These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

134 related articles for article (PubMed ID: 37350771)

  • 1. Understanding mechanism governing the inflammatory potential of metal oxide nanoparticles using periodic table-based descriptors: a nano-QSAR approach.
    Roy J; Roy K
    SAR QSAR Environ Res; 2023; 34(6):459-474. PubMed ID: 37350771
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Modeling and mechanistic understanding of cytotoxicity of metal oxide nanoparticles (MeOxNPs) to
    Roy J; Roy K
    Nanotoxicology; 2022 Mar; 16(2):152-164. PubMed ID: 35166631
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Quantitative Structure-Activity Relationship Models for Predicting Inflammatory Potential of Metal Oxide Nanoparticles.
    Huang Y; Li X; Xu S; Zheng H; Zhang L; Chen J; Hong H; Kusko R; Li R
    Environ Health Perspect; 2020 Jun; 128(6):67010. PubMed ID: 32692251
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Nano-read-across predictions of toxicity of metal oxide engineered nanoparticles (MeOx ENPS) used in nanopesticides to BEAS-2B and RAW 264.7 cells.
    Roy J; Roy K
    Nanotoxicology; 2022 Jun; 16(5):629-644. PubMed ID: 36260491
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: a mechanistic QSTR approach.
    Kar S; Gajewicz A; Puzyn T; Roy K; Leszczynski J
    Ecotoxicol Environ Saf; 2014 Sep; 107():162-9. PubMed ID: 24949897
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Nano-QSAR modeling for predicting the cytotoxicity of metallic and metal oxide nanoparticles: A review.
    Li J; Wang C; Yue L; Chen F; Cao X; Wang Z
    Ecotoxicol Environ Saf; 2022 Sep; 243():113955. PubMed ID: 35961199
    [TBL] [Abstract][Full Text] [Related]  

  • 7. The way to cover prediction for cytotoxicity for all existing nano-sized metal oxides by using neural network method.
    Fjodorova N; Novic M; Gajewicz A; Rasulev B
    Nanotoxicology; 2017 May; 11(4):475-483. PubMed ID: 28330416
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles.
    Puzyn T; Rasulev B; Gajewicz A; Hu X; Dasari TP; Michalkova A; Hwang HM; Toropov A; Leszczynska D; Leszczynski J
    Nat Nanotechnol; 2011 Mar; 6(3):175-8. PubMed ID: 21317892
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Using experimental data of Escherichia coli to develop a QSAR model for predicting the photo-induced cytotoxicity of metal oxide nanoparticles.
    Pathakoti K; Huang MJ; Watts JD; He X; Hwang HM
    J Photochem Photobiol B; 2014 Jan; 130():234-40. PubMed ID: 24362319
    [TBL] [Abstract][Full Text] [Related]  

  • 10. From basic physics to mechanisms of toxicity: the "liquid drop" approach applied to develop predictive classification models for toxicity of metal oxide nanoparticles.
    Sizochenko N; Rasulev B; Gajewicz A; Kuz'min V; Puzyn T; Leszczynski J
    Nanoscale; 2014 Nov; 6(22):13986-93. PubMed ID: 25317542
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: hints from nano-QSAR studies.
    Gajewicz A; Schaeublin N; Rasulev B; Hussain S; Leszczynska D; Puzyn T; Leszczynski J
    Nanotoxicology; 2015 May; 9(3):313-25. PubMed ID: 24983896
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Introducing third-generation periodic table descriptors for nano-qRASTR modeling of zebrafish toxicity of metal oxide nanoparticles.
    Kar S; Yang S
    Beilstein J Nanotechnol; 2024; 15():1142-1152. PubMed ID: 39290525
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Correlation intensity index: mathematical modeling of cytotoxicity of metal oxide nanoparticles.
    Ahmadi S; Toropova AP; Toropov AA
    Nanotoxicology; 2020 Oct; 14(8):1118-1126. PubMed ID: 32877261
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Toxicity of 11 Metal Oxide Nanoparticles to Three Mammalian Cell Types In Vitro.
    Ivask A; Titma T; Visnapuu M; Vija H; Kakinen A; Sihtmae M; Pokhrel S; Madler L; Heinlaan M; Kisand V; Shimmo R; Kahru A
    Curr Top Med Chem; 2015; 15(18):1914-29. PubMed ID: 25961521
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO
    Roy J; Pore S; Roy K
    Beilstein J Nanotechnol; 2023; 14():939-950. PubMed ID: 37736658
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Risk assessment of heterogeneous TiO
    Roy J; Ojha PK; Roy K
    Nanotoxicology; 2019 Jun; 13(5):701-716. PubMed ID: 30938199
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Developing random forest based QSAR models for predicting the mixture toxicity of TiO
    Trinh TX; Seo M; Yoon TH; Kim J
    NanoImpact; 2022 Jan; 25():100383. PubMed ID: 35559889
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Use of metal/metal oxide spherical cluster and hydroxyl metal coordination complex for descriptor calculation in development of nanoparticle cytotoxicity classification model.
    Shin HK; Kim KY; Park JW; No KT
    SAR QSAR Environ Res; 2017 Nov; 28(11):875-888. PubMed ID: 29189078
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Optimal nano-descriptors as translators of eclectic data into prediction of the cell membrane damage by means of nano metal-oxides.
    Toropova AP; Toropov AA; Benfenati E; Korenstein R; Leszczynska D; Leszczynski J
    Environ Sci Pollut Res Int; 2015 Jan; 22(1):745-57. PubMed ID: 25223357
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Insights into nanoparticle toxicity against aquatic organisms using multivariate regression, read-across, and ML algorithms: Predictive models for Daphnia magna and Danio rerio.
    Roy J; Roy K
    Aquat Toxicol; 2024 Nov; 276():107114. PubMed ID: 39396443
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.