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      Is the Infection of the SARS-CoV-2 Delta Variant Associated With the Outcomes of COVID-19 Patients?

      research-article
      1 , * , 2 , 3 , 1 , 4 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 2 , 13 , 7 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 3 , 3 , 14 , 14 , 5 , 8 , 2 , 2
      Frontiers in Medicine
      Frontiers Media S.A.
      comorbidity, Ct value, delta variant, hospitalization, mortality, SARS-CoV-2, viral load, whole genome sequencing

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          Abstract

          Background: Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) Delta variant (B.1.617.2) has been responsible for the current increase in Coronavirus disease 2019 (COVID-19) infectivity rate worldwide. We compared the impact of the Delta variant and non-Delta variant on the COVID-19 outcomes in patients from Yogyakarta and Central Java provinces, Indonesia.

          Methods: In this cross-sectional study, we ascertained 161 patients, 69 with the Delta variant and 92 with the non-Delta variant. The Illumina MiSeq next-generation sequencer was used to perform the whole-genome sequences of SARS-CoV-2.

          Results: The mean age of patients with the Delta variant and the non-Delta variant was 27.3 ± 20.0 and 43.0 ± 20.9 ( p = 3 × 10 −6). The patients with Delta variant consisted of 23 males and 46 females, while the patients with the non-Delta variant involved 56 males and 36 females ( p = 0.001). The Ct value of the Delta variant (18.4 ± 2.9) was significantly lower than that of the non-Delta variant (19.5 ± 3.8) ( p = 0.043). There was no significant difference in the hospitalization and mortality of patients with Delta and non-Delta variants ( p = 0.80 and 0.29, respectively). None of the prognostic factors were associated with the hospitalization, except diabetes with an OR of 3.6 (95% CI = 1.02–12.5; p = 0.036). Moreover, the patients with the following factors have been associated with higher mortality rate than the patients without the factors: age ≥65 years, obesity, diabetes, hypertension, and cardiovascular disease with the OR of 11 (95% CI = 3.4–36; p = 8 × 10 −5), 27 (95% CI = 6.1–118; p = 1 × 10 −5), 15.6 (95% CI = 5.3–46; p = 6 × 10 −7), 12 (95% CI = 4–35.3; p = 1.2 × 10 −5), and 6.8 (95% CI = 2.1–22.1; p = 0.003), respectively. Multivariate analysis showed that age ≥65 years, obesity, diabetes, and hypertension were the strong prognostic factors for the mortality of COVID-19 patients with the OR of 3.6 (95% CI = 0.58–21.9; p = 0.028), 16.6 (95% CI = 2.5–107.1; p = 0.003), 5.5 (95% CI = 1.3–23.7; p = 0.021), and 5.8 (95% CI = 1.02–32.8; p = 0.047), respectively.

          Conclusions: We show that the patients infected by the SARS-CoV-2 Delta variant have a lower Ct value than the patients infected by the non-Delta variant, implying that the Delta variant has a higher viral load, which might cause a more transmissible virus among humans. However, the Delta variant does not affect the COVID-19 outcomes in our patients. Our study also confirms that older age and comorbidity increase the mortality rate of patients with COVID-19.

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          Most cited references37

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          MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

          The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.
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            A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences.

            Some simple formulae were obtained which enable us to estimate evolutionary distances in terms of the number of nucleotide substitutions (and, also, the evolutionary rates when the divergence times are known). In comparing a pair of nucleotide sequences, we distinguish two types of differences; if homologous sites are occupied by different nucleotide bases but both are purines or both pyrimidines, the difference is called type I (or "transition" type), while, if one of the two is a purine and the other is a pyrimidine, the difference is called type II (or "transversion" type). Letting P and Q be respectively the fractions of nucleotide sites showing type I and type II differences between two sequences compared, then the evolutionary distance per site is K = -(1/2) ln [(1-2P-Q) square root of 1-2Q]. The evolutionary rate per year is then given by k = K/(2T), where T is the time since the divergence of the two sequences. If only the third codon positions are compared, the synonymous component of the evolutionary base substitutions per site is estimated by K'S = -(1/2) ln (1-2P-Q). Also, formulae for standard errors were obtained. Some examples were worked out using reported globin sequences to show that synonymous substitutions occur at much higher rates than amino acid-altering substitutions in evolution.
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              CONFIDENCE LIMITS ON PHYLOGENIES: AN APPROACH USING THE BOOTSTRAP.

              The recently-developed statistical method known as the "bootstrap" can be used to place confidence intervals on phylogenies. It involves resampling points from one's own data, with replacement, to create a series of bootstrap samples of the same size as the original data. Each of these is analyzed, and the variation among the resulting estimates taken to indicate the size of the error involved in making estimates from the original data. In the case of phylogenies, it is argued that the proper method of resampling is to keep all of the original species while sampling characters with replacement, under the assumption that the characters have been independently drawn by the systematist and have evolved independently. Majority-rule consensus trees can be used to construct a phylogeny showing all of the inferred monophyletic groups that occurred in a majority of the bootstrap samples. If a group shows up 95% of the time or more, the evidence for it is taken to be statistically significant. Existing computer programs can be used to analyze different bootstrap samples by using weights on the characters, the weight of a character being how many times it was drawn in bootstrap sampling. When all characters are perfectly compatible, as envisioned by Hennig, bootstrap sampling becomes unnecessary; the bootstrap method would show significant evidence for a group if it is defined by three or more characters.
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                Author and article information

                Contributors
                Journal
                Front Med (Lausanne)
                Front Med (Lausanne)
                Front. Med.
                Frontiers in Medicine
                Frontiers Media S.A.
                2296-858X
                09 December 2021
                2021
                09 December 2021
                : 8
                : 780611
                Affiliations
                [1] 1Pediatric Surgery Division, Department of Surgery/Genetics Working Group, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada , Yogyakarta, Indonesia
                [2] 2Department of Microbiology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada , Yogyakarta, Indonesia
                [3] 3Disease Investigation Center, Ministry of Agriculture Indonesia , Yogyakarta, Indonesia
                [4] 4National Institute of Health Research and Development, Ministry of Health , Jakarta, Indonesia
                [5] 5Pulmonology Division, Department of Internal Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada/Dr. Sardjito Hospital , Yogyakarta, Indonesia
                [6] 6Centre of Tropical Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada , Yogyakarta, Indonesia
                [7] 7Department of Clinical Pathology and Laboratory Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada/Dr. Sardjito Hospital , Yogyakarta, Indonesia
                [8] 8Department of Child Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada , Yogyakarta, Indonesia
                [9] 9Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada , Yogyakarta, Indonesia
                [10] 10Department of Physiology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada/UGM Academic Hospital , Yogyakarta, Indonesia
                [11] 11Balai Besar Teknik Kesehatan Lingkungan dan Pengendalian Penyakit , Yogyakarta, Indonesia
                [12] 12Department of Anatomical Pathology/Genetics Working Group, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada , Yogyakarta, Indonesia
                [13] 13Department of Pharmacology and Therapy/Genetics Working Group, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada , Yogyakarta, Indonesia
                [14] 14RSUD Dr. Loekmono Hadi , Kudus, Indonesia
                Author notes

                Edited by: Bin Luo, Lanzhou University, China

                Reviewed by: Nur Izzah Ismail, The Chinese University of Hong Kong, China; Zengliang Ruan, Southeast University, China

                *Correspondence: Gunadi drgunadi@ 123456ugm.ac.id

                This article was submitted to Infectious Diseases - Surveillance, Prevention and Treatment, a section of the journal Frontiers in Medicine

                Article
                10.3389/fmed.2021.780611
                8695874
                34957154
                5cb35401-4857-4981-a837-44b0ea1a019f
                Copyright © 2021 Gunadi, Hakim, Wibawa, Marcellus, Setiawaty, Slamet, Trisnawati, Supriyati, El Khair, Iskandar, Afiahayati, Siswanto, Irene, Anggorowati, Daniwijaya, Nugrahaningsih, Puspadewi, Puspitarani, Tania, Vujira, Ardlyamustaqim, Gabriela, Eryvinka, Nirmala, Geometri, Darutama, Kuswandani, Lestari, Irianingsih, Khoiriyah, Lestari, Ananda, Arguni, Nuryastuti and Wibawa.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 21 September 2021
                : 09 November 2021
                Page count
                Figures: 1, Tables: 3, Equations: 0, References: 43, Pages: 9, Words: 6025
                Categories
                Medicine
                Original Research

                comorbidity,ct value,delta variant,hospitalization,mortality,sars-cov-2,viral load,whole genome sequencing

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