scholarly journals Clinical Performance of Paraoxonase-1-Related Variables and Novel Markers of Inflammation in Coronavirus Disease-19. A Machine Learning Approach

Antioxidants ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 991
Author(s):  
Elisabet Rodríguez-Tomàs ◽  
Simona Iftimie ◽  
Helena Castañé ◽  
Gerard Baiges-Gaya ◽  
Anna Hernández-Aguilera ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Machine Learning ◽  
Clinical Performance ◽  
Paraoxonase 1 ◽  
Pon1 Activity ◽  
Learning Approach ◽  
Markers Of Inflammation ◽  
Machine Learning Approach ◽  
Galectin 3 ◽  
Circulating Levels

SARS-CoV-2 infection produces a response of the innate immune system causing oxidative stress and a strong inflammatory reaction termed ‘cytokine storm’ that is one of the leading causes of death. Paraoxonase-1 (PON1) protects against oxidative stress by hydrolyzing lipoperoxides. Alterations in PON1 activity have been associated with pro-inflammatory mediators such as the chemokine (C-C motif) ligand 2 (CCL2), and the glycoprotein galectin-3. We aimed to investigate the alterations in the circulating levels of PON1, CCL2, and galectin-3 in 126 patients with COVID-19 and their interactions with clinical variables and analytical parameters. A machine learning approach was used to identify predictive markers of the disease. For comparisons, we recruited 45 COVID-19 negative patients and 50 healthy individuals. Our approach identified a synergy between oxidative stress, inflammation, and fibrogenesis in positive patients that is not observed in negative patients. PON1 activity was the parameter with the greatest power to discriminate between patients who were either positive or negative for COVID-19, while their levels of CCL2 and galectin-3 were similar. We suggest that the measurement of serum PON1 activity may be a useful marker for the diagnosis of COVID-19.

scholarly journals Clinical Performance of Paraoxonase-1-Related Variables and Novel Markers of Inflammation in Coronavirus Disease-19. A Machine Learning Approach

2021 ◽  
Author(s):  
Elisabet Rodríguez-Tomàs ◽  
Simona Iftimie ◽  
Helena Castañé ◽  
Gerard Baiges-Gaya ◽  
Anna Hernández-Aguilera ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Machine Learning ◽  
Clinical Performance ◽  
Paraoxonase 1 ◽  
Pon1 Activity ◽  
Learning Approach ◽  
Markers Of Inflammation ◽  
Machine Learning Approach ◽  
Galectin 3 ◽  
Circulating Levels

SARS-CoV-2 infection produces a response of the innate immune system causing oxidative stress and a strong inflammatory reaction termed ‘cytokine storm’ that is one of the leading causes of death. Paraoxonase-1 (PON1) protects against oxidative stress by hydrolyzing lipoperoxides. Alterations in PON1 activity have been associated with pro-inflammatory mediators such as the chemokine (C-C motif) ligand 2 (CCL2) and the glycoprotein galectin-3. We aimed to investigate the alterations in the circulating levels of PON1, CCL2 and galectin-3 in 126 patients with COVID-19 and their interactions with clinical variables and analytical parameters. A machine learning approach was used to identify predictive markers of the disease. For comparisons, we recruited 45 COVID-19 negative patients and 50 healthy individuals. Our approach identified a synergy between oxidative stress, inflammation and fibrogenesis in positive patients that is not observed in negative patients. PON1 activity was the parameter with the greatest power to discriminate between patients who were either positive or negative for COVID-19, while their levels of CCL2 and galectin-3 were similar. We suggest that the measurement of serum PON1 activity may be a useful marker for the diagnosis of COVID-19.

scholarly journals A machine-learning approach to the prediction of oxidative stress in chronic inflammatory disease

Redox Report ◽  
2009 ◽  
Vol 14 (1) ◽  
pp. 23-33 ◽  
Author(s):  
A. Magon de la Villehuchet ◽  
M. Brack ◽  
G. Dreyfus ◽  
Y. Oussar ◽  
D. Bonnefont-Rousselot ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Machine Learning ◽  
Inflammatory Disease ◽  
Chronic Inflammatory Disease ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Low Paraoxonase 1 Activity Predicts Mortality in Surgical Patients with Sepsis

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Suzana Bojic ◽  
Jelena Kotur-Stevuljevic ◽  
Nevena Kalezic ◽  
Zorana Jelic-Ivanovic ◽  
Aleksandra Stefanovic ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Disease Severity ◽  
Paraoxonase 1 ◽  
Surgical Patients ◽  
Pon1 Activity ◽  
Healthy Controls ◽  
Risk Of Death ◽  
Markers Of Inflammation ◽  
Severity Scores ◽  
Disease Severity Scores

Introduction. State of severe oxidative stress is encountered in sepsis. Paraoxonase 1 (PON1) protects against oxidative stress but also undergoes inactivation upon that condition. We investigated PON1 activity in surgical patients with sepsis in relation to oxidative stress status, inflammation, disease severity, and survival.Methods. Prospective observational study. Sixty-nine surgical patients with sepsis were compared to 69 age/sex matched healthy controls. PON1 paraoxonase and diazoxonase activities, selected biochemical, hematological and oxidative stress parameters were measured on admission to ICU and 24, 48, 72, and 96 hours later. Disease severity scores were calculated daily.Results. Septic patients had significantly lower PON1 activities compared to control group at all time points. PON1 activities had good capacity to differentiate septic patients from healthy controls. Low PON1 activities were associated with higher disease severity scores and higher risk of death. Correlation between PON1 activity and markers of inflammation failed to reach significance. Decrease in PON1 activity was correlated with an increase in reducing components in plasma.Conclusion. Our study demonstrated lower PON1 activity in surgical patients with sepsis compared to healthy controls. PON1 activity also reflected severity of the disease. Low PON1 activity was associated with higher mortality of surgical patients with sepsis.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

2020 ◽  
Author(s):  
Clifford A. Brown ◽  
Jonny Dowdall ◽  
Brian Whiteaker ◽  
Lauren McIntyre
Keyword(s):  
Machine Learning ◽  
Surface Interaction ◽  
Learning Approach ◽  
Noise Modeling ◽  
Machine Learning Approach
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