Data Science Commons^™

Inpatient Costs Of Treating Patients With Covid-19, Kandice A Kapinos, Richard M Peters, Robert E Murphy, Samuel F Hohmann, Ankita Podichetty, Raymond S Greenberg

Faculty, Staff and Student Publications

IMPORTANCE: With more than 6.2 million hospitalizations due to COVID-19 in the US, recognition of the average hospital costs to provide inpatient care during the pandemic is necessary to understanding the national medical resource use and improving public health readiness and related policies.

OBJECTIVE: To examine the mean cost to provide inpatient care to treat COVID-19 and how it varied through the pandemic waves and by important sociodemographic patient characteristics.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used inpatient-level data from March 1, 2020, to March 31, 2022, extracted from a repository of clinical, administrative, and financial information covering 97% …

Erratum: Toward Standardization, Harmonization, And Integration Of Social Determinants Of Health Data: A Texas Clinical And Translational Science Award Institutions Collaboration - Corrigendum, Catherine K Craven, Linda Highfield, Mujeeb Basit, Elmer V Bernstam, Byeong Yeob Choi, Robert L Ferrer, Jonathan A Gelfond, Sandi L Pruitt, Vaishnavi Kannan, Paula K Shireman, Heidi Spratt, Kayla J Torres Morales, Chen-Pin Wang, Zhan Wang, Meredith N Zozus, Edward C Sankary, Susanne Schmidt

Faculty, Staff and Student Publications

This corrects the article "Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration" in volume 8, e17.

A Real-World Disproportionality Analysis Of Everolimus: Data Mining Of The Public Version Of Fda Adverse Event Reporting System, Bin Zhao, Yumei Fu, Shichao Cui, Xiangning Chen, Shu Liu, Lan Luo

Faculty, Staff and Student Publications

Background: Everolimus is an inhibitor of the mammalian target of rapamycin and is used to treat various tumors. The presented study aimed to evaluate the Everolimus-associated adverse events (AEs) through data mining of the US Food and Drug Administration Adverse Event Reporting System (FAERS).

Methods: The AE records were selected by searching the FDA Adverse Event Reporting System database from the first quarter of 2009 to the first quarter of 2022. Potential adverse event signals were mined using the disproportionality analysis, including reporting odds ratio the proportional reporting ratio the Bayesian confidence propagation neural network and the empirical Bayes geometric …

Prioritizing Clinically Significant Lung Cancer Somatic Mutations For Targeted Therapy Through Efficient Ngs Data Filtering System, Jinlian Wang, Hui Li, Hongfang Liu

Faculty, Staff and Student Publications

In the realm of lung cancer treatment, where genetic heterogeneity presents formidable challenges, precision oncology demands an exacting approach to identify and hierarchically sort clinically significant somatic mutations. Current Next-Generation Sequencing (NGS) data filtering pipelines, while utilizing various external databases for mutation screening, often fall short in comprehensive integration and flexibility needed to keep pace with the evolving landscape of clinical data. Our study introduces a sophisticated NGS data filtering system, which not only aggregates but effectively synergizes diverse data sources, encompassing genetic variants, gene functions, clinical evidence, and an extensive body of literature. This system is distinguished by a …

Community Scientist Program Provides Bi-Directional Communication And Co-Learning Between Researchers And Community Members, Jessica Alvarado, Larkin L Strong, Birnur Buzcu-Guven, Leonetta B Thompson, Erica Cantu, Chelsea C Carrier, Chiamaka D Chukwu, Cassandra L Harris, Luz K Melendez, Crystal L Roberson, Angela M Ross, Sophia C Russell, Pablo Sanchez, Amirali Tahanan, Blair C Zdenek, Belinda M Reininger, Lorna H Mcneill

Faculty, Staff and Student Publications

Community involvement in research is key to translating science into practice, and new approaches to engaging community members in research design and implementation are needed. The Community Scientist Program, established at the MD Anderson Cancer Center in Houston in 2018 and expanded to two other Texas institutions in 2021, provides researchers with rapid feedback from community members on study feasibility and design, cultural appropriateness, participant recruitment, and research implementation. This paper aims to describe the Community Scientist Program and assess Community Scientists' and researchers' satisfaction with the program. We present the analysis of the data collected from 116 Community Scientists …

Visualsphere: A Web-Based Interactive Visualization System For Clinical Research Data, Shiwei Lin, Shiqiang Tao, Wei-Chun Chou, Guo-Qiang Zhang, Xiaojin Li

Faculty, Staff and Student Publications

Clinical research data visualization is integral to making sense of biomedical research and healthcare data. The complexity and diversity of data, along with the need for solid programming skills, can hinder advances in clinical research data visualization. To overcome these challenges, we introduce VisualSphere, a web-based interactive visualization system that directly interfaces with clinical research data repositories, streamlining and simplifying the visualization workflow. VisualSphere is founded on three primary component modules: Connection, Configuration, and Visualization. An end-user can set up connections to the data repositories, create charts by selecting the desired tables and variables, and render visualization dashboards generated by …

Predicting Endothelium-Dependent Diastolic Function (Fmd)And Its Correlation With The Degree Of Coronary Artery Disease (Cad) And Plaque Vulnerability For Cardiovascular Events, Guangming Zhang, Jing Yang, Hanghang Xing, Hongning Yin, Guoqing Gu

Faculty, Staff and Student Publications

OBJECTIVE: This study aims to investigate the correlation between vascular endothelium-dependent diastolic function (FMD) and the degree of coronary artery disease (CAD), plaque vulnerability, and its predictive value for cardiovascular events.

METHODS: Initially, patients (n=100) who were admitted from January 2020 to January 2021 and intended to undergo percutaneous coronary intervention (PCI) were selected. Further, FMD in all patients was determined before the procedure and divided into a high-FMD group (≥4.2%) and a low-FMD group (

RESULTS: No significant differences were observed concerning general information, number of coronary arteries-associated branches, lesion type, involvement of the left main stem (LM), the …

An Automated Approach For Identifying Erroneous Is-A Relations In Snomed Ct, Ran Hu, Jay Shi, Licong Cui, Rashmie Abeysinghe

Faculty, Staff and Student Publications

SNOMED CT is the most comprehensive clinical terminology employed worldwide and enhancing its accuracy is of utmost importance. In this work, we introduce an automated approach to identifying erroneous IS-A relations in SNOMED CT. We first extract linked concept-pairs from which we generate Term Difference Pairs (TDPs) that contain differences between the concepts. Given a TDP, if the reversed TDP also exists and the number of linked-pairs generating this TDP is less than those generating the reversed TDP, then we suggest the former linked-pairs as potentially erroneous IS-A relations. We applied this approach to the Clinical finding and Procedure subhierarchies …

Linking Cancer Clinical Trials To Their Result Publications, Evan Pan, Kirk Roberts

Faculty, Staff and Student Publications

The results of clinical trials are a valuable source of evidence for researchers, policy makers, and healthcare professionals. However, online trial registries do not always contain links to the publications that report on their results, instead requiring a time-consuming manual search. Here, we explored the application of pre-trained transformer-based language models to automatically identify result-reporting publications of cancer clinical trials by computing dense vectors and performing semantic search. Models were fine-tuned on text data from trial registry fields and article metadata using a contrastive learning approach. The best performing model was PubMedBERT, which achieved a mean average precision of 0.592 …

Xuebijing Improves Intestinal Microcirculation Dysfunction In Septic Rats By Regulating The Vegf-A/Pi3k/Akt Signaling Pathway, A-Ling Tang, Yan Li, Li-Chao Sun, Xiao-Yu Liu, Nan Gao, Sheng-Tao Yan, Guo-Qiang Zhang

Faculty, Staff and Student Publications

BACKGROUND: This study aims to explore whether Xuebijing (XBJ) can improve intestinal microcirculation dysfunction in sepsis and its mechanism.

METHODS: A rat model of sepsis was established by cecal ligation and puncture (CLP). A total of 30 male SD rats were divided into four groups: sham group, CLP group, XBJ + axitinib group, and XBJ group. XBJ was intraperitoneally injected 2 h before CLP. Hemodynamic data (blood pressure and heart rate) were recorded. The intestinal microcirculation data of the rats were analyzed via microcirculation imaging. Enzyme-linked immunosorbent assay (ELISA) kits were used to detect the serum levels of interleukin-6 (IL-6), …

Disentangling Accelerated Cognitive Decline From The Normal Aging Process And Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach, Yulin Dai, Yu-Chun Hsu, Brisa S Fernandes, Kai Zhang, Xiaoyang Li, Nitesh Enduru, Andi Liu, Astrid M Manuel, Xiaoqian Jiang, Zhongming Zhao, Alzheimer’S Disease Neuroimaging Initiative

Faculty, Staff and Student Publications

BACKGROUND: The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points.

OBJECTIVE: To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach.

METHODS: We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. …

The Enact Network Is Acting On Housing Instability And The Unhoused Using The Open Health Natural Language Processing Toolkit, Daniel R Harris, Sunyang Fu, Andrew Wen, Alexandria Corbeau, Darren Henderson, Jordan Hilsman, David Oniani, Yanshan Wang

Faculty, Staff and Student Publications

Housing is an environmental social determinant of health that is linked to mortality and clinical outcomes. We developed a lexicon of housing-related concepts and rule-based natural language processing methods for identifying these housing-related concepts within clinical text. We piloted our methods on several test cohorts: a synthetic cohort generated by ChatGPT for initial infrastructure testing, a cohort with substance use disorders (SUD), and a cohort diagnosed with problems related to housing and economic circumstances (HEC). Our methods successfully identified housing concepts in our ChatGPT notes (recall = 1.0, precision = 1.0), our SUD population (recall = 0.9798, precision = 0.9898), …

Descriptor:Benchmarking Secure Neural Network Evaluation Methods For Protein Sequence Classification (Idash24), Arif Harmanci, Luyao Chen, Miran Kim, Xiaoqian Jiang

Faculty, Staff and Student Publications

To uniformly test and benchmark the secure evaluation of transformer-based models, we designed the iDASH24 homomorphic encryption track dataset. The dataset comprises a protein family classification model with a transformer architecture and an example dataset that is used to build and test the secure evaluation strategies. This dataset was used in the challenge period of iDASH24 Genomic Privacy Competition, where the teams designed secure evaluation of the classification model using a homomorphic encryption scheme. Combined with the benchmarking results and companion methods, iDASH24 dataset is a unique resource that can be used to benchmark secure evaluation of neural network models.

A Transformer-Based Deep Learning Approach For Fairly Predicting Post-Liver Transplant Risk Factors, Can Li, Xiaoqian Jiang, Kai Zhang

Faculty, Staff and Student Publications

Liver transplantation is a life-saving procedure for patients with end-stage liver disease. There are two main challenges in liver transplant: finding the best matching patient for a donor and ensuring transplant equity among different subpopulations. The current MELD scoring system evaluates a patient's mortality risk if not receiving an organ within 90 days. However, the donor-patient matching should also consider post-transplant risk factors, such as cardiovascular disease, chronic rejection, etc., which are all common complications after transplant. Accurate prediction of these risk scores remains a significant challenge. In this study, we used predictive models to solve the above challenges. Specifically, …

Linking Artificial Sweetener Intake With Kidney Function: Insights From Nhanes 2003-2006 And Findings From Mendelian Randomization Research, Zhuoling Ran, Yuxuan Zheng, Lin Yu, Yuxian Zhang, Zhenjiang Zhang, Huijie Li, Xuhan Li, Jing Song, Li Zhang, Ran Zhang, Chang Lu, Yang Gong, Jian Gong

Faculty, Staff and Student Publications

BACKGROUND: The current investigation examines the association between artificial sweetener (AS) consumption and the likelihood of developing chronic kidney disease (CKD), along with its impact on kidney function.

METHODS: We utilized data from the National Health and Nutrition Examination Survey from 2003-2006 to conduct covariance analysis and weighted adjusted logistic regression, aiming to assess the association between artificial sweetener intake and CKD risk, as well as kidney function indicators. Subsequently, we employed Mendelian randomization methods to validate the causal relationship between the intake of artificial sweeteners, CKD risk, and kidney function indicators. Instrumental variable analysis using inverse-variance weighting and Robust …

Modernizing And Harmonizing Regulatory Data Requirements For Genetically Modified Crops-Perspectives From A Workshop, Nicholas P Storer, Abigail R Simmons, Jordan Sottosanto, Jennifer A Anderson, Ming Hua Huang, Debbie Mahadeo, Carey A Mathesius, Mitscheli Sanches Da Rocha, Shuang Song, Ewa Urbanczyk-Wochniak

Faculty, Staff and Student Publications

Genetically modified (GM) crops that have been engineered to express transgenes have been in commercial use since 1995 and are annually grown on 200 million hectares globally. These crops have provided documented benefits to food security, rural economies, and the environment, with no substantiated case of food, feed, or environmental harm attributable to cultivation or consumption. Despite this extensive history of advantages and safety, the level of regulatory scrutiny has continually increased, placing undue burdens on regulators, developers, and society, while reinforcing consumer distrust of the technology. CropLife International held a workshop at the 16th International Society of Biosafety Research …

Vagus Nerve Stimulation For The Therapy Of Dravet Syndrome: A Systematic Review And Meta-Analysis, Shuang Chen, Man Li, Ming Huang

Faculty, Staff and Student Publications

OBJECTIVE: Dravet syndrome (DS) is a refractory developmental and epileptic encephalopathy characterized by seizures, developmental delay and cognitive impairment with a variety of comorbidities, including autism-like behavior, speech dysfunction, and ataxia. Vagus nerve stimulation (VNS) is one of the common therapies for DS. Here, we aim to perform a meta-analysis and systematic review of the efficacy of VNS in DS patients.

METHODS: We systematically searched four databases (PubMed, Embase, Cochrane and CNKI) to identify potentially eligible studies from their inception to January 2024. These studies provided the effective rate of VNS in treating patients with DS. The proportions of DS …

Sequencing Conversational Turns In Peer Interactions: An Integrated Approach For Evidence-Based Conversational Agent For Just-In-Time Nicotine Cravings Intervention, Tavleen Singh, Michael Truong, Kirk Roberts, Sahiti Myneni

Faculty, Staff and Student Publications

BACKGROUND: Risky health behaviors place an enormous toll on public health systems. While relapse prevention support is integrated with most behavior modification programs, the results are suboptimal. Recent advances in artificial intelligence (AI) applications provide us with unique opportunities to develop just-in-time adaptive behavior change solutions.

METHODS: In this study, we present an innovative framework, grounded in behavioral theory, and enhanced with social media sequencing and communications scenario builder to architect a conversational agent (CA) specialized in the prevention of relapses in the context of tobacco cessation. We modeled peer interaction data (n = 1000) using the taxonomy of behavior …

Effect Of S-Ketamine On Postoperative Nausea And Vomiting In Patients Undergoing Video-Assisted Thoracic Surgery: A Randomized Controlled Trial, Yu Qi, Meiyan Zhou, Wenting Zheng, Yaqi Dong, Weihua Li, Long Wang, Haijun Xu, Miao Zhang, Dunpeng Yang, Liwei Wang, Hai Zhou

Faculty, Staff and Student Publications

PURPOSE: Postoperative nausea and vomiting (PONV) frequently occur in patients after surgery. In this study, the authors investigated whether perioperative S-ketamine infusion could decrease the incidence of PONV in patients undergoing video-assisted thoracoscopic surgery (VATS) lobectomy.

PATIENTS AND METHODS: This prospective, randomized, double-blinded, controlled study was conducted a total of 420 patients from September 2021 to May 2023 at Xuzhou Central Hospital in China, who underwent elective VATS lobectomy under general anesthesia with tracheal intubation. The patients were randomly assigned to either the S-ketamine group or the control group. The S-ketamine group received a bolus injection of 0.5 mg/kg S-ketamine …

Human Equilibrative Nucleoside Transporter 1: Novel Biomarker And Prognostic Indicator For Patients With Gemcitabine-Treated Pancreatic Cancer, Jianchun Xiao, Fangyu Zhao, Wenhao Luo, Gang Yang, Yicheng Wang, Jiangdong Qiu, Yueze Liu, Lei You, Lianfang Zheng, Taiping Zhang

Faculty, Staff and Student Publications

AIM: This article aimed to find appropriate pancreatic cancer (PC) patients to treat with Gemcitabine with better survival outcomes by detecting hENT1 levels.

METHODS: We collected surgical pathological tissues from PC patients who received radical surgery in our hospital from September 2004 to December 2014. A total of 375 PC tissues and paired adjacent nontumor tissues were employed for the construction of 4 tissue microarrays (TMAs). The quality of the 4 TMAs was examined by HE staining. We performed immunohistochemistry analysis to evaluate hENT1 expression in the TMAs. Moreover, we detected hENT1 expression level and proved the role of hENT1 …

Preparing Healthcare Education For An Ai-Augmented Future, Jiajie Zhang, Susan H Fenton

Faculty, Staff and Student Publications

Artificial intelligence (AI) fundamentally transforms healthcare education as a knowledge enterprise, creating a distributed cognitive system composed of the human brain, which remains relatively unchanged, and AI-based knowledge and cognitive functions, which have accelerated exponentially in scale and power. Education must focus on developing skills to collaborate with AI and on achieving outcomes like problems solved and discoveries made. Curriculum and education policies also need to adapt to this transformation.

Statins Are Rarely Prescribed For Incidentally Discovered Covert Cerebrovascular Disease: A Retrospective Cohort In A Large Electronic Health Record (Ehr) Identified Using Natural Language Processing, Lester Y Leung, Eric Puttock, David F Kallmes, Patrick Luetmer, Sunyang Fu, Chengyi X Zheng, Hongfang Liu, Wansu Chen, David M Kent

Faculty, Staff and Student Publications

Introduction: While incidentally discovered covert cerebrovascular diseases (id-CCD) are associated with future stroke, it is not known if patients with id-CCD are prescribed statins.

Methods: Patients age ≥50 with id-CCD on neuroimaging from 2009 to 2019 with no prior ischaemic stroke, transient ischaemic attack or dementia were identified using natural language processing in a large real-world cohort. Robust Poisson multivariable regression was used to assess statin prescription among patients without prior statins.

Results: Among 2 41 050 patients, 74 975 patients (31.1%; 4.7% with covert brain infarcts (CBI); 29.0% with white matter disease (WMD)) had id-CCD. 53.5% (95% CI 53.2 …

Siglec15, Negatively Correlated With Pd-L1 In Hcc, Could Induce Cd8+ T Cell Apoptosis To Promote Immune Evasion, Zheng Chen, Mincheng Yu, Bo Zhang, Lei Jin, Qiang Yu, Shuang Liu, Binghai Zhou, Jiuliang Yan, Wentao Zhang, Xiaoqiang Li, Yongfeng Xu, Yongsheng Xiao, Jian Zhou, Jia Fan, Mien-Chie Hung, Qinghai Ye, Hui Li, Lei Guo

Faculty, Staff and Student Publications

Functional roles of SIGLEC15 in hepatocellular carcinoma (HCC) were not clear, which was recently found to be an immune inhibitor with similar structure of inhibitory B7 family members. SIGLEC15 expression in HCC was explored in public databases and further examined by PCR analysis. SIGLEC15 and PD-L1 expression patterns were examined in HCC samples through immunohistochemistry. SIGLEC15 expression was knocked-down or over-expressed in HCC cell lines, and CCK8 tests were used to examine cell proliferative ability in vitro. Influences of SIGLEC15 expression on tumor growth were examined in immune deficient and immunocompetent mice respectively. Co-culture system of HCC cell lines and …

Combination Chemotherapy Optimization With Discrete Dosing, Temitayo Ajayi, Seyedmohammadhossein Hosseinian, Andrew J Schaefer, Clifton D Fuller

Faculty, Staff and Student Publications

Chemotherapy drug administration is a complex problem that often requires expensive clinical trials to evaluate potential regimens; one way to alleviate this burden and better inform future trials is to build reliable models for drug administration. This paper presents a mixed-integer program for combination chemotherapy (utilization of multiple drugs) optimization that incorporates various important operational constraints and, besides dose and concentration limits, controls treatment toxicity based on its effect on the count of white blood cells. To address the uncertainty of tumor heterogeneity, we also propose chance constraints that guarantee reaching an operable tumor size with a high probability in …

Lesion Detection In Women Breast’S Dynamic Contrast-Enhanced Magnetic Resonance Imaging Using Deep Learning, Sudarshan Saikia, Tapas Si, Darpan Deb, Kangkana Bora, Saurav Mallik, Ujjwal Maulik, Zhongming Zhao

Faculty, Staff and Student Publications

Breast cancer is one of the most common cancers in women and the second foremost cause of cancer death in women after lung cancer. Recent technological advances in breast cancer treatment offer hope to millions of women in the world. Segmentation of the breast's Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is one of the necessary tasks in the diagnosis and detection of breast cancer. Currently, a popular deep learning model, U-Net is extensively used in biomedical image segmentation. This article aims to advance the state of the art and conduct a more in-depth analysis with a focus on the use …

Mitochondrial Dna Variants At Low-Level Heteroplasmy And Decreased Copy Numbers In Chronic Kidney Disease (Ckd) Tissues With Kidney Cancer, Yuki Kanazashi, Kazuhiro Maejima, Todd A Johnson, Shota Sasagawa, Ryosuke Jikuya, Hisashi Hasumi, Naomichi Matsumoto, Shigekatsu Maekawa, Wataru Obara, Hidewaki Nakagawa

Faculty, Staff and Student Publications

The human mitochondrial genome (mtDNA) is a circular DNA molecule with a length of 16.6 kb, which contains a total of 37 genes. Somatic mtDNA mutations accumulate with age and environmental exposure, and some types of mtDNA variants may play a role in carcinogenesis. Recent studies observed mtDNA variants not only in kidney tumors but also in adjacent kidney tissues, and mtDNA dysfunction results in kidney injury, including chronic kidney disease (CKD). To investigate whether a relationship exists between heteroplasmic mtDNA variants and kidney function, we performed ultra-deep sequencing (30,000×) based on long-range PCR of DNA from 77 non-tumor kidney …

Association Between Viral Infections And Glioma Risk: A Two-Sample Bidirectional Mendelian Randomization Analysis, Sheng Zhong, Wenzhuo Yang, Zhiyun Zhang, Yangyiran Xie, Lin Pan, Jiaxin Ren, Fei Ren, Yifan Li, Haoqun Xie, Hongyu Chen, Davy Deng, Jie Lu, Hui Li, Bo Wu, Youqi Chen, Fei Peng, Vinay K Puduvalli, Ke Sai, Yunqian Li, Ye Cheng, Yonggao Mou

Faculty, Staff and Student Publications

Background: Glioma is one of the leading types of brain tumor, but few etiologic factors of primary glioma have been identified. Previous observational research has shown an association between viral infection and glioma risk. In this study, we used Mendelian randomization (MR) analysis to explore the direction and magnitude of the causal relationship between viral infection and glioma.

Methods: We conducted a two-sample bidirectional MR analysis using genome-wide association study (GWAS) data. Summary statistics data of glioma were collected from the largest meta-analysis GWAS, involving 12,488 cases and 18,169 controls. Single-nucleotide polymorphisms (SNPs) associated with exposures were used as instrumental …

Targeted Gene Expression Profiling Predicts Meningioma Outcomes And Radiotherapy Responses, William C Chen, Abrar Choudhury, Mark W Youngblood, Mei-Yin C Polley, Calixto-Hope G Lucas, Kanish Mirchia, Sybren L N Maas, Abigail K Suwala, Minhee Won, James C Bayley, Akdes S Harmanci, Arif O Harmanci, Tiemo J Klisch, Minh P Nguyen, Harish N Vasudevan, Kathleen Mccortney, Theresa J Yu, Varun Bhave, Tai-Chung Lam, Jenny Kan-Suen Pu, Lai-Fung Li, Gilberto Ka-Kit Leung, Jason W Chan, Haley K Perlow, Joshua D Palmer, Christine Haberler, Anna S Berghoff, Matthias Preusser, Theodore P Nicolaides, Christian Mawrin, Sameer Agnihotri, Adam Resnick, Brian R Rood, Jessica Chew, Jacob S Young, Lauren Boreta, Steve E Braunstein, Jessica Schulte, Nicholas Butowski, Sandro Santagata, David Spetzler, Nancy Ann Oberheim Bush, Javier E Villanueva-Meyer, James P Chandler, David A Solomon, C Leland Rogers, Stephanie L Pugh, Minesh P Mehta, Penny K Sneed, Mitchel S Berger, Craig M Horbinski, Michael W Mcdermott, Arie Perry, Wenya Linda Bi, Akash J Patel, Felix Sahm, Stephen T Magill, David R Raleigh

Faculty, Staff and Student Publications

Surgery is the mainstay of treatment for meningioma, the most common primary intracranial tumor, but improvements in meningioma risk stratification are needed and indications for postoperative radiotherapy are controversial. Here we develop a targeted gene expression biomarker that predicts meningioma outcomes and radiotherapy responses. Using a discovery cohort of 173 meningiomas, we developed a 34-gene expression risk score and performed clinical and analytical validation of this biomarker on independent meningiomas from 12 institutions across 3 continents (N = 1,856), including 103 meningiomas from a prospective clinical trial. The gene expression biomarker improved discrimination of outcomes compared with all other systems …

Virtual Reality In Simulation-Based Emergency Skills Training: A Systematic Review With A Narrative Synthesis, Jonathan R Abbas, Michael M H Chu, Ceyon Jeyarajah, Rachel Isba, Antony Payton, Brendan Mcgrath, Neil Tolley, Iain Bruce

Faculty, Staff and Student Publications

OBJECTIVE: An important role is predicted for virtual reality (VR) in the future of medical education. We performed a systematic review of the literature with a narrative synthesis, to examine the current evidence for VR in simulation-based emergency skills training. We broadly define emergency skills as any clinical skill used in the emergency care of patients across all clinical settings.

METHODS: This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guidelines. The data sources accessed during this study included: PubMed, CINAHL, EMBASE, AMED, EMCARE, HMIC, BNI, PsychINFO, Medline, CENTRAL, SCOPUS, Web of Science, BIOSIS …

Fimap A Fast Identity-By-Descent Mapping Test For Biobank-Scale Cohorts, Han Chen, Ardalan Naseri, Degui Zhi

Faculty, Staff and Student Publications

Although genome-wide association studies (GWAS) have identified tens of thousands of genetic loci, the genetic architecture is still not fully understood for many complex traits. Most GWAS and sequencing association studies have focused on single nucleotide polymorphisms or copy number variations, including common and rare genetic variants. However, phased haplotype information is often ignored in GWAS or variant set tests for rare variants. Here we leverage the identity-by-descent (IBD) segments inferred from a random projection-based IBD detection algorithm in the mapping of genetic associations with complex traits, to develop a computationally efficient statistical test for IBD mapping in biobank-scale cohorts. …