Automated, flow-based chemiluminescence microarray immunoassay for the rapid multiplex detection of IgG antibodies to SARS-CoV-2 in human serum and plasma (CoVRapid CL-MIA).

In the face of the COVID-19 pandemic, the need for rapid serological tests that allow multiplexing emerged, as antibody seropositivity can instruct about individual immunity after an infection with SARS-CoV-2 or after vaccination. As many commercial antibody tests are either time-consuming or tend to produce false negative or false positive results when only one antigen is considered, we developed an automated, flow-based chemiluminescence microarray immunoassay (CL-MIA) that allows for the detection of IgG antibodies to SARS-CoV-2 receptor-binding domain (RBD), spike protein (S1 fragment), and nucleocapsid protein (N) in human serum and plasma in less than 8 min. The CoVRapid CL-MIA was tested with a set of 65 SARS-CoV-2 serology positive or negative samples, resulting in 100% diagnostic specificity and 100% diagnostic sensitivity, thus even outcompeting commercial tests run on the same sample set. Additionally, the prospect of future quantitative assessments (i.e., quantifying the level of antibodies) was demonstrated. Due to the fully automated process, the test can easily be operated in hospitals, medical practices, or vaccination centers, offering a valuable tool for COVID-19 serosurveillance. Graphical abstract.

Clinical outcome prediction in COVID-19 using self-supervised vision transformer representations

Automated analysis of chest imaging in coronavirus disease (COVID-19) has mostly been performed on smaller datasets leading to overfitting and poor generalizability. Training of deep neural networks on large datasets requires data labels. This is not always available and can be expensive to obtain. Self-supervision is being increasingly used in various medical imaging tasks to leverage large amount of unlabeled data during pretraining. Our proposed approach pretrains a vision transformer to perform two self-supervision tasks - image reconstruction and contrastive learning on a Chest Xray (CXR) dataset. In the process, we generate more robust image embeddings. The reconstruction module models visual semantics within the lung fields by reconstructing the input image through a mechanism which mimics denoising and autoencoding. On the other hand, the constrastive learning module learns the concept of similarity between two texture representations. After pretraining, the vision transformer is used as a feature extractor towards a clinical outcome prediction task on our target dataset. The pretraining multi-kaggle dataset comprises 27499 CXR scans while our target dataset contains 530 images. Specifically, our framework predicts ventilation and mortality outcomes for COVID-19 positive patients using baseline CXR. We compare our method against a baseline approach using pretrained ResNet50 features. Experimental results demonstrate that our proposed approach outperforms the supervised method. © 2022 SPIE.

Catalysis-in-a-Box: Robotic Screening of Catalytic Materials in the Time of COVID-19 and Beyond.

This work describes the design and implementation of an automated device for catalytic materials testing by direct modifications to a gas chromatograph (GC). The setup can be operated as a plug-flow isothermal reactor and enables the control of relevant parameters such as reaction temperature and reactant partial pressures directly from the GC. High-quality kinetic data (including reaction rates, product distributions, and activation barriers) can be obtained at almost one-tenth of the fabrication cost of analogous commercial setups. With these key benefits including automation, low cost, and limited experimental equipment instrumentation, this implementation is intended as a high-throughput catalyst screening reactor that can be readily utilized by materials synthesis researchers to assess the catalytic properties of their synthesized structures in vapor-phase chemistries.