MétaCan
Menu
Back to cohort

The Role of Voltage-Gated Calcium Channels in Basal Ganglia NeurodegenerativeDisorders

2022· article· en· W4221091436 on OpenAlexafffund

Bibliographic record

VenueCurrent Neuropharmacology · 2022
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBasal gangliaVoltage-dependent calcium channelDiseaseNeurodegenerationParkinson's diseaseCalciumHomeostasisMitochondrionCentral nervous system

Abstract

fetched live from OpenAlex

Abstract: Calcium (Ca2+) plays a central role in regulating many cellular processes and influences cell survival. Several mechanisms can disrupt Ca2+ homeostasis to trigger cell death, including oxidative stress, mitochondrial damage, excitotoxicity, neuroinflammation, autophagy, and apoptosis. Voltage-gated Ca2+ channels (VGCCs) act as the main source of Ca2+ entry into electrically excitable cells, such as neurons, and they are also expressed in glial cells such as astrocytes and oligodendrocytes. The dysregulation of VGCC activity has been reported in both Parkinson's disease (PD) and Huntington's (HD). PD and HD are progressive neurodegenerative disorders (NDs) of the basal ganglia characterized by motor impairment as well as cognitive and psychiatric dysfunctions. This review will examine the putative role of neuronal VGCCs in the pathogenesis and treatment of central movement disorders, focusing on PD and HD. The link between basal ganglia disorders and VGCC physiology will provide a framework for understanding the neurodegenerative processes that occur in PD and HD, as well as a possible path towards identifying new therapeutic targets for the treatment of these debilitating disorders.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.036
GPT teacher head0.313
Teacher spread0.277 · 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 designNot applicable
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

Citations37
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueCurrent NeuropharmacologySame topicGenetic Neurodegenerative DiseasesFrench-language works237,207