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Record W2472816442 · doi:10.3233/jad-160408

Expression of the Alzheimer’s Disease Mutations <i>AβPP695sw</i> and <i>PSEN1M146I</i> in Double-Transgenic Göttingen Minipigs

2016· article· en· W2472816442 on OpenAlexafffund
Jannik E. Jakobsen, Marianne Johansen, Mette Schmidt, Ying Liu, Rong Li, Henrik Callesen, Margarita Melnikova, Mette Habekost, Carmela Matrone, Yvonne Bouter, Thomas A. Bayer, Anders Lade Nielsen, Monika Duthie, Paul E. Fraser, Ida E. Holm, Arne Lund Jørgensen

Bibliographic record

VenueJournal of Alzheimer s Disease · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPrion Diseases and Protein Misfolding
Canadian institutionsOccupational Cancer Research CentreUniversity of Toronto
FundersCanadian Institutes of Health ResearchLundbeckfondenAlzheimer Society
KeywordsTransgeneGenetically modified mouseDiseaseExpression (computer science)BiologyGeneticsGeneMedicinePathologyComputer science

Abstract

fetched live from OpenAlex

Mutations in the amyloid-β protein precursor gene (AβPP), the presenilin 1 gene (PSEN1) or the presenilin 2 gene (PSEN2) that increase production of the AβPP-derived peptide Aβ42 cause early-onset Alzheimer's disease. Rodent models of the disease show that further increase in Aβ42 production and earlier brain pathology can be obtained by coexpressing AβPP and PSEN1 mutations. To generate such elevated Aβ42 level in a large animal model, we produced Göttingen minipigs carrying in their genome one copy of a human PSEN1 cDNA with the Met146Ile (PSEN1M146I) mutation and three copies of a human AβPP695 cDNA with the Lys670Asn/Met671Leu (AβPPsw) double-mutation. Both transgenes were expressed in fibroblasts and in the brain, and their respective proteins were processed normally. Immunohistochemical staining with Aβ42-specific antibodies detected intraneuronal accumulation of Aβ42 in brains from a 10- and an 18-month-old pig. Such accumulation may represent an early event in the pathogenesis of Alzheimer's disease.

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 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 score0.151
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.020
GPT teacher head0.273
Teacher spread0.253 · 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

Citations52
Published2016
Admission routes2
Has abstractyes

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