Digital Commons Network^™

Dynamic Cbct Imaging Using Prior Model-Free Spatiotemporal Implicit Neural Representation (Pmf-Stinr), Hua-Chieh Shao, Tielige Mengke, Tinsu Pan, You Zhang

Faculty, Staff and Student Publications

Objective. Dynamic cone-beam computed tomography (CBCT) can capture high-spatial-resolution, time-varying images for motion monitoring, patient setup, and adaptive planning of radiotherapy. However, dynamic CBCT reconstruction is an extremely ill-posed spatiotemporal inverse problem, as each CBCT volume in the dynamic sequence is only captured by one or a few x-ray projections, due to the slow gantry rotation speed and the fast anatomical motion (e.g. breathing).Approach. We developed a machine learning-based technique, prior-model-free spatiotemporal implicit neural representation (PMF-STINR), to reconstruct dynamic CBCTs from sequentially acquired x-ray projections. PMF-STINR employs a joint image reconstruction and registration approach to address the …

Prediction Of Disease-Free Survival For Precision Medicine Using Cooperative Learning On Multi-Omic Data, Georg Hahn, Dmitry Prokopenko, Julian Hecker, Sharon M Lutz, Kristina Mullin, Leinal Sejour, Winston Hide, Ioannis Vlachos, Stacia Desantis, Rudolph E Tanzi, Christoph Lange

Faculty, Staff and Student Publications

In precision medicine, both predicting the disease susceptibility of an individual and forecasting its disease-free survival are areas of key research. Besides the classical epidemiological predictor variables, data from multiple (omic) platforms are increasingly available. To integrate this wealth of information, we propose new methodology to combine both cooperative learning, a recent approach to leverage the predictive power of several datasets, and polygenic hazard score models. Polygenic hazard score models provide a practitioner with a more differentiated view of the predicted disease-free survival than the one given by merely a point estimate, for instance computed with a polygenic risk score. …

Gte: A Graph Learning Framework For Prediction Of T-Cell Receptors And Epitopes Binding Specificity, Feng Jiang, Yuzhi Guo, Hehuan Ma, Saiyang Na, Wenliang Zhong, Yi Han, Tao Wang, Junzhou Huang

Faculty, Staff and Student Publications

The interaction between T-cell receptors (TCRs) and peptides (epitopes) presented by major histocompatibility complex molecules (MHC) is fundamental to the immune response. Accurate prediction of TCR–epitope interactions is crucial for advancing the understanding of various diseases and their prevention and treatment. Existing methods primarily rely on sequence-based approaches, overlooking the inherent topology structure of TCR–epitope interaction networks. In this study, we present , a novel heterogeneous Graph neural network model based on inductive learning to capture the topological structure between TCRs and Epitopes. Furthermore, we address the challenge of constructing negative samples within the graph by proposing a dynamic edge …

Generalizing Parkinson’S Disease Detection Using Keystroke Dynamics: A Self-Supervised Approach, Shikha Tripathi, Alejandro Acien, Ashley A Holmes, Teresa Arroyo-Gallego, Luca Giancardo

Faculty, Staff and Student Publications

Objective: Passive monitoring of touchscreen interactions generates keystroke dynamic signals that can be used to detect and track neurological conditions such as Parkinson's disease (PD) and psychomotor impairment with minimal burden on the user. However, this typically requires datasets with clinically confirmed labels collected in standardized environments, which is challenging, especially for a large subject pool. This study validates the efficacy of a self-supervised learning method in reducing the reliance on labels and evaluates its generalizability.

Materials and methods: We propose a new type of self-supervised loss combining Barlow Twins loss, which attempts to create similar feature representations with reduced …

The Long-Term Effects Of Blood Urea Nitrogen Levels On Cardiovascular Disease And All-Cause Mortality In Diabetes: A Prospective Cohort Study, Hongfang Liu, Xiaoqin Xin, Jinghui Gan, Jungao Huang

Faculty, Staff and Student Publications

BACKGROUND: The long-term effects of blood urea nitrogen(BUN) in patients with diabetes remain unknown. Current studies reporting the target BUN level in patients with diabetes are also limited. Hence, this prospective study aimed to explore the relationship of BUN with all-cause and cardiovascular mortalities in patients with diabetes.

METHODS: In total, 10,507 participants with diabetes from the National Health and Nutrition Examination Survey (1999-2018) were enrolled. The causes and numbers of deaths were determined based on the National Death Index mortality data from the date of NHANES interview until follow-up (December 31, 2019). Multivariate Cox proportional hazard regression models were …

Misstatements, Misperceptions, And Mistakes In Controlling For Covariates In Observational Research, Xiaoxin Yu, Roger S Zoh, David A Fluharty, Luis M Mestre, Danny Valdez, Carmen D Tekwe, Colby J Vorland, Yasaman Jamshidi-Naeini, Sy Han Chiou, Stella T Lartey, David B Allison

Children’s Nutrition Research Center Staff Publications

We discuss 12 misperceptions, misstatements, or mistakes concerning the use of covariates in observational or nonrandomized research. Additionally, we offer advice to help investigators, editors, reviewers, and readers make more informed decisions about conducting and interpreting research where the influence of covariates may be at issue. We primarily address misperceptions in the context of statistical management of the covariates through various forms of modeling, although we also emphasize design and model or variable selection. Other approaches to addressing the effects of covariates, including matching, have logical extensions from what we discuss here but are not dwelled upon heavily. The misperceptions, …

Accelerated Somatic Mutation Calling For Whole-Genome And Whole-Exome Sequencing Data From Heterogenous Tumor Samples, Shuangxi Ji, Tong Zhu, Ankit Sethia, Wenyi Wang

Faculty, Staff and Student Publications

Accurate detection of somatic mutations in DNA sequencing data is a fundamental prerequisite for cancer research. Previous analytical challenges were overcome by consensus mutation calling from four to five popular callers. This, however, increases the already nontrivial computing time from individual callers. Here, we launch MuSE 2, powered by multistep parallelization and efficient memory allocation, to resolve the computing time bottleneck. MuSE 2 speeds up 50 times more than MuSE 1 and eight to 80 times more than other popular callers. Our benchmark study suggests combining MuSE 2 and the recently accelerated Strelka2 achieves high efficiency and accuracy in analyzing …

Casi: A Framework For Cross-Timepoint Analysis Of Single-Cell Rna Sequencing Data, Yizhuo Wang, Christopher R Flowers, Michael Wang, Xuelin Huang, Ziyi Li

Faculty, Staff and Student Publications

Single-cell RNA sequencing (scRNA-seq) technology has been widely used to study the differences in gene expression at the single cell level, providing insights into the research of cell development, differentiation, and functional heterogeneity. Various pipelines and workflows of scRNA-seq analysis have been developed but few considered multi-timepoint data specifically. In this study, we develop CASi, a comprehensive framework for analyzing multiple timepoints' scRNA-seq data, which provides users with: (1) cross-timepoint cell annotation, (2) detection of potentially novel cell types emerged over time, (3) visualization of cell population evolution, and (4) identification of temporal differentially expressed genes (tDEGs). Through comprehensive simulation …

Data Normalization For Addressing The Challenges In The Analysis Of Single-Cell Transcriptomic Datasets, Raquel Cuevas-Diaz Duran, Haichao Wei, Jiaqian Wu

Faculty, Staff and Student Publications

BACKGROUND: Normalization is a critical step in the analysis of single-cell RNA-sequencing (scRNA-seq) datasets. Its main goal is to make gene counts comparable within and between cells. To do so, normalization methods must account for technical and biological variability. Numerous normalization methods have been developed addressing different sources of dispersion and making specific assumptions about the count data.

MAIN BODY: The selection of a normalization method has a direct impact on downstream analysis, for example differential gene expression and cluster identification. Thus, the objective of this review is to guide the reader in making an informed decision on the most …

Cancer Stem Cell Hypothesis 2.0 In Glioblastoma: Where Are We Now And Where Are We Going?, Anthony R Sloan, Daniel J Silver, Sam Kint, Marco Gallo, Justin D Lathia

Faculty, Staff and Students Publications

Over the past 2 decades, the cancer stem cell (CSC) hypothesis has provided insight into many malignant tumors, including glioblastoma (GBM). Cancer stem cells have been identified in patient-derived tumors and in some mouse models, allowing for a deeper understanding of cellular and molecular mechanisms underlying GBM growth and therapeutic resistance. The CSC hypothesis has been the cornerstone of cellular heterogeneity, providing a conceptual and technical framework to explain this longstanding phenotype in GBM. This hypothesis has evolved to fit recent insights into how cellular plasticity drives tumor growth to suggest that CSCs do not represent a distinct population but …

Age At Lung Cancer Diagnosis In Females Versus Males Who Never Smoke By Race And Ethnicity, Batel Blechter, Jason Y Y Wong, Li-Hsin Chien, Kouya Shiraishi, Xiao-Ou Shu, Qiuyin Cai, Wei Zheng, Bu-Tian Ji, Wei Hu, Mohammad L Rahman, Hsin-Fang Jiang, Fang-Yu Tsai, Wen-Yi Huang, Yu-Tang Gao, Xijing Han, Mark D Steinwandel, Gong Yang, Yihe G Daida, Su-Ying Liang, Scarlett L Gomez, Mindy C Derouen, W Ryan Diver, Ananya G Reddy, Alpa V Patel, Loïc Le Marchand, Christopher Haiman, Takashi Kohno, Iona Cheng, I-Shou Chang, Chao Agnes Hsiung, Nathaniel Rothman, Qing Lan

Faculty, Staff and Student Publications

BACKGROUND: We characterized age at diagnosis and estimated sex differences for lung cancer and its histological subtypes among individuals who never smoke.

METHODS: We analyzed the distribution of age at lung cancer diagnosis in 33,793 individuals across 8 cohort studies and two national registries from East Asia, the United States (US) and the United Kingdom (UK). Student's t-tests were used to assess the study population differences (Δ years) in age at diagnosis comparing females and males who never smoke across subgroups defined by race/ethnicity, geographic location, and histological subtypes.

RESULTS: We found that among Chinese individuals diagnosed with lung cancer …

Development And Validation Of A Rule-Based Algorithm To Identify Periodontal Diagnosis Using Structured Electronic Health Record Data, Bunmi Tokede, Ryan Brandon, Chun-Teh Lee, Guo-Hao Lin, Joel White, Alfa Yansane, Xiaoqian Jiang, Elsbeth Kalenderian, Muhammad Walji

Faculty, Staff and Student Publications

AIM: To develop and validate an automated electronic health record (EHR)-based algorithm to suggest a periodontal diagnosis based on the 2017 World Workshop on the Classification of Periodontal Diseases and Conditions.

MATERIALS AND METHODS: Using material published from the 2017 World Workshop, a tool was iteratively developed to suggest a periodontal diagnosis based on clinical data within the EHR. Pertinent clinical data included clinical attachment level (CAL), gingival margin to cemento-enamel junction distance, probing depth, furcation involvement (if present) and mobility. Chart reviews were conducted to confirm the algorithm's ability to accurately extract clinical data from the EHR, and then …

Improvements In Appropriate Placement Of Dental Sealants After Implementation Of A Clinical Decision Support System, Joanna Mullins, Ryan Brandon, Nicholas Skourtes, Elsbeth Kalenderian, Muhammad Walji

Faculty, Staff and Student Publications

BACKGROUND: Dental sealants are effective for the prevention of caries in children at elevated risk levels, and increasing the proportion of children and adolescents who have dental sealants on 1 or more molars is a Healthy People 2030 objective. Electronic health record (EHR)-based clinical decision support systems (CDSSs) have the ability to improve patient care. A dental quality measure related to dental sealant placement for children at elevated risk of caries was targeted for improvement using a CDSS.

METHODS: A validated dental quality measure was adapted to assess a patient's need for dental sealant placement. A CDSS was implemented to …

Bacteria Synergized With Pd-1 Blockade Enhance Positive Feedback Loop Of Cancer Cells-M1 Macrophages-T Cells In Glioma, Qi Chen, Yuyi Zheng, Xiaojie Chen, Yuan Xing, Jiajie Zhang, Xinyi Yan, Qi Zhang, Di Wu, Zhong Chen

Faculty, Staff and Student Publications

Cancer immunotherapy is an attractive strategy because it stimulates immune cells to target malignant cells by regulating the intrinsic activity of the immune system. However, due to lacking many immunologic markers, it remains difficult to treat glioma, a representative "cold" tumor. Herein, to wake the "hot" tumor immunity of glioma, Porphyromonas gingivalis (Pg) is customized with a coating to create an immunogenic tumor microenvironment and further prove the effect in combination with the immune checkpoint agent anti-PD-1, exhibiting elevated therapeutic efficacy. This is accomplished not by enhancing the delivery of PD-1 blockade to enhance the effect of immunotherapy, but by …

Montelukast As A Repurposable Additive Drug For Standard-Efficacy Multiple Sclerosis Treatment: Emulating Clinical Trials With Retrospective Administrative Health Claims Data, Astrid M Manuel, Assaf Gottlieb, Leorah Freeman, Zhongming Zhao

Faculty, Staff and Student Publications

BACKGROUND: Effective and safe treatment options for multiple sclerosis (MS) are still needed. Montelukast, a leukotriene receptor antagonist (LTRA) currently indicated for asthma or allergic rhinitis, may provide an additional therapeutic approach.

OBJECTIVE: The study aimed to evaluate the effects of montelukast on the relapses of people with MS (pwMS).

METHODS: In this retrospective case-control study, two independent longitudinal claims datasets were used to emulate randomized clinical trials (RCTs). We identified pwMS aged 18-65 years, on MS disease-modifying therapies concomitantly, in de-identified claims from Optum's Clinformatics

RESULTS: pwMS treated with montelukast demonstrated a statistically significant 23.6% reduction in relapses compared …

Igwas: Image-Based Genome-Wide Association Of Self-Supervised Deep Phenotyping Of Retina Fundus Images, Ziqian Xie, Tao Zhang, Sangbae Kim, Jiaxiong Lu, Wanheng Zhang, Cheng-Hui Lin, Man-Ru Wu, Alexander Davis, Roomasa Channa, Luca Giancardo, Han Chen, Sui Wang, Rui Chen, Degui Zhi

Faculty, Staff and Student Publications

Existing imaging genetics studies have been mostly limited in scope by using imaging-derived phenotypes defined by human experts. Here, leveraging new breakthroughs in self-supervised deep representation learning, we propose a new approach, image-based genome-wide association study (iGWAS), for identifying genetic factors associated with phenotypes discovered from medical images using contrastive learning. Using retinal fundus photos, our model extracts a 128-dimensional vector representing features of the retina as phenotypes. After training the model on 40,000 images from the EyePACS dataset, we generated phenotypes from 130,329 images of 65,629 British White participants in the UK Biobank. We conducted GWAS on these phenotypes …

Astrocytes As A Mechanism For Contextually-Guided Network Dynamics And Function, Lulu Gong, Fabio Pasqualetti, Thomas Papouin, Shinung Ching

2020-Current year OA Pubs

Astrocytes are a ubiquitous and enigmatic type of non-neuronal cell and are found in the brain of all vertebrates. While traditionally viewed as being supportive of neurons, it is increasingly recognized that astrocytes play a more direct and active role in brain function and neural computation. On account of their sensitivity to a host of physiological covariates and ability to modulate neuronal activity and connectivity on slower time scales, astrocytes may be particularly well poised to modulate the dynamics of neural circuits in functionally salient ways. In the current paper, we seek to capture these features via actionable abstractions within …

Cross-Cohort Replicable Resting-State Functional Connectivity In Predicting Symptoms And Cognition Of Schizophrenia, Chunzhi Zhao, Rongtao Jiang, Juan Bustillo, Peter Kochunov, Jessica A Turner, Chuang Liang, Zening Fu, Daoqiang Zhang, Shile Qi, Vince D Calhoun

Faculty, Staff and Student Publications

Schizophrenia (SZ) is a debilitating mental illness characterized by adolescence or early adulthood onset of psychosis, positive and negative symptoms, as well as cognitive impairments. Despite a plethora of studies leveraging functional connectivity (FC) from functional magnetic resonance imaging (fMRI) to predict symptoms and cognitive impairments of SZ, the findings have exhibited great heterogeneity. We aimed to identify congruous and replicable connectivity patterns capable of predicting positive and negative symptoms as well as cognitive impairments in SZ. Predictable functional connections (FCs) were identified by employing an individualized prediction model, whose replicability was further evaluated across three independent cohorts (BSNIP, SZ …

Alteration In Resting-State Brain Activity In Stroke Survivors After Repetitive Finger Stimulation, Dorothy He, William A Sikora, Shirley A James, Jordan N Williamson, Louis V Lepak, Carolyn F Cheema, Evgeny Sidorov, Sheng Li, Yuan Yang

Faculty, Staff and Student Publications

Objective: This quasi-experimental study examined the effect of repetitive finger stimulation on brain activation in eight stroke and seven control subjects, measured by quantitative electroencephalogram.

Methods: We applied 5 mins of 2-Hz repetitive bilateral index finger transcutaneous electrical nerve stimulation and compared differences pre- and post-transcutaneous electrical nerve stimulation using quantitative electroencephalogram metrics delta/alpha ratio and delta-theta/alpha-beta ratio.

Results: Between-group differences before and after stimulation were significantly different in the delta/alpha ratio ( z = -2.88, P = 0.0040) and the delta-theta/alpha-beta ratio variables ( z = -3.90 with P < 0.0001). Significant decrease in the delta/alpha ratio and delta-theta/alpha-beta ratio variables after the transcutaneous electrical nerve stimulation was detected only in the stroke group (delta/alpha ratio diff = 3.87, P = 0.0211) (delta-theta/alpha-beta ratio diff = 1.19, P = 0.0074).

Conclusions: The decrease in quantitative electroencephalogram metrics in the stroke …

Dynamics Of Karyotype Evolution, Elena Kuzmin, Toby M Baker, Peter Van Loo, Leon Glass

Faculty, Staff and Student Publications

In the evolution of species, the karyotype changes with a timescale of tens to hundreds of thousand years. In the development of cancer, the karyotype often is modified in cancerous cells over the lifetime of an individual. Characterizing these changes and understanding the mechanisms leading to them has been of interest in a broad range of disciplines including evolution, cytogenetics, and cancer genetics. A central issue relates to the relative roles of random vs deterministic mechanisms in shaping the changes. Although it is possible that all changes result from random events followed by selection, many results point to other non-random …

Artificial Intelligence-Powered Assessment Of Pathologic Response To Neoadjuvant Atezolizumab In Patients With Nsclc: Results From The Lcmc3 Study, Sanja Dacic, William D Travis, Jennifer M Giltnane, Filip Kos, John Abel, Stephanie Hilz, Junya Fujimoto, Lynette Sholl, Jon Ritter, Farah Khalil, Yi Liu, Amaro Taylor-Weiner, Murray Resnick, Hui Yu, Fred R Hirsch, Paul A Bunn, David P Carbone, Valerie Rusch, David J Kwiatkowski, Bruce E Johnson, Jay M Lee, Stephanie R Hennek, Ilan Wapinski, Alan Nicholas, Ann Johnson, Katja Schulze, Mark G Kris, Ignacio I Wistuba

Faculty, Staff and Student Publications

Introduction: Pathologic response (PathR) by histopathologic assessment of resected specimens may be an early clinical end point associated with long-term outcomes with neoadjuvant therapy. Digital pathology may improve the efficiency and precision of PathR assessment. LCMC3 (NCT02927301) evaluated neoadjuvant atezolizumab in patients with resectable NSCLC and reported a 20% major PathR rate.

Methods: We determined PathR in primary tumor resection specimens using guidelines-based visual techniques and developed a convolutional neural network model using the same criteria to digitally measure the percent viable tumor on whole-slide images. Concordance was evaluated between visual determination of percent viable tumor (n = …

Circulating Immune Signatures In Chronic Pancreatitis With And Without Preceding Acute Pancreatitis: A Pilot Study, Rasmus Hagn-Meincke, Dhiraj Yadav, Dana K Andersen, Santhi Swaroop Vege, Evan L Fogel, Jose Serrano, Melena D Bellin, Mark D Topazian, Darwin L Conwell, Liang Li, Stephen K Van Den Eeden, Asbjørn M Drewes, Stephen J Pandol, Chris E Forsmark, William E Fisher, Phil A Hart, Søren S Olesen, Walter G Park, Consortium For The Study Of Chronic Pancreatitis, Diabetes, And Pancreatic Cancer (Cpdpc)

Faculty, Staff and Student Publications

Objective: To investigate profiles of circulating immune signatures in healthy controls and chronic pancreatitis patients (CP) with and without a preceding history of acute pancreatitis (AP).

Methods: We performed a phase 1, cross-sectional analysis of prospectively collected serum samples from the PROspective Evaluation of Chronic Pancreatitis for EpidEmiologic and Translation StuDies (PROCEED) study. All samples were collected during a clinically quiescent phase. CP subjects were categorized into two subgroups based on preceding episode(s) of AP. Healthy controls were included for comparison. Blinded samples were analyzed using an 80-plex Luminex assay of cytokines, chemokines, and adhesion molecules. Group and pairwise comparisons …

Cardiac Conduction Diseases: Understanding The Molecular Mechanisms To Uncover Targets For Future Treatments, Tingting Li, Qussay Marashly, Jitae A Kim, Na Li, Mihail G Chelu

Faculty, Staff and Students Publications

Introduction: The cardiac conduction system (CCS) is crucial for maintaining adequate cardiac frequency at rest and modulation during exercise. Furthermore, the atrioventricular node and His-Purkinje system are essential for maintaining atrioventricular and interventricular synchrony and consequently maintaining an adequate cardiac output.

Areas covered: In this review article, we examine the anatomy, physiology, and pathophysiology of the CCS. We then discuss in detail the most common genetic mutations and the molecular mechanisms of cardiac conduction disease (CCD) and provide our perspectives on future research and therapeutic opportunities in this field.

Expert opinion: Significant advancement has been made in understanding the molecular …

Comprehensive Analysis Of Bulk And Single-Cell Transcriptomic Data Reveals A Novel Signature Associated With Endoplasmic Reticulum Stress, Lipid Metabolism, And Liver Metastasis In Pancreatic Cancer, Xiaohong Liu, Bo Ren, Yuan Fang, Jie Ren, Xing Wang, Minzhi Gu, Feihan Zhou, Ruiling Xiao, Xiyuan Luo, Lei You, Yupei Zhao

Faculty, Staff and Student Publications

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with high probability of recurrence and distant metastasis. Liver metastasis is the predominant metastatic mode developed in most pancreatic cancer cases, which seriously affects the overall survival rate of patients. Abnormally activated endoplasmic reticulum stress and lipid metabolism reprogramming are closely related to tumor growth and metastasis. This study aims to construct a prognostic model based on endoplasmic reticulum stress and lipid metabolism for pancreatic cancer, and further explore its correlation with tumor immunity and the possibility of immunotherapy.

METHODS: Transcriptomic and clinical data are acquired from TCGA, ICGC, and GEO …

Differentiating Epileptic And Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis Of Eeg Plot Images, Steven Fussner, Aidan Boyne, Albert Han, Lauren A Nakhleh, Zulfi Haneef

Faculty, Staff and Students Publications

The treatment of epilepsy, the second most common chronic neurological disorder, is often complicated by the failure of patients to respond to medication. Treatment failure with anti-seizure medications is often due to the presence of non-epileptic seizures. Distinguishing non-epileptic from epileptic seizures requires an expensive and time-consuming analysis of electroencephalograms (EEGs) recorded in an epilepsy monitoring unit. Machine learning algorithms have been used to detect seizures from EEG, typically using EEG waveform analysis. We employed an alternative approach, using a convolutional neural network (CNN) with transfer learning using MobileNetV2 to emulate the real-world visual analysis of EEG images by epileptologists. …

Using Functional Principal Component Analysis (Fpca) To Quantify Sitting Patterns Derived From Wearable Sensors., Rong W. Zablocki, Sheri J. Hartman, Chongzhi Di, Jingjing Zou, Jordan A. Carlson, Paul R. Hibbing, Dori E. Rosenberg, Mikael Anne Greenwood-Hickman, Lindsay Dillon, Andrea Z. Lacroix, Loki Natarajan

Manuscripts, Articles, Book Chapters and Other Papers

BACKGROUND: Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers in SB research. The former measures body movement and the latter measures body posture. The goal of the current study is to quantify the pattern and variation of movement (by ActiGraph activity counts) during activPAL-identified sitting events, and examine associations between patterns and health-related outcomes, such as systolic and diastolic blood pressure (SBP and DBP).

METHODS: The current study included 314 overweight postmenopausal women, who were instructed to wear an activPAL (at thigh) and ActiGraph (at waist) simultaneously …

Glycemia And Gluconeogenesis With Metformin And Liraglutide: A Randomized Trial In Youth-Onset Type 2 Diabetes, Katrina B Dietsche, Sheela N Magge, Sydney A Dixon, Faith S Davis, Andrea Krenek, Aruba Chowdhury, Lilian Mabundo, Michael Stagliano, Amber B Courville, Shanna Yang, Sara Turner, Hongyi Cai, Kannan Kasturi, Arthur S Sherman, Joon Ha, Eileen Shouppe, Mary Walter, Peter J Walter, Kong Y Chen, Robert J Brychta, Cody Peer, Yi Zeng, William Figg, Fran Cogen, D Elizabeth Estrada, Shaji Chacko, Stephanie T Chung

Children’s Nutrition Research Center Staff Publications

OBJECTIVE: Elevated rates of gluconeogenesis are an early pathogenic feature of youth-onset type 2 diabetes (Y-T2D), but targeted first-line therapies are suboptimal, especially in African American (AA) youth. We evaluated glucose-lowering mechanisms of metformin and liraglutide by measuring rates of gluconeogenesis and β-cell function after therapy in AA Y-T2D.

METHODS: In this parallel randomized clinical trial, 22 youth with Y-T2D-age 15.3 ± 2.1 years (mean ± SD), 68% female, body mass index (BMI) 40.1 ± 7.9 kg/m2, duration of diagnosis 1.8 ± 1.3 years-were randomized to metformin alone (Met) or metformin + liraglutide (Lira) (Met + Lira) and evaluated before …

Advanced Nano-Drug Delivery Systems In The Treatment Of Ischemic Stroke, Jiajie Zhang, Zhong Chen, Qi Chen

Faculty, Staff and Student Publications

In recent years, the frequency of strokes has been on the rise year by year and has become the second leading cause of death around the world, which is characterized by a high mortality rate, high recurrence rate, and high disability rate. Ischemic strokes account for a large percentage of strokes. A reperfusion injury in ischemic strokes is a complex cascade of oxidative stress, neuroinflammation, immune infiltration, and mitochondrial damage. Conventional treatments are ineffective, and the presence of the blood-brain barrier (BBB) leads to inefficient drug delivery utilization, so researchers are turning their attention to nano-drug delivery systems. Functionalized nano-drug …

Ml241 Antagonizes Erk 1/2 Activation And Inhibits Rotavirus Proliferation, Jinlan Wang, Xiaoqing Hu, Jinyuan Wu, Xiaochen Lin, Rong Chen, Chenxing Lu, Xiaopeng Song, Qingmei Leng, Yan Li, Xiangjing Kuang, Jinmei Li, Lida Yao, Xianqiong Tang, Jun Ye, Guangming Zhang, Maosheng Sun, Yan Zhou, Hongjun Li

Faculty, Staff and Student Publications

Rotavirus (RV) is the main pathogen that causes severe diarrhea in infants and children under 5 years of age. No specific antiviral therapies or licensed anti-rotavirus drugs are available. It is crucial to develop effective and low-toxicity anti-rotavirus small-molecule drugs that act on novel host targets. In this study, a new anti-rotavirus compound was selected by ELISA, and cell activity was detected from 453 small-molecule compounds. The anti-RV effects and underlying mechanisms of the screened compounds were explored. In vitro experimental results showed that the small-molecule compound ML241 has a good effect on inhibiting rotavirus proliferation and has low cytotoxicity …

Tissue-Specific Atlas Of Trans-Models For Gene Regulation Elucidates Complex Regulation Patterns, Robert Dagostino, Assaf Gottlieb

Faculty, Staff and Student Publications

BACKGROUND: Deciphering gene regulation is essential for understanding the underlying mechanisms of healthy and disease states. While the regulatory networks formed by transcription factors (TFs) and their target genes has been mostly studied with relation to cis effects such as in TF binding sites, we focused on trans effects of TFs on the expression of their transcribed genes and their potential mechanisms.

RESULTS: We provide a comprehensive tissue-specific atlas, spanning 49 tissues of TF variations affecting gene expression through computational models considering two potential mechanisms, including combinatorial regulation by the expression of the TFs, and by genetic variants within the …