ReviewNanoscale advances2023
Machine learning assisted-nanomedicine using magnetic nanoparticles for central nervous system diseases.
Review in Nanoscale advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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.
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.
Who cites it
17 citing papers in PubMed.
- Pharmacological modulation of cGAS-STING-NLRP3 signaling by nano-immunomodulators in Alzheimer and Parkinson disease.Inflammopharmacology · 2026Review
- Rebalancing α-Synuclein Clearance: Novel Therapeutic Frontiers in Parkinson's Disease.Neuromolecular medicine · 2026Review
- In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives.NPJ systems biology and applications · 2026Review
- Advances in Strategies to Transport Nanoparticles Across the Blood-Brain Barrier for Drug Delivery into the Brain for the Treatment of Alzheimer's Disease.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Polysaccharide-functionalized gold, silver, and iron oxide nanoparticles for siRNA delivery: The role of artificial intelligence in design and optimization.Materials today. Bio · 2026Review
- Recent Advances in the Non-viral Delivery of Genes to Central Nervous System Disorders.Cellular and molecular neurobiology · 2026Review
- Nanomaterials for Alzheimer's disease: emerging strategies in diagnosis and therapy.Journal of nanobiotechnology · 2026Review
- Machine Learning on Systematically Curated Data Reveals Key Determinants of Magnetic Hyperthermia Performance.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- AI-driven nanomedicine for cancer theranostics.Molecular cancer · 2026Review
- Material-based neuroimaging and biomarker detection for central nervous system disorder.Materials today. Bio · 2025Review
- Nanoradiopharmaceuticals: Design Principles, Radiolabeling Strategies, and Biomedicine Applications.Pharmaceutics · 2025Review
- Trojan Horse Delivery Strategies of Natural Medicine Monomers: Challenges and Limitations in Improving Brain Targeting.Pharmaceutics · 2025Review
- Artificial Intelligence-Driven Innovations in Pharmaceutical Development and Drug Delivery Systems.Current topics in medicinal chemistry · 2025Review
- Machine Learning and Deep Learning Applications in Magnetic Particle Imaging.Journal of magnetic resonance imaging : JMRI · 2025Review
- Roadmap on magnetic nanoparticles in nanomedicine.Nanotechnology · 2024Review
- Twenty years ofFrontiers in toxicology · 2024Article
- Drug Delivery to the Brain: Recent Advances and Unmet Challenges.Pharmaceutics · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Magnetic nanoparticles possess unique properties distinct from other types of nanoparticles developed for biomedical applications. Their unique magnetic properties and multifunctionalities are especially beneficial for central nervous system (CNS) disease therapy and diagnostics, as well as targeted and personalized applications using image-guided therapy and theranostics. This review discusses the recent development of magnetic nanoparticles for CNS applications, including Alzheimer's disease, Parkinson's disease, epilepsy, multiple sclerosis, and drug addiction. Machine learning (ML) methods are increasingly applied towards the processing, optimization and development of nanomaterials. By using data-driven approach, ML has the potential to bridge the gap between basic research and clinical research. We review ML approaches used within the various stages of nanomedicine development, from nanoparticle synthesis and characterization to performance prediction and disease diagnosis.
Identifiers
What OpenQuestion holds
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.