Structural Attention Graph Neural Network for Diagnosis and Prediction of COVID-19 Severity.

With rapid worldwide spread of Coronavirus Disease 2019 (COVID-19), jointly identifying severe COVID-19 cases from mild ones and predicting the conversion time (from mild to severe) is essential to optimize the workflow and reduce the clinician's workload. In this study, we propose a novel framework for COVID-19 diagnosis, termed as Structural Attention Graph Neural Network (SAGNN), which can combine the multi-source information including features extracted from chest CT, latent lung structural distribution, and non-imaging patient information to conduct diagnosis of COVID-19 severity and predict the conversion time from mild to severe. Specifically, we first construct a graph to incorporate structural information of the lung and adopt graph attention network to iteratively update representations of lung segments. To distinguish different infection degrees of left and right lungs, we further introduce a structural attention mechanism. Finally, we introduce demographic information and develop a multi-task learning framework to jointly perform both tasks of classification and regression. Experiments are conducted on a real dataset with 1687 chest CT scans, which includes 1328 mild cases and 359 severe cases. Experimental results show that our method achieves the best classification (e.g., 86.86% in terms of Area Under Curve) and regression (e.g., 0.58 in terms of Correlation Coefficient) performance, compared with other comparison methods.

Atomically dispersed Mn boosting photoelectrochemical SARS-CoV-2 spike protein immunosensing on carbon nitride.

The sudden outbreak of coronavirus disease (COVID-19) triggered by SARS-CoV-2 infection has created a terrifying situation around the world. The spike protein of SARS-CoV-2 can act as an early biomarker for COVID-19. Therefore, controlling the spread of COVID-19 requires a low-cost, fast-response, and sensitive monitoring technique of spike protein. Herein, a photoelectrochemical (PEC) immunosensor for the detection of spike protein was constructed using the nanobody and an Mn (Ⅱ) modified graphitic carbon nitride (Mn/g-C3N4). The introduction of atomically dispersed Mn (Ⅱ) can accelerate the effective transfer and separation of photogenerated electron-hole pairs, which significantly boosts PEC performance of g-C3N4, thereby improving the detection sensitivity. As a recognition site, nanobody can achieve high-affinity binding to the spike protein, leading to a high sensitivity. The linear detection range of the proposed PEC immunosensor was 75 fg mL-1 to 150 pg mL-1, and the limit of detection was calculated to be 1.22 fg mL-1. This stable and feasible PEC immunosensor would be a promising diagnostic tool for sensitively detecting spike protein of SARS-CoV-2.

Atomically Dispersed Mn Boosting Photoelectrochemical SARS-CoV-2 Spike Protein Immunosensing on Carbon Nitride

The sudden outbreak of coronavirus disease (COVID-19) triggered by SARS-CoV-2 infection has created a terrifying situation around the world. The Spike protein of SARS-CoV-2 may act as an early biomarker for leading to receptor binding and virus entry. Therefore, controlling the spread of COVID-19 requires a low-cost, fast-response, and sensitive monitoring technique of spike protein. Herein, a photoelectrochemical (PEC) immunosensor for the detection of spike protein was constructed using the nanobody and an Mn (Ⅱ) modified graphitic carbon nitride (Mn/g-C3N4). The introduction of atomically dispersed Mn (Ⅱ) accelerates the effective transfer and separation of photogenerated electron-hole pairs, which significantly boosts PEC performance of g-C3N4, thereby improving the detection sensitivity. As a recognition site, nanobody can achieve high-affinity binding to the spike protein for detection. The linear detection range of the proposed PEC immunosensor was 75 fg mL–1 to 150 pg mL–1, and the limit of detection was calculated to be 1.22 fg mL–1. This stable and feasible PEC immunosensor is a promising diagnostic tool for sensitively detecting SARS-CoV-2 spike protein. Graphical

COVID-19 IDD: Findings from a global survey exploring family members' and paid staff's perceptions of the impact of COVID-19 on individuals with intellectual and developmental disabilities (IDD) and their caregivers.

Background: A growing body of evidence attests to the disproportionate impact of COVID-19 on persons with intellectual and developmental disabilities (IDD) during the pandemic. This study asked caregivers about their perceptions of how COVID-19 impacted them and the people they support. Method: An online survey was conducted in 12 countries during August-September 2020 and sought information on demographics, support practices, information and training, experiences of COVID-19, social distancing, and wellbeing, as measured by the DASS12. This study reports on 3,754 family members, direct support professionals, and managers who participated in the survey. Results: Caregivers observed increases in depression/anxiety, stereotyped behaviours, aggression towards others and weight gain in the person(s) they supported. They also reported difficulties supporting the person(s) to access healthcare.  Families reported reducing or ceasing employment and absorbed additional costs when supporting their family member. Direct support professionals experienced changes in staff shifts, staff absences, increased workload and hiring of casual staff. Caregivers' wellbeing revealed high levels of stress, depression, and less so anxiety. The strongest predictor of wellbeing among families was observation of changes in mood in the person(s) they supported, while for direct support professionals, the strongest predictors of wellbeing were reorganisation of staff shifts and increases in new direct support staff.  Discussion: Findings support the contention of this population experiencing a disproportionate burden during the COVID-19 pandemic, reflecting historical inequities in access to healthcare and other human rights violations which are now protected under the United Nations Convention on the Rights of Persons with Disabilities.

Nanobody-based label-free photoelectrochemical immunoassay for highly sensitive detection of SARS-CoV-2 spike protein.

Until now, COVID-19 caused by SARS-CoV-2 is engulfing the worldwide and still ranging to date, continuing to threaten the public health. The main challenge facing COVID-19 epidemic is short of fast-response and high-efficiency methods to determine SARS-CoV-2 viral pathogens. Herein, a nanobody-based label-free photoelectrochemical (PEC) immunosensor has been fabricated for rapidly detecting SARS-CoV-2 spike protein. As a small-size and high-stability antibody, nanobody was directly and well immobilized with Au nanoparticles and TiO2 spheres by the interaction. Au deposited TiO2 nanomaterial possessed 8.5 times photoelectric performance in comparison with TiO2 in the presence of electron donor owing to surface plasma resonance effect of Au. Based on the steric hindrance effect, this immunoassay platform realized the linear detection from 0.015 to 15000 pg mL-1, and a limit of detection was low as 5 fg mL-1. The label-free PEC immunoassay design provides a new idea for convenient, rapid, and efficient test of SARS-CoV-2 spike protein and broadens further application of nanobody as an identification agent to specific biomarkers.