ArticleCell reports2024
Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models.
Article in Cell reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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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.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Does advancement in marker-less pose-estimation mean more quality research? A systematic review.Frontiers in behavioral neuroscience · 2025Pooled it
- Considerations for the selection and phenotyping of mouse models for the study of Alzheimer's disease.STAR protocols · 2026Review
- Therapeutic targeting of fibrin-microglia interactions ameliorates Alzheimer's disease-related hyperexcitability and brain network dysfunction.bioRxiv : the preprint server for biology · 2026Article
- Article
- Sleep-Wake Transitions Are Impaired in thebioRxiv : the preprint server for biology · 2025Article
- Development of a humanized anti-fibrin monoclonal antibody for the treatment of neuroinflammatory and retinal diseases.Journal of neuroinflammation · 2025Article
- APOE4-Aβ synergy drives brain network dysfunction and neuronal lysosomal-ER proteostasis dysregulation a preclinical Alzheimer's disease model.bioRxiv : the preprint server for biology · 2025Article
- Article
- AI-driven transformation of precision medicine: a comprehensive narrative review of key application areas, emerging paradigms, and future directions.Frontiers in public health · 2025Review
Corrections and comments
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Authors and funding
25 authors.
Funding
Abstract
Computer-vision and machine-learning (ML) approaches are being developed to provide scalable, unbiased, and sensitive methods to assess mouse behavior. Here, we used the ML-based variational animal motion embedding (VAME) segmentation platform to assess spontaneous behavior in humanized App knockin and transgenic APP models of Alzheimer's disease (AD) and to test the role of AD-related neuroinflammation in these behavioral manifestations. We found marked alterations in spontaneous behavior in App
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Registered trials
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.