peptide receptor neurons
Bibliographic record
Abstract
There are sex differences in somatosensory sensitivity. Circulating estrogens appear to have a pronociceptive effect that explains why females are reported to be more sensitive to pain than males. Although itch symptoms develop during pregnancy in many women, the underlying mechanism of female-specific pruritus is unknown. Here, we demonstrate that estradiol, but not progesterone, enhances histamine-evoked scratching behavior indicative of itch in female rats. Estradiol increased the expression of the spinal itch mediator, gastrin-releasing peptide (GRP), and increased the histamine-evoked activity of itch-processing neurons that express the GRP receptor (GRPR) in the spinal dorsal horn. The enhancement of itch behavior by estradiol was suppressed by intrathecal administration of a GRPR blocker. In vivo electrophysiological analysis showed that estradiol increased the histamine-evoked firing frequency and prolonged the response of spinal GRP-sensitive neurons in female rats. On the other hand, estradiol did not affect the threshold of noxious thermal pain and decreased touch sensitivity, indicating that estradiol separately affects itch, pain, and touch modalities. Thus, estrogens selectively enhance histamine-evoked itch in females via the spinal GRP/GRPR system. This may explain why itch sensation varies with estrogen levels and provides a basis for treating itch in females by targeting GRPR.
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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) | 0.002 | 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".