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Record W4207035561

Cryo-EM structures of amyloid-β 42 filaments from human brains

2022· article· en· W4207035561 on OpenAlexaff
Yang Yang, Diana Arseni, Wenjuan Zhang, Melissa Huang, Sofia Lövestam, Manuel Schweighauser, Abhay Kotecha, Alexey G. Murzin, Sew‐Yeu Peak‐Chew, Jennifer A. Macdonald, Isabelle Lavenir, Holly J. Garringer, Ellen Gelpí, Kathy L. Newell, Gábor G. Kovács, Rubén Vidal, Bernardino Ghetti, Benjamin Falcon, Sjors H. W. Scheres, Michel Goedert

Bibliographic record

VenueUCL Discovery (University College London) · 2022
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsOccupational Cancer Research Centre
Fundersnot available
KeywordsProtein filamentIntermediate filamentElectron microscopeAmyloid (mycology)NeurofilamentBiophysicsHuman brainAlzheimer's diseaseFibrilChemistryBiologyNeuroscienceDiseaseCytoskeletonPathologyMedicineBiochemistryImmunohistochemistryCellPhysicsImmunology
DOInot available

Abstract

fetched live from OpenAlex

Alzheimer’s disease is characterized by a loss of memory and other cognitive functions and the filamentous assembly of Aβ and tau in the brain. The assembly of Aβ peptides into filaments that end at residue 42 is a central event. Yang et al. used electron cryo–electron microscopy to determine the structures of Aβ42 filaments from human brain (see the Perspective by Willem and Fändrich). They identified two types of related S-shaped filaments, each consisting of two identical protofilaments. These structures will inform the development of better in vitro and animal models, inhibitors of Aβ42 assembly, and imaging agents with increased specificity and sensitivity. —SMH

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.252
Teacher spread0.236 · 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

Citations422
Published2022
Admission routes1
Has abstractyes

Explore more

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