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Record W2159906772 · doi:10.1186/1756-6606-4-39

DIP/WISH deficiency enhances synaptic function and performance in the Barnes maze

2011· article· en· W2159906772 on OpenAlexaff
Suhail Asrar, Keiko Kaneko, Keizo Takao, Jaina Negandhi, Makoto Matsui, Koji Shibasaki, Tsuyoshi Miyakawa, Robert V. Harrison, Zhengping Jia, Michael W. Salter, Makoto Tominaga, Tomoko Fukumi‐Tominaga

Bibliographic record

VenueMolecular Brain · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective Tissue Growth Factor Research
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsLong-term potentiationNeuroscienceWishSynaptic plasticityHippocampal formationSynapseActin cytoskeletonGlutamatergicCytoskeletonBiologyCell biologyChemistryPsychologyBiochemistryGlutamate receptorReceptorCellArt

Abstract

fetched live from OpenAlex

BACKGROUND: DIP (diaphanous interacting protein)/WISH (WASP interacting SH3 protein) is a protein involved in cytoskeletal signaling which regulates actin cytoskeleton dynamics and/or microtubules mainly through the activity of Rho-related proteins. Although it is well established that: 1) spine-head volumes change dynamically and reflect the strength of the synapse accompanying long-term functional plasticity of glutamatergic synaptic transmission and 2) actin organization is critically involved in spine formation, the involvement of DIP/WISH in these processes is unknown. RESULTS: We found that DIP/WISH-deficient hippocampal CA1 neurons exhibit enhanced long-term potentiation via modulation of both pre- and post-synaptic events. Consistent with these electrophysiological findings, DIP/WISH-deficient mice, particularly at a relatively young age, found the escape hole more rapidly in the Barnes maze test. CONCLUSIONS: We conclude that DIP/WISH deletion improves performance in the Barnes maze test in mice probably through increased hippocampal long-term potentiation.

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)0.000
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 score0.540
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

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 science0.0000.000
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 head0.018
GPT teacher head0.247
Teacher spread0.229 · 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

Citations5
Published2011
Admission routes1
Has abstractyes

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