Forecasting of Oxidant/Antioxidant levels of COVID-19 patients by using Expert models with biomarkers used in the Diagnosis/Prognosis of COVID-19

Huyut, Mehmet Tahir; Huyut, Zübeyir

doi:10.1016/j.intimp.2021.108127

Cited by 25 publications

(37 citation statements)

References 47 publications

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“…The new severe acute respiratory syndrome coronavirus (SARS-CoV-2), first identified in 2019, has rapidly affected the world and caused a pandemic [1,2]. The disease, identified as coronavirus 2019 (COVID- 19), can cause severe pneumonia and fatal acute respiratory distress syndrome (ARDS) [3][4][5][6]. While the disease may be asymptomatic, severe ARDS is thought to be caused by an inflammatory cytokine storm that may be encountered during the disease period [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have demonstrated the clinical importance of changes in routine blood parameters (RBV) in the diagnosis and prediction of prognosis of infectious diseases [1][2][3][4][10][11][12]. Similarly, many abnormalities have been reported in the peripheral blood of patients infected with COVID-19 [6,7,11].…”

Section: Introductionmentioning

confidence: 99%

“…There are several artificial intelligence methods to predict the diagnosis and mortality of COVID-19 [4,17]. Most of these studies have relied on computed tomography (CT) [19], while far fewer studies relied on RBVs [4,5,20]. Imaging-based solutions are costly, timeconsuming, and require specialized equipment [20].…”

Section: Introductionmentioning

confidence: 99%

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Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network

Huyut

Velichko

2022

Sensors

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Since February 2020, the world has been engaged in an intense struggle with the COVID-19 disease, and health systems have come under tragic pressure as the disease turned into a pandemic. The aim of this study is to obtain the most effective routine blood values (RBV) in the diagnosis and prognosis of COVID-19 using a backward feature elimination algorithm for the LogNNet reservoir neural network. The first dataset in the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 tests. The LogNNet-model achieved the accuracy rate of 99.5% in the diagnosis of the disease with 46 features and the accuracy of 99.17% with only mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, and activated partial prothrombin time. The second dataset consists of a total of 3899 patients with a diagnosis of COVID-19 who were treated in hospital, of which 203 were severe patients and 3696 were mild patients. The model reached the accuracy rate of 94.4% in determining the prognosis of the disease with 48 features and the accuracy of 82.7% with only erythrocyte sedimentation rate, neutrophil count, and C reactive protein features. Our method will reduce the negative pressures on the health sector and help doctors to understand the pathogenesis of COVID-19 using the key features. The method is promising to create mobile health monitoring systems in the Internet of Things.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network

Huyut

Velichko

2022

Sensors

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“…Previous studies have shown that the change in routine blood parameters has certain clinical application value in predicting the progression of infectious diseases [ [1] , [10] , [37] ]. Indeed, many abnormalities have been reported in the peripheral blood of some infected patients [2] , [6] , [9] , [11] .…”

Section: Introductionmentioning

confidence: 99%

“…However, information on the early predictive indicators for particularly severe and fatal cases is relatively limited and further research is needed [ [9] , [37] ]. In addition, the rapid spread of the disease raises concerns about the need for intensive care that can overwhelm health care system resources [13] .…”

Section: Introductionmentioning

confidence: 99%

What is the impact and efficacy of routine immunological, biochemical and hematological biomarkers as predictors of COVID-19 mortality?

Huyut

Ilkbahar

et al. 2022

International Immunopharmacology

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It remains important to investigate the changing and impact of routine blood values (RBVs) in order to predict mortality and follow an appropriate treatment in COVID-19 patients. In the study, the importance of RBVs in the mortality of patients with COVID-19 was investigated. The changes in the biochemical, hematological, and immunological parameters of patients who recovered (n = 4364) and died (n = 233) from COVID-19 over time and their relationship with the mortality of the disease were evaluated retrospectively. Odds ratios of the parameters affecting one-month mortality were calculated by running multiple-logistic-regression analysis. The cut off values and diagnostic efficiencies of the parameters that posed a risk for mortality were obtained via receiver operating curve analysis. It was determined that the C-reactive protein (CRP), D-dimer, procalcitonin, erythrocyte-sedimentation-rate (ESR), troponin values were at abnormal levels until death occurred in the patients who died. In addition, the procalcitonin levels were consistently high in patients who died. The patients who died generally had a sustained increase in their leukocyte and neutrophil levels and biochemical variables, and an ongoing decrease in lymphopenia and eosinopenia levels. Although significant changes were observed in liver function tests, cardiac troponin, hemogram values, kidney function tests and parameters related to inflammation in deceased patients, high ESR, international-normalized-ratio (INR), prothrombin-time (PT), CRP, D-dimer, ferritin and red-cell-distribution width (RDW) values, respectively, were the most effective predictive mortality risk biomarkers of COVID-19. In addition, neutrophilia, leukocytosis, thrombocytopenia, erythrocytopenia were other risk predictors of mortality. Indicators was found in this study can be successfully used to predict mortality from COVID-19.

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An Analysis of Antioxidant Therapies for COVID‐19

Holt,

Davies

2023

Advcd Theory and Sims

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Oxidative stress and inflammation are observed in critically ill patients with severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). As oxidative stress is due to free radicals, free radical scavenging agents, such as vitamin C, are used in the hope of mitigating the severity of COVID‐19. In this paper an analytical model of free radical encounters within a cell is derived. It computes the effectiveness of free radical scavenging and the subsequent reduction in oxidative damage due to therapeutic intervention of exogenous antioxidants. Various publications make claims of success, albeit modest, treating critically ill COVID‐19 patients with high doses of vitamin C. The findings cast doubt on these claims and challenge the notion that any benefits attributed to vitamin C are due to free radical scavenging.

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Forecasting of Oxidant/Antioxidant levels of COVID-19 patients by using Expert models with biomarkers used in the Diagnosis/Prognosis of COVID-19

Abstract: Graphical abstract

Cited by 25 publications

References 47 publications

Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network

Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network

What is the impact and efficacy of routine immunological, biochemical and hematological biomarkers as predictors of COVID-19 mortality?

An Analysis of Antioxidant Therapies for COVID‐19

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