SynthesisACS nano2025
Meta-Analysis and Machine Learning Prediction of Protein Corona Composition across Nanoparticle Systems in Biological Media.
Synthesis in ACS nano, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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.
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.
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Who cites it
21 citing papers in PubMed.
- Artificial intelligence-guided nanozyme engineering for chronic wound healing: from rational design to precision therapeutics.Bioactive materials · 2027Review
- Engineering Oral Nanoparticles: Navigating Biological Barriers in the Gastrointestinal Tract.Pharmaceutical research · 2026Review
- Peptide-Enabled Nanoplatforms for Malaria and Leishmaniasis: From Intracellular Targeting to Translational Diagnostic Perspectives.ChemMedChem · 2026Review
- Review
- Multidimensional Protein Corona Analysis Toward Predictive Nano-Bio Interface Design.Small methods · 2026Review
- Review
- Living Inorganic Nanomaterials: Design, Preparation, and Biomedical Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Are similar nanoforms comparable in biodistribution? Data-driven approaches for optimizing nanomedicine design.Materials today. Nano · 2026Article
- Revisiting Colloid Theory for Biomedicine: Reconciling Simple Models in Complex Environments.ACS nano medicine · 2026Article
- Nanomedicine Meets Immunotherapy: Advancing Adoptive Cell Therapy with Nanoparticles in the Treatment of Cancer with Sustainability Perspectives.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Smart Nanodelivery Systems for Immunometabolic Modulation in Osteoarthritis.Exploration (Beijing, China) · 2026Review
- Determinants of protein corona adsorption and abundance revealed by interpretable machine learning across nanoparticle systems.Scientific reports · 2026Article
- The protein corona at the nano-bio interface: the need for standardized methodology and opportunities for neurodegenerative disease intervention.RSC advances · 2026Review
- Advancing nanomedicine with machine learning: predicting protein corona and nano-bio interactions.Nanomedicine (London, England) · 2026Review
- Artificial intelligence empowered biomaterials for cancer therapy: From rational design to clinical translation.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- AI-driven nanomedicine for cancer theranostics.Molecular cancer · 2026Review
- Empowering photodynamic therapy with artificial intelligence: current trends and future directions.Frontiers in oncology · 2026Review
- Decoy Nanoparticles and Protein Corona Modulation: A Novel Frontier in Sepsis Treatment.International journal of nanomedicine · 2026Review
- Cancer-Associated Fibroblast-Targeted Nanomedicine in Solid Tumor Therapy: From Mechanisms of Therapeutic Resistance to Precision Stromal Modulation.International journal of nanomedicine · 2026Review
- Combining Fluorescence and Magnetic Resonance Imaging in Drug Discovery-A Review.Pharmaceuticals (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
A comprehensive understanding of protein corona (PC) composition is critical for engineering nanoparticles (NPs) with optimal safety and therapeutic performance, because the PC governs NP pharmacokinetics, biodistribution, and cellular interactions. Yet systematic analyses are hampered by the absence of standardized, richly annotated data sets. Here, we introduce the Protein Corona Database (PC-DB), which compiles data from 83 studies (2000-2024) and integrates 817 NP formulations with quantitative profiles of 2497 adsorbed proteins. The PC-DB exposes pronounced heterogeneity in NP materials (metal 28.8%, silica 22.8%, lipid-based 14.8%), surface modifications, sizes (1-1400 nm), and ζ-potentials (-70 to +70 mV). Subsequent meta-analysis shows that silica, polystyrene, and lipid-based NPs smaller than 100 nm with moderately negative to neutral ζ-potentials preferentially bind the lipoproteins APOE and APOB-100, which are linked to receptor-mediated uptake and enhanced delivery efficiency. In contrast, metal and metal-oxide NPs carrying highly negative surface charge enrich complement component C3, indicating a greater likelihood of immune recognition and clearance. Interpretable machine learning models (LightGBM and XGBoost; ROC-AUC > 0.85) confirm NP size, ζ-potential, and incubation time as the most influential predictors of protein adsorption. These results delineate how physicochemical parameters dictate PC composition and illustrate the power of predictive modeling to guide rational NP design.
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Registered trials
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