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Record W7000630523

EXPERIMENTAL ANIMAL MODELS FOR ALZHEIMER DISEASE

2022· review· en· W7000630523 on OpenAlexaboutno aff

Bibliographic record

VenueDspace Repository (Marmara Üniversitesi) · 2022
Typereview
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsNeuropathologyGenetically modified mouseTransgeneAnimal modelAlzheimer's diseaseIntracellularDiseaseAmyloid betaTau proteinExtracellular
DOInot available

Abstract

fetched live from OpenAlex

Alzheimer's disease (AD) which is an age related disorder is characterized by progressive cognitive decline. Accumulation of extracellular amyloid plaques, intracellular neurofibrillary tangles and neuronal loss is observed in AD brain. Exploring the neuropathology of AD in human pre-clinical stages is not easy. Mechanisms which causes AD in preclinical stage and potential new therapeutic targets are understood by transgenic animal models. McGill-R-Thy1-APP rat model is the only model to reproduce AD-like amyloid pathology with a single transgene. A beta has been the most cited probable causative factor in the onset and progression of AD. In A beta-based rodent models, there is a significant correlation between increased A beta levels and cognitive decline. Risk genes in the development of AD are amiloid precursor protein (APP), presenilin-1 (PS-1), presenilin-2 (PS-2) which contain autosomal dominant mutations. In rat models of Tau pathology, transgenic rats which overexpress mutant APP/PS1 display increased Tau alterations in the brain. Injection of A beta into rat brain is an alternative AD animal model to the use of transgenic animals. In this model, after A beta 1-42 is injected into the CA3 region of hippocampus, progressive decline in behavioral responses are observed. Despite the large variety of therapeutic approaches, AD remains incurable. Because, in clinical diagnosis stage, the brain has suffered irreversible and extensive damage.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.742
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.102
GPT teacher head0.369
Teacher spread0.267 · 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 designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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