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Record W2118598201 · doi:10.1080/15548627.2015.1067364

Genetic perspective on the role of the autophagy-lysosome pathway in Parkinson disease

2015· review· en· W2118598201 on OpenAlexaff
Ziv Gan‐Or, Patrick A. Dion, Guy A. Rouleau

Bibliographic record

VenueAutophagy · 2015
Typereview
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsAutophagyLysosomePINK1MitophagyLRRK2BiologyParkinson's diseaseGeneticsDiseaseCell biologyPathogenesisLafora diseaseMechanism (biology)ATG16L1GeneMutationImmunologyPhosphorylationEnzymeBiochemistryPhosphataseMedicine

Abstract

fetched live from OpenAlex

Parkinson disease (PD), once considered as a prototype of a sporadic disease, is now known to be considerably affected by various genetic factors, which interact with environmental factors and the normal process of aging, leading to PD. Large studies determined that the hereditary component of PD is at least 27%, and in some populations, single genetic factors are responsible for more than 33% of PD patients. Interestingly, many of these genetic factors, such as LRRK2, GBA, SMPD1, SNCA, PARK2, PINK1, PARK7, SCARB2, and others, are involved in the autophagy-lysosome pathway (ALP). Some of these genes encode lysosomal enzymes, whereas others correspond to proteins that are involved in transport to the lysosome, mitophagy, or other autophagic-related functions. Is it possible that all these factors converge into a single pathway that causes PD? In this review, we will discuss these genetic findings and the role of the ALP in the pathogenesis of PD and will try to answer this question. We will suggest a novel hypothesis for the pathogenic mechanism of PD that involves the lysosome and the different autophagy pathways.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.030
GPT teacher head0.316
Teacher spread0.286 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations264
Published2015
Admission routes1
Has abstractyes

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