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Record W4391773618 · doi:10.1523/eneuro.0170-23.2023

Mice Expressing A53T/A30P Mutant Alpha-Synuclein in Dopamine Neurons Do Not Display Behavioral Deficits

2024· article· en· W4391773618 on OpenAlexaff
Cameron Keomanivong, Josephine H. Schamp, Ervina Tabakovic, Ramasamy Thangavel, Georgina M. Aldridge, Andrew A. Pieper, Nandakumar S. Narayanan

Bibliographic record

VenueeNeuro · 2024
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsYork University
FundersNational Institute of Neurological Disorders and StrokeNational Heart, Lung, and Blood Institute
KeywordsTyrosine hydroxylaseDopamineMutantAlpha-synucleinDementia with Lewy bodiesNeuroscienceParkinson's diseaseAlpha (finance)Genetically modified mouseLewy bodyTransgeneBiologyDiseaseMedicineInternal medicineGeneGeneticsDementia

Abstract

fetched live from OpenAlex

Alpha-synuclein has been implicated in neurodegenerative diseases such as Parkinson's disease and dementia with Lewy bodies, with A53T and A30P mutations shown to be disease causing. It has been reported that hemizygous transgenic mice with tyrosine hydroxylase promotor-driven expression of A53T/A30P mutant alpha-synuclein in dopamine neurons provide a useful preclinical model of these conditions by virtue of developing behavioral deficits. Here, we report a lack of replication of this finding. Despite detecting robust overexpression of A53T/A30P mutant alpha-synuclein in dopamine neurons, we did not observe decreased tyrosine hydroxylase immunofluorescence or behavioral deficits in these mice. Our results demonstrate that preclinical models of synucleinopathy need careful validation in the field.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.029
GPT teacher head0.300
Teacher spread0.271 · 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
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

Citations3
Published2024
Admission routes1
Has abstractyes

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