Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery.

Climate change and the COVID-19 pandemic have disrupted the food supply chain across the globe and adversely affected food security. Early estimation of staple crops can assist relevant government agencies to take timely actions for ensuring food security. Reliable crop type maps can play an essential role in monitoring crops, estimating yields, and maintaining smooth food supplies. However, these maps are not available for developing countries until crops have matured and are about to be harvested. The use of remote sensing for accurate crop-type mapping in the first few weeks of sowing remains challenging. Smallholder farming systems and diverse crop types further complicate the challenge. For this study, a ground-based survey is carried out to map fields by recording the coordinates and planted crops in respective fields. The time-series images of the mapped fields are acquired from the Sentinel-2 satellite. A deep learning-based long short-term memory network is used for the accurate mapping of crops at an early growth stage. Results show that staple crops, including rice, wheat, and sugarcane, are classified with 93.77% accuracy as early as the first four weeks of sowing. The proposed method can be applied on a large scale to effectively map crop types for smallholder farms at an early stage, allowing the authorities to plan a seamless availability of food.

Effectiveness of Mavrilimumab in Viral Infections Including SARS-CoV-2 Infection - A Brief Review.

Hyperinflammation and cytokine storm has been noted as a poor prognostic factor in patients with severe pneumonia related to coronavirus disease 2019 (COVID-19). In COVID-19, pathogenic myeloid cell overactivation is found to be a vital mediator of damage to tissues, hypercoagulability, and the cytokine storm. These cytokines unselectively infiltrate various tissues, such as the lungs and heart, and nervous system. This cytokine storm can hence cause multi-organ dysfunction and life-threatening complications. Mavrilimumab is a monoclonal antibody (mAb) that may be helpful in some cases with COVID-19. During an inflammation, Granulocyte-macrophage colony-stimulating factor (GM-CSF) release is crucial to driving both innate and adaptive immune responses. The GM-CSF immune response is triggered when an antigen attaches to the host cell and induces the signaling pathway. Mavrilimumab antagonizes the action of GM-CSF and decreases the hyperinflammation associated with pneumonia in COVID-19, therefore strengthening the rationale that mavrilimumab when added to the standard protocol of treatment could improve the clinical outcomes in COVID-19 patients, specifically those patients with pneumonia. With this review paper, we aim to demonstrate the inhibitory effect of mavrilimumab on cytokine storms in patients with COVID-19 by reviewing published clinical trials and emphasize the importance of extensive future trials.

Immune dysregulation and system pathology in COVID-19.

The coronavirus disease 19 (COVID-19) caused by the novel coronavirus known as SARS-CoV-2 has caused a global public health crisis. As of 7 January 2021, 87,640,402 confirmed cases and 1,891,692 mortalities have been reported worldwide. Studies focusing on the epidemiological and clinical characteristics of COVID-19 patients have suggested a dysregulated immune response characterized by lymphopenia and cytokine storm in these patients. The exaggerated immune response induced by the cytokine storm causes septic shock, acute respiratory distress syndrome (ARDS), and/or multiple organs failure, which increases the fatality rate of patients with SARS-CoV-2 infection. Herein, we review the recent research progress on epidemiology, clinical features, and system pathology in COVID-19. Moreover, we summarized the recent therapeutic strategies, which are either approved, under clinical trial, and/or under investigation by the local or global health authorities. We assume that treatments should focus on the use of antiviral drugs in combination with immunomodulators as well as treatment of the underlying comorbidities.

Novel biomarkers for the prediction of COVID-19 progression a retrospective, multi-center cohort study.

A pandemic designated as Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading worldwide. Up to date, there is no efficient biomarker for the timely prediction of the disease progression in patients. To analyze the inflammatory profiles of COVID-19 patients and demonstrate their implications for the illness progression of COVID-19. Retrospective analysis of 3,265 confirmed COVID-19 cases hospitalized between 10 January 2020, and 26 March 2020 in three medical centers in Wuhan, China. Patients were diagnosed as COVID-19 and hospitalized in Leishenshan Hospital, Zhongnan Hospital of Wuhan University and The Seventh Hospital of Wuhan, China. Univariable and multivariable logistic regression models were used to determine the possible risk factors for disease progression. Moreover, cutoff values, the sensitivity and specificity of inflammatory parameters for disease progression were determined by MedCalc Version 19.2.0. Age (95%CI, 1.017 to 1.048; P < 0.001), serum amyloid A protein (SAA) (95%CI, 1.216 to 1.396; P < 0.001) and erythrocyte sedimentation rate (ESR) (95%CI, 1.006 to 1.045; P < 0.001) were likely the risk factors for the disease progression. The Area under the curve (AUC) of SAA for the progression of COVID-19 was 0.923, with the best predictive cutoff value of SAA of 12.4 mg/L, with a sensitivity of 83.9% and a specificity of 97.67%. SAA-containing parameters are novel promising ones for predicting disease progression in COVID-19.