Search for: All records

Abstract The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure. Although various DT initiatives have been underway in the industry, government, and military, DT4H is still in its early stages. This paper presents an overview of the current applications of DTs in healthcare, examines consortium research centers and their limitations, and surveys the current landscape of emerging research and development opportunities in healthcare. We envision the emergence of a collaborative global effort among stakeholders to enhance healthcare and improve the quality of life for millions of individuals worldwide through pioneering research and development in the realm of DT technology. 

Abstract The rise of antibiotic resistance motivates a revived interest in phage therapy. However, bacteria possess dozens of anti-bacteriophage immune systems that confer resistance to therapeutic phages. Chemical inhibitors of these anti-phage immune systems could be employed as adjuvants to overcome resistance in phage-based therapies. Here, we report that anti-phage systems can be selectively inhibited by small molecules, thereby sensitizing phage-resistant bacteria to phages. We discovered a class of chemical inhibitors that inhibit the type II Thoeris anti-phage immune system. These inhibitors block the biosynthesis of a histidine-ADPR intracellular ‘alarm’ signal by ThsB and prevent ThsA from arresting phage replication. These inhibitors promiscuously inhibit type II Thoeris systems from diverse bacteria—including antibiotic-resistant pathogens. Chemical inhibition of the Thoeris defense improved the efficacy of a model phage therapy against a phage-resistant strain ofP. aeruginosain a mouse infection, suggesting a therapeutic potential. Furthermore, these inhibitors may be employed as chemical tools to dissect the importance of the Thoeris system for phage defense in natural microbial communities. 

Oxidation of the sub-arc mantle driven by slab-derived fluids has been hypothesized to contribute to the formation of gold deposits in magmatic arc environments that host the majority of metal resources on Earth. However, the mechanism by which the infiltration of slab-derived fluids into the mantle wedge changes its oxidation state and affects Au enrichment remains poorly understood. Here, we present the results of a numerical model that demonstrates that slab-derived fluids introduce large amounts of sulfate (S⁶⁺) into the overlying mantle wedge that increase its oxygen fugacity by up to 3 to 4 log units relative to the pristine mantle. Our model predicts that as much as 1 wt.% of the total dissolved sulfur in slab-derived fluids reacting with mantle rocks is present as the trisulfur radical ion, S₃^–. This sulfur ligand stabilizes the aqueous Au(HS)S₃^–complex, which can transport Au concentrations of several grams per cubic meter of fluid. Such concentrations are more than three orders of magnitude higher than the average abundance of Au in the mantle. Our data thus demonstrate that an aqueous fluid phase can extract 10 to 100 times more Au than in a fluid-absent rock-melt system during mantle partial melting at redox conditions close to the sulfide-sulfate boundary. We conclude that oxidation by slab-derived fluids is the primary cause of Au mobility and enrichment in the mantle wedge and that aqueous fluid-assisted mantle melting is a prerequisite for formation of Au-rich magmatic hydrothermal and orogenic gold systems in subduction zone settings. 

Abstract Survival and second malignancy prediction models can aid clinical decision making. Most commonly, survival analysis studies are performed using traditional proportional hazards models, which require strong assumptions and can lead to biased estimates if violated. Therefore, this study aims to implement an alternative, machine learning (ML) model for survival analysis: Random Survival Forest (RSF). In this study, RSFs were built using the U.S. Surveillance Epidemiology and End Results to (1) predict 30-year survival in pediatric, adolescent, and young adult cancer survivors; and (2) predict risk and site of a second tumor within 30 years of the first tumor diagnosis in these age groups. The final RSF model for pediatric, adolescent, and young adult survival has an average Concordance index (C-index) of 92.9%, 94.2%, and 94.4% and average time-dependent area under the receiver operating characteristic curve (AUC) at 30-years since first diagnosis of 90.8%, 93.6%, 96.1% respectively. The final RSF model for pediatric, adolescent, and young adult second malignancy has an average C-index of 86.8%, 85.2%, and 88.6% and average time-dependent AUC at 30-years since first diagnosis of 76.5%, 88.1%, and 99.0% respectively. This study suggests the robustness and potential clinical value of ML models to alleviate physician burden by quickly identifying highest risk individuals. 

Abstract Objective. This study proposes and evaluates a new figure of merit (FOMn) for dose optimization of Dual-energy cone-beam CT (DE-CBCT) scanning protocols based on size-dependent modeling of radiation dose and multi-scale image quality.Approach. FOMn was defined using Z-score normalization and was proportional to the dose efficiency providing better multi-scale image quality, including comprehensive contrast-to-noise ratio (CCNR) and electron density (CED) for CatPhan604 inserts of various materials. Acrylic annuluses were combined with CatPhan604 to create four phantom sizes (diameters of the long axis are 200 mm, 270 mm, 350 mm, and 380 mm, respectively). DE-CBCT was decomposed using image-domain iterative methods based on Varian kV-CBCT images acquired using 25 protocols (100 kVp and 140 kVp combined with 5 tube currents).Main results. The accuracy of CED was approximately 1% for all protocols, but degraded monotonically with the increased phantom sizes. Combinations of lower voltage + higher current and higher voltage + lower current were optimal protocols balancing CCNR and dose. The most dose-efficient protocols for CED and CCNR were inconsistent, underlining the necessity of including multi-scale image quality in the evaluation and optimization of DE-CBCT. Pediatric and adult anthropomorphic phantom tests confirmed dose-efficiency of FOMn-recommended protocols.Significance. FOMn is a comprehensive metric that collectively evaluates radiation dose and multi-scale image quality for DE-CBCT. The models and data can also serve as lookup tables, suggesting personalized dose-efficient protocols for specific clinical imaging purposes.