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Record W3167101229 · doi:10.1126/sciadv.abg4855

Following spatial Aβ aggregation dynamics in evolving Alzheimer’s disease pathology by imaging stable isotope labeling kinetics

2021· article· en· W3167101229 on OpenAlexaff
Wojciech Michno, Katie Stringer, Thomas Enzlein, Melissa K. Passarelli, Stéphane Escrig, Karina S. Vitanova, Jack Wood, Kaj Blennow, Henrik Zetterberg, Anders Meibom, Carsten Hopf, Frances A. Edwards, Jörg Hanrieder

Bibliographic record

VenueScience Advances · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsConcordia University
FundersNational Institute on AgingMedical Research CouncilDemensfondenSahlgrenska UniversitetssjukhusetÅhlén-stiftelsenCure Alzheimer's FundVetenskapsrådetLinköpings UniversitetAlzheimer’s Research UKAlzheimerfondenBundesministerium für Bildung und ForschungSahlgrenska AkademinGun och Bertil Stohnes StiftelseGöteborgs UniversitetTorsten Söderbergs Stiftelse
KeywordsKineticsNeuroimagingIsotopeDynamics (music)Mass spectrometry imagingPathologyIsotopic labelingTau pathologyMass spectrometryStable isotope ratioNeuroscienceAlzheimer's diseaseDiseaseChemistryBiophysicsMedicineBiologyPsychologyPhysics

Abstract

fetched live from OpenAlex

knock-in mice together with mass spectrometry imaging to monitor the earliest seeds of Aβ deposition through ongoing plaque development. This allowed visualizing Aβ aggregation dynamics within single plaques across different brain regions. We show that formation of structurally distinct plaques is associated with differential Aβ peptide deposition. Specifically, Aβ1-42 is forming an initial core structure followed by radial outgrowth and late secretion and deposition of Aβ1-38. These data describe a detailed picture of the earliest events of precipitating amyloid pathology at scales not previously possible.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.563

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 head0.007
GPT teacher head0.266
Teacher spread0.259 · 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 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

Citations46
Published2021
Admission routes1
Has abstractyes

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