No abstract

International audienceAn imidazolium ionic liquid was synthesized, characterized and used as a catalyst for conversion of polyethylene terephthalate (PET) and soybean oil to polyester polyol (PE polyol). The degradation of PET waste was carried out using glycerol and low cost soybean oil that resulted in the formation of PE polyols. Formed PE polyols were characterized using Fourier transform infrared (FT-IR) and mass spectra method, thermo gravimetric and differential thermal analysis and gel permeation chromatoghraphy. The first step in the overall process is proposed to be the transesterification of soybean oil with glycerol to form monoglyceride or/and diglyceride of soybean oil fatty acids. In the second step, the obtained glycerides can react with PET to form PE polyol. Both steps could be combined in one process and acidic catalyzed by an ionic liquid. Ionic liquid can be used as active catalyst and show a high reusability. The influence of some factors such as amount of glycerol used in transesterification of soybean oil with glycerol, PET degradation time, and temperature on PET conversion were investigated to find the suitable conditions for the process. Under suggested optimum parameters (mass ratio of soybean oil to glycerol of 2:1, a time of 8 h and a temperature of 180 °C for PET degradation), a PET conversion of 87.3% was reache

IntroductionAccurate prognosis is important either after acute infection or during long-term follow-up of patients infected by SARS-CoV-2. This study aims to predict COVID-19 severity based on clinical and biological indicators, and to identify biomarkers for prognostic assessment.MethodsWe included 261 Vietnamese COVID-19 patients, who were classified into moderate and severe groups. Disease severity prediction based on biomarkers and clinical parameters was performed by applying machine learning and statistical methods using the combination of clinical and biological data.ResultsThe random forest model could predict with 97% accuracy the likelihood of COVID-19 patients who subsequently worsened to severe condition. The most important indicators were IL-6, Ferritin, and D-dimer. The model could still predict with 92% accuracy after removing IL-6 from analysis to generalize applicability of the model to hospitals with limited capacity for IL-6 testing. The five most effective indicators were CRP, D-dimer, IL-6, Ferritin, and dyspnoea. Two different sets of biomarkers (D-dimer, IL-6, and Ferritin; and CRP, D-dimer, and IL-6) are applicable for the assessment of disease severity and prognosis. The two biomarker sets were further tested through the machine learning algorithms random forest; and relatively validated on two Danish COVID-19 patient groups (n=32; and n=100). The results indicated various biomarker sets combined with clinical data can be used for detection of potential develop severe conditions.ConclusionThis study provided a simple and reliable model using two different sets of biomarkers to assess disease severity and predict clinical outcomes in COVID-19 patients in Vietnam.

No abstract