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Record W4402859932 · doi:10.1016/j.xcrm.2024.101749

A candidate loss-of-function variant in SGIP1 causes synaptic dysfunction and recessive parkinsonism

2024· article· en· W4402859932 on OpenAlexaff
Marianna Decet, Patrick Scott, Sabine Kuenen, Douja Meftah, Jef Swerts, Carles Calatayud, Sandra F. Gallego, Natalie Kaempf, Eliana Nachman, Roman Praschberger, Nils Schoovaerts, Chris C. Tang, David Eidelberg, Samir Al‐Adawi, Abdullah Al‐Asmi, Ramachandiran Nandhagopal, Patrik Verstreken

Bibliographic record

VenueCell Reports Medicine · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
FundersNational Institutes of HealthH2020 European Research CouncilVlaamse regeringKU LeuvenFonds Wetenschappelijk Onderzoek
KeywordsParkinsonismLoss functionNeuroscienceMedicineGeneticsBiologyPhenotypeInternal medicineGeneDisease

Abstract

fetched live from OpenAlex

F]-fluorodeoxyglucose positron emission tomography scans shows patterns very similar to those of idiopathic Parkinson's disease. We show that the identified SGIP1 mutation causes a loss of protein function, and analyses in newly created Drosophila models reveal movement defects, synaptic transmission dysfunction, and neurodegeneration, including dopaminergic synapse loss. Histology and correlative light and electron microscopy reveal the absence of synaptic multivesicular bodies and the accumulation of degradative organelles. This research delineates a putative form of recessive parkinsonism, converging on defective synaptic proteostasis and opening avenues for diagnosis, genetic counseling, and treatment.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.321
Teacher spread0.288 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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