Coronary Artery Disease (CAD) is the leading cause of death in the United States, and 81% of Acute Kidney Injury (AKI) patients in the renal fibrosis stage later develop CAD. In this study, Mathew and Joykutty aimed to create a cost-effective strategy to treat AKI and thus prevent CAD using a model of the zebrafish, Danio rerio. They first tested whether AKI is induced in Danio rerio upon exposure to environmental toxins, then evaluated nitrotyrosine as an early biomarker for toxin-induced AKI. Finally, they evaluated 4 treatments of renal fibrosis, the last stage of AKI, and found that the compound SB431542 was the most effective treatment (reduced fibrosis by 99.97%). Their approach to treating AKI patients, and potentially prevent CAD, is economically feasible for translation into the clinic in both developing and developed countries.

One of the greatest challenges we face today is the sustainable production, storage, and distribution of electrical power. One emerging technology with great promise in this area is that of metal-air fuel cells—a long-term and reusable electricity storage system made from a reactive metal anode and a saline solution. In this study the authors tested several different types of metal to determine which was the most suitable for this application. They found that a fuel cell with a magnesium anode was superior to fuel cells made from aluminum or zinc, producing a voltage and current sufficient for real-world applications such as charging a mobile phone.

Here, seeking to identify a possible explanation for the more frequent diagnosis of autism spectrum disorder (ASD) in males than females, they sought to investigate a potential sex bias in the expression of ASD-associated genes. Based on their analysis, they identified 17 ASD-associated candidate genes that showed stronger collective sex-dependent expression.

Many common respiratory illnesses like bronchitis, asthma, and chronic obstructive pulmonary disease (COPD) lead to bronchial inflammation and, subsequently, a blockage. However, there are many difficulties in measuring the severity of the blockage. A numeric metric to determine the degree of the blockage severity is necessary. To tackle this demand, we aimed to develop a novel human respiratory model and design a deep-learning program that can constantly monitor and report bronchial blockage by recording breath sounds in a non-intrusive way.

Spinal degeneration has been linked to critical conditions such as osteoarthritis in adults aged 40+; while this condition is considered to be irreversible, we took interest in magnetic resonance imaging (MRI) for early detection of the condition. Ultimately, our purpose was to determine the effectiveness of a relatively novel T1rho method in the early detection of spinal degeneration, and we hypothesized that the early to mild progression of spinal degeneration would affect T1rho values following an MRI scan.

Poor air quality is associated with negative effects on human health but can be difficult to measure in an accurate and cost-effective manner. The authors design and test a monitor for measuring indoor air quality using low-cost components.

In this study the authors looked at the ability of artificial intelligence to detect tempo, rhythm, and intonation of a piece played on violin. Technology such as this would allow for students to practice and get feedback without the need of a teacher.

The authors seek to accelerate wound healing by reducing inflammation with a cocktail containing growth factors and bioactive modulators.

The authors designed a system that runs off of body heat to track body temperature that could help prevent injuries that result from elevated body temperature.

Current drug discovery processes can cost billions of dollars and usually take five to ten years. People have been researching and implementing various computational approaches to search for molecules and compounds from the chemical space, which can be on the order of 1060 molecules. One solution involves deep generative models, which are artificial intelligence models that learn from nonlinear data by modeling the probability distribution of chemical structures and creating similar data points from the trends it identifies. Aiming for faster runtime and greater robustness when analyzing high-dimensional data, we designed and implemented a Hybrid Quantum-Classical Generative Adversarial Network (QGAN) to synthesize molecules.