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Record W2762087636 · doi:10.15190/d.2017.8

Strains of Pathological Protein Aggregates in Neurodegenerative Diseases

2017· review· en· W2762087636 on OpenAlexafffund
Xinzhu Wang, Zeinab Noroozian, Madelaine Lynch, Nicholas Armstrong, Raphaël Schneider, Mingzhe Liu, Farinaz Ghodrati, Ashley B. Zhang, Yoo Jeong Yang, Amanda C. Hall, Michael Solarski, Samuel A. Killackey, Joel C. Watts

Bibliographic record

VenueDiscoveries · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPrion Diseases and Protein Misfolding
Canadian institutionsSunnybrook Health Science CentreOccupational Cancer Research CentreUniversity of Toronto
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaParkinson CanadaAlzheimer Society
KeywordsDiseaseProtein aggregationPathologicalPrion proteinNeuroscienceBiologyNeurodegenerationMedicinePathologyGenetics

Abstract

fetched live from OpenAlex

The presence of protein aggregates in the brain is a hallmark of neurodegenerative disorders such as Alzheimer's disease (AD) and Parkinson's disease (PD). Considerable evidence has revealed that the pathological protein aggregates in many neurodegenerative diseases are able to self-propagate, which may enable pathology to spread from cell-to-cell within the brain. This property is reminiscent of what occurs in prion diseases such as Creutzfeldt-Jakob disease. A widely recognized feature of prion disorders is the existence of distinct strains of prions, which are thought to represent unique protein aggregate structures. A number of recent studies have pointed to the existence of strains of protein aggregates in other, more common neurodegenerative illnesses such as AD, PD, and related disorders. In this review, we outline the pathobiology of prion strains and discuss how the concept of protein aggregate strains may help to explain the heterogeneity inherent to many human neurodegenerative disorders.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.075
GPT teacher head0.375
Teacher spread0.300 · 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.

Study designOther design
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

Citations9
Published2017
Admission routes2
Has abstractyes

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