Evidence map›Paper›PMID 29273078›Full record

ReviewMolecular neurodegeneration2017

Practical considerations for choosing a mouse model of Alzheimer's disease.

Joanna L Jankowsky, Hui Zheng

Abstract readReview
In one paragraph

Review in Molecular neurodegeneration, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 264 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
264citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

264 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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204 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Joanna L JankowskyDepartment of Neuroscience, Baylor College of Medicine, Houston, TX, 77030, USA. jankowsk@bcm.edu.
Hui ZhengDepartment of Neuroscience, Baylor College of Medicine, Houston, TX, 77030, USA. huiz@bcm.edu.

Funding

Knockin Mouse Models of Alzheimers DiseaseR01AG020670 · NIA · BAYLOR COLLEGE OF MEDICINE · PI Hui Zheng · 2002 to 2026
$9.1M
Pathophysiology of APP in vivo.R01AG032051 · NIA · BAYLOR COLLEGE OF MEDICINE · PI ZHENG, HUI · 2008 to 2017
$3.5M
Plasticity of the entorhinal-hippocampal circuit as a vulnerability in Alzheimer’s DiseaseRF1AG058188 · NIA · BAYLOR COLLEGE OF MEDICINE · PI JANKOWSKY, JOANNA L · 2017 to 2020
$3.2M
Decoding the role of diverse astrocyte populations in aging and ADRF1AG054111 · NIA · BAYLOR COLLEGE OF MEDICINE · PI DENEEN, BENJAMIN, ZHENG, HUI · 2016 to 2016
$3.1M
ROLE OF TFEB IN TAUOPATHYR01NS093652 · NINDS · BAYLOR COLLEGE OF MEDICINE · PI ZHENG, HUI · 2015 to 2025
$3.1M
Interrogating the link between aging and AD with temporally-controlled transgenesRF1AG054160 · NIA · BAYLOR COLLEGE OF MEDICINE · PI JANKOWSKY, JOANNA L · 2016 to 2016
$2.5M
Knockin mouse models of Alzheimer's DiseaseRF1AG020670 · NIA · BAYLOR COLLEGE OF MEDICINE · PI ZHENG, HUI · 2022 to 2022
$2.3M
Deconstructing the pathogenic effect of APP in memory circuitsR01NS092615 · NINDS · BAYLOR COLLEGE OF MEDICINE · PI JANKOWSKY, JOANNA L · 2015 to 2018
$1.9M
ROLE OF TFEB IN TAUOPATHYRF1NS093652 · NINDS · BAYLOR COLLEGE OF MEDICINE · PI ZHENG, HUI · 2021 to 2021
$1.5M
Modeling genetic modifiers of cognitive reserve in ADR21AG056028 · NIA · BAYLOR COLLEGE OF MEDICINE · PI JANKOWSKY, JOANNA L · 2017 to 2018
$436k
NIA NIH HHS AG020670NIA NIH HHS AG032051NIA NIH HHS AG054111NIA NIH HHS AG054160NIA NIH HHS AG056028NIA NIH HHS AG058188NIA NIH HHS R01 AG020670NIA NIH HHS R01 AG032051NIA NIH HHS R21 AG056028NIA NIH HHS RF1 AG020670NIA NIH HHS RF1 AG054111NIA NIH HHS RF1 AG054160NIA NIH HHS RF1 AG058188NINDS NIH HHS NS092615NINDS NIH HHS NS093652NINDS NIH HHS R01 NS092615NINDS NIH HHS R01 NS093652NINDS NIH HHS RF1 NS093652
6 · The paper itself

Abstract

Alzheimer's disease (AD) is behaviorally identified by progressive memory impairment and pathologically characterized by the triad of β-amyloid plaques, neurofibrillary tangles, and neurodegeneration. Genetic mutations and risk factors have been identified that are either causal or modify the disease progression. These genetic and pathological features serve as basis for the creation and validation of mouse models of AD. Efforts made in the past quarter-century have produced over 100 genetically engineered mouse lines that recapitulate some aspects of AD clinicopathology. These models have been valuable resources for understanding genetic interactions that contribute to disease and cellular reactions that are engaged in response. Here we focus on mouse models that have been widely used stalwarts of the field or that are recently developed bellwethers of the future. Rather than providing a summary of each model, we endeavor to compare and contrast the genetic approaches employed and to discuss their respective advantages and limitations. We offer a critical account of the variables which may contribute to inconsistent findings and the factors that should be considered when choosing a model and interpreting the results. We hope to present an insightful review of current AD mouse models and to provide a practical guide for selecting models best matched to the experimental question at hand.

Indexed as

Disease Models, AnimalMice, TransgenicAlzheimer DiseaseAmyloid beta-Protein PrecursorAnimalsCerebral Amyloid AngiopathyCognitive DysfunctionHumansMiceMice, KnockoutNeurodegenerative DiseasesNeurofibrillary TanglesPlaque, AmyloidAmyloid beta-Protein PrecursorAmyloid precursor proteinApoEApolipoprotein EAPPAβKnock-inKnockoutMAPTTauTransgenic mouseTREM2

Identifiers

PMID29273078
PMCPMC5741956

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.