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Record W3185856250 · doi:10.1186/s13041-021-00813-7

Modeling temperature- and Cav3 subtype-dependent alterations in T-type calcium channel mediated burst firing

2021· article· en· W3185856250 on OpenAlexafffund
Fernando R. Fernandez, Mircea Iftinca, Gerald W. Zamponi, Ray W. Turner

Bibliographic record

VenueMolecular Brain · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIon channel regulation and function
Canadian institutionsAlberta Children's HospitalHotchkiss Brain InstituteUniversity of Calgary
FundersCanadian Institutes of Health ResearchCanada Research ChairsGovernment of Canada
KeywordsT-type calcium channelBurstingNeuroscienceVoltage-dependent calcium channelCalcium channelChannel (broadcasting)Neuronal firingCalciumChemistryBiologyElectrophysiologyMedicineComputer scienceInternal medicineComputer network

Abstract

fetched live from OpenAlex

T-type calcium channels are important regulators of neuronal excitability. The mammalian brain expresses three T-type channel isoforms (Cav3.1, Cav3.2 and Cav3.3) with distinct biophysical properties that are critically regulated by temperature. Here, we test the effects of how temperature affects spike output in a reduced firing neuron model expressing specific Cav3 channel isoforms. The modeling data revealed only a minimal effect on baseline spontaneous firing near rest, but a dramatic increase in rebound burst discharge frequency for Cav3.1 compared to Cav3.2 or Cav3.3 due to differences in window current or activation/recovery time constants. The reduced response by Cav3.2 could optimize its activity where it is expressed in peripheral tissues more subject to temperature variations than Cav3.1 or Cav3.3 channels expressed prominently in the brain. These tests thus reveal that aspects of neuronal firing behavior are critically dependent on both temperature and T-type calcium channel subtype.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.252
Teacher spread0.237 · 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 designSimulation or modeling
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

Citations6
Published2021
Admission routes2
Has abstractyes

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