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Record W164388161 · doi:10.1093/jnen/62.3.304

Changes in Neuronal Migration in Neocortex of Connexin43 Null Mutant Mice

2003· article· en· W164388161 on OpenAlexaff
Shinji Fushiki, José Luis Pérez Velázquez, Liang Zhang, John F. Bechberger, Peter L. Carlen, Christian C. Naus

Bibliographic record

VenueJournal of Neuropathology & Experimental Neurology · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnexins and lens biology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNeocortexNeurogenesisBromodeoxyuridineBiologyNeuroscienceHippocampal formationMutantCell biologyCerebral cortexElectrophysiologyGeneticsCell growth

Abstract

fetched live from OpenAlex

To identify a neural phenotype in connexin43 null mutant mice, electrophysiological properties, intercellular communication and neuronal migration were studied in the developing neocortex. In acute slice preparations from newborn mice, electrophysiological characteristics of cortical and hippocampal neurons were not significantly different between wild type and null mutant mice. However, gap junctional coupling as assessed by fluorescence recovery after photobleaching was significantly attenuated in neocortical brain slices of null mutant mice. To assess neuronal migration, dividing cells were labeled with bromodeoxyuridine (BrdU) on embryonic days 12, 14 and 16, respectively, corresponding to the period of cortical neurogenesis, and the neocortex examined 2 or 3 days after the labeling. BrdU-labeled cells were distributed in the neocortical wall with a significant change in the pattern in the neocortex of the null mutant, where labeled cells accumulated in the intermediate zone or in the inner part of the cortical plate. The result suggests a significant delay in neocortical neuronal migration in the connexin43 null mutants, and a possible role of connexin43 in this process through yet unidentified mechanisms.

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 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 score0.072
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.253
Teacher spread0.243 · 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

Citations106
Published2003
Admission routes1
Has abstractyes

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