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Xu, Y., Ma, Q., Ren, J., Chen, L., Guo, W., Feng, K., et al. (2023) Using Machine Learning Methods in Identifying Genes Associated with COVID-19 in Cardiomyocytes and Cardiac Vascular Endothelial Cells. Life, 13, Article No. 1011.
https://doi.org/10.3390/life13041011

has been cited by the following article:

• TITLE: Long COVID: A Molecular, Cellular and Histopathology Overview

  AUTHORS: Bárbara Rafaela da Silva Barros, Leonardo Carvalho de Oliveira Cruz, Georon Ferreira de Sousa, Guilherme Antonio Souza-Silva, Marília R. C. F. de Lima, Elifrances G. Oliveira, Anna Jéssica Duarte Silva, Larissa S. Macêdo, Lígia R. S. Leal, Bianca F. São Marcos, Carolina Elsztein, Maria da Conceição V. Invenção, Antonio Carlos de Freitas, Cristiane Moutinho-Melo

  KEYWORDS: Long COVID, COVID-19, Sequelae, Public Health, Surveillance

  JOURNAL NAME: Journal of Biosciences and Medicines, Vol.11 No.9, September 6, 2023

  ABSTRACT: Long COVID has been studied as different sequelae that some individuals can develop after the acute phase of the disease. Persistent symptoms such as dry cough, fatigue, and dyspnea can remain after six months of COVID-19 cure. Others such as lung fibrosis, kidney injury, and thrombotic risk also are observed. Here, a deep review of each human organ and system infected by the virus was performed aiming to show how molecules expression and cell signaling can induce the organism cure or injuries and, subsequently sequelae. The review also suggests the importance of public health surveillance for these cases including a more comprehensive analysis of molecular biology tools that can clarify and assist in the prognosis, treatment, and preventive methods for potentially more serious disorders in post-COVID-19 patients.