MétaCan
Menu
Back to cohort
Record W4241291724 · doi:10.1097/nen.0b013e318053716a

The Etiopathogenesis of Parkinson Disease and Suggestions for Future Research. Part II

2007· article· en· W4241291724 on OpenAlexaff
Irene Litvan, Marie‐Françoise Chesselet, Thomas Gasser, Donato A. Di Monte, Davis Parker, Theo Hagg, John Hardy, Peter Jenner, Richard H. Myers, Donald D. Price, Mark Hallett, William Langston, Anthony E. Lang, Glenda M. Halliday, Walter A. Rocca, Charles Duyckaerts, Dennis W. Dickson, Yoav Ben‐Shlomo, Christopher G. Goetz, Eldad Melamed

Bibliographic record

VenueJournal of Neuropathology & Experimental Neurology · 2007
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsToronto Western Hospital
FundersNational Institute on AgingParkinson's UK
KeywordsDiseaseEtiologyParkinson's diseasePathogenesisBasic researchMedicineBioinformaticsNeurosciencePsychologyPathologyBiologyComputer science

Abstract

fetched live from OpenAlex

We are at a critical juncture in our knowledge of the etiology and pathogenesis of Parkinson disease (PD). It is clear that PD is not a single entity simply resulting from a dopaminergic deficit; rather it is most likely caused by a combination of genetic and environmental factors. Although there is extensive new information on the etiology and pathogenesis of PD, which may advance its treatment, new syntheses of this information are needed. The second part of this two-part, state-of-the-art review by leaders in PD research critically examines the research field to identify areas for which new knowledge and ideas might be helpful for treatment purposes. Topics reviewed in Part II are genetics, animal models, and oxidative stress. There have been 2 important approaches to genetic research studies in Parkinson disease (PD). The first focuses on rare families with parkinsonism apparently following Mendelian inheritance and using the classic methodology of linkage analysis and positional cloning. In the other, an attempt is made to evaluate the PD population as a whole, using association studies and nonparametric linkage methodology and trying to define risk alleles that contribute to the sporadic form of the disease. Over the past years, genetic research following the first approach has been highly effective in identifying the genes underlying monogenic PD, in particular α-synuclein (1) (Park-1) and LRRK2 (Park-8) (2, 3) in dominant families, as well as the recessive PD genes parkin(Park-2) (4), PINK1(Park-6) (5), and DJ-1(Park-7) (6). These mutations probably cause the disease in only a very small subset of families, with the exception of LRRK2, which is responsible for a significant proportion of familial PD (5.1%-18.7%) and a smaller portion of sporadic PD (1.5%-6.1%), although Ashkenazi Jews and Arabs have a higher frequency in the majority of populations studied. Nevertheless, these discoveries have been extremely fruitful, leading the way to some molecular pathways involved in nigral degeneration, which includes protein aggregation, defective proteasomal degradation, mitochondrial dysfunction, and oxidative stress (7). The findings that LRRK2 encodes a protein from the family of mitogen-activated protein kinases and that mutations may lead to an increase of kinase activity (8) may translate relatively quickly into a novel treatment option involving kinase inhibition. However, the contribution of the mechanisms of these and other genes to idiopathic PD as a whole is still poorly defined.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.003

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 head0.034
GPT teacher head0.345
Teacher spread0.311 · 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 designTheoretical or conceptual
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

Citations45
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Neuropathology & Experimental NeurologySame topicParkinson's Disease Mechanisms and TreatmentsFrench-language works237,207