Gliomas, which originate from glial cells, are considered the most aggressive type of brain tumors. Gliomas are categorized into four grades based on malignancy with lower-grade gliomas (LGG) being less severe than high grade gliomas (HGG). Current research efforts are focused mainly on HGGs, however LGGs are equally important research targets as they often develop into HGGs. This project aims to analyze MRIs containing LGGs via segmentation and utilize extracted tumor shape features to predict genomic subtypes. Segmentation, the process of outlining tumors in MRIs, is crucial to the diagnosis and treatment of a patient with an LGG. Generally, segmentation is performed manually by radiologists; however, this process is tedious, time-consuming, and often leads to inter-observer variability. Once an LGGs segmentation is complete, invasive biopsies and genetic tests such as RNAseq, miRNA sequencing, and RPPA, etc. are performed on the tumor cells. Recent studies suggest strong correlation between tumor shape and genomic subtypes stemming from the genetic tests. These relationships could be leveraged to devise a novel noninvasive method of identifying subtypes, which play a large role in diagnosis, treatment, and prognosis of LGGs. The specific aims of this project included analyzing LGGs through deep learning-based segmentation and shape feature extraction. Then, these shape features were utilized to predict the genomic subtypes of the LGG. For automatic segmentation, two models were created using different convolutional neural networks (CNN). The highest performing model, which used a U-Net with a ResNeXt-50 backbone, yielded a 91.4% accuracy after testing. Shape feature extraction included computation of seven features which quantified tumor shape in 2D and 3D. Among the seven genetic tests of interest, six were shown to be correlated with shape features. These relationships were utilized in a deep-learning classifier which was capable of predicting genomic subtypes with accuracies between 60% and 80%. Altogether, this novel approach only requires patient MRIs for LGG segmentation and genomic subtype classification. Furthermore, this technology could increase time and cost efficiency, assist radiologists, and reduce the need for invasive diagnostic tests.
Kay’s rule was used to calculate the pseudocritical temperature and pressure as well as the acentric factor for several compositions of toluene/ethanol mixture. An in-house Python utility was used to calculate the isothermal compressibility based on the Peng-Robinson equation of state and resulting pseudocritical temperature, pseudocritical pressure, and acentric factor. Calculated isothermal compressibility was used in isothermal-isobaric molecular dynamics simulations at several compositions to predict density, molar volume, and excess volume which were then compared to literature values. The use of Kay’s rule with the Peng-Robinson equation of state may be sufficient for thermophysical property prediction of non-ideal binary mixtures, given that accurate molecular dynamics parameters are used. Qualitative conclusions for positive or negative deviations can likely be drawn based on simulations.
Alzheimer’s disease (AD) is a progressive neurodegenerative disease that impairs memory, cognitive function, and decreases the life expectancy in aging populations world-wide. It the leading cause of dementia and is associated with a significant social and economic burden. This multifactorial disease is associated with many genetic, epigenetic, social, and lifestyle risk factors, as well as many mechanisms and pathways for pathology development. One defining characterization of AD is amyloid β deposition. Tau pathology, or tauopathy, is another hallmark feature of AD. Tauopathies are characterized by specific strains of abnormal tau, a prion-like spread of pathology, and insoluble, filamentous tau deposits. Tau proteins are a native protein that help stabilize microtubules in the cell. There are many mechanisms that result in aberrant tau proteins and their propagation in tauopathies. There is compelling evidence that supports these findings on tau strains and the fidelity of their proliferation or spread. This review focuses on the prion-like mechanisms of tau strains, discussing the multiple mechanisms at play, the connection to prion pathology, the structural features of distinct tau fibrils, the variety of clinical behaviors, and current detection methods.
Pathogen detection is currently limited by a lack of widespread access to equipment that is portable and accurate. Point of care detection techniques strive to make detection more accessible for healthcare applications. One example is a lab-on-a-chip device which detects changes in properties of the chip, including electrical resistance and optical absorbance, after running a sample through the channel. Substrates in the chip would bind to biomarkers of interest from the sample, and thus change the overall properties of the chip. These devices are often effective in either detecting molecules in a sample (called sensitivity) or differentiating similar molecules (called selectivity). However, they may fail to achieve high sensitivity and selectivity simultaneously, thus reducing accuracy by either creating false negatives or false positives, respectively. In order to achieve higher sensitivity a new technique is being developed, in which carbon nanotubes (CNT) or other materials are packed into the channel, creating a web through which the sample has to navigate. This creates turbulent flow to enhance mixing and creates more active locations for the biomarkers to bind. Additionally it enhances the shear force of the device, thus improving selectivity by washing away different molecules with similar structures.