BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

169 related articles for article (PubMed ID: 32841907)

  • 21. Review: Do engineered nanoparticles pose a significant threat to the aquatic environment?
    Scown TM; van Aerle R; Tyler CR
    Crit Rev Toxicol; 2010 Aug; 40(7):653-70. PubMed ID: 20662713
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Review and Prospects on the Ecotoxicity of Mixtures of Nanoparticles and Hybrid Nanomaterials.
    Zhang F; Wang Z; Peijnenburg WJGM; Vijver MG
    Environ Sci Technol; 2022 Nov; 56(22):15238-15250. PubMed ID: 36196869
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Interactions between engineered nanoparticles and dissolved organic matter: A review on mechanisms and environmental effects.
    Yu S; Liu J; Yin Y; Shen M
    J Environ Sci (China); 2018 Jan; 63():198-217. PubMed ID: 29406103
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Langendorff heart: a model system to study cardiovascular effects of engineered nanoparticles.
    Stampfl A; Maier M; Radykewicz R; Reitmeir P; Göttlicher M; Niessner R
    ACS Nano; 2011 Jul; 5(7):5345-53. PubMed ID: 21630684
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Genotoxic and carcinogenic potential of engineered nanoparticles: an update.
    Kumar A; Dhawan A
    Arch Toxicol; 2013 Nov; 87(11):1883-1900. PubMed ID: 24068037
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Monitoring characteristics and genotoxic effects of engineered nanoparticle-protein corona.
    Senapati VA; Kansara K; Shanker R; Dhawan A; Kumar A
    Mutagenesis; 2017 Oct; 32(5):479-490. PubMed ID: 29048576
    [TBL] [Abstract][Full Text] [Related]  

  • 27. DeepRF: A deep learning method for predicting metabolic pathways in organisms based on annotated genomes.
    Shah HA; Liu J; Yang Z; Zhang X; Feng J
    Comput Biol Med; 2022 Aug; 147():105756. PubMed ID: 35759992
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Revealing disease-associated pathways by network integration of untargeted metabolomics.
    Pirhaji L; Milani P; Leidl M; Curran T; Avila-Pacheco J; Clish CB; White FM; Saghatelian A; Fraenkel E
    Nat Methods; 2016 Sep; 13(9):770-6. PubMed ID: 27479327
    [TBL] [Abstract][Full Text] [Related]  

  • 29. [Application of metabolomics in nanotoxicity].
    Zou XX; Wang HJ; Liu LH; Zhang B
    Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi; 2020 Sep; 38(9):712-717. PubMed ID: 33036542
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Metal-based engineered nanoparticles in the drinking water treatment systems: A critical review.
    Sousa VS; Ribau Teixeira M
    Sci Total Environ; 2020 Mar; 707():136077. PubMed ID: 31863978
    [TBL] [Abstract][Full Text] [Related]  

  • 31. The mechanisms and environmental implications of engineered nanoparticles dispersion.
    Zhang D; Qiu J; Shi L; Liu Y; Pan B; Xing B
    Sci Total Environ; 2020 Jun; 722():137781. PubMed ID: 32199363
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Can the properties of engineered nanoparticles be indicative of their functions and effects in plants?
    Liu Y; Pan B; Li H; Lang D; Zhao Q; Zhang D; Wu M; Steinberg CEW; Xing B
    Ecotoxicol Environ Saf; 2020 Dec; 205():111128. PubMed ID: 32827963
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Untargeted metabolomics for Achilles heel of engineered nanomaterials' risk assessment.
    Ahmad F; Abubshait SA; Abubshait HA
    Chemosphere; 2021 Jan; 262():128058. PubMed ID: 33182140
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Effects of low-level engineered nanoparticles on the quorum sensing of Pseudomonas aeruginosa PAO1.
    Li N; Wang L; Yan H; Wang M; Shen D; Yin J; Shentu J
    Environ Sci Pollut Res Int; 2018 Mar; 25(7):7049-7058. PubMed ID: 29273994
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Health implications of engineered nanoparticles in infants and children.
    Tang S; Wang M; Germ KE; Du HM; Sun WJ; Gao WM; Mayer GD
    World J Pediatr; 2015 Aug; 11(3):197-206. PubMed ID: 26253410
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Metabolomics techniques for nanotoxicity investigations.
    Lv M; Huang W; Chen Z; Jiang H; Chen J; Tian Y; Zhang Z; Xu F
    Bioanalysis; 2015; 7(12):1527-44. PubMed ID: 26168257
    [TBL] [Abstract][Full Text] [Related]  

  • 37. A deep learning architecture for metabolic pathway prediction.
    Baranwal M; Magner A; Elvati P; Saldinger J; Violi A; Hero AO
    Bioinformatics; 2020 Apr; 36(8):2547-2553. PubMed ID: 31879763
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Neutron activation of engineered nanoparticles as a tool for tracing their environmental fate and uptake in organisms.
    Oughton DH; Hertel-Aas T; Pellicer E; Mendoza E; Joner EJ
    Environ Toxicol Chem; 2008 Sep; 27(9):1883-7. PubMed ID: 19086315
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming.
    Wu SG; Wang Y; Jiang W; Oyetunde T; Yao R; Zhang X; Shimizu K; Tang YJ; Bao FS
    PLoS Comput Biol; 2016 Apr; 12(4):e1004838. PubMed ID: 27092947
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Metabolomics meets machine learning: Longitudinal metabolite profiling in serum of normal versus overconditioned cows and pathway analysis.
    Ghaffari MH; Jahanbekam A; Sadri H; Schuh K; Dusel G; Prehn C; Adamski J; Koch C; Sauerwein H
    J Dairy Sci; 2019 Dec; 102(12):11561-11585. PubMed ID: 31548056
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 9.