The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had severe consequences for health and the global economy. To control the transmission, there is an urgent demand for early diagnosis and treatment in the general population. In the present study, an automatic system for SARS-CoV-2 diagnosis is designed and built to deliver high specification, high sensitivity, and high throughput with minimal workforce involvement. The system, set up with cross-priming amplification (CPA) rather than conventional reverse transcription-polymerase chain reaction (RT-PCR), was evaluated using more than 1000 real-world samples for direct comparison. This fully automated robotic system performed SARS‐CoV‐2 nucleic acid-based diagnosis with 192 samples in under 180 min at 100 copies per reaction in a “specimen in data out” manner. This throughput translates to a daily screening capacity of 800–1000 in an assembly-line manner with limited workforce involvement. The sensitivity of this device could be further improved using a CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-based assay, which opens the door to mixed samples, potentially include SARS-CoV-2 variants screening in extensively scaled testing for fighting COVID-19. Supplementary Information The online version contains supplementary material available at 10.1007/s00449-021-02674-9.

Graph Convolutional Network (GCN) has been commonly applied for semi-supervised learning tasks. However, the established GCN frequently only considers the given labels in the topology optimization, which may not deliver the best performance for semi-supervised learning tasks. In this paper, we propose a novel Graph Convolutional Network with Estimated labels (E-GCN) for semi-supervised learning. The core design of E-GCN is to learn a suitable network topology for semi-supervised learning by linking both estimated labels and given labels in a centralized network framework. The major enhancement is that both given labels and estimated labels are utilized for the topology optimization in E-GCN, which assists the graph convolution implementation for unknown labels evaluation. Experimental results demonstrate that E-GCN is significantly better than state-of-the-art (SOTA) baselines without estimated labels.

No abstract