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Record W4206423250 · doi:10.1016/j.xpro.2021.101112

Characterizing neurotrophic factor-induced synaptic growth in primary mouse neuronal cultures

2022· article· en· W4206423250 on OpenAlexafffund
Guiqin He, Xinyue Wang, Zhengping Jia, Zikai Zhou

Bibliographic record

VenueSTAR Protocols · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsCanada Research ChairsHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health ResearchChina Postdoctoral Science FoundationNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsNeuroscienceNeurotrophic factorsNeurotrophinAMPA receptorNeurotransmissionImmunocytochemistryBrain-derived neurotrophic factorBiologyCiliary neurotrophic factorDendritic spineCell biologyGlutamate receptorReceptorHippocampal formationEndocrinologyBiochemistry

Abstract

fetched live from OpenAlex

Neurotrophic factors and their signaling cascades play important roles in synaptic growth, which can be investigated in cultured primary neurons to better control the concentrations and timing of neurotrophic factor treatment. Here, we provide a protocol detailing the preparation of cultured primary mouse neurons and the neurotrophic factor treatment. We then describe electrophysiological recording of synaptic transmission, immunocytochemistry of AMPA receptor expression, and imaging analysis of dendritic spines. This platform enables characterization of synaptic growth at functional and morphological levels. For complete details on the use and execution of this profile, please refer to Zhou et al. (2021).

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.003

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 head0.053
GPT teacher head0.295
Teacher spread0.242 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations3
Published2022
Admission routes2
Has abstractyes

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