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Record W4309614494 · doi:10.3389/fncel.2022.994585

Voltage-gated sodium channels in diabetic sensory neuropathy: Function, modulation, and therapeutic potential

2022· review· en· W4309614494 on OpenAlexafffund
Stephanie Bigsby, Joseph Neapetung, Verónica A. Campanucci

Bibliographic record

VenueFrontiers in Cellular Neuroscience · 2022
Typereview
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaUniversities Space Research Association
KeywordsMedicineDiabetic neuropathySensory systemSodium channelDiabetes mellitusSensory nerveNeuropathic painNeurosciencePhysical medicine and rehabilitationAnesthesiaPsychologyEndocrinologySodium

Abstract

fetched live from OpenAlex

Voltage-gated sodium channels (Na V ) are the main contributors to action potential generation and essential players in establishing neuronal excitability. Na V channels have been widely studied in pain pathologies, including those that develop during diabetes. Diabetic sensory neuropathy (DSN) is one of the most common complications of the disease. DSN is the result of sensory nerve damage by the hyperglycemic state, resulting in a number of debilitating symptoms that have a significant negative impact in the quality of life of diabetic patients. Among those symptoms are tingling and numbness of hands and feet, as well as exacerbated pain responses to noxious and non-noxious stimuli. DSN is also a major contributor to the development of diabetic foot, which may lead to lower limb amputations in long-term diabetic patients. Unfortunately, current treatments fail to reverse or successfully manage DSN. In the current review we provide an updated report on Na V channels including structure/function and contribution to DSN. Furthermore, we summarize current research on the therapeutic potential of targeting Na V channels in pain pathologies, including DSN.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.266
Teacher spread0.229 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations10
Published2022
Admission routes2
Has abstractyes

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