Digital Commons Network^™

Cellular Geometry And Epithelial-Mesenchymal Plasticity Intersect With Piezo1 In Breast Cancer Cells, Choon Leng So, Mélanie Robitaille, Francisco Sadras, Michael H Mccullough, Michael J G Milevskiy, Geoffrey J Goodhill, Sarah J Roberts-Thomson, Gregory R Monteith

2020-Current year OA Pubs

Differences in shape can be a distinguishing feature between different cell types, but the shape of a cell can also be dynamic. Changes in cell shape are critical when cancer cells escape from the primary tumor and undergo major morphological changes that allow them to squeeze between endothelial cells, enter the vasculature, and metastasize to other areas of the body. A shift from rounded to spindly cellular geometry is a consequence of epithelial-mesenchymal plasticity, which is also associated with changes in gene expression, increased invasiveness, and therapeutic resistance. However, the consequences and functional impacts of cell shape changes and the …

Longitudinal Regression Of Covariance Matrix Outcomes, Yi Zhao, Brian S Caffo, Xi Luo

Faculty, Staff and Student Publications

In this study, a longitudinal regression model for covariance matrix outcomes is introduced. The proposal considers a multilevel generalized linear model for regressing covariance matrices on (time-varying) predictors. This model simultaneously identifies covariate-associated components from covariance matrices, estimates regression coefficients, and captures the within-subject variation in the covariance matrices. Optimal estimators are proposed for both low-dimensional and high-dimensional cases by maximizing the (approximated) hierarchical-likelihood function. These estimators are proved to be asymptotically consistent, where the proposed covariance matrix estimator is the most efficient under the low-dimensional case and achieves the uniformly minimum quadratic loss among all linear combinations of the …

Identifying Covariate-Related Subnetworks For Whole-Brain Connectome Analysis, Shuo Chen, Yuan Zhang, Qiong Wu, Chuan Bi, Peter Kochunov, L Elliot Hong

Faculty, Staff and Student Publications

Whole-brain connectome data characterize the connections among distributed neural populations as a set of edges in a large network, and neuroscience research aims to systematically investigate associations between brain connectome and clinical or experimental conditions as covariates. A covariate is often related to a number of edges connecting multiple brain areas in an organized structure. However, in practice, neither the covariate-related edges nor the structure is known. Therefore, the understanding of underlying neural mechanisms relies on statistical methods that are capable of simultaneously identifying covariate-related connections and recognizing their network topological structures. The task can be challenging because of false-positive …

Non-Traumatic Osteonecrosis Of The Femoral Head Induced By Steroid And Alcohol Exposure Is Associated With Intestinal Flora Alterations And Metabolomic Profiles, Qing-Yuan Zheng, Ye Tao, Lei Geng, Peng Ren, Ming Ni, Guo-Qiang Zhang

Faculty, Staff and Student Publications

OBJECTIVE: Osteonecrosis of the femoral head (ONFH) is a severe disease that primarily affects the middle-aged population, imposing a significant economic and social burden. Recent research has linked the progression of non-traumatic osteonecrosis of the femoral head (NONFH) to the composition of the gut microbiota. Steroids and alcohol are considered major contributing factors. However, the relationship between NONFH caused by two etiologies and the microbiota remains unclear. In this study, we examined the gut microbiota and fecal metabolic phenotypes of two groups of patients, and analyzed potential differences in the pathogenic mechanisms from both the microbial and metabolic perspectives.

METHODS: …

Synovial Fibroblast Gene Expression Is Associated With Sensory Nerve Growth And Pain In Rheumatoid Arthritis, Zilong Bai, Nicholas Bartelo, Maryam Aslam, Elisabeth A Murphy, Caryn R Hale, Nathalie E Blachere, Salina Parveen, Edoardo Spolaore, Edward Dicarlo, Ellen M Gravallese, Melanie H Smith, Accelerating Medicines Partnership Ra/Sle Network, Mayu O Frank, Caroline S Jiang, Haotan Zhang, Christina Pyrgaki, Myles J Lewis, Shafaq Sikandar, Costantino Pitzalis, Joseph B Lesnak, Khadijah Mazhar, Theodore J Price, Anne-Marie Malfait, Rachel E Miller, Fan Zhang, Susan Goodman, Robert B Darnell, Fei Wang, Dana E Orange

Faculty, Staff and Student Publications

It has been presumed that rheumatoid arthritis (RA) joint pain is related to inflammation in the synovium; however, recent studies reveal that pain scores in patients do not correlate with synovial inflammation. We developed a machine-learning approach (graph-based gene expression module identification or GbGMI) to identify an 815-gene expression module associated with pain in synovial biopsy samples from patients with established RA who had limited synovial inflammation at arthroplasty. We then validated this finding in an independent cohort of synovial biopsy samples from patients who had early untreated RA with little inflammation. Single-cell RNA sequencing analyses indicated that most of …

Data-Driven Modeling Of Core Gene Regulatory Network Underlying Leukemogenesis In Idh Mutant Aml., Ataur Katebi, Xiaowen Chen, Daniel Ramirez, Sheng Li, Mingyang Lu

Faculty Research 2024

Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a …

Improved Cardiac Auscultation Competency Interweaving Visual, Auditory, And Tactile Stimuli: A Preliminary Study, Harrison A Patrizio, Riley Phyu, Bum Kim, Nils V Brolis

Rowan-Virtua School of Osteopathic Medicine Departmental Research

METHODS: A pilot randomized controlled trial was conducted at our institution's simulation center with 32 first year medical students from a single medical institution. Participants were randomly divided into two equal groups and completed an educational module on the identification and pathophysiology of five common cardiac sounds. The control group utilized traditional education methods, while the interventional group incorporated multisensory stimuli. Afterwards, participants listened to randomly selected cardiac sounds and competency data was collected through a multiple-choice post-assessment in both groups. Mann-Whitney U test was used to analyze the data.

Results: Data were analyzed using the Mann-Whitney U test. Diagnostic …

Application Of Deep Learning On Mammographies To Discriminate Between Low And High-Risk Dcis For Patient Participation In Active Surveillance Trials, Sena Alaeikhanehshir, Madelon M Voets, Frederieke H Van Duijnhoven, Esther H Lips, Emma J Groen, Marja C J Van Oirsouw, Shelley E Hwang, Joseph Y Lo, Jelle Wesseling, Ritse M Mann, Jonas Teuwen

Faculty, Staff and Student Publications

BACKGROUND: Ductal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS lesions never will. Therefore, four clinical trials (COMET, LORIS, LORETTA, AND LORD) test whether active surveillance for women with low-risk Ductal carcinoma In Situ is safe (E. S. Hwang et al., BMJ Open, 9: e026797, 2019, A. Francis et al., Eur J Cancer. 51: 2296-2303, 2015, Chizuko Kanbayashi et al. The international collaboration of active surveillance trials for low-risk DCIS (LORIS, LORD, COMET, LORETTA), L. E. Elshof et al., Eur J Cancer, 51, 1497-510, 2015). Low-risk is defined as grade I or II DCIS. Because …

Leveraging Explainable Artificial Intelligence To Optimize Clinical Decision Support, Siru Liu, Allison B Mccoy, Josh F Peterson, Thomas A Lasko, Dean F Sittig, Scott D Nelson, Jennifer Andrews, Lorraine Patterson, Cheryl M Cobb, David Mulherin, Colleen T Morton, Adam Wright

Faculty, Staff and Student Publications

OBJECTIVE: To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches.

METHODS: We extracted data on alerts generated from January 1, 2019 to December 31, 2020, at Vanderbilt University Medical Center. We developed machine learning models to predict user responses to alerts. We applied XAI techniques to generate global explanations and local explanations. We evaluated the generated suggestions by comparing with alert's historical change logs and stakeholder interviews. Suggestions that either matched (or partially matched) changes already made to the alert or were considered clinically correct were classified as helpful. …

Robust Capital Cost Optimization Of Generation And Multitimescale Storage Requirements For A 100% Renewable Australian Electricity Grid, Raheel A Shaikh, David J Vowles, Alex Dinovitser, Andrew Allison, Derek Abbott

Faculty, Staff and Student Publications

Transitioning from a fossil-fuel-dependent economy to one based on renewable energy requires significant investment and technological advancement. While wind and solar technologies provide lower cost electricity, enhanced energy storage and transmission infrastructure come at a cost for managing renewable intermittency. Energy storage systems vary in characteristics and costs, and future grids will incorporate multiple technologies, yet the optimal combination of storage technologies and the role of interconnectors in alleviating storage needs are not widely explored. This study focuses on optimal generation-storage capacity requirements to elucidate associated investments. We propose a multitimescale storage solution consisting of three storage categories and an …

Identification Of Novel F2-Isoprostane Metabolites By Specific Udp-Glucuronosyltransferases, Ginger L Milne, Marina S Nogueira, Benlian Gao, Stephanie C Sanchez, Warda Amin, Sarah Thomas, Camille Oger, Jean-Marie Galano, Harvey J Murff, Gong Yang, Thierry Durand

Faculty, Staff and Student Publications

UDP-glucuronosyltransferases (UGTs) catalyze the conjugation of glucuronic acid with endogenous and exogenous lipophilic small molecules to facilitate their inactivation and excretion from the body. This represents approximately 35 % of all phase II metabolic transformations. Fatty acids and their oxidized eicosanoid derivatives can be metabolized by UGTs. F2-isoprostanes (F2-IsoPs) are eicosanoids formed from the free radical oxidation of arachidonic acid. These molecules are potent vasoconstrictors and are widely used as biomarkers of endogenous oxidative damage. An increasing body of evidence demonstrates the efficacy of measuring the β-oxidation metabolites of F2-IsoPs rather than the unmetabolized F2-IsoPs to quantify oxidative damage in …

Social And Behavior Factors Of Alzheimer's Disease And Related Dementias: A National Study In The Us, David Ciciora, Elizabeth Vásquez, Edward Valachovic, Lifang Hou, Yinan Zheng, Hua Xu, Xiaoqian Jiang, Kun Huang, Kelley Pettee Gabriel, Hong-Wen Deng, Mary P Gallant, Kai Zhang

Faculty, Staff and Student Publications

Introduction: Considerable research has linked many risk factors to Alzheimer's Disease and Related Dementias (ADRD). Without a clear etiology of ADRD, it is advantageous to rank the known risk factors by their importance and determine if disparities exist. Statistical-based ranking can provide insight into which risk factors should be further evaluated.

Methods: This observational, population-based study assessed 50 county-level measures and estimates related to ADRD in 3,155 counties in the U.S. using data from 2010 to 2021. Statistical analysis was performed in 2022-2023. The machine learning method, eXtreme Gradient Boosting, was utilized to rank the importance of these variables by …

Potential Impact Of Annual Vaccination With Reformulated Covid-19 Vaccines: Lessons From The Us Covid-19 Scenario Modeling Hub, Sung-Mok Jung, Sara L Loo, Emily Howerton, Lucie Contamin, Claire P Smith, Erica C Carcelén, Katie Yan, Samantha J Bents, John Levander, Jessi Espino, Joseph C Lemaitre, Koji Sato, Clifton D Mckee, Alison L Hill, Matteo Chinazzi, Jessica T Davis, Kunpeng Mu, Alessandro Vespignani, Erik T Rosenstrom, Sebastian A Rodriguez-Cartes, Julie S Ivy, Maria E Mayorga, Julie L Swann, Guido España, Sean Cavany, Sean M Moore, T Alex Perkins, Shi Chen, Rajib Paul, Daniel Janies, Jean-Claude Thill, Ajitesh Srivastava, Majd Al Aawar, Kaiming Bi, Shraddha Ramdas Bandekar, Anass Bouchnita, Spencer J Fox, Lauren Ancel Meyers, Przemyslaw Porebski, Srini Venkatramanan, Aniruddha Adiga, Benjamin Hurt, Brian Klahn, Joseph Outten, Jiangzhuo Chen, Henning Mortveit, Amanda Wilson, Stefan Hoops, Parantapa Bhattacharya, Dustin Machi, Anil Vullikanti, Bryan Lewis, Madhav Marathe, Harry Hochheiser, Michael C Runge, Katriona Shea, Shaun Truelove, Cécile Viboud, Justin Lessler

Faculty, Staff and Student Publications

BACKGROUND: Coronavirus Disease 2019 (COVID-19) continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval).

METHODS AND FINDINGS: The COVID-19 Scenario Modeling Hub …

Machine Learning-Enabled Maternal Risk Assessment For Women With Pre-Eclampsia (The Piers-Ml Model): A Modelling Study, Tünde Montgomery-Csobán, Kimberley Kavanagh, Paul Murray, Chris Robertson, Sarah J E Barry, U Vivian Ukah, Beth A Payne, Kypros H Nicolaides, Argyro Syngelaki, Olivia Ionescu, Ranjit Akolekar, Jennifer A Hutcheon, Laura A Magee, Peter Von Dadelszen, Piers Consortium

Faculty, Staff and Student Publications

BACKGROUND: Affecting 2-4% of pregnancies, pre-eclampsia is a leading cause of maternal death and morbidity worldwide. Using routinely available data, we aimed to develop and validate a novel machine learning-based and clinical setting-responsive time-of-disease model to rule out and rule in adverse maternal outcomes in women presenting with pre-eclampsia.

METHODS: We used health system, demographic, and clinical data from the day of first assessment with pre-eclampsia to predict a Delphi-derived composite outcome of maternal mortality or severe morbidity within 2 days. Machine learning methods, multiple imputation, and ten-fold cross-validation were used to fit models on a development dataset (75% of …

Analytical Challenges In Omics Research On Asthma And Allergy: A National Institute Of Allergy And Infectious Diseases Workshop, Supinda Bunyavanich, Patrice M Becker, Matthew C Altman, Jessica Lasky-Su, Carole Ober, Karsten Zengler, Evgeny Berdyshev, Richard Bonneau, Talal Chatila, Nilanjan Chatterjee, Kian Fan Chung, Colleen Cutcliffe, Wendy Davidson, Gang Dong, Gang Fang, Patricia Fulkerson, Blanca E Himes, Liming Liang, Rasika A Mathias, Shuji Ogino, Joseph Petrosino, Nathan D Price, Eric Schadt, James Schofield, Max A Seibold, Hanno Steen, Lisa Wheatley, Hongmei Zhang, Alkis Togias, Kohei Hasegawa

Center for Medical Ethics and Health Policy Staff Publications

Studies of asthma and allergy are generating increasing volumes of omics data for analysis and interpretation. The National Institute of Allergy and Infectious Diseases (NIAID) assembled a workshop comprising investigators studying asthma and allergic diseases using omics approaches, omics investigators from outside the field, and NIAID medical and scientific officers to discuss the following areas in asthma and allergy research: genomics, epigenomics, transcriptomics, microbiomics, metabolomics, proteomics, lipidomics, integrative omics, systems biology, and causal inference. Current states of the art, present challenges, novel and emerging strategies, and priorities for progress were presented and discussed for each area. This workshop report summarizes …

A Scoping Review Of Artificial Intelligence In Medical Education: Beme Guide No 84, Morris Gordon, Michelle Daniel, Aderonke Ajiboye, Hussein Uraiby, Nicole Y Xu, Rangana Bartlett, Janice Hanson, Mary Haas, Maxwell Spadafore, Ciaran Grafton-Clarke, Rayhan Yousef Gasiea, Colin Michie, Janet Corral, Brian Kwan, Diana Dolmans, Satid Thammasitboon

Faculty, Staff and Students Publications

Background: Artificial Intelligence (AI) is rapidly transforming healthcare, and there is a critical need for a nuanced understanding of how AI is reshaping teaching, learning, and educational practice in medical education. This review aimed to map the literature regarding AI applications in medical education, core areas of findings, potential candidates for formal systematic review and gaps for future research.

Methods: This rapid scoping review, conducted over 16 weeks, employed Arksey and O'Malley's framework and adhered to STORIES and BEME guidelines. A systematic and comprehensive search across PubMed/MEDLINE, EMBASE, and MedEdPublish was conducted without date or language restrictions. Publications included in …

Development And Publication Of Clinical Practice Parameters, Reviews, And Meta-Analyses: A Report From The Society Of Cardiovascular Anesthesiologists Presidential Task Force, Miklos D Kertai, Benu Makkad, Bruce A Bollen, Hilary P Grocott, Bessie Kachulis, Michael L Boisen, Jacob Raphael, Tjorvi E Perry, Hong Liu, Michael C Grant, Jacob Gutsche, Wanda M Popescu, Nadia B Hensley, Michael A Mazzeffi, Roman M Sniecinski, Emily Teeter, Nirvik Pal, Jennie Y Ngai, Alexander Mittnacht, Yianni G T Augoustides, Stephanie O Ibekwe, Archer Kilbourne Martin, Amanda J Rhee, Rachel L Walden, Kathryn Glas, Andrew D Shaw, Linda Shore-Lesserson

Faculty, Staff and Students Publications

The Society of Cardiovascular Anesthesiologists (SCA) is committed to improving the quality, safety, and value that cardiothoracic anesthesiologists bring to patient care. To fulfill this mission, the SCA supports the creation of peer-reviewed manuscripts that establish standards, produce guidelines, critically analyze the literature, interpret preexisting guidelines, and allow experts to engage in consensus opinion. The aim of this report, commissioned by the SCA President, is to summarize the distinctions among these publications and describe a novel SCA-supported framework that provides guidance to SCA members for the creation of these publications. The ultimate goal is that through a standardized and transparent …

Using Starling Murmuration As A Model For Creating A Global Health Community Of Practice To Advance Equity In Scholarship, Diane Nguyen, Rogers N Ssebunya, Kajal Hirani, Anna Mandalakas, Jennifer Benjamin, B Lee Ligon, Satid Thammasitboon

Faculty, Staff and Students Publications

Background: Disparities in scholarship exist between authors in low- or middle-income countries (LMIC) and high-income countries. Recognizing these disparities in our global network providing pediatric, adolescent, and maternal healthcare to vulnerable populations in LMIC, we sought to improve access and provide resources to address educational needs and ultimately impact the broader scholarship disparity.

Methods: We created a virtual community of practice (CoP) program underpinned by principles from starling murmuration to promote interdisciplinary scholarship. We developed guiding principles- autonomy, mastery and purpose- to direct the Global Health Scholarship Community of Practice Program. Program components included a continuing professional development (CPD) program, …

Visual Analysis Of Bone Malignancies Immunotherapy: A Bibliometric Analysis From 2010 To 2023, Kaiyu Shen, Liu Yang, Shuaiyi Ke, Wencang Gao

Faculty, Staff and Student Publications

Background: Bone malignancies (BM), including osteosarcoma, Ewing's sarcoma, chondrosarcoma, and chordoma, are characterized by high rates of recurrence and mortality, despite the availability of diverse treatment approaches. Immunotherapy has gained increasing importance in cancer treatment. However, there is a lack of comprehensive studies that utilize bibliometric analysis to explore immunotherapy for BM.

Methods: A literature search of English studies on BM and immunotherapy from 2010 to 2023 was conducted in the Web of Science Core Collection database. Bibliometric analysis tools such as VOSviewer, CiteSpace, and R Studio were utilized to examine global trends and research hotspots in this field.

Results: …

Faster Detection Of Asymptomatic Covid-19 Cases Among Care Home Staff In England Through The Combination Of Sars-Cov-2 Testing Technologies, Finola Ryan, Joanna Cole-Hamilton, Niharika Dandamudi, Matthias E Futschik, Alexander Needham, Rida Saquib, Raghavendran Kulasegaran-Shylini, Edward Blandford, Michael Kidd, Éamonn O'Moore, Ian Hall, Malur Sudhanva, Paul Klapper, Andrew Dodgson, Adam Moore, Madeleine Duke, Sarah Tunkel, Chris Kenny, Tom Fowler

School of Biomedical Sciences

To detect SARS-CoV-2 amongst asymptomatic care home staff in England, a dual-technology weekly testing regime was introduced on 23 December 2020. A lateral flow device (LFD) and quantitative reverse transcription polymerase chain reaction (qRT-PCR) test were taken on the same day (day 0) and a midweek LFD test was taken three to four days later. We evaluated the effectiveness of using dual-technology to detect SARS-CoV-2 between December 2020 to April 2021. Viral concentrations derived from qRT-PCR were used to determine the probable stage of infection and likely level of infectiousness. Day 0 PCR detected 1,493 cases of COVID-19, of which …

Synthetic Pet From Ct Improves Diagnosis And Prognosis For Lung Cancer: Proof Of Concept, Morteza Salehjahromi, Tatiana V Karpinets, Sheeba J Sujit, Mohamed Qayati, Pingjun Chen, Muhammad Aminu, Maliazurina B Saad, Rukhmini Bandyopadhyay, Lingzhi Hong, Ajay Sheshadri, Julie Lin, Mara B Antonoff, Boris Sepesi, Edwin J Ostrin, Iakovos Toumazis, Peng Huang, Chao Cheng, Tina Cascone, Natalie I Vokes, Carmen Behrens, Jeffrey H Siewerdsen, John D Hazle, Joe Y Chang, Jianhua Zhang, Yang Lu, Myrna C B Godoy, Caroline Chung, David Jaffray, Ignacio Wistuba, J Jack Lee, Ara A Vaporciyan, Don L Gibbons, Gregory Gladish, John V Heymach, Carol C Wu, Jianjun Zhang, Jia Wu

Faculty, Staff and Student Publications

[18F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of …

Automated Seizure Detection Based On State-Space Model Identification, Zhuo Wang, Michael Sperling, Dale Wyeth, Allon Guez

Department of Neuroscience Faculty Papers

In this study, we developed a machine learning model for automated seizure detection using system identification techniques on EEG recordings. System identification builds mathematical models from a time series signal and uses a small number of parameters to represent the entirety of time domain signal epochs. Such parameters were used as features for the classifiers in our study. We analyzed 69 seizure and 55 non-seizure recordings and an additional 10 continuous recordings from Thomas Jefferson University Hospital, alongside a larger dataset from the CHB-MIT database. By dividing EEGs into epochs (1 s, 2 s, 5 s, and 10 s) and …

An Exposome Atlas Of Serum Reveals The Risk Of Chronic Diseases In The Chinese Population, Lei You, Jing Kou, Mengdie Wang, Guoqin Ji, Xiang Li, Chang Su, Fujian Zheng, Mingye Zhang, Yuting Wang, Tiantian Chen, Ting Li, Lina Zhou, Xianzhe Shi, Chunxia Zhao, Xinyu Liu, Surong Mei, Guowang Xu

Faculty, Staff and Student Publications

Although adverse environmental exposures are considered a major cause of chronic diseases, current studies provide limited information on real-world chemical exposures and related risks. For this study, we collected serum samples from 5696 healthy people and patients, including those with 12 chronic diseases, in China and completed serum biomonitoring including 267 chemicals via gas and liquid chromatography-tandem mass spectrometry. Seventy-four highly frequently detected exposures were used for exposure characterization and risk analysis. The results show that region is the most critical factor influencing human exposure levels, followed by age. Organochlorine pesticides and perfluoroalkyl substances are associated with multiple chronic diseases, …

Genomic Evolution Shapes Prostate Cancer Disease Type, Dan J Woodcock, Atef Sahli, Ruxandra Teslo, Vinayak Bhandari, Andreas J Gruber, Aleksandra Ziubroniewicz, Gunes Gundem, Yaobo Xu, Adam Butler, Ezequiel Anokian, Bernard J Pope, Chol-Hee Jung, Maxime Tarabichi, Stefan C Dentro, J Henry R Farmery, Cruk Icgc Prostate Group, Peter Van Loo, Anne Y Warren, Vincent Gnanapragasam, Freddie C Hamdy, G Steven Bova, Christopher S Foster, David E Neal, Yong-Jie Lu, Zsofia Kote-Jarai, Michael Fraser, Robert G Bristow, Paul C Boutros, Anthony J Costello, Niall M Corcoran, Christopher M Hovens, Charlie E Massie, Andy G Lynch, Daniel S Brewer, Rosalind A Eeles, Colin S Cooper, David C Wedge

Faculty, Staff and Student Publications

The development of cancer is an evolutionary process involving the sequential acquisition of genetic alterations that disrupt normal biological processes, enabling tumor cells to rapidly proliferate and eventually invade and metastasize to other tissues. We investigated the genomic evolution of prostate cancer through the application of three separate classification methods, each designed to investigate a different aspect of tumor evolution. Integrating the results revealed the existence of two distinct types of prostate cancer that arise from divergent evolutionary trajectories, designated as the Canonical and Alternative evolutionary disease types. We therefore propose the evotype model for prostate cancer evolution wherein Alternative-evotype …

Pharmacokinetics Of Propofol In Severely Obese Surgical Patients, Martin Rygh Braathen, Ann E. Rigby-Jones, Johan Ræder, Olav Spigset, Tom Heier

Peninsula Medical School

Background: Existing PK models of propofol include sparse data from very obese patients. The aim of this study was to develop a PK model based on standardised surgical conditions and spanning from normal-weight up to, and including, a high number of very obese patients. Methods: Adult patients scheduled for laparoscopic cholecystectomy or bariatric surgery were studied. Anaesthesia was induced with propofol 2 mg/kg adjusted body weight over 2 min followed by 6 mg/kg/h adjusted body weight over 30 min. For the remainder of the operation anaesthesia was maintained with sevoflurane. Remifentanil was dosed according to clinical need. Eight arterial samples …

The Acceptance And Use Of Digital Technologies For Self-Reporting Medication Safety Events After Care Transitions To Home In Patients With Cancer: Survey Study, Yun Jiang, Misun Hwang, Youmin Cho, Christopher R Friese, Sarah T Hawley, Milisa Manojlovich, John C Krauss, Yang Gong

Faculty, Staff and Student Publications

BACKGROUND: Actively engaging patients with cancer and their families in monitoring and reporting medication safety events during care transitions is indispensable for achieving optimal patient safety outcomes. However, existing patient self-reporting systems often cannot address patients' various experiences and concerns regarding medication safety over time. In addition, these systems are usually not designed for patients' just-in-time reporting. There is a significant knowledge gap in understanding the nature, scope, and causes of medication safety events after patients' transition back home because of a lack of patient engagement in self-monitoring and reporting of safety events. The challenges for patients with cancer in …

Deep Learning Model For Personalized Prediction Of Positive Mrsa Culture Using Time-Series Electronic Health Records, Masayuki Nigo, Laila Rasmy, Bingyu Mao, Bijun Sai Kannadath, Ziqian Xie, Degui Zhi

Faculty, Staff and Student Publications

Methicillin-resistant Staphylococcus aureus (MRSA) poses significant morbidity and mortality in hospitals. Rapid, accurate risk stratification of MRSA is crucial for optimizing antibiotic therapy. Our study introduced a deep learning model, PyTorch_EHR, which leverages electronic health record (EHR) time-series data, including wide-variety patient specific data, to predict MRSA culture positivity within two weeks. 8,164 MRSA and 22,393 non-MRSA patient events from Memorial Hermann Hospital System, Houston, Texas are used for model development. PyTorch_EHR outperforms logistic regression (LR) and light gradient boost machine (LGBM) models in accuracy (AUROC

Liax Is A Surrogate Marker For Cell Envelope Stress And Daptomycin Non-Susceptibility In Enterococcus Faecium, Dierdre B Axell-House, Shelby R Simar, Diana Panesso, Sandra Rincon, William R Miller, Ayesha Khan, Orville A Pemberton, Lizbet Valdez, April H Nguyen, Kara S Hood, Kirsten Rydell, Andrea M Detranaltes, Mary N Jones, Rachel Atterstrom, Jinnethe Reyes, Pranoti V Sahasrabhojane, Geehan Suleyman, Marcus Zervos, Samuel A Shelburne, Kavindra V Singh, Yousif Shamoo, Blake M Hanson, Truc T Tran, Cesar A Arias

Faculty, Staff and Student Publications

Daptomycin (DAP) is often used as a first-line therapy to treat vancomycin-resistant

The Immunopathological Landscape Of Human Pre-Tcrα Deficiency: From Rare To Common Variants., Marie Materna, Ottavia M Delmonte, Marita Bosticardo, Mana Momenilandi, Peyton E Conrey, Bénédicte Charmeteau-De Muylder, Clotilde Bravetti, Rebecca Bellworthy, Axel Cederholm, Frederik Staels, Christian A Ganoza, Samuel Darko, Samir Sayed, Corentin Le Floc'h, Masato Ogishi, Darawan Rinchai, Andrea Guenoun, Alexandre Bolze, Taushif Khan, Adrian Gervais, Renate Krüger, Mirjam Völler, Boaz Palterer, Mahnaz Sadeghi-Shabestari, Anne Langlois De Septenville, Chaim A Schramm, Sanjana Shah, John J Tello-Cajiao, Francesca Pala, Kayla Amini, Jose S Campos, Noemia Santana Lima, Daniel Eriksson, Romain Lévy, Yoann Seeleuthner, Soma Jyonouchi, Manar Ata, Fatima Al Ali, Anna Stittrich, Caroline Deswarte, Anaïs Pereira, Jérôme Mégret, Tom Le Voyer, Paul Bastard, Laureline Berteloot, Michaël Dussiot, Natasha Vladikine, Paula P Cardenas, Emmanuelle Jouanguy, Mashael Alqahtani, Amal Hasan, Thangavel Alphonse Thanaraj, Jérémie Rosain, Fahd Al Qureshah, Vito Sabato, Marie Alexandra Alyanakian, Marianne Leruez-Ville, Flore Rozenberg, Elie Haddad, Jose R Regueiro, Maria L Toribio, Judith R Kelsen, Mansoor Salehi, Shahram Nasiri, Mehdi Torabizadeh, Hassan Rokni-Zadeh, Majid Changi-Ashtiani, Nasimeh Vatandoost, Hossein Moravej, Seyed Mohammad Akrami, Mohsen Mazloomrezaei, Aurélie Cobat, Isabelle Meyts, Etsushi Toyofuku, Madoka Nishimura, Kunihiko Moriya, Tomoyuki Mizukami, Kohsuke Imai, Laurent Abel, Bernard Malissen, Fahd Al-Mulla, Fowzan Sami Alkuraya, Nima Parvaneh, Horst Von Bernuth, Christian Beetz, Frédéric Davi, Daniel C Douek, Rémi Cheynier, David Langlais, Nils Landegren, Nico Marr, Tomohiro Morio, Mohammad Shahrooei, Rik Schrijvers, Sarah E Henrickson, Hervé Luche, Luigi D Notarangelo, Jean-Laurent Casanova, Vivien Béziat

Faculty Research 2024

We describe humans with rare biallelic loss-of-function PTCRA variants impairing pre–a T cell receptor (pre-TCRa) expression. Low circulating naive ab T cell counts at birth persisted over time, with normal memory ab and high gd T cell counts. Their TCRa repertoire was biased, which suggests that noncanonical thymic differentiation pathways can rescue ab T cell development. Only a minority of these individuals were sick, with infection, lymphoproliferation, and/or autoimmunity. We also report that 1 in 4000 individuals from the Middle East and South Asia are homozygous for a common hypomorphic PTCRA variant. They had normal circulating naive ab T cell …

Spec: A Soft Prompt-Based Calibration On Performance Variability Of Large Language Model In Clinical Notes Summarization, Yu-Neng Chuang, Ruixiang Tang, Xiaoqian Jiang, Xia Hu

Faculty, Staff and Student Publications

Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes. These records are crucial for enabling healthcare providers to make well-informed decisions regarding patient care. Summarizing clinical notes further assists healthcare professionals in pinpointing potential health risks and making better-informed decisions. This process contributes to reducing errors and enhancing patient outcomes by ensuring providers have access to the most pertinent and current patient data. Recent research has shown that incorporating instruction prompts with large language models (LLMs) substantially boosts the efficacy of summarization tasks. However, we show that this approach also …