Detection of Target Genes for Drug Repurposing to Treat Skeletal Muscle Atrophy in Mice Flown in Spaceflight

Exploring the complexities of drug formulation selection, storage, and shelf-life for exploration spaceflight

Medications have been a part of space travel dating back to the Apollo missions. Currently, medical kits aboard the International Space Station (ISS) contain medications and supplies to treat a variety of possible medical events. As we prepare for more distant exploration missions to Mars and beyond, risk management planning for astronaut healthcare should include the assembly of a medication formulary that is comprehensive enough to prevent or treat anticipated medical events, remains safe and chemically stable, and retains sufficient potency to last for the duration of the mission. Emerging innovation and technologies in pharmaceutical development, delivery, quality maintenance, and validation offer promise for addressing these challenges. The present editorial will summarize the current state of knowledge regarding innovative formulary optimization strategies, pharmaceutical stability assessment techniques, and storage and packaging solutions that could enhance drug safety and efficacy for future exploration spaceflight missions.

Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities

The incorporation of data analytics in the healthcare industry has made significant progress, driven by the demand for efficient and effective big data analytics solutions. Knowledge graphs (KGs) have proven utility in this arena and are rooted in a number of healthcare applications to furnish better data representation and knowledge inference. However, in conjunction with a lack of a representative KG construction taxonomy, several existing approaches in this designated domain are inadequate and inferior. This paper is the first to provide a comprehensive taxonomy and a bird's eye view of healthcare KG construction. Additionally, a thorough examination of the current state-of-the-art techniques drawn from academic works relevant to various healthcare contexts is carried out. These techniques are critically evaluated in terms of methods used for knowledge extraction, types of the knowledge base and sources, and the incorporated evaluation protocols. Finally, several research findings and existing issues in the literature are reported and discussed, opening horizons for future research in this vibrant area.

Patient-Oriented Herb Recommendation System Based on Multi-Graph Convolutional Network

The presented herb recommendation system aims to analyze the patients’ symptoms and recommends a set of herbs as the prescription to treat diseases. In addition to symptoms, the patients’ personal properties and induced diagnoses are also essential for treatment making. Specifically, for different age groups, the treatments are different. However, the existing studies only use symptoms to represent patients and ignore the patients’ multidimensional features modeling. Thus, these models are insufficiently personalized. Meanwhile, most of these existing herb recommendation models based on graphs have not distinguished the effects of different node types. To address the above limitations, we propose a model named Patient-Oriented Multi-Graph Convolutional Network-based Herb Recommendation system (PMGCN). The prediction model contains two effective modules, patient portraits modeling and herb interactions modeling, to learn representations for patients and enhance herb interactions. First, we depict the patient portrait to enrich the individualized features. To distinguish personal properties, symptoms, and diagnoses, we adopt the type-aware attention mechanism, thereby improving the accuracy of personalized herb recommendation. Next, we build two herb-interaction graphs and design type-aware multigraph convolution networks to capture the interactions of herbs and patient features. In this way, our model emphasizes the impact of the patient portrait on diagnosis induction and herb selection. Experimental studies demonstrate that our method outperforms the compared methods and confirms the significance of patient portraits. In conclusion, this research proposes type-aware multigraph convolution networks and adds patient portraits modeling to simulate TCM prescriptions making.