RESUMEN
BACKGROUND: The Coronavirus disease 2019 (COVID-19) outbreak quickly has spread and became a pandemic. However, no approved therapeutics or effective treatment is available for the treatment of these patients. The present study was done to retrospectively assess the treatment strategies (e.g., pharmaceutical care services) for COVID-19 patients in selected hospitals and highlight the importance of such services in the management of a pandemic. MATERIALS AND METHODS: Data from a series of COVID-19 patients (978 patients; 658 males [66.9%] and 324 females [33.1%]) admitted to the selected hospitals in Tehran from 20 February to 19 March 2020 were retrieved retrospectively from the Health Information System (HIS) of the hospitals. The statistical tests were used for analyzing the effect and correlation of the variables (drugs) with the average length of stay (ALOS) in the hospital. RESULTS: Diverse medication classes and old drugs with or without strong evidence of therapeutic effects against the novel coronavirus, some previously tried as a treatment for SARS-CoV and MERS-CoV, were mostly used for the treatment of patients in the hospitals. Many medications (broad-spectrum antibiotics and antivirals) or combination therapies are used without evidence of their therapeutic effects during pandemics. CONCLUSION: Therefore, guidelines should be provided for the off-label use of these drugs by policymakers and stakeholders during a pandemic emergency due to high demands. Also, monitoring of the HIS data can play an important role in improving public health response to emerging diseases.
RESUMEN
BACKGROUND & OBJECTIVE: Microarray and next generation sequencing (NGS) data are the important sources to find helpful molecular patterns. Also, the great number of gene expression data increases the challenge of how to identify the biomarkers associated with cancer. The random forest (RF) is used to effectively analyze the problems of large-p and small-n. Therefore, RF can be used to select and rank the genes for the diagnosis and effective treatment of cancer. METHODS: The microarray gene expression data of colon, leukemia, and prostate cancers were collected from public databases. Primary preprocessing was done on them using limma package, and then, the RF classification method was implemented on datasets separately in R software. Finally, the selected genes in each of the cancers were evaluated and compared with those of previous experimental studies and their functionalities were assessed in molecular cancer processes. RESULT: The RF method extracted very small sets of genes while it retained its predictive performance. About colon cancer data set DIEXF, GUCA2A, CA7, and IGHA1 key genes with the accuracy of 87.39 and precision of 85.45 were selected. The SNCA, USP20, and SNRPA1 genes were selected for prostate cancer with the accuracy of 73.33 and precision of 66.67. Also, key genes of leukemia data set were BAG4, ANKHD1-EIF4EBP3, PLXNC1, and PCDH9 genes, and the accuracy and precision were 100 and 95.24, respectively. CONCLUSION: The current study results showed most of the selected genes involved in the processes and cancerous pathways were previously reported and had an important role in shifting from normal cell to abnormal.
