COVID-19 portal: Profiling researchers, bio-entities, and institutions

The outbreak of COVID-19 has a severe impact on our families, communities, and businesses. White House released the COVID19 literature dataset (called CORD-19 dataset) which has grown exponentially into a gigantic collection of over 500,000 articles. Researchers, practitioners, and administrators need a tool to help them digest this enormous amount of knowledge to address various scientific questions related to COVID-19. This paper showcases the COVID-19 portal to portray the research profiles of scientists, bio entities (e.g., gene, drug, disease), and institutions based on the integration of CORD-19 research literature, COVID-19 related clinical trials, PubMed knowledge graph, and the drug discovery knowledge graph. This portal provides the following profiles related to COVID-19: 1) the profile of a research scientist with his/her COVID-19 related publications and clinical trials which can be ranked by year or by the number of tweets; 2) the profile of a bio entity which could be a gene, a drug, or a disease with articles and clinical trials mentioned this bio entity; and 3) the profile of an institution with papers authored by researchers from this institution

Reversal of cancer gene expression identifies repurposed drugs for diffuse intrinsic pontine glioma

Diffuse intrinsic pontine glioma (DIPG) is an aggressive incurable brainstem tumor that targets young children. Complete resection is not possible, and chemotherapy and radiotherapy are currently only palliative. This study aimed to identify potential therapeutic agents using a computational pipeline to perform an in silico screen for novel drugs. We then tested the identified drugs against a panel of patient-derived DIPG cell lines. Using a systematic computational approach with publicly available databases of gene signature in DIPG patients and cancer cell lines treated with a library of clinically available drugs, we identified drug hits with the ability to reverse a DIPG gene signature to one that matches normal tissue background. The biological and molecular effects of drug treatment was analyzed by cell viability assay and RNA sequence. In vivo DIPG mouse model survival studies were also conducted. As a result, two of three identified drugs showed potency against the DIPG cell lines Triptolide and mycophenolate mofetil (MMF) demonstrated significant inhibition of cell viability in DIPG cell lines. Guanosine rescued reduced cell viability induced by MMF. In vivo, MMF treatment significantly inhibited tumor growth in subcutaneous xenograft mice models. In conclusion, we identified clinically available drugs with the ability to reverse DIPG gene signatures and anti-DIPG activity in vitro and in vivo. This novel approach can repurpose drugs and significantly decrease the cost and time normally required in drug discovery

Comparison of Supervised Machine Algorithms by Classifying a Cardiotocography Data Set

PURPOSE: To compare the performance and visualize the results of five different Supervised Machine Learning algorithms by classifying Cardiotocography dataset. SUBJECTS: Cardiotocography is a technique to record the fetal heart rate and uterine contractions during pregnancy to examine the maternal and fetal health status. The UCI Machine Learning Repository Cardiotocography dataset contains 2126 automatically processed cardiotocograms with 21 attributes. The two-way classification of the dataset as 10-class morphological patterns and 3-class fetal status was done by three expert obstetricians. The 10-class classification was attempted in this project. METHODS AND MATERIALS: Five different classification models based on Recursive Partitioning, Random Forest, Conditional Inference Trees, Linear Discriminant Analysis and Naïve Bayes were built. 70-30% data-splitting was used for Training-Testing process. The performances of models’ were compared in terms of accuracy and Kappa value. Confusion-matrices were converted to heat map for visual assessment of individual model performance. Visual comparison of models was done by plotting class mismatch percentages across every model. R statistical programming and Tableau software were used for model building and visualization respectively. RESULTS: RandomForest model shown highest accuracy (86%) and kappa (.84) whereas Naive Bayes model showed lowest accuracy (55%) and Kappa (0.49). Heat map visualization of individual algorithms and class-wise mismatch percentages of every model aided in the analysis. CONCLUSION: RandomForest algorithm has potential to classify future cardiotocography datasets. Visualization techniques such as Heatmap and Mismatch plotting should be considered while assessing the performance of the multi-class classifier

Impact of EHR Usability on Provider Efficiency and Patient Safety

Healthcare organizations may reap substantial benefits when transitioning to electronic health records (EHRs), such as decreased healthcare costs and better care. However, severe unintended consequences from implementation and design of these systems have emerged. Poorly implemented EHR systems may endanger the integrity of clinical or administrative data. That in turn can lead to errors that may jeopardize patient safety or decrease quality of care. In addition, poor design quality of EHRs can significantly increase the mental workload of clinicians, thereby increasing frustration, reducing user satisfaction, and causing unproductive workarounds. Our literature review identified how EHR implementation and design can impact clinical use, workload, patient safety, and quality. This literature review contributes to efforts on how to improve accuracy, reliability, and integrity of healthcare information as stored in EHRs

The Relationship between Leptin, the Leptin Receptor and FGFR1 in Primary Human Breast Tumors

Obesity is associated with increased breast cancer risk and poorer cancer outcomes; however, the precise etiology of these observations has not been fully identified. Our previous research suggests that adipose tissue-derived fibroblast growth factor-2 (FGF2) promotes the malignant transformation of epithelial cells through the activation of fibroblast growth factor receptor-1 (FGFR1). FGF2 is increased in the context of obesity, and increased sera levels have been associated with endocrine-resistant breast cancer. Leptin is a marker of obesity and promotes breast carcinogenesis through several mechanisms. In this study, we leverage public gene expression datasets to evaluate the associations between FGFR1, leptin, and the leptin receptor (LepR) in breast cancer. We show a positive association between FGFR1 and leptin protein copy number in primary breast tumors. These observations coincided with a positive association between Janus kinase 2 (Jak2) mRNA with both leptin receptor (LepR) mRNA and FGFR1 mRNA. Moreover, two separate Jak2 inhibitors attenuated both leptin+FGF2-stimulated and mouse adipose tissue-stimulated MCF-10A transformation. These results demonstrate how elevated sera FGF2 and leptin in obese patients may promote cancer progression in tumors that express elevated FGFR1 and LepR through Jak2 signaling. Therefore, Jak2 is a potential therapeutic target for FGFR1 amplified breast cancer, especially in the context of obesity