Digital Commons Network^™

Fairly Predicting Graft Failure In Liver Transplant For Organ Assigning, Sirui Ding, Ruixiang Tang, Daochen Zha, Na Zou, Kai Zhang, Xiaoqian Jiang, Xia Hu

Faculty, Staff and Student Publications

Liver transplant is an essential therapy performed for severe liver diseases. The fact of scarce liver resources makes the organ assigning crucial. Model for End-stage Liver Disease (MELD) score is a widely adopted criterion when making organ distribution decisions. However, it ignores post-transplant outcomes and organ/donor features. These limitations motivate the emergence of machine learning (ML) models. Unfortunately, ML models could be unfair and trigger bias against certain groups of people. To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant. Specifically, knowledge distillation is employed to handle dense and sparse …

Facilitating Federated Genomic Data Analysis By Identifying Record Correlations While Ensuring Privacy, Leonard Dervishi, Xinyue Wang, Wentao Li, Anisa Halimi, Jaideep Vaidya, Xiaoqian Jiang, Erman Ayday

Faculty, Staff and Student Publications

With the reduction of sequencing costs and the pervasiveness of computing devices, genomic data collection is continually growing. However, data collection is highly fragmented and the data is still siloed across different repositories. Analyzing all of this data would be transformative for genomics research. However, the data is sensitive, and therefore cannot be easily centralized. Furthermore, there may be correlations in the data, which if not detected, can impact the analysis. In this paper, we take the first step towards identifying correlated records across multiple data repositories in a privacy-preserving manner. The proposed framework, based on random shuffling, synthetic record …

Caries Risk Documentation And Prevention: Emeasures For Dental Electronic Health Records, Suhasini Bangar, Ana Neumann, Joel M White, Alfa Yansane, Todd R Johnson, Gregory W Olson, Shwetha V Kumar, Krishna K Kookal, Aram Kim, Enihomo Obadan-Udoh, Elizabeth Mertz, Kristen Simmons, Joanna Mullins, Ryan Brandon, Muhammad F Walji, Elsbeth Kalenderian

Faculty, Staff and Student Publications

BACKGROUND: Longitudinal patient level data available in the electronic health record (EHR) allows for the development, implementation, and validations of dental quality measures (eMeasures).

OBJECTIVE: We report the feasibility and validity of implementing two eMeasures. The eMeasures determined the proportion of patients receiving a caries risk assessment (eCRA) and corresponding appropriate risk-based preventative treatments for patients at elevated risk of caries (appropriateness of care [eAoC]) in two academic institutions and one accountable care organization, in the 2019 reporting year.

METHODS: Both eMeasures define the numerator and denominator beginning at the patient level, populations' specifications, and validated the automated queries. For …

Multi-Antigen-Targeted T-Cell Therapy To Treat Patients With Relapsed/Refractory Breast Cancer, Valentina Hoyos, Spyridoula Vasileiou, Manik Kuvalekar, Ayumi Watanabe, Ifigeneia Tzannou, Yovana Velazquez, Matthew French-Kim, Wingchi Leung, Suhasini Lulla, Catherine Robertson, Claudette Foreman, Tao Wang, Shaun Bulsara, Natalia Lapteva, Bambi Grilley, Matthew Ellis, Charles Kent Osborne, Angela Coscio, Julie Nangia, Helen E Heslop, Cliona M Rooney, Juan F Vera, Premal Lulla, Mothaffar Rimawi, Ann M Leen

Faculty, Staff and Students Publications

PURPOSE: Adoptively transferred, ex vivo expanded multi-antigen-targeted T cells (multiTAA-T) represent a new, potentially effective, and nontoxic therapeutic approach for patients with breast cancer (BC). In this first-in-human trial, we investigated the safety and clinical effects of administering multiTAA T cells targeting the tumor-expressed antigens, Survivin, NY-ESO-1, MAGE-A4, SSX2, and PRAME, to patients with relapsed/refractory/metastatic BC.

MATERIALS AND METHODS: MultiTAA T-cell products were generated from the peripheral blood of heavily pre-treated patients with metastatic or locally recurrent unresectable BC of all subtypes and infused at a fixed dose level of 2 × 107/m2. Patients received two infusions of cells 4 …

Biomarkers Of Response And Resistance To Palbociclib Plus Letrozole In Patients With Er+/Her2- Breast Cancer, Mitch Dowsett, Lucy Kilburn, Mothaffar F Rimawi, C Kent Osborne, Katherine Pogue-Geile, Yuan Liu, Samuel A Jacobs, Melanie Finnigan, Shannon Puhalla, Andrew Dodson, Vera Martins, Maggie Cheang, Sophie Perry, Chris Holcombe, Nick Turner, Claire Swift, Judith M Bliss, Stephen Johnston

Faculty, Staff and Students Publications

PURPOSE: To determine (i) the relationship between candidate biomarkers of the antiproliferative (Ki67) response to letrozole and palbociclib alone and combined in ER

EXPERIMENTAL DESIGN: 307 postmenopausal women with ER+/HER2− primary breast cancer were randomly assigned to neoadjuvant treatment with letrozole for 14 weeks; letrozole for 2 weeks, then letrozole+palbociclib to 14 weeks; palbociclib for 2 weeks, then letrozole+palbociclib to 14 weeks; or letrozole+palbociclib for 14 weeks. Biopsies were taken at baseline, 2 and 14 weeks and surgery at varying times after stopping palbociclib. Immunohistochemical analyses were conducted for Ki67, c-PARP, ER, PgR, RB1, CCNE1, and CCND1.

RESULTS: Higher baselines …

An Unusual Case Of Breast Implant-Associated Anaplastic Large Cell Lymphoma, Sarah Premji, Andreia Barbieri, Christine Roth, Eric M Rohren, Gustavo Rivero, Sravanti P Teegavarapu

Faculty, Staff and Students Publications

INTRODUCTION: Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is a rare disease entity associated with textured breast implants. Though the clinical course is typically indolent, BIA-ALCL can occasionally invade through the capsule into the breast parenchyma with spread to the regional lymph nodes and beyond including chest wall invasive disease.

CASE: We present the case of a 51-year-old female with a history of bilateral silicone breast implants placed approximately twenty years ago who presented with two months of progressively enlarging right breast mass. Ultrasound-guided biopsy of right breast mass and right axillary lymph node showed CD 30-positive

CONCLUSION: Treatment guidelines …

The Effects Of Glucocorticoids And Immunosuppressants On Cancer Outcomes In Checkpoint Inhibitor Therapy, Sebastian Bruera, Maria E Suarez-Almazor

Faculty, Staff and Students Publications

The emergence of checkpoint inhibitors has created a paradigm shift for the treatment of various malignancies. However, although these therapies are associated with improved survival rates, they also carry the risk of immune-related adverse events (irAEs). Moderate to severe irAEs are typically treated with glucocorticoids, sometimes with the addition of immunosuppressants as steroid-sparing therapy. However, it is unclear how glucocorticoids and immunosuppressants may impact cancer survival and the efficacy of immune checkpoint therapy on cancer. In this narrative review, we discuss the effects of glucocorticoids and immunosuppressants including methotrexate, hydroxychloroquine, azathioprine, mycophenolate mofetil, tumor-necrosis factor (TNF)-inhibitors, interleukin-6 inhibitors, interleukin-1 inhibitors, …

Improving Pharmacovigilance Signal Detection From Clinical Notes With Locality Sensitive Neural Concept Embeddings, Justin Mower, Elmer Bernstam, Hua Xu, Sahiti Myneni, Devika Subramanian, Trevor Cohen

Faculty, Staff and Student Publications

Although pharmaceutical products undergo clinical trials to profile efficacy and safety, some adverse drug reactions (ADRs) are only discovered after release to market. Post-market drug safety surveillance - pharmacovigilance - leverages information from various sources to proactively identify such ADRs. Clinical notes are one source of observational data that could assist this process, but their inherent complexity can obfuscate possible ADR signals. In previous research, embeddings trained on observational reports have improved detection of such signals over commonly used statistical measures. Moreover, neural embedding methods which further encode juxtapositional information have shown promise on analogical retrieval tasks, suggesting proximity-based alternatives …

Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, And Beyond, Robert El-Kareh, Dean F Sittig

Faculty, Staff and Student Publications

Patient care in intensive care environments is complex, time-sensitive, and data-rich, factors that make these settings particularly well-suited to clinical decision support (CDS). A wide range of CDS interventions have been used in intensive care unit environments. The field needs well-designed studies to identify the most effective CDS approaches. Evolving artificial intelligence and machine learning models may reduce information-overload and enable teams to take better advantage of the large volume of patient data available to them. It is vital to effectively integrate new CDS into clinical workflows and to align closely with the cognitive processes of frontline clinicians.

Advances In Non-Invasive Biosensing Measures To Monitor Wound Healing Progression, Walker D Short, Oluyinka O Olutoye, Benjamin W Padon, Umang M Parikh, Daniel Colchado, Hima Vangapandu, Shayan Shams, Taiyun Chi, Jangwook P Jung, Swathi Balaji

Faculty, Staff and Student Publications

Impaired wound healing is a significant financial and medical burden. The synthesis and deposition of extracellular matrix (ECM) in a new wound is a dynamic process that is constantly changing and adapting to the biochemical and biomechanical signaling from the extracellular microenvironments of the wound. This drives either a regenerative or fibrotic and scar-forming healing outcome. Disruptions in ECM deposition, structure, and composition lead to impaired healing in diseased states, such as in diabetes. Valid measures of the principal determinants of successful ECM deposition and wound healing include lack of bacterial contamination, good tissue perfusion, and reduced mechanical injury and …

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 …

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, …

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 …

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; …

Patent Foramen Ovale In Children: Unique Pediatric Challenges And Lessons Learned From Adult Literature, Sunil Saharan, Joseph Vettukattil, Aarti Bhat, Venu Amula, Manish Bansal, Devyani Chowdhury, Umesh Dyamenahalli, Saurabh Kumar Gupta, Bibhuti Das, T K Susheel Kumar, Ashok Muralidaran, Kalyani Trivedi, Sethuraman Swaminathan, Neha Bansal, Unnati Doshi, Arvind Hoskoppal, Seshadri Balaji

Faculty, Staff and Students Publications

A patent foramen ovale (PFO) is a frequent incidental finding during echocardiography in otherwise healthy children. In most healthy children with a diagnosis of isolated incidental PFO, no further follow-up or intervention is necessary. In some children, PFO is associated with certain clinical syndromes such as cryptogenic stroke, decompression sickness, migraine, and platypnea-orthodeoxia syndrome. This review discusses PFO anatomy, diagnostic imaging, PFO-associated clinical situations, management options, and the role of PFO in certain congenital heart disease. This review also highlights the current deficiency of pediatric data guiding management of these uncommon but important PFO-associated conditions. Future multicenter randomized controlled studies …

Flow Dynamics In Anomalous Aortic Origin Of A Coronary Artery In Children: Importance Of The Intramural Segment, Hoda Hatoum, Rajesh Krishnamurthy, Jayanthi Parthasarathy, Dorma C Flemister, Carly M Krull, Benjamin A Walter, Carlos M Mery, Silvana Molossi, Lakshmi Prasad Dasi

Faculty, Staff and Students Publications

This study aims to assess the differences in pressure, fractional flow reserve (FFR) and coronary flow (with increasing pressure) of the proximal coronary artery in patients with anomalous aortic origin of a coronary artery with a confirmed ischemic event, without ischemic events, and before and after unroofing surgery, and compare to a patient with normal coronary arteries. Patient-specific flow models were 3D printed for 3 subjects with anomalous right coronary arteries with intramural course, 2 of them had documented ischemia, and compared with a patient with normal coronaries. The models were placed in the aortic position of a pulse duplicator …

The Nhlbi Study On Long-Term Outcomes After The Multisystem Inflammatory Syndrome In Children (Music): Design And Objectives, Dongngan T Truong, Felicia L Trachtenberg, Gail D Pearson, Audrey Dionne, Matthew D Elias, Kevin Friedman, Kerri H Hayes, Lynn Mahony, Brian W Mccrindle, Matthew E Oster, Victoria Pemberton, Andrew J Powell, Mark W Russell, Lara S Shekerdemian, Mary Beth Son, Michael Taylor, Jane W Newburger

Faculty, Staff and Students Publications

BACKGROUND: The Long-terM OUtcomes after the Multisystem Inflammatory Syndrome In Children (MUSIC) study aims to characterize the frequency and time course of acute and long-term cardiac and non-cardiac sequelae in multisystem inflammatory syndrome in children associated with COVID-19 (MIS-C), which are currently poorly understood.

METHODS: This multicenter observational cohort study will enroll at least 600 patientsyears, and optional genetic testing. Cardiac imaging at specific time points includes standardized echocardiographic assessment (all participants) and cardiac magnetic resonance imaging (CMR) in those with left ventricular ejection fraction (LVEF)

CONCLUSION: The MUSIC study, with the largest cohort of MIS-C patients and the longest …

Current State Of The Art In Hypoplastic Left Heart Syndrome, Aditya K Birla, Sunita Brimmer, Walker D Short, Oluyinka O Olutoye, Jason A Shar, Suriya Lalwani, Philippe Sucosky, Anitha Parthiban, Sundeep G Keswani, Christopher A Caldarone, Ravi K Birla

Faculty, Staff and Students Publications

Hypoplastic left heart syndrome (HLHS) is a complex congenital heart condition in which a neonate is born with an underdeveloped left ventricle and associated structures. Without palliative interventions, HLHS is fatal. Treatment typically includes medical management at the time of birth to maintain patency of the ductus arteriosus, followed by three palliative procedures: most commonly the Norwood procedure, bidirectional cavopulmonary shunt, and Fontan procedures. With recent advances in surgical management of HLHS patients, high survival rates are now obtained at tertiary treatment centers, though adverse neurodevelopmental outcomes remain a clinical challenge. While surgical management remains the standard of care for …

Optimal Fenestration Of The Fontan Circulation, Zan Ahmad, Lynn H Jin, Daniel J Penny, Craig G Rusin, Charles S Peskin, Charles Puelz

Faculty, Staff and Students Publications

In this paper, we develop a pulsatile compartmental model of the Fontan circulation and use it to explore the effects of a fenestration added to this physiology. A fenestration is a shunt between the systemic and pulmonary veins that is added either at the time of Fontan conversion or at a later time for the treatment of complications. This shunt increases cardiac output and decreases systemic venous pressure. However, these hemodynamic benefits are achieved at the expense of a decrease in the arterial oxygen saturation. The model developed in this paper incorporates fenestration size as a parameter and describes both …

An Automated Risk Index For Diabetic Ketoacidosis In Pediatric Patients With Type 1 Diabetes: The Ri-Dka, David D Schwartz, Rosa Banuelos, Serife Uysal, Mili Vakharia, Kristen R Hendrix, Kelly Fegan-Bohm, Sarah K Lyons, Rona Sonabend, Sheila K Gunn, Selorm Dei-Tutu

Faculty, Staff and Students Publications

Identifying patients at high risk for diabetic ketoacidosis (DKA) is crucial for informing efforts at preventive intervention. This study sought to develop and validate an electronic medical record (EMR)-based tool for predicting DKA risk in pediatric patients with type 1 diabetes. Based on analysis of data from 1,864 patients with type 1 diabetes, three factors emerged as significant predictors of DKA: most recent A1C, type of health insurance (public vs. private), and prior DKA. A prediction model was developed based on these factors and tested to identify and categorize patients at low, moderate, and high risk for experiencing DKA within …

Paraventricular Vitamin D Receptors Are Required For Glucose Tolerance In Males But Not Females, Jessie Beck, Silvania Da Silva Teixeira, Keisha Harrison, Gabrielle Phillips, Yanlin He, Stephanie Sisley

Faculty, Staff and Students Publications

When delivered directly into the brain, vitamin D, can improve glucose levels in male mice. Additionally, the loss of the vitamin D receptor (VDR) in male mice's paraventricular hypothalamus (PVH) results in impaired glucose tolerance. Data in humans shows that low vitamin D levels are detrimental to glucose homeostasis, an effect that may be more prominent in men. However, it is unknown if vitamin D action in the brain is required for normal glucose regulation in female mice. This study shows that in both viral and genetic models, male mice with obesity and PVH VDR loss have impaired glucose tolerance …

Adult Food Protein-Induced Enterocolitis Syndrome, Sara Anvari, Melanie A Ruffner

Faculty, Staff and Students Publications

Food protein-induced enterocolitis syndrome (FPIES) is a non-IgE, cell-mediated food allergy, commonly diagnosed in infants and young children. In recent years, new-onset adult FPIES has been recognized. The underlying pathogenic mechanism of FPIES has yet to be elucidated, thus disease-specific diagnostic biomarkers have yet to be determined and an oral food challenge (OFC) remains the gold-standard for the diagnosis. Pediatric patients with FPIES classically present with symptoms of delayed, repetitive vomiting approximately 1 to 4 hours following ingestion of a food allergen. However, adults with FPIES have been reported to have a different symptom profile and different food triggers compared …

A 6-Year-Old Child With A New Diagnosis Of Perinatal Human Immunodeficiency Virus Infection, Kristen G Valencia Deray, Cassie Weyland, Diana Jeang, Gayatri Mirani, Lindsay H Cameron

Faculty, Staff and Students Publications

Perinatal human immunodeficiency virus transmission, while rare in the United States, should be considered in children with a history of recurrent infections, chronic respiratory symptoms and developmental delay. A delayed diagnosis of human immunodeficiency virus in children can lead to significant morbidity and mortality. We present a 6-year-old male who presented for evaluation and management of antibiotic refractory chronic cough and purulent nasal secretions, with a history of recurrent bacterial pneumonias and sinus infections, disseminated varicella zoster, and global developmental delay. He likely had perinatally acquired human immunodeficiency virus. At the time of his human immunodeficiency virus diagnosis, he met …

Features Of Burnout Amongst Pathologists: A Reassessment, Michael B Cohen, Marisa Saint Martin, David J Gross, Kristen Johnson, Stanley J Robboy, Thomas M Wheeler, Rebecca L Johnson, W Stephen Black-Schaffer

Faculty, Staff and Students Publications

There has been little rigorous assessment of burnout among pathologists and pathology trainees. Given this relative dearth of relevant literature on pathologist burnout, this report aims to raise awareness of the issue among those working in and around this specialty. Our results are based on a survey given in conjunction with the American Board of Pathology's (ABPath) biennial Continuing Certification (CC) reporting of activities required of diplomates to maintain certification. The survey was voluntary, open to all diplomates participating in CC, and conducted over two consecutive years (2019 and 2020), with alternate years comprising different sets of diplomates. The data …