In the battle against Alzheimer's disease, early detection is critical to mitigating symptoms in patients. Here, the authors use a collection of MRI scans, layering with deep learning computer modeling, to investigate early stages of AD which can be hard to catch by human eye. Their model is successful, able to outperform previous models, and detected regions of interest in the brain for further consideration.
The number of bacterial infections in humans is rising, and a major contributor is foodborne illnesses, which affect a large portion of the population and result in many hospitalizations and deaths. Common household cleaners are an effective strategy to combat foodborne illness, but they are often costly and contain harmful chemicals. Thus, the authors sought to test the antimicrobial effectiveness of spices (clove, nutmeg, astragalus, cinnamon, turmeric, and garlic) on microbes cultured from refrigerator handles and cutting boards. Results from this study demonstrate long-lasting, antimicrobial effects of multiple spices that support their use as alternatives to common household cleaners.
Agenesis of the Corpus Callosum (ACC) is a birth defect where an infant’s corpus callosum, the structure linking the brain’s two hemispheres to allow interhemispheric communication, fails to develop in a typical manner during pregnancy. Existing research on the connection between ACC and epilepsy leaves significant gaps, due to the lack of focused investigation. One important gap is the degree to which ACC may impact the course of epilepsy treatment and outcomes. The present study was conducted to test the hypotheses that epilepsy is highly prevalent among individuals with ACC, and that those with both ACC and epilepsy have a lower response rate to anticonvulsant drugs than other patients treated with anticonvulsant drugs. A weighted average of epilepsy rates was calculated from a review of existing literature, which supported the hypothesis that epilepsy was more common among individuals with ACC (25.11%) than in the general population (1.2%). An empirical survey administered to 57 subjects or parents of subjects showed that rate of intractable epilepsy among study subjects with both ACC and epilepsy was substantially higher than the rate found in the general population, indicating that individuals with both conditions had a lower response rate to the anticonvulsant drugs. This study contributes novel results regarding the potential for concurrence of ACC and epilepsy to interfere with anticonvulsant drug treatment. We also discuss implications for how medical professionals may use the findings of this study to add depth to their treatment decisions.
Despite major advances in gender equality, men still far outnumber women in science, technology, engineering and math (STEM) professions. The purpose of this project was to determine whether mindset could affect a student’s future career choices and whether this effect differed based on gender. When looking within the gender groups, 86% of females who had a growth mindset were likely to consider a “male” career, whereas only 16% of females with fixed mindset would likely to consider a “male” career. Especially for girls, cultivating a growth mindset may be a great strategy to address the problem of fewer girls picking STEM careers.
Every year, around 40% of undergraduate students in the United States discontinue their studies, resulting in a loss of valuable education for students and a loss of money for colleges. Even so, colleges across the nation struggle to discover the underlying causes of these high dropout rates. In this paper, the authors discuss the use of machine learning to find correlations between the built environment factors and the retention rates of colleges. They hypothesized that one way for colleges to improve their retention rates could be to improve the physical characteristics of their campus to be more pleasing. The authors used image classification techniques to look at images of colleges and correlate certain features like colors, cars, and people to higher or lower retention rates. With three possible options of high, medium, and low retention rates, the probability that their models reached the right conclusion if they simply chose randomly was 33%. After finding that this 33%, or 0.33 mark, always fell outside of the 99% confidence intervals built around their models’ accuracies, the authors concluded that their machine learning techniques can be used to find correlations between certain environmental factors and retention rates.
Since cancer cells inhibit T-cell activity, the authors investigated a method to reverse T-cell disfunction with gene therapy, so that the T-cells would become effective once again in fighting cancer cells. They used the inhibition of proprotein convertases (PCSK1) in T cells and programmed death-ligand 1 (CD274) in cancer cells. They observed the recovery of IL-2 expression in Jurkat cells, with increased recovery noted in a co-culture sample. This study suggests a novel strategy to reactivate T cells.
In this report, Bhardwaj and Sharma tested whether placing specific plants indoors can reduce levels of indoor air pollution that can lead to lung-related illnesses. Using machine learning, they show that plants improved overall indoor air quality and reduced levels of particulate matter. They suggest that plant-based interventions coupled with sensors may be a useful long-term solution to reducing and maintaining indoor air pollution.
Sequence accessibility is an important factor affecting gene expression. Sequence accessibility or openness impacts the likelihood that a gene is transcribed and translated into a protein and performs functions and manifests traits. There are many potential factors that affect the accessibility of a gene. In this study, our hypothesis was that the content of nucleotides in a genetic sequence predicts its accessibility. Using a machine learning linear regression model, we studied the relationship between nucleotide content and accessibility.
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.