An allosteric inhibitor of sirtuin 2 blocks hepatitis B virus covalently closed circular DNA establishment and its transcriptional activity.

296 million people worldwide are predisposed to developing severe end-stage liver diseases due to chronic hepatitis B virus (HBV) infection. HBV forms covalently closed circular DNA (cccDNA) molecules that persist as episomal DNA in the nucleus of infected hepatocytes and drive viral replication. Occasionally, the HBV genome becomes integrated into host chromosomal DNA, a process that is believed to significantly contribute to circulating HBsAg levels and HCC development.

Inhibition of SIRT2 promotes death of human cytomegalovirus-infected peripheral blood monocytes via apoptosis and necroptosis.

Peripheral blood monocytes are the cells predominantly responsible for systemic dissemination of human cytomegalovirus (HCMV) and a significant cause of morbidity and mortality in immunocompromised patients. HCMV establishes a silent/quiescent infection in monocytes, which is defined by the lack of viral replication and lytic gene expression. The absence of replication shields the virus within infected monocytes from the current available antiviral drugs that are designed to suppress active replication.

An allosteric inhibitor of sirtuin 2 deacetylase activity exhibits broad-spectrum antiviral activity.

Most drugs used to treat viral disease target a virus-coded product. They inhibit a single virus or virus family, and the pathogen can readily evolve resistance. Host-targeted antivirals can overcome these limitations.

Small-molecule inhibitors that disrupt the MTDH-SND1 complex suppress breast cancer progression and metastasis.

Metastatic breast cancer is a leading health burden worldwide. Previous studies have shown that metadherin (MTDH) promotes breast cancer initiation, metastasis and therapy resistance; however, the therapeutic potential of targeting MTDH remains largely unexplored. Here, we used genetically modified mice and demonstrate that genetic ablation of Mtdh inhibits breast cancer development through disrupting the interaction with staphylococcal nuclease domain-containing 1 (SND1), which is required to sustain breast cancer progression in established tumors.

Optimizing decision tree structures for spectral histopathology (SHP).

This paper reviews methods to arrive at optimum decision tree or label tree structures to analyze large SHP datasets. Supervised methods of analysis can utilize either sequential or (flat) multi-classifiers depending on the variance in the data, and on the number of spectral classes to be distinguished. For small number of spectral classes, multi-classifiers have been used in the past, but for the analysis of datasets containing large numbers (∼20) of disease or tissue types, mixed decision tree structures were found to be advantageous.