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Record W4408543400 · doi:10.1186/s13041-025-01196-9

The terpenes alpha-bisabolol and camphene modulate pruritus via an action on Cav3.2 T-type calcium channels

2025· article· en· W4408543400 on OpenAlexafffund
Flavia Tasmin Techera Antunes, Vinícius M. Gadotti, Gerald W. Zamponi

Bibliographic record

VenueMolecular Brain · 2025
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersCanadian Institutes of Health ResearchAlberta Innovates
KeywordsCampheneItchingPharmacologyChemistryTerpeneHistaminergicAnalgesicScratchingCalciumHistamineAnesthesiaMedicineStereochemistryDermatologyEssential oilOrganic chemistry

Abstract

fetched live from OpenAlex

Alpha-bisabolol and camphene have demonstrated analgesic effects in inflammatory pain models by blocking Cav3.2 calcium channels. As the pain pathway overlaps with mechanisms for itch, and because Cav3.2 channels have been associated with itch in our previous work, we aimed to investigate the potential anti-itch effects of these two terpenes. Although both terpenes failed to show anti-pruritogenic properties when dissolved in aqueous PBS, when diluted in Hydroxypropyl-beta-cyclodextrin their bioactivity significantly increased. Both compounds significantly reduced scratching in the histaminergic itch model, whether administered subcutaneously or intraperitoneally. Camphene reduced itching in the non-histaminergic model regardless of the route of administration, whereas alpha-bisabolol did not alleviate chloroquine-induced itching. When tested in Cav3.2-/- mice, neither camphene nor alpha-bisabolol significantly reduced histamine-induced scratching behavior. This suggests that the anti-pruritic actions of these terpenes may involve Cav3.2 block to mitigate itch.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.016
GPT teacher head0.321
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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