MétaCan
Menu
Back to cohort
Record W2957783309 · doi:10.1073/pnas.1901805116

Pleiotropic effects for Parkin and LRRK2 in leprosy type-1 reactions and Parkinson’s disease

2019· article· en· W2957783309 on OpenAlexafffund
Vinicius M. Fava, Yong Zhong Xu, Guillaume Lettre, Nguyen Van Thuc, Marianna Orlova, Vu Hong Thai, Shao Tao, Nathalie Croteau, Mohamed A. Eldeeb, Emma J. MacDougall, Geison Cambri, Ramanuj Lahiri, Linda B. Adams, Edward A. Fon, Jean‐François Trempe, Aurélie Cobat, Alexandre Alcaïs, Laurent Abel, Erwin Schurr

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldMedicine
TopicLeprosy Research and Treatment
Canadian institutionsMcGill UniversityMontreal Neurological Institute and HospitalMontreal Heart InstituteUniversité de MontréalMcGill University Health Centre
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchParkinson CanadaCanada Research ChairsAgence Nationale de la RechercheCompute CanadaGovernment of CanadaMcGill University
KeywordsParkinLRRK2Parkinson's diseaseImmune systemPathogenesisGeneticsBiologyMutationDiseaseLeprosyGeneImmunologyMedicinePathology

Abstract

fetched live from OpenAlex

Significance Type-1 reactions (T1R) are pathological immune responses in leprosy and a frequent cause of peripheral nerve damage. Employing a candidate gene approach combined with deep resequencing, we identified amino acid mutations in the E3 ligase Parkin and the polyfunctional kinase LRRK2 that were associated with T1R. This finding directly linked both proteins with the extent of the immune response in an infectious disease. Moreover, amino acids associated with T1R mutations were significantly enriched for mutations found in patients suffering from Parkinson’s disease (PD). These findings confirm Parkin and LRRK2 as 2 key inflammatory regulators and suggest that T1R and PD share overlapping pathways of pathogenesis.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.121

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.034
GPT teacher head0.337
Teacher spread0.303 · 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 designObservational
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

Citations68
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicLeprosy Research and TreatmentFrench-language works237,207