Outcome prediction model and prognostic biomarkers for COVID-19 patients in Vietnam

Background Accurate prognosis is important either after acute infection or during long-term follow-up of patients infected by severe acute respiratory syndrome coronavirus 2. This study aims to predict coronavirus disease 2019 (COVID-19) severity based on clinical and biological indicators, and to identify biomarkers for prognostic assessment. Methods We 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. Results The random forest model could predict with 97% accuracy the likelihood of COVID-19 patients who subsequently worsened to the severe condition. The most important indicators were interleukin (IL)-6, ferritin and D-dimer. The model could still predict with 92% accuracy after removing IL-6 from the analysis to generalise the applicability of the model to hospitals with limited capacity for IL-6 testing. The five most effective indicators were C-reactive protein (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 machine learning algorithms and relatively validated on two Danish COVID-19 patient groups (n=32 and n=100). The results indicated that various biomarker sets combined with clinical data can be used for detection of the potential to develop the severe condition. Conclusion This 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

Protection agroécologique des cultures pour une production agricole durable

International audienceCrop losses from pests threaten global food security and safety. In the last six decades, pest control using chemical pesticides has resulted in important yield gains per unit area, worldwide. However, the long-term sustainability of chemical pest control has been increasingly thrown into doubt due to the negative impact on human health, biodiversity, and the environment. Consequently, there is an urgent need to improve the science of crop protection in order to tackle the five key challenges of 21st century agriculture holistically: (i) maintaining or improving agricultural productivity, (ii) producing healthy food, (iii) reducing the negative impacts of agriculture on ecosystem and human health, (iv) ensuring the economic viability of farms, and (v) adapting agriculture to climate change. Agroecological Crop Protection (ACP) can be a powerful approach to address these challenges, as we demonstrate in this paper. ACP is the application of the principles of agroecology to crop protection in order to promote virtuous and sustainable changes in agriculture and food systems. ACP combines multiple approaches and disciplines including ecology, agroecology, and Integrated Pest Management. It promotes a crop protection system compatible with healthy agricultural and food systems, agroecological principles and the “one health” approach. We predict that ACP will meet the challenge of pesticide-free agriculture in the future. In this paper, we will first present the scientific, agricultural and social components of ACP. We will then analyze the research approaches, questions, methods and tools needed to adopt ACP. Finally, we suggest key mechanisms to facilitate the transition to ACP, which will ultimately provide sustainable food, feed, and fuel in a context of major global change