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Blood Metabolites Predicting Mild Cognitive Impairment In The Study Of Latinos-Investigation Of Neurocognitive Aging (Hchs/So), Shan He, Einat Granot-Hershkovitz, Ying Zhang, Jan Bressler, Wassim Tarraf, Bing Yu, Tianyi Huang, Donglin Zeng, Sylvia Wassertheil-Smoller, Melissa Lamar, Martha Daviglus, Maria J Marquine, Jianwen Cai, Thomas Mosley, Robert Kaplan, Eric Boerwinkle, Myriam Fornage, Charles Decarli, Bruce Kristal, Hector M Gonzalez, Tamar Sofer

Faculty, Staff and Student Publications

INTRODUCTION: Blood metabolomics-based biomarkers may be useful to predict measures of neurocognitive aging.

METHODS: We tested the association between 707 blood metabolites measured in 1451 participants from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), with mild cognitive impairment (MCI) and global cognitive change assessed 7 years later. We further used Lasso penalized regression to construct a metabolomics risk score (MRS) that predicts MCI, potentially identifying a different set of metabolites than those discovered in individual-metabolite analysis.

RESULTS: We identified 20 metabolites predicting prevalent MCI and/or global cognitive change. Six of them were novel and 14 were previously reported as …

Using Implementation Mapping For The Adoption And Implementation Of Target:Bp In Community Health Centers, Melissa A Valerio-Shewmaker, Natalia I Heredia, Catherine Pulicken, Patenne D Mathews, Roshanda Chenier, Tracy L Swoboda, Ella R Garza, Fernanda Velasco-Huerta, Maria E Fernandez

Faculty, Staff and Student Publications

BACKGROUND: Despite the availability of multilevel evidence-based interventions for blood pressure management, poor hypertension control is common among community health center patient populations across the state of Texas and the United States.

METHODS: We used Implementation Mapping (IM) to identify barriers and facilitators influencing the adoption and implementation of the

RESULTS: As part of the needs and capacity assessment, we collected data through interviews with CHC staff, examining gaps in needs and services (e.g., what do clinics need to implement

DISCUSSION: This paper provides an example of using Implementation Mapping to develop strategies to increase the adoption and implementation of …

Incidence Rate Of Psychiatric Disorders In 2020: The Pivotal Role Played By Sars-Cov-2 Infection, Antonio L Teixeira, Regina M Hansen, Joseph S Wozny, Caroline M Schaefer, Rodrigo Machado-Vieira, Lokesh Shahani, Scott D Lane, Jair C Soares, Trudy M Krause

Faculty, Staff and Student Publications

IMPORTANCE: The Coronavirus Disease (COVID-19) pandemic has significantly impacted mental health outcomes. While the frequency of anxiety and depressive symptoms has increased in the whole population, the relationship between COVID-19 and new psychiatric diagnoses remains unclear.

OBJECTIVE: to compare the population incidence rate of emergence of de novo psychiatric disorders in 2020 compared to the previous years, and to compare the incidence rate of new psychiatric disorder diagnoses between people with vs without COVID-19.

DESIGN, SETTING, AND PARTICIPANTS: This study utilized administrative claims data from the Clinformatics® Data Mart database, licensed from Optum®. The study is a cross-sectional analysis that …

Population-Based Correlates Of Covid-19 Infection: An Analysis From The Dfw Covid-19 Prevalence Study, Amit G Singal, Andrew Masica, Kate Esselink, Caitlin C Murphy, Jill A Dever, Annika Reczek, Matthew Bensen, Nicole Mack, Ellen Stutts, Jamie L Ridenhour, Evan Galt, Jordan Brainerd, Noa Kopplin, Sruthi Yekkaluri, Chris Rubio, Shelby Anderson, Kathryn Jan, Natalie Whitworth, Jacqueline Wagner, Stephen Allen, Alagar R Muthukumar, Jasmin Tiro

Faculty, Staff and Student Publications

BACKGROUND: COVID-19 has resulted in over 1 million deaths in the U.S. as of June 2022, with continued surges after vaccine availability. Information on related attitudes and behaviors are needed to inform public health strategies. We aimed to estimate the prevalence of COVID-19, risk factors of infection, and related attitudes and behaviors in a racially, ethnically, and socioeconomically diverse urban population.

METHODS: The DFW COVID-19 Prevalence Study Protocol 1 was conducted from July 2020 to March 2021 on a randomly selected sample of adults aged 18-89 years, living in Dallas or Tarrant Counties, Texas. Participants were asked to complete a …

Inhibition Of The Cd47-Sirpα Axis For Cancer Therapy: A Systematic Review And Meta-Analysis Of Emerging Clinical Data, Ji Son, Rodney Cheng-En Hsieh, Heather Y Lin, Kate J Krause, Ying Yuan, Amadeo B Biter, James Welsh, Michael A Curran, David S Hong

Faculty, Staff and Student Publications

CD47-SIRPα interaction acts as a "don't eat me" signal and is exploited by cancer to downregulate innate and adaptive immune surveillance. There has been intense interest to develop a mechanism of blockade, and we aimed to analyze the emerging data from early clinical trials. We performed a systematic review and meta-analysis of relevant databases and conference abstracts including clinical trials using CD47 and/or SIRPα inhibitors in cancer treatment. Nonlinear mixed models were applied for comparison of response and toxicity. We retrieved 317 articles, 24 of which were eligible. These included 771 response-evaluable patients with hematologic (47.1%) and solid tumors (52.9%). …

Research Career Intentions Among Non-Native English-Speaking Graduate And Postdoctoral Trainees In Stem-Results From Cross-Sectional And Longitudinal Studies, Hwa Young Lee, Shine Chang, Cheryl B Anderson, Erin K Dahlstrom, Carrie Cameron

Faculty, Staff and Student Publications

International graduate trainees, many of whom are non-native English-speaking (L2) trainees, comprise more than half of graduate-level trainees in STEM, but little is known regarding factors that influence their career intentions, especially those that foster their growth as scientists to achieve their full potential in research. Thus, the purpose of our studies was to examine the relationship between L2 status and contextual factors that help shape the learning experiences and plans for research-focused careers. Study 1 collected cross-sectional survey data from doctoral and postdoctoral trainees (N=510) from research institutes in the Texas Medical Center in Houston. We examined which factors …

Cancer Prevention Behaviors In Workers Of A Referral Cancer Center In Mexico City: A Pilot Study On Early Detection Awareness For Cancer, Nancy Reynoso-Noverón, Shine Chang, Luis Alonso Herrera-Montalvo, Abelardo Meneses-García

Faculty, Staff and Student Publications

Background: Prevention strategies for cancer are necessary. Health workers who often serve as role models bear responsibility for prevention counseling and programs. However, whether their habits and behaviors reflect prevention goals are unknown. We describe the prevalence of cancer risk factors and prevention behaviors in health workers of a referral cancer center in Mexico City.

Methods: Cross-sectional study in which workers of the National Cancer Institute were invited to participate in a prevention program, risk factor survey, and nutrition, psychological, and genetic counseling were included. The likelihood of cancer was calculated based on the presence of risk factors. Factors associated …

Head-Mounted Digital Metamorphopsia Suppression As A Countermeasure For Macular-Related Visual Distortions For Prolonged Spaceflight Missions And Terrestrial Health, Joshua Ong, Nasif Zaman, Ethan Waisberg, Sharif Amit Kamran, Andrew G Lee, Alireza Tavakkoli

Faculty, Staff and Student Publications

During long-duration spaceflight, astronauts are exposed to various risks including spaceflight-associated neuro-ocular syndrome, which serves as a risk to astronaut vision and a potential physiological barrier to future spaceflight. When considering exploration missions that may expose astronauts to longer periods of microgravity, radiation exposure, and natural aging processes during spaceflight, more severe changes to functional vision may occur. The macula plays a critical role in central vision and disruptions to this key area in the eye may compromise functional vision and mission performance. In this article, we describe the development of a countermeasure technique to digitally suppress monocular central visual …

Emerging Current Trends And Research Focus Related To Pancreatic Cancer Metabolism: A Bibliometric And Visualized Analysis, Qian Shen, Chuanlong Zhang, Xiaochen Jiang, Junchen Li, Fudong Liu, Xiyuan Zhang, Ge'er En, Bo Pang

Faculty, Staff and Student Publications

BACKGROUND: As a malignant digestive system tumor, pancreatic cancer has unique metabolic characteristics. In recent years, the study of pancreatic cancer metabolism is in full swing, which provides a new direction for the treatment of pancreatic cancer patients. However, there is no systematic report of pancreatic cancer metabolism. In this paper, bibliometrics and visualization methods were used to analyze the number of publications, countries/regions, authors, institutions, journals, co-cited references, and keywords of pancreatic cancer metabolism articles, to summarize the research trends and predict research hotspots.

METHODS: We searched, screened and downloaded articles on pancreatic cancer metabolism through the Web of …

A Novel Group Of Genes That Cause Endocrine Resistance In Breast Cancer Identified By Dynamic Gene Expression Analysis, Arvand Asghari, Katherine Wall, Michael Gill, Natascha Del Vecchio, Farnaz Allahbakhsh, Jacky Wu, Nan Deng, W Jim Zheng, Hulin Wu, Michihisa Umetani, Vahed Maroufy

Faculty, Staff and Student Publications

Breast cancer (BC) is the most common type of cancer diagnosed in women. Among female cancer deaths, BC is the second leading cause of death worldwide. For estrogen receptor-positive (ER-positive) breast cancers, endocrine therapy is an effective therapeutic approach. However, in many cases, an ER-positive tumor becomes unresponsive to endocrine therapy, and tumor regrowth occurs after treatment. While some genetic mutations contribute to resistance in some patients, the underlying causes of resistance to endocrine therapy are mostly undetermined. In this study, we utilized a recently developed statistical approach to investigate the dynamic behavior of gene expression during the development of …

Effects Of Tamoxifen Inducible Mercremer On Gene Expression In Cardiac Myocytes In Mice, Leila Rouhi, Siyang Fan, Sirisha M Cheedipudi, Melis Olcum, Hyun-Hwan Jeong, Zhongming Zhao, Priyatansh Gurha, Ali J Marian

Faculty, Staff and Student Publications

The Cre-LoxP technology, including the tamoxifen (TAM) inducible MerCreMer (MCM), is increasingly used to delineate gene function, understand the disease mechanisms, and test therapeutic interventions. We set to determine the effects of TAM-MCM on cardiac myocyte transcriptome. Expression of the MCM was induced specifically in cardiac myocytes upon injection of TAM to myosin heavy chain 6-MCM (Myh6-Mcm) mice for 5 consecutive days. Cardiac function, myocardial histology, and gene expression (RNA-sequencing) were analyzed 2 weeks after TAM injection. A total of 346 protein coding genes (168 up- and 178 down-regulated) were differentially expressed. Transcript levels of 85 genes, analyzed …

An Ensemble-Based Deep Convolutional Neural Network For Computer-Aided Polyps Identification From Colonoscopy, Pallabi Sharma, Bunil Kumar Balabantaray, Kangkana Bora, Saurav Mallik, Kunio Kasugai, Zhongming Zhao

Faculty, Staff and Student Publications

Colorectal cancer (CRC) is the third leading cause of cancer death globally. Early detection and removal of precancerous polyps can significantly reduce the chance of CRC patient death. Currently, the polyp detection rate mainly depends on the skill and expertise of gastroenterologists. Over time, unidentified polyps can develop into cancer. Machine learning has recently emerged as a powerful method in assisting clinical diagnosis. Several classification models have been proposed to identify polyps, but their performance has not been comparable to an expert endoscopist yet. Here, we propose a multiple classifier consultation strategy to create an effective and powerful classifier for …

A Deep Learning-Based Framework For Supporting Clinical Diagnosis Of Glioblastoma Subtypes, Sana Munquad, Tapas Si, Saurav Mallik, Asim Bikas Das, Zhongming Zhao

Faculty, Staff and Student Publications

Understanding molecular features that facilitate aggressive phenotypes in glioblastoma multiforme (GBM) remains a major clinical challenge. Accurate diagnosis of GBM subtypes, namely classical, proneural, and mesenchymal, and identification of specific molecular features are crucial for clinicians for systematic treatment. We develop a biologically interpretable and highly efficient deep learning framework based on a convolutional neural network for subtype identification. The classifiers were generated from high-throughput data of different molecular levels, i.e., transcriptome and methylome. Furthermore, an integrated subsystem of transcriptome and methylome data was also used to build the biologically relevant model. Our results show that deep learning model outperforms …

Crucial Roles Of Microrna-16-5p And Microrna-27b-3p In Ameloblast Differentiation Through Regulation Of Genes Associated With Amelogenesis Imperfecta, Akiko Suzuki, Hiroki Yoshioka, Teng Liu, Aania Gull, Naina Singh, Thanh Le, Zhongming Zhao, Junichi Iwata

Faculty, Staff and Student Publications

Amelogenesis imperfecta is a congenital disorder within a heterogeneous group of conditions characterized by enamel hypoplasia. Patients suffer from early tooth loss, social embarrassment, eating difficulties, and pain due to an abnormally thin, soft, fragile, and discolored enamel with poor aesthetics and functionality. The etiology of amelogenesis imperfecta is complicated by genetic interactions. To identify mouse amelogenesis imperfecta-related genes (mAIGenes) and their respective phenotypes, we conducted a systematic literature review and database search and found and curated 70 mAIGenes across all of the databases. Our pathway enrichment analysis indicated that these genes were enriched in tooth development-associated pathways, forming four …

Dimensionality Reduction And Louvain Agglomerative Hierarchical Clustering For Cluster-Specified Frequent Biomarker Discovery In Single-Cell Sequencing Data, Soumita Seth, Saurav Mallik, Tapas Bhadra, Zhongming Zhao

Faculty, Staff and Student Publications

The major interest domains of single-cell RNA sequential analysis are identification of existing and novel types of cells, depiction of cells, cell fate prediction, classification of several types of tumor, and investigation of heterogeneity in different cells. Single-cell clustering plays an important role to solve the aforementioned questions of interest. Cluster identification in high dimensional single-cell sequencing data faces some challenges due to its nature. Dimensionality reduction models can solve the problem. Here, we introduce a potential cluster specified frequent biomarkers discovery framework using dimensionality reduction and hierarchical agglomerative clustering Louvain for single-cell RNA sequencing data analysis. First, we pre-filtered …

Explanation-Driven Deep Learning Model For Prediction Of Brain Tumour Status Using Mri Image Data, Loveleen Gaur, Mohan Bhandari, Tanvi Razdan, Saurav Mallik, Zhongming Zhao

Faculty, Staff and Student Publications

Cancer research has seen explosive development exploring deep learning (DL) techniques for analysing magnetic resonance imaging (MRI) images for predicting brain tumours. We have observed a substantial gap in explanation, interpretability, and high accuracy for DL models. Consequently, we propose an explanation-driven DL model by utilising a convolutional neural network (CNN), local interpretable model-agnostic explanation (LIME), and Shapley additive explanation (SHAP) for the prediction of discrete subtypes of brain tumours (meningioma, glioma, and pituitary) using an MRI image dataset. Unlike previous models, our model used a dual-input CNN approach to prevail over the classification challenge with images of inferior quality …

Breast Cancer Detection: Shallow Convolutional Neural Network Against Deep Convolutional Neural Networks Based Approach, Himanish Shekhar Das, Akalpita Das, Anupal Neog, Saurav Mallik, Kangkana Bora, Zhongming Zhao

Faculty, Staff and Student Publications

Introduction: Of all the cancers that afflict women, breast cancer (BC) has the second-highest mortality rate, and it is also believed to be the primary cause of the high death rate. Breast cancer is the most common cancer that affects women globally. There are two types of breast tumors: benign (less harmful and unlikely to become breast cancer) and malignant (which are very dangerous and might result in aberrant cells that could result in cancer).

Methods: To find breast abnormalities like masses and micro-calcifications, competent and educated radiologists often examine mammographic images. This study focuses on computer-aided diagnosis to help …

The Wnt/Β-Catenin Pathway Regulates Expression Of The Genes Involved In Cell Cycle Progression And Mitochondrial Oxidative Phosphorylation In The Postmitotic Cardiac Myocytes, Melis Olcum, Sirisha M Cheedipudi, Leila Rouhi, Siyang Fan, Hyun-Hwan Jeong, Zhongming Zhao, Priyatansh Gurha, Ali J Marian

Faculty, Staff and Student Publications

INTRODUCTION: Aging is associated with cardiac myocyte loss, sarcopenia, and cardiac dysfunction. Adult cardiac myocytes are postmitotic cells with an insufficient proliferative capacity to compensate for myocyte loss. The canonical WNT (cWNT) pathway is involved in the regulation of cell cycle reentry in various cell types. The effects of the cWNT pathway on the expression of genes involved in cell cycle reentry in the postmitotic cardiac myocytes are unknown.

AIM: The aim of the study was to identify genes whose expression is regulated by the β-catenin, the indispensable component to the cWNT signaling, in the postmitotic myocytes.

METHODS AND RESULTS: …

Prediction Of Intensive Care Unit Admission (>24h) After Surgery In Elective Noncardiac Surgical Patients Using Machine Learning Algorithms, Lan Lan, Fangwei Chen, Jiawei Luo, Mengjiao Li, Xuechao Hao, Yao Hu, Jin Yin, Tao Zhu, Xiaobo Zhou

Faculty, Staff and Student Publications

BACKGROUND: To develop a highly discriminative machine learning model for the prediction of intensive care unit admission (>24h) using the easily available preoperative information from electronic health records. An accurate prediction model for ICU admission after surgery is of great importance for surgical risk assessment and appropriate utilization of ICU resources.

METHOD: Data were collected retrospectively from a large hospital, comprising 135,442 adult patients who underwent surgery except for cardiac surgery between 1 January 2014, and 31 July 2018 in China. Multiple existing predictive machine learning algorithms were explored to construct the prediction model, including logistic regression, random forest, …

Prediction Of Fluid Intelligence From T1-W Mri Images: A Precise Two-Step Deep Learning Framework, Mingliang Li, Mingfeng Jiang, Guangming Zhang, Yujun Liu, Xiaobo Zhou

Faculty, Staff and Student Publications

The Adolescent Brain Cognitive Development (ABCD) Neurocognitive Prediction Challenge (ABCD-NP-Challenge) is a community-driven competition that challenges competitors to develop algorithms to predict fluid intelligence scores from T1-w MRI images. In this work, a two-step deep learning pipeline is proposed to improve the prediction accuracy of fluid intelligence scores. In terms of the first step, the main contributions of this study include the following: (1) the concepts of the residual network (ResNet) and the squeeze-and-excitation network (SENet) are utilized to improve the original 3D U-Net; (2) in the segmentation process, the pixels in symmetrical brain regions are assigned the same label; …

Distinguish Bipolar And Major Depressive Disorder In Adolescents Based On Multimodal Neuroimaging: Results From The Adolescent Brain Cognitive Development Stud, Yujun Liu, Kai Chen, Yangyang Luo, Jiqiu Wu, Qu Xiang, Li Peng, Jian Zhang, Weiling Zhao, Mingliang Li, Xiaobo Zhou

Faculty, Staff and Student Publications

BACKGROUND: Major depressive disorder and bipolar disorder in adolescents are prevalent and are associated with cognitive impairment, executive dysfunction, and increased mortality. Early intervention in the initial stages of major depressive disorder and bipolar disorder can significantly improve personal health.

METHODS: We collected 309 samples from the Adolescent Brain Cognitive Development study, including 116 adolescents with bipolar disorder, 64 adolescents with major depressive disorder, and 129 healthy adolescents, and employed a support vector machine to develop classification models for identification. We developed a multimodal model, which combined functional connectivity of resting-state functional magnetic resonance imaging and four anatomical measures of …

Exploring The Feasibility Of Virtually Delivered Auricular Point Acupressure In Self-Managing Chronic Pain: Qualitative Study, Jennifer Kawi, Chao Hsing Yeh, Nada Lukkahatai, Robin L Hardwicke, Thomas Murphy, Paul J Christo

Faculty, Staff and Student Publications

BACKGROUND: Chronic pain remains highly prevalent. Current pharmacological and non-pharmacological strategies have not adequately managed chronic pain which has contributed to disability and high healthcare costs. With existing challenges in providing adequate pain care and access, we tested vAPA, a virtually delivered, self-management intervention using Auricular Point Acupressure (APA) by mobile app and virtual consultations (telehealth). Our key purpose was to evaluate the feasibility of the vAPA in self-managing chronic pain in preparation for a future randomized controlled trial.

METHODS: We conducted a descriptive, qualitative study evaluating our 4-week vAPA intervention among 18 participants. We used directed qualitative content analysis. …

The Current State Of Fellowship Leadership In Dermatology: Trends In Workforce Demographics And Scholarly Productivity, Steven Idell, David D Mcpherson, Jessica Martin, Andrew Dentino, Luis Ostrosky-Zeichner, Julia V Philley, Paul Mcgaha, Megan Devine, Jacqueline Bronicki, Daniel D Karp

Faculty, Staff and Student Publications

In 2018, The University of Texas Health Science Center-Tyler and University of Texas Rio Grande Valley were invited to develop clinical research units for an existing Clinical and Translational Science Award (CTSA) consortium with the objective to equip medically underserved, economically disadvantaged communities and subsequently to deploy COVID-19 clinical trials in response to a public health emergency.

Invasive Haemophilus Influenza Type B In An Infant During The Covid-19 Pandemic: The Return Of Diseases We Hoped Never To See Again…, Christy Tabarani, Stephen A Fletcher, Gloria P Heresi, Susan H Wootton

Faculty, Staff and Student Publications

No abstract provided.

Regenerative Medicine Technologies Applied To Transplant Medicine An Update, Astgik Petrosyan, Filippo Montali, Andrea Peloso, Antonio Citro, Lori N Byers, Catherine La Pointe, Mara Suleiman, Alice Marchetti, Eoin P Mcneill, Allison L Speer, Wai Hoe Ng, Xi Ren, Benedetta Bussolati, Laura Perin, Paolo Di Nardo, Vincenzo Cardinale, Jerome Duisit, Alexandra Rose Monetti, John Richard Savino, Amish Asthana, Giuseppe Orlando

Faculty, Staff and Student Publications

Regenerative medicine (RM) is changing how we think and practice transplant medicine. In regenerative medicine, the aim is to develop and employ methods to regenerate, restore or replace damaged/diseased tissues or organs. Regenerative medicine investigates using tools such as novel technologies or techniques, extracellular vesicles, cell-based therapies, and tissue-engineered constructs to design effective patient-specific treatments. This review illustrates current advancements in regenerative medicine that may pertain to transplant medicine. We highlight progress made and various tools designed and employed specifically for each tissue or organ, such as the kidney, heart, liver, lung, vasculature, gastrointestinal tract, and pancreas. By combing both …

Case Report: Novel Pathogenic Variant In Nfix In Two Sisters With Malan Syndrome Due To Germline Mosaicism, Elizabeth Langley, Laura S Farach, Kate Mowrey

Faculty, Staff and Student Publications

Malan syndrome is an autosomal dominant disorder caused by pathogenic variants in NFIX with less than 100 cases reported thus far. NFIX is important for stem cell proliferation, quiescence, and differentiation during development and its protein plays a role in replication, signal transduction, and transcription. As a result of pathogenic variants, symptoms of Malan syndrome include overgrowth, intellectual disability, speech delay, and dysmorphic features. Currently, the recurrence risk for this disorder is indicated at less than 1%, standard for de novo autosomal dominant disorders. Herein, we report an additional set of sisters with the same novel pathogenic variant in NFIX …

Impact Of Covid-19 On Nationwide Pediatric Otolaryngology Practice: Adenotonsillectomies (Ta) And Tonsil-Related Diagnoses Trends, David Z Allen, Sai Challapalli, Kyung Hyun Lee, Cynthia S Bell, Soham Roy, Sarah Bowe, Karthik Balakrishnan, C W David Chang, Zhen Huang

Faculty, Staff and Student Publications

Objectives/hypothesis: To determine the effects of the COVID-19 pandemic on Adenotonsillectomies (TA), Tonsil Related Cases (TC), and Peritonsillar Abscess (PTA) Trends.

Study design: Retrospective Cohort Study.

Methods: This is a retrospective cohort study using the Pediatric Health Information System® (PHIS) database, which consists of 51 children's hospitals. Regions were defined according to PHIS rules with at least five children's hospitals per region. We compared monthly total TA, TC, TC as a proportion of all hospital visits, and PTA from all encounters at each hospital from January 1, 2019, through December 31, 2021.

Results: Compared to 2019, April 2020 saw mean …

The Psychological Impact Of The Covid-19 Outbreak On Pregnancy And Mother-Infant Prenatal Bonding, Muhammad O Awiwi, Khaled M Elsayes, Yehia I Mohamed, Lina Altameemi, Migena Gjoni, Omayr Muhammad Irshad, Ahmed Sayed Ahmed, Ahmad O Kaseb, Usama Salem

Faculty, Staff and Student Publications

PURPOSE: To identify prognostic clinical and radiologic features in patients with unresectable hepatocellular carcinoma (HCC) treated with atezolizumab plus bevacizumab.

PATIENTS AND METHODS: Clinical and imaging records of patients with unresectable HCC were retrospectively reviewed, and baseline features were recorded. Patients' records and imaging studies were used to determine the patients' overall survival (OS) and progression-free survival (PFS). Univariate and multivariate analyses were performed to determine prognostic features. Subanalyses of treatment-naïve patients (who never received local or systemic therapy) and previously treated patients were also performed.

RESULTS: Fifty-five patients were included in the final analysis, 23 (41.8%) of whom were …

Impairment Of Autophagy After Spinal Cord Injury Potentiates Neuroinflammation And Motor Function Deficit In Mice, Yun Li, Zhuofan Lei, Rodney M Ritzel, Junyun He, Hui Li, Harry M C Choi, Marta M Lipinski, Junfang Wu

Faculty, Staff and Student Publications

Autophagy is a catabolic process that degrades cytoplasmic constituents and organelles in the lysosome, thus serving an important role in cellular homeostasis and protection against insults. We previously reported that defects in autophagy contribute to neuronal cell damage in traumatic spinal cord injury (SCI). Recent data from other inflammatory models implicate autophagy in regulation of immune and inflammatory responses, with low levels of autophagic flux associated with pro-inflammatory phenotypes. In the present study, we examined the effects of genetically or pharmacologically manipulating autophagy on posttraumatic neuroinflammation and motor function after SCI in mice.

Methods: Young adult male C57BL/6, CX3CR1-GFP, autophagy …

A Haplotype Resolved Chromosomal Level Avocado Genome Allows Analysis Of Novel Avocado Genes, Onkar Nath, Stephen J Fletcher, Alice Hayward, Lindsay M Shaw, Ardashir Kharabian Masouleh, Agnelo Furtado, Robert J Henry, Neena Mitter

Faculty, Staff and Student Publications

Avocado (Persea americana) is a member of the magnoliids, an early branching lineage of angiosperms that has high value globally with the fruit being highly nutritious. Here, we report a chromosome-level genome assembly for the commercial avocado cultivar Hass, which represents 80% of the world’s avocado consumption. The DNA contigs produced from Pacific Biosciences HiFi reads were further assembled using a previously published version of the genome supported by a genetic map. The total assembly was 913 Mb with a contig N50 of 84 Mb. Contigs assigned to the 12 chromosomes represented 874 Mb and covered 98.8% of …