This commentary reflects on the launch of the new, state-of-the-art, mixed-ownership Guiqian International General Hospital in Guiyang and explores its potential as a template for digitally enabled hospitals of the future and economic development hubs.

Biomechanical models of blood flows in the last century were devoted to the handling of flow pattern in simple models more or less representative of rigid and deformable segments of the vasculature with bends and branchings, in normal and pathological conditions. With the development of medical three-dimensional (3D) imaging techniques, availability of 3D reconstruction software, improved fluid–structure interaction methods, and high-performance computing, computational models of blood flows are now aimed at guiding medical decision in the framework of precision medicine. However, most often, input data remain unavailable for a given patient, especially those related to blood and vascular wall rheology. This review summarizes the main blood flow features and yields the state-of-the-art.

Precision medicine is based on the premise that genomic analysis radically changes the nature and scope of therapeutic medicine. While genome sequencing has revolutionized the study of human disease, to date progress in translating genomic knowledge into treatments for inherited disorders has been less apparent. However, increasing attention on the relationship(s) between rare diseases (RDs) and precision medicine should significantly accelerate this process, as evidenced by the increasing numbers of molecular therapies receiving approval from the U.S. Food and Drug Administration. There are an estimated 7000 RDs, a majority of which affect <1/million people; but, collectively, it has been calculated that in high-income countries ≥1/17 persons have a RD. RDs represent both a significant health challenge and a major economic burden for many affected individuals and their families, and although many are life-threatening, they often remain undiagnosed or misdiagnosed. Unfortunately, global progress in resolving these issues has been hindered by basic problems such as differing national and regional definitions, from a RD prevalence of 1 to 5/10,000 affected persons in Europe and Australia to 1/500,000 in China. There also has been failure to recognize that many RDs originated as founder mutations and are community-specific, an especially important consideration in populations where community endogamy is the rule and close-kin marriage is traditional. The near-global transition from a communicable to a predominantly noncommunicable disease profile has, however, served to highlight the importance of genetics in medicine, and resulted in a rapidly growing focus on RDs. Against this background, collaborative international programs to facilitate the prevention and curative treatment of RDs merit urgent adoption and support.

Background and Objectives: Nonintrusive heart rate (HR) monitoring can be a useful tool for health monitoring. By creating capacitively coupled textile electrodes, a comfortable monitoring system can be integrated into seating or bedding that can monitor HR through clothing. This work empirically studied two factors for a system of this type: the electrode size and the material worn by the subject. Materials and Methods: HR measurements were taken using six different sizes of the rectangular textile electrode with four subjects and the signal-to-noise ratio (SNR) of the signals were analyzed. A further set of experiments were conducted with a single subject and a fixed electrode size where different materials were worn. Results: Electrode size was seen to have a statistically insignificant effect on the collected signal quality. The SNR was also largely unaffected by the worn material type. Conclusion: This study provided empirical data relating to two important factors for nonintrusive, textile, and HR monitoring systems. This data will be helpful for designing a seat-based HR monitoring system or to understand the operational limitations of a system of this type.

Background: A decline in physical activity (PA) during pregnancy, despite its benefits to the mother and fetus, invites concerns for innovative platforms for its implementation. This cross-sectional study assessed knowledge, attitude, and “willingness to use” and “willingness to pay” for smartphone applications (apps) for PA. Materials and Methods: A total of 196 consenting pregnant women participated in this study. Eligible respondents were pregnant women who were 18 years and older, on at least a second antenatal visit and uses a mobile phone. Three purposively selected antenatal care facilities were surveyed. A self-developed questionnaire pilot tested for face and content validity was used as the research tool. Results: The mean age of the respondents was 27.5 ± 3.42 years, and 52% of them were occasionally engaged in PA. The point prevalence for smartphone use for general purpose and the usability rate of smartphone app were 72.4% and 84.2%, respectively. The rates for willingness to use PA apps for pregnancy and willingness to pay for the apps were 64.3% and 63.8%, respectively. There was a significant association between the attitude of respondents toward smartphone apps use for PA and number of parity (χ² = 7.119; P = 0.028). There was no significant association between knowledge of the use of smartphone apps for PA in pregnancy and each of the educational qualification (χ² = 13.046; P = 0.523), income (χ² = 11.086; P = 0.679), age (χ² = 4.552; P = 0.804), gravidity status (χ² = 5.302; P = 0.506), and number of parity (χ² = 1.878; P = 0.758). Conclusion: Nigerian pregnant women have good knowledge, positive attitude, and willingness to use smartphone apps for PA in pregnancy. There was a significant association between the number of parity and each of the attitude and knowledge of the use of smartphone apps for PA.

Background and Objectives: Precision medicine and drug repurposing provide an opportunity to ameliorate the challenges of declining pharmaceutical R&D productivity, rising costs of new drugs, and poor patient response rates to existing medications. Multifactorial “disease signatures” provide unique insights into the architecture of complex disease populations that can be used to better stratify patient groups, aiding the delivery of precision medicine. Methods: Analysis of a complex disease (breast cancer) population was undertaken to identify the combinations of single-nucleotide polymorphisms that are associated with different disease subgroups. Target genes associated with the disease risk of these subgroups were examined, followed by identification and evaluation of existing active chemical leads as drug repurposing candidates. Results: One hundred and seventy-five disease-associated gene targets relevant to different subpopulations of breast cancer patients were identified. Twenty-three of these genes were prioritized as both promising novel drug targets and repurposing candidates. Two targets, P4HA2 and TGM2, have high repurposing potential and a strong mechanistic link to breast cancer. Conclusions: This study showed that detailed analysis of combinatorial genomic (and other) features can be used to accurately stratify patient populations and identify highly plausible drug repurposing candidates systematically across all disease-associated targets.