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Novel molecular targets in pain control

2003· article· en· W2094118978 on OpenAlexaff
Andy Dray

Bibliographic record

VenueCurrent Opinion in Anaesthesiology · 2003
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsAstraZeneca (Canada)
Fundersnot available
KeywordsMedicineNeuroscienceReceptorChronic painMetabotropic receptorBioinformaticsGlutamate receptorInternal medicineBiologyPsychiatry

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The complexity of pain processing in clinical pain conditions and in animal models has revealed many time-related changes and an abundance of molecular drug targets. There continues to be insecurity, however, about new target validation in clinical pain and thus most analgesia development is of high risk for evolving new pain therapies. The present review highlights a number of molecular targets being pursued for pain control. RECENT FINDINGS: Many pain targets are critically dependent on the pain model/lesion type. Neural and glial plasticity, ranging from changes in molecular expression and receptor phosphorylation to profound morphological reorganization, has been described under these conditions. Pain modulation has been shown to involve all major families of regulatory proteins such as the G-protein coupled receptors, ion channels, regulatory enzymes, neurotrophins, and kinases, offering an abundance of targets and therapeutic opportunities for symptomatic pain relief. SUMMARY: Many molecular targets have been highlighted with some being the focus of current analgesia research. Some of these (e.g. vanilloid receptor 1, cannabinoid receptor 1, sodium channel NaV 1.8) have been evaluated in animal studies and in preliminary clinical studies, but others are highly novel and riskier analgesia pain targets (e.g. metabotropic glutamate receptors, sensory neurone specific receptors, kinase inhibitors).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.039
GPT teacher head0.320
Teacher spread0.281 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations12
Published2003
Admission routes1
Has abstractyes

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