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Record W2019260165 · doi:10.1002/ptr.3174

Intrathecal eugenol administration alleviates neuropathic pain in male Sprague‐Dawley rats

2010· article· en· W2019260165 on OpenAlexaff
Ludivine Lionnet, Francis Beaudry, Pascal Vachon

Bibliographic record

VenuePhytotherapy Research · 2010
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsNeuropathic painMedicineIntrathecalEugenolAnesthesiaPharmacologyChemistry

Abstract

fetched live from OpenAlex

The main objective of this study was to determine the central effect of eugenol on neuropathic pain when injected intrathecally at the level of the lumbar spinal cord. In a preliminary study the penetrability of eugenol was evaluated in the CNS of rats. Blood, brain and spinal cord samples were collected at selected time points following eugenol administration and concentrations were determined by tandem liquid chromatography-mass spectrometry. Brain-to-plasma and spinal cord-to-plasma ratios (3.3 and 6.7, respectively) suggest that eugenol penetrates relatively well the CNS of rats, with a preferential distribution in the spinal cord. Following the induction of neuropathic pain in rats using the sciatic nerve ligation model, intrathecal injections of eugenol were done to evaluate the central effect of eugenol. Treatment with 50 μg of eugenol significantly decreased secondary mechanical allodynia after 15 min, 2 h and 4 h (p < 0.05; <0.005; <0.05, respectively) and improved thermal hyperalgesia after 2 h and 4 h (p < 0.001 and p < 0.05). The results support the hypothesis that eugenol may alleviate neuropathic pain, both allodynia and hyperalgesia, by acting centrally most probably at the level of the dorsal horn of the spinal cord where vanilloid receptors can be found.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.062
GPT teacher head0.391
Teacher spread0.329 · 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

Citations37
Published2010
Admission routes1
Has abstractyes

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