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Rodent models of Alzheimer's disease: Critical analysis of current hypotheses and pathways for future research

2025· review· en· W4413848379 on OpenAlexafffund
Pasindu Hansana Singhaarachchi, Péter Antal, Frédéric Calon, Carsten Culmsee, Jean-Christophe Delpech, Martin Feldotto, J. Geertsema, Emmy E. Hoeksema, Anikó Kőrösi, Sophie Layé, Jonathan L. McQualter, Susanne R. de Rooij, Christoph Rummel, Mary Slayo, Luba Sominsky, Sarah J. Spencer

Bibliographic record

VenueProgress in Neurobiology · 2025
Typereview
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversité Laval
FundersAustralian Research CouncilMedical Research CouncilBundesministerium für Bildung und ForschungAgence Nationale de la RechercheRMIT UniversityAustralian GovernmentNational Health and Medical Research CouncilEuropean CommissionCanadian Institutes of Health ResearchEU Joint Programme – Neurodegenerative Disease Research
KeywordsNeuroscienceDiseaseCurrent (fluid)Alzheimer's diseaseRodent modelPsychologyBrain researchBiologyMedicinePathology

Abstract

fetched live from OpenAlex

Alzheimer's disease (AD) was first described over a century ago. However, the mechanisms underlying the disease are not well understood to this day. This has negatively impacted our ability to create animal models to design and test targeted reliable treatments for the disease. Amyloid β plaque accumulation, aggregation of neurofibrillary tangles, neuroinflammation, neurodegeneration, and, of course, cognitive decline, are few of the many observed pathological features associated with AD. However, there is a concern that the animal models of AD that are based on these frameworks may not be accurately representing AD in people. As such, the results from preclinical trials have not historically translated well to the clinic. In this article, we review the current major hypotheses to describe AD; we outline the major strengths and weaknesses of the commonly used rodent models used to replicate features of these hypotheses; and we provide a strategy for the field for future research.

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)0.001
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 score0.908
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.240
GPT teacher head0.495
Teacher spread0.254 · 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

Citations5
Published2025
Admission routes2
Has abstractyes

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