Comparative modeling and benchmarking data sets for human histone deacetylases and sirtuin families.

Histone deacetylases (HDACs) are an important class of drug targets for the treatment of cancers, neurodegenerative diseases, and other types of diseases. Virtual screening (VS) has become fairly effective approaches for drug discovery of novel and highly selective histone deacetylase inhibitors (HDACIs). To facilitate the process, we constructed maximal unbiased benchmarking data sets for HDACs (MUBD-HDACs) using our recently published methods that were originally developed for building unbiased benchmarking sets for ligand-based virtual screening (LBVS). The MUBD-HDACs cover all four classes including Class III (Sirtuins family) and 14 HDAC isoforms, composed of 631 inhibitors and 24609 unbiased decoys. Its ligand sets have been validated extensively as chemically diverse, while the decoy sets were shown to be property-matching with ligands and maximal unbiased in terms of "artificial enrichment" and "analogue bias". We also conducted comparative studies with DUD-E and DEKOIS 2.0 sets against HDAC2 and HDAC8 targets and demonstrate that our MUBD-HDACs are unique in that they can be applied unbiasedly to both LBVS and SBVS approaches. In addition, we defined a novel metric, i.e. NLBScore, to detect the "2D bias" and "LBVS favorable" effect within the benchmarking sets. In summary, MUBD-HDACs are the only comprehensive and maximal-unbiased benchmark data sets for HDACs (including Sirtuins) that are available so far. MUBD-HDACs are freely available at http://www.xswlab.org/ .

Using mHealth to Improve Timeliness and Quality of Maternal and Newborn Health in the Primary Health Care System in Ethiopia.

The Last Ten Kilometers 2020 Project (L10K 2020) designed a strategy for piloting, implementing, and scaling a mobile health (mHealth) digital solution to improve the quality of community-level maternal and child health service delivery in Ethiopia. L10K 2020 first conducted a landscape assessment to design a context-appropriate smartphone-based mHealth solution for the Health Extension Workers and tablets for their supervisors and the midwives managing the same clients at the health center level. These applications included multiple modules and packages including client registration and appointment management; follow-up and notifications; digital job aids for each of the maternal and child health program packages (for Health Extension Workers only); and referral and client tracking systems.Findings from the process evaluation of the mHealth app usage and user experience indicated that the application was user-friendly and facilitated real-time information exchange, defaulter tracing, referral, and feedback systems. It improved the timely identification and registration of pregnant mothers. Adherence to treatment protocols also increased in all domains across the pregnancy continuum of care.L10K 2020 has developed a user-friendly model for implementing mHealth solutions at the community level through stakeholder engagement across levels when developing, testing, and deploying the applications, which was critical to effectively cultivating ownership as well as skills and knowledge transfer at all levels. To replicate and scale this model, context-based scoping, resource analysis, and mapping are essential to determine the infrastructure, cost, time, risks, and key stakeholders involved throughout the implementation of the intervention. During implementation, vigilance in consistently mitigating the challenges related to mHealth infrastructure, such as mobile data capacity, electricity, smartphones and tablets, solar chargers, and internet connectivity, is critical for continued success.