Clofazimine derivatives as potent broad-spectrum antiviral agents with dual-target mechanism.

Thirty-two clofazimine derivatives, of which twenty-two were new, were synthesized and evaluated for their antiviral effects against both rabies virus and pseudo-typed SARS-CoV-2, taking clofazimine (1) as the lead. Among them, compound 15f bearing 4-methoxy-2-pyridyl at the N5-position showed superior or comparable antiviral activities to lead 1, with the EC50 values of 1.45 µM and 14.6 µM and the SI values of 223 and 6.1, respectively. Compound 15f inhibited rabies and SARS-CoV-2 by targeting G or S protein to block membrane fusion, as well as binding to L protein or nsp13 to inhibit intracellular biosynthesis respectively, and thus synergistically exerted a broad-spectrum antiviral effect. The results provided useful scientific data for the development of clofazimine derivatives into a new class of broad-spectrum antiviral candidates.

Progressive back-projection network for COVID-CT super-resolution.

BACKGROUND AND OBJECTIVE: Recently, the COVID-19 epidemic has become more and more serious around the world, how to improve the image resolution of COVID-CT is a very important task. The network based on progressive upsampling for COVID-CT super-resolution increases the reconstruction error. This paper proposes a progressive back-projection network (PBPN) for COVID-CT super-resolution to solve this problem. METHODS: In this paper, we propose a progressive back-projection network (PBPN) for COVID-CT super-resolution. PBPN is divided into two stages, and each stage consists of back-projection, deep feature extraction and upscaling. We design an up-projection and down-projection residual module to minimize the reconstruction error and construct a residual attention module to extract deep features. In each stage, firstly, PBPN performs back-projection to extract shallow features by two up-projection and down-projection residual modules; then, PBPN extracts deep features from the shallow features by two residual attention modules; finally, PBPN upsamples the deep features through sub-pixel convolution. RESULTS: The proposed method achieves the improvements of about 0.14~0.47 dB/0.0012~0.0060 for × 2 scale factor, 0.02~0.08 dB/0.0024~0.0059 for × 3 scale factor, and 0.08~0.41 dB/ 0.0040~0.0147 for × 4 scale factor than state-of-the-art methods (Bicubic, SRCNN, FSRCNN, VDSR, LapSRN, DRCN and DSRN) in terms of PSNR/SSIM on benchmark datasets. CONCLUSIONS: The proposed mehtod obtains better performance for COVID-CT super-resolution and reconstructs high-quality high-resolution COVID-CT images that contain more details and edges.