Therapeutic targets and potential agents for the treatment of COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has become a global crisis. As of November 9, COVID-19 has already spread to more than 190 countries with 50,000,000 infections and 1,250,000 deaths. Effective therapeutics and drugs are in high demand. The structure of SARS-CoV-2 is highly conserved with those of SARS-CoV and Middle East respiratory syndrome-CoV. Enzymes, including RdRp, Mpro /3CLpro , and PLpro , which play important roles in viral transcription and replication, have been regarded as key targets for therapies against coronaviruses, including SARS-CoV-2. The identification of readily available drugs for repositioning in COVID-19 therapy is a relatively rapid approach for clinical treatment, and a series of approved or candidate drugs have been proven to be efficient against COVID-19 in preclinical or clinical studies. This review summarizes recent progress in the development of drugs against SARS-CoV-2 and the targets involved.

Iterative dual energy material decomposition from spatial mismatched raw data sets.

Today's clinical dual energy computed tomography (DECT) scanners generally measure different rays for different energy spectra and acquire spatial mismatched raw data sets. The deficits in clinical DECT technologies suggest that mainly image based material decomposition methods are in use nowadays. However, the image based material decomposition is an approximate technique, and beam hardening artifacts remain in decomposition results. A recently developed image based iterative method for material decomposition from inconsistent rays (MDIR) can achieve much better image quality than the conventional image based methods. Inspired by the MDIR method, this paper proposes an iterative method to indirectly perform raw data based DECT even with completely mismatched raw data sets. The iterative process is initialized by density images that were obtained from an image based material decomposition. Then the density images are iteratively corrected by comparing the estimated polychromatic projections and the measured polychromatic projections. Only three iterations of the method are sufficient to greatly improve the qualitative and quantitative information in material density images. Compared with the MDIR method, the proposed method needs not to perform additional water precorrection. The advantages of the method are verified with numerical experiments from inconsistent noise free and noisy raw data.