Network pharmacology-based predictions of active components and pharmacological mechanisms of Artemisia annua L. for the treatment of the novel Corona virus disease 2019 (COVID-19).

BACKGROUND: Novel Corona Virus Disease 2019 (COVID-19) is closely associated with cytokines storms. The Chinese medicinal herb Artemisia annua L. (A. annua) has been traditionally used to control many inflammatory diseases, such as malaria and rheumatoid arthritis. We performed network analysis and employed molecular docking and network analysis to elucidate active components or targets and the underlying mechanisms of A. annua for the treatment of COVID-19. METHODS: Active components of A. annua were identified through the TCMSP database according to their oral bioavailability (OB) and drug-likeness (DL). Moreover, target genes associated with COVID-19 were mined from GeneCards, OMIM, and TTD. A compound-target (C-T) network was constructed to predict the relationship of active components with the targets. A Compound-disease-target (C-D-T) network has been built to reveal the direct therapeutic target for COVID-19. Molecular docking, molecular dynamics simulation studies (MD), and MM-GBSA binding free energy calculations were used to the closest molecules and targets between A. annua and COVID-19. RESULTS: In our network, GO, and KEGG analysis indicated that A. annua acted in response to COVID-19 by regulating inflammatory response, proliferation, differentiation, and apoptosis. The molecular docking results manifested excellent results to verify the binding capacity between the hub components and hub targets in COVID-19. MD and MM-GBSA data showed quercetin to be the more effective candidate against the virus by target MAPK1, and kaempferol to be the other more effective candidate against the virus by target TP53. We identified A. annua's potentially active compounds and targets associated with them that act against COVID-19. CONCLUSIONS: These findings suggest that A. annua may prevent and inhibit the inflammatory processes related to COVID-19.

Low-Cost and Scalable Platform with Multiplexed Microwell Array Biochip for Rapid Diagnosis of COVID-19.

Sensitive detection of SARS-CoV-2 is of great importance for inhibiting the current pandemic of COVID-19. Here, we report a simple yet efficient platform integrating a portable and low-cost custom-made detector and a novel microwell array biochip for rapid and accurate detection of SARS-CoV-2. The instrument exhibits expedited amplification speed that enables colorimetric read-out within 25 minutes. A polymeric chip with a laser-engraved microwell array was developed to process the reaction between the primers and the respiratory swab RNA extracts, based on reverse transcriptase loop-mediated isothermal amplification (RT-LAMP). To achieve clinically acceptable performance, we synthesized a group of six primers to identify the conserved regions of the ORF1ab gene of SARS-CoV-2. Clinical trials were conducted with 87 PCR-positive and 43 PCR-negative patient samples. The platform demonstrated both high sensitivity (95.40%) and high specificity (95.35%), showing potentials for rapid and user-friendly diagnosis of COVID-19 among many other infectious pathogens.

Association of menopausal status with COVID-19 outcomes: a propensity score matching analysis.

BACKGROUND: Despite the growing number of studies on the coronavirus disease-19 (COVID-19), little is known about the association of menopausal status with COVID-19 outcomes. MATERIALS AND METHODS: In this retrospective study, we included 336 COVID-19 inpatients between February 15, 2020 and April 30, 2020 at the Taikang Tongji Hospital (Wuhan), China. Electronic medical records including patient demographics, laboratory results, and chest computed tomography (CT) images were reviewed. RESULTS: In total, 300 patients with complete clinical outcomes were included for analysis. The mean age was 65.3 years, and most patients were women (n = 167, 55.7%). Over 50% of patients presented with comorbidities, with hypertension (63.5%) being the most common comorbidity. After propensity score matching, results showed that men had significantly higher odds than premenopausal women for developing severe disease type (23.7% vs. 0%, OR 17.12, 95% CI 1.00-293.60; p = 0.003) and bilateral lung infiltration (86.1% vs. 64.7%, OR 3.39, 95% CI 1.08-10.64; p = 0.04), but not for mortality (2.0% vs. 0%, OR 0.88, 95% CI 0.04-19.12, p = 1.00). However, non-significant difference was observed among men and postmenopausal women in the percentage of severe disease type (32.7% vs. 41.7%, OR 0.68, 95% CI 0.37-1.24, p = 0.21), bilateral lung infiltration (86.1% vs. 91.7%, OR 0.56, 95% CI 0.22-1.47, p = 0.24), and mortality (2.0% vs. 6.0%, OR 0.32, 95% CI 0.06-1.69, p = 0.25). CONCLUSIONS: Men had higher disease severity than premenopausal women, while the differences disappeared between postmenopausal women and men. These findings support aggressive treatment for the poor prognosis of postmenopausal women in clinical practice.

A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images.

Segmentation of pneumonia lesions from CT scans of COVID-19 patients is important for accurate diagnosis and follow-up. Deep learning has a potential to automate this task but requires a large set of high-quality annotations that are difficult to collect. Learning from noisy training labels that are easier to obtain has a potential to alleviate this problem. To this end, we propose a novel noise-robust framework to learn from noisy labels for the segmentation task. We first introduce a noise-robust Dice loss that is a generalization of Dice loss for segmentation and Mean Absolute Error (MAE) loss for robustness against noise, then propose a novel COVID-19 Pneumonia Lesion segmentation network (COPLE-Net) to better deal with the lesions with various scales and appearances. The noise-robust Dice loss and COPLE-Net are combined with an adaptive self-ensembling framework for training, where an Exponential Moving Average (EMA) of a student model is used as a teacher model that is adaptively updated by suppressing the contribution of the student to EMA when the student has a large training loss. The student model is also adaptive by learning from the teacher only when the teacher outperforms the student. Experimental results showed that: (1) our noise-robust Dice loss outperforms existing noise-robust loss functions, (2) the proposed COPLE-Net achieves higher performance than state-of-the-art image segmentation networks, and (3) our framework with adaptive self-ensembling significantly outperforms a standard training process and surpasses other noise-robust training approaches in the scenario of learning from noisy labels for COVID-19 pneumonia lesion segmentation.