Increased Oxidative Stress and Decreased Citrulline in Blood Associated with Severe Novel Coronavirus Pneumonia in Adult Patients

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(15), P. 8370 - 8370

Published: July 31, 2024

This study investigated the correlation between oxidative stress and blood amino acids associated with nitric oxide metabolism in adult patients coronavirus disease (COVID-19) pneumonia. Clinical data serum samples were prospectively collected from 100 hospitalized for COVID-19 July 2020 August 2021. Patients categorized into three groups analysis based on lung infiltrates, oxygen inhalation upon admission, initiation of therapy after admission. Blood data, stress-related biomarkers, acid levels admission compared these groups. infiltrations requiring or starting post-admission exhibited higher hydroperoxides lower citrulline to control group. No remarkable differences observed nitrite/nitrate, asymmetric dimethylarginine, arginine levels. Serum correlated significantly lactate dehydrogenase C-reactive protein A significant negative was found hydroperoxides. Levels decreased, increased during recovery period extensive pneumonia poor oxygenation showed reduced those fewer pulmonary complications. These findings suggest that combined abnormal may play a role pathogenesis

Language: Английский

Oxidative stress markers and prediction of severity by a machine learning approach in hospitalized patients with COVID-19 and severe lung disease: a feasibility study (Preprint)

Published: Sept. 15, 2024

Oxidative Stress (OS) is an imbalance between the production of free radicals and body's ability to neutralize them, leading damages cells, proteins deoxyribonucleic. To identify relevant biomarkers OS which could be associated severity hospitalized patients a possible correlation clinical status COVID-19 with severe lung disease at hospital admission. All adult Infirmerie Protestante (Lyon, France) from 9th February 2022 18th May were included, regardless care service. The final sample consisted in 28 patients. Ten collected per patient (Zinc (Zn), Copper (Cu), Cu/Zn, Selenium, Uric acid, CRplus, Oxidized LDL, Glutathione peroxidase, reductase Thiols), as well demographic variables comorbidities. A support vector machine (SVM) model was used predict grade patient, based on data training set. : Three severity; Zn, Cu/Zn Thiols especially for 0 (asymptomatic) 1 (mild moderate severity). SVM predicted level biological analysis only 7.14% discrepancy dataset. In case infection, symptomatic are lowered zinc level, plasma thiol increased CRPus ratio among panel ten OS.

Language: Английский

Oxidative stress markers and prediction of severity by a machine learning approach in hospitalized patients with COVID-19 and severe lung disease: a feasibility study (Preprint)

JMIR Formative Research, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 15, 2024

Serious pulmonary pathologies of infectious, viral, or bacterial origin are accompanied by inflammation and an increase in oxidative stress (OS). In these situations, biological measurements OS technically difficult to obtain, their results interpret. assays that do not require complex preanalytical methods, as well machine learning methods for improving interpretation the results, would be very useful tools medical care teams. We aimed identify relevant biomarkers associated with severity hospitalized patients' condition possible correlations between clinical status patients COVID-19 severe lung disease at time hospital admission. All adult Infirmerie Protestante (Lyon, France) from February 9, 2022, May 18, were included, regardless service they used, during respiratory infectious epidemic. collected serous (zinc [Zn], copper [Cu], Cu/Zn ratio, selenium, uric acid, high-sensitivity C-reactive protein [hs-CRP], oxidized low-density lipoprotein, glutathione peroxidase, reductase, thiols), demographic variables comorbidities. A support vector (SVM) model was used predict based on data a training set. total 28 included: 8 asymptomatic admission (grade 0), 14 had mild moderate symptoms 1) 6 critical 3). As first outcome, we found 3 (Zn, especially grades 0 1 2. second SVM could level analysis OS, only 7% misclassification dataset. illustrative example, simulated different profiles (named A, B, C) submitted them model. Profile B significantly high Zn, low hs-CRP, thiols, corresponding grade 0. C The damage predicted using model; plasma thiol, increased ratio among panel 10 OS. Since this does method, it can studied other such chronic diseases.

Language: Английский