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Filtros : "DIAGNÓSTICO" "ACS Sensors" Removidos: "2008" "Journal of Photochemistry and Photobiology B" Limpar

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  • Artigo de periodico

    On the Challenges for the diagnosis of SARS-CoV2 based on a review of current methodologies (2020)

    Mattioli, Isabela A. ; Hassan, Ayaz ; Oliveira Junior, Osvaldo Novais de ; Crespilho, Frank Nelson

    Source: ACS Sensors. Unidades: IFSC, IQSC

    Subjects: COVID-19, DIAGNÓSTICO

    Versão PublicadaAcesso à fonteDOIHow to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      MATTIOLI, Isabela A. et al. On the Challenges for the diagnosis of SARS-CoV2 based on a review of current methodologies. ACS Sensors, v. 5, n. 12, p. 3655-3677, 2020Tradução . . Disponível em: https://doi.org/10.1021/acssensors.0c01382. Acesso em: 17 set. 2024.

    • APA

      Mattioli, I. A., Hassan, A., Oliveira Junior, O. N. de, & Crespilho, F. N. (2020). On the Challenges for the diagnosis of SARS-CoV2 based on a review of current methodologies. ACS Sensors, 5( 12), 3655-3677. doi:10.1021/acssensors.0c01382

    • NLM

      Mattioli IA, Hassan A, Oliveira Junior ON de, Crespilho FN. On the Challenges for the diagnosis of SARS-CoV2 based on a review of current methodologies [Internet]. ACS Sensors. 2020 ; 5( 12): 3655-3677.[citado 2024 set. 17 ] Available from: https://doi.org/10.1021/acssensors.0c01382

    • Vancouver

      Mattioli IA, Hassan A, Oliveira Junior ON de, Crespilho FN. On the Challenges for the diagnosis of SARS-CoV2 based on a review of current methodologies [Internet]. ACS Sensors. 2020 ; 5( 12): 3655-3677.[citado 2024 set. 17 ] Available from: https://doi.org/10.1021/acssensors.0c01382

  • Artigo de periodico

    Converging Multidimensional Sensor and Machine Learning Toward High-Throughput and Biorecognition Element-Free Multidetermination of Extracellular Vesicle Biomarkers (2020)

    Nicoliche, Caroline, Y. N ; Oliveira, Ricardo A. G. de; Silva, Giulia S da; Ferreira, Larissa F.; Rodrigues, Ian L.; Faria, Ronaldo Censi ; Fazzio, Adalberto ; Carrilho, Emanuel ; Pontes, Leticia Gomes de ; Schleder, Gabriel Ravanhani ; Lima, Renato Sousa

    Source: ACS Sensors. Unidades: IQSC, IF

    Subjects: SENSORES BIOMÉDICOS, BIOMARCADORES, DIAGNÓSTICO, NEOPLASIAS

    Versão PublicadaAcesso à fonteDOIHow to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      NICOLICHE, Caroline et al. Converging Multidimensional Sensor and Machine Learning Toward High-Throughput and Biorecognition Element-Free Multidetermination of Extracellular Vesicle Biomarkers. ACS Sensors, v. 5, n. 7, p. 1864–1871, 2020Tradução . . Disponível em: https://doi.org/10.1021/acssensors.0c00599. Acesso em: 17 set. 2024.

    • APA

      Nicoliche, C., Oliveira, R. A. G. de, Silva, G. S. da, Ferreira, L. F., Rodrigues, I. L., Faria, R. C., et al. (2020). Converging Multidimensional Sensor and Machine Learning Toward High-Throughput and Biorecognition Element-Free Multidetermination of Extracellular Vesicle Biomarkers. ACS Sensors, 5( 7), 1864–1871. doi:10.1021/acssensors.0c00599

    • NLM

      Nicoliche C, Oliveira RAG de, Silva GS da, Ferreira LF, Rodrigues IL, Faria RC, Fazzio A, Carrilho E, Pontes LG de, Schleder GR, Lima RS. Converging Multidimensional Sensor and Machine Learning Toward High-Throughput and Biorecognition Element-Free Multidetermination of Extracellular Vesicle Biomarkers [Internet]. ACS Sensors. 2020 ; 5( 7): 1864–1871.[citado 2024 set. 17 ] Available from: https://doi.org/10.1021/acssensors.0c00599

    • Vancouver

      Nicoliche C, Oliveira RAG de, Silva GS da, Ferreira LF, Rodrigues IL, Faria RC, Fazzio A, Carrilho E, Pontes LG de, Schleder GR, Lima RS. Converging Multidimensional Sensor and Machine Learning Toward High-Throughput and Biorecognition Element-Free Multidetermination of Extracellular Vesicle Biomarkers [Internet]. ACS Sensors. 2020 ; 5( 7): 1864–1871.[citado 2024 set. 17 ] Available from: https://doi.org/10.1021/acssensors.0c00599
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