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Record W2128254998 · doi:10.1113/jphysiol.2006.121848

Extracellular chelation of zinc does not affect hippocampal excitability and seizure‐induced cell death in rats

2006· article· en· W2128254998 on OpenAlexafffund
N. Lavoie, Modesto R. Peralta, Marilou Chiasson, Kathleen Lafortune, Luca Pellegrini, László Seress, Katalin Tóth

Bibliographic record

VenueThe Journal of Physiology · 2006
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsExtracellularZincKainic acidIntracellularHippocampal formationChemistryZinc toxicitySynaptic vesicleBiophysicsNeuroscienceBiochemistryCell biologyBiologyMembraneGlutamate receptorVesicleReceptor

Abstract

fetched live from OpenAlex

In the nervous system, zinc can influence synaptic responses and at extreme concentrations contributes to epileptic and ischaemic neuronal injury. Zinc can originate from synaptic vesicles, the extracellular space and from intracellular stores. In this study, we aimed to determine which of these zinc pools is responsible for the increased hippocampal excitability observed in zinc-depleted animals or following zinc chelation. Also, we investigated the source of intracellularly accumulating zinc in vulnerable neurons. Our data show that membrane-permeable and membrane-impermeable zinc chelators had little or no effect on seizure activity in the CA3 region. Furthermore, extracellular zinc chelation could not prevent the accumulation of lethal concentrations of zinc in dying neurons following epileptic seizures. At the electron microscopic level, zinc staining significantly increased at the presynaptic membrane of mossy fibre terminals in kainic acid-treated animals. These data indicate that intracellular but not extracellular zinc chelators could influence neuronal excitability and seizure-induced zinc accumulation observed in the cytosol of vulnerable neurons.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.676
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.022
GPT teacher head0.295
Teacher spread0.272 · 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 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

Citations48
Published2006
Admission routes2
Has abstractyes

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