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Record W2758704391 · doi:10.1016/j.ibror.2017.08.001

Susceptibility to hippocampal kindling seizures is increased in aging C57 black mice

2017· article· en· W2758704391 on OpenAlexaff
Kurt R. Stover, Stellar Lim, Terri-Lin Zhou, Paul M. Stafford, Jonathan J. Chow, Haoyuan Li, Nila Sivanenthiran, Sivakami M. Mylvaganam, Chiping Wu, Donald F. Weaver, James H. Eubanks, Liang Zhang

Bibliographic record

VenueIBRO Reports · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsHippocampal formationKindlingEpilepsyNeuroscienceStimulationPsychologyDementiaHippocampusMedicineInternal medicine

Abstract

fetched live from OpenAlex

The incidence of seizures increases with old age. Stroke, dementia and brain tumors are recognized risk factors for new-onset seizures in the aging populations and the incidence of these conditions also increased with age. Whether aging is associated with higher seizure susceptibility in the absence of the above pathologies remains unclear. We used classic kindling to explore this issue as the kindling model is highly reproducible and allows close monitoring of electrographic and motor seizure activities in individual animals. We kindled male young and aging mice (C57BL/6 strain, 2-3 and 18-22 months of age) via daily hippocampal CA3 stimulation and monitored seizure activity via video and electroencephalographic recordings. The aging mice needed fewer stimuli to evoke stage-5 motor seizures and exhibited longer hippocampal afterdischarges and more frequent hippocampal spikes relative to the young mice, but afterdischarge thresholds and cumulative afterdischarge durations to stage 5 motor seizures were not different between the two age groups. While hippocampal injury and structural alterations at cellular and micro-circuitry levels remain to be examined in the kindled mice, our present observations suggest that susceptibility to hippocampal CA3 kindling seizures is increased with aging in male C57 black mice.

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)0.004
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.071
GPT teacher head0.388
Teacher spread0.317 · 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

Citations25
Published2017
Admission routes1
Has abstractyes

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