Engineering antiviral immune-like systems for autonomous virus detection and inhibition in mice.

The ongoing COVID-19 pandemic has demonstrated that viral diseases represent an enormous public health and economic threat to mankind and that individuals with compromised immune systems are at greater risk of complications and death from viral diseases. The development of broad-spectrum antivirals is an important part of pandemic preparedness. Here, we have engineer a series of designer cells which we term autonomous, intelligent, virus-inducible immune-like (ALICE) cells as sense-and-destroy antiviral system. After developing a destabilized STING-based sensor to detect viruses from seven different genera, we have used a synthetic signal transduction system to link viral detection to the expression of multiple antiviral effector molecules, including antiviral cytokines, a CRISPR-Cas9 module for viral degradation and the secretion of a neutralizing antibody. We perform a proof-of-concept study using multiple iterations of our ALICE system in vitro, followed by in vivo functionality testing in mice. We show that dual output ALICESaCas9+Ab system delivered by an AAV-vector inhibited viral infection in herpetic simplex keratitis (HSK) mouse model. Our work demonstrates that viral detection and antiviral countermeasures can be paired for intelligent sense-and-destroy applications as a flexible and innovative method against virus infection.

A deep ensemble learning-based automated detection of COVID-19 using lung CT images and Vision Transformer and ConvNeXt.

Since the outbreak of COVID-19, hundreds of millions of people have been infected, causing millions of deaths, and resulting in a heavy impact on the daily life of countless people. Accurately identifying patients and taking timely isolation measures are necessary ways to stop the spread of COVID-19. Besides the nucleic acid test, lung CT image detection is also a path to quickly identify COVID-19 patients. In this context, deep learning technology can help radiologists identify COVID-19 patients from CT images rapidly. In this paper, we propose a deep learning ensemble framework called VitCNX which combines Vision Transformer and ConvNeXt for COVID-19 CT image identification. We compared our proposed model VitCNX with EfficientNetV2, DenseNet, ResNet-50, and Swin-Transformer which are state-of-the-art deep learning models in the field of image classification, and two individual models which we used for the ensemble (Vision Transformer and ConvNeXt) in binary and three-classification experiments. In the binary classification experiment, VitCNX achieves the best recall of 0.9907, accuracy of 0.9821, F1-score of 0.9855, AUC of 0.9985, and AUPR of 0.9991, which outperforms the other six models. Equally, in the three-classification experiment, VitCNX computes the best precision of 0.9668, an accuracy of 0.9696, and an F1-score of 0.9631, further demonstrating its excellent image classification capability. We hope our proposed VitCNX model could contribute to the recognition of COVID-19 patients.

Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative brain disease, and it is challenging to mine features that distinguish AD and healthy control (HC) from multiple datasets. Brain network modeling technology in AD using single-modal images often lacks supplementary information regarding multi-source resolution and has poor spatiotemporal sensitivity. In this study, we proposed a novel multi-modal LassoNet framework with a neural network for AD-related feature detection and classification. Specifically, data including two modalities of resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were adopted for predicting pathological brain areas related to AD. The results of 10 repeated experiments and validation experiments in three groups prove that our proposed framework outperforms well in classification performance, generalization, and reproducibility. Also, we found discriminative brain regions, such as Hippocampus, Frontal_Inf_Orb_L, Parietal_Sup_L, Putamen_L, Fusiform_R, etc. These discoveries provide a novel method for AD research, and the experimental study demonstrates that the framework will further improve our understanding of the mechanisms underlying the development of AD.

Notch4 signaling limits regulatory T-cell-mediated tissue repair and promotes severe lung inflammation in viral infections.

A cardinal feature of COVID-19 is lung inflammation and respiratory failure. In a prospective multi-country cohort of COVID-19 patients, we found that increased Notch4 expression on circulating regulatory T (Treg) cells was associated with disease severity, predicted mortality, and declined upon recovery. Deletion of Notch4 in Treg cells or therapy with anti-Notch4 antibodies in conventional and humanized mice normalized the dysregulated innate immunity and rescued disease morbidity and mortality induced by a synthetic analog of viral RNA or by influenza H1N1 virus. Mechanistically, Notch4 suppressed the induction by interleukin-18 of amphiregulin, a cytokine necessary for tissue repair. Protection by Notch4 inhibition was recapitulated by therapy with Amphiregulin and, reciprocally, abrogated by its antagonism. Amphiregulin declined in COVID-19 subjects as a function of disease severity and Notch4 expression. Thus, Notch4 expression on Treg cells dynamically restrains amphiregulin-dependent tissue repair to promote severe lung inflammation, with therapeutic implications for COVID-19 and related infections.

Relating Government Policies to Covid-19 Pandemic

The Covid-19 Pandemic has threatening the human being as a whole starting from the January of 2020. In response to the crisis, government authorities across the globe have come up with varied range of reactions and precautions in controlling the spread of the virus, as what we have taken from the Government Reactions Dataset. In the Report, we examine the level of reactions of governments by constructing a Government Reaction Index (GRI) by nations, following a timeline from January to August;combining GRI and with growth rates, confirmed cases and other numerical indications of the virus spread to explore whether these reactions are effective. We draw a solid overall conclusion of how governments take actions in response to the crisis, and particularly few representative countries for a more detailed observation. The research gives clear indications of the and helps to construct the idea of governments' future actions.