Evidence map›Paper›PMID 42074014›Full record

ReviewInternational journal of molecular sciences2026

CRISPR Applications in Alzheimer's Disease: From High-Throughput Genetic Screening to Precision Editing and CNS Delivery.

You Li, Shixin Ma, Teng Fei

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

You LiKey Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern University, Shenyang 110819, China.ORCID 0000-0002-3325-0730
Shixin MaKey Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern University, Shenyang 110819, China.ORCID 0000-0002-7724-3807
Teng FeiKey Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern University, Shenyang 110819, China.ORCID 0000-0001-9620-0450

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease is a devastating progressive neurodegenerative disorder characterized by extracellular amyloid-beta plaques and intracellular tau tangles. Despite recent advancements in amyloid-beta-targeting immunotherapies, achieving safe and definitive disease control remains a profound clinical challenge. The CRISPR/Cas9 system has emerged as a powerful technology for precision neurogenetics, offering significant potential to address the fundamental questions behind Alzheimer's disease. This comprehensive review delineates the trajectory of CRISPR applications in Alzheimer's disease research and therapeutics. First, we explore the integration of CRISPR in engineering high-fidelity in vitro models, such as isogenic induced pluripotent stem cells and three-dimensional cerebral organoids, alongside advanced in vivo mammalian models. Second, we examine how these platforms facilitate unbiased high-throughput genetic screening to uncover molecular underpinnings regulating tau, lipid metabolism, and neuroinflammation. Third, we critically evaluate precision editing strategies targeting core risk genes (

Indexed as

Alzheimer DiseaseCRISPR-Cas SystemsGene EditingGenetic TestingGenetic TherapyAnimalsCentral Nervous SystemHigh-Throughput Screening AssaysHumansAlzheimer’s diseaseCRISPRdisease modelinggene editinghigh-throughput screening

Identifiers

PMID42074014
PMCPMC13116923

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.