Medicine and Health Sciences Commons^™

Sound Speed Estimation For Distributed Aberration Correction In Laterally Varying Media, Rehman Ali, Trevor M Mitcham, Melanie Singh, Marvin M Doyley, Richard R Bouchard, Jeremy J Dahl, Nebojsa Duric

Faculty, Staff and Student Publications

Spatial variation in sound speed causes aberration in medical ultrasound imaging. Although our previous work has examined aberration correction in the presence of a spatially varying sound speed, practical implementations were limited to layered media due to the sound speed estimation process involved. Unfortunately, most models of layered media do not capture the lateral variations in sound speed that have the greatest aberrative effect on the image. Building upon a Fourier split-step migration technique from geophysics, this work introduces an iterative sound speed estimation and distributed aberration correction technique that can model and correct for aberrations resulting from laterally varying …

Naproxen Chemoprevention Induces Proliferation Of Cytotoxic Lymphocytes In Lynch Syndrome Colorectal Mucosa, Charles M Bowen, Nan Deng, Laura Reyes-Uribe, Edwin Roger Parra, Pedro Rocha, Luisa M Solis, Ignacio I Wistuba, Valerie O Sepeda, Lana Vornik, Marjorie Perloff, Eva Szabo, Asad Umar, Krishna M Sinha, Powel H Brown, Eduardo Vilar

Faculty, Staff and Student Publications

BACKGROUND: Recent clinical trial data from Lynch Syndrome (LS) carriers demonstrated that naproxen administered for 6-months is a safe primary chemoprevention that promotes activation of different resident immune cell types without increasing lymphoid cellularity. While intriguing, the precise immune cell types enriched by naproxen remained unanswered. Here, we have utilized cutting-edge technology to elucidate the immune cell types activated by naproxen in mucosal tissue of LS patients.

METHODS: Normal colorectal mucosa samples (pre- and post-treatment) from a subset of patients enrolled in the randomized and placebo-controlled 'Naproxen Study' were obtained and subjected to a tissue microarray for image mass cytometry …

Membranous Nephropathy In Chronic Lymphocytic Leukemia Responsive To Ibrutinib: A Case Report, Anna-Eve Turcotte, William F Glass, Jamie S Lin, Jan A Burger

Faculty, Staff and Student Publications

Membranous nephropathy (MN) is an uncommon renal presentation in patients with chronic lymphocytic leukemia (CLL), and as such, there is no standard therapy for these patients. A few cases of MN in CLL have been described with varying success in MN treatment involving alkylating agents and fludarabine. Here we report the first case of MN in a patient with CLL treated with ibrutinib with complete renal response. This presentation underlines the importance of recognizing rare glomerular diseases that may occur with CLL and offers a new therapeutic avenue to the treatment of CLL-associated MN.

Machine Learning-Based Prediction Of Acute Mortality In Emergency Department Patients Using Twelve-Lead Electrocardiogram, Po-Cheng Chang, Zhi-Yong Liu, Yu-Chang Huang, Yu-Chun Hsu, Jung-Sheng Chen, Ching-Heng Lin, Richard Tsai, Chung-Chuan Chou, Ming-Shien Wen, Hung-Ta Wo, Wen-Chen Lee, Hao-Tien Liu, Chun-Chieh Wang, Chang-Fu Kuo

Faculty, Staff and Student Publications

BACKGROUND: The risk of mortality is relatively high among patients who visit the emergency department (ED), and stratifying patients at high risk can help improve medical care. This study aimed to create a machine-learning model that utilizes the standard 12-lead ECG to forecast acute mortality risk in ED patients.

METHODS: The database included patients who visited the EDs and underwent standard 12-lead ECG between October 2007 and December 2017. A convolutional neural network (CNN) ECG model was developed to classify survival and mortality using 12-lead ECG tracings acquired from 345,593 ED patients. For machine learning model development, the patients were …

Artificial Intelligence-Enabled Electrocardiographic Screening For Left Ventricular Systolic Dysfunction And Mortality Risk Prediction, Yu-Chang Huang, Yu-Chun Hsu, Zhi-Yong Liu, Ching-Heng Lin, Richard Tsai, Jung-Sheng Chen, Po-Cheng Chang, Hao-Tien Liu, Wen-Chen Lee, Hung-Ta Wo, Chung-Chuan Chou, Chun-Chieh Wang, Ming-Shien Wen, Chang-Fu Kuo

Faculty, Staff and Student Publications

BACKGROUND: Left ventricular systolic dysfunction (LVSD) characterized by a reduced left ventricular ejection fraction (LVEF) is associated with adverse patient outcomes. We aimed to build a deep neural network (DNN)-based model using standard 12-lead electrocardiogram (ECG) to screen for LVSD and stratify patient prognosis.

METHODS: This retrospective chart review study was conducted using data from consecutive adults who underwent ECG examinations at Chang Gung Memorial Hospital in Taiwan between October 2007 and December 2019. DNN models were developed to recognize LVSD, defined as LVEF

RESULTS: The mean age of patients in the testing dataset was 63.7 ± 16.3 years (46.3% …

Distinct Patterns Of Auto-Reactive Antibodies Associated With Organ-Specific Immune-Related Adverse Events, Mehmet Altan, Quan-Zhen Li, Qi Wang, Natalie I Vokes, Ajay Sheshadri, Jianjun Gao, Chengsong Zhu, Hai T Tran, Saumil Gandhi, Mara B Antonoff, Stephen Swisher, Jing Wang, Lauren A Byers, Noha Abdel-Wahab, Maria C Franco-Vega, Yinghong Wang, J Jack Lee, Jianjun Zhang, John V Heymach

Faculty, Staff and Student Publications

UNLABELLED: The roles of preexisting auto-reactive antibodies in immune-related adverse events (irAEs) associated with immune checkpoint inhibitor therapy are not well defined. Here, we analyzed plasma samples longitudinally collected at predefined time points and at the time of irAEs from 58 patients with immunotherapy naïve metastatic non-small cell lung cancer treated on clinical protocol with ipilimumab and nivolumab. We used a proteomic microarray system capable of assaying antibody reactivity for IgG and IgM fractions against 120 antigens for systemically evaluating the correlations between auto-reactive antibodies and certain organ-specific irAEs. We found that distinct patterns of auto-reactive antibodies at baseline were …

Efficient Federated Kinship Relationship Identification, Xinyue Wang, Leonard Dervishi, Wentao Li, Xiaoqian Jiang, Erman Ayday, Jaideep Vaidya

Faculty, Staff and Student Publications

Kinship relationship estimation plays a significant role in today's genome studies. Since genetic data are mostly stored and protected in different silos, retrieving the desirable kinship relationships across federated data warehouses is a non-trivial problem. The ability to identify and connect related individuals is important for both research and clinical applications. In this work, we propose a new privacy-preserving kinship relationship estimation framework: Incremental Update Kinship Identification (INK). The proposed framework includes three key components that allow us to control the balance between privacy and accuracy (of kinship estimation): an incremental process coupled with the use of auxiliary information and …

Sensitive Data Detection With High-Throughput Machine Learning Models In Electrical Health Records, Kai Zhang, Xiaoqian Jiang

Faculty, Staff and Student Publications

In the era of big data, there is an increasing need for healthcare providers, communities, and researchers to share data and collaborate to improve health outcomes, generate valuable insights, and advance research. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) is a federal law designed to protect sensitive health information by defining regulations for protected health information (PHI). However, it does not provide efficient tools for detecting or removing PHI before data sharing. One of the challenges in this area of research is the heterogeneous nature of PHI fields in data across different parties. This variability makes rule-based …

Large Language Models For Healthcare Data Augmentation: An Example On Patient-Trial Matching, Jiayi Yuan, Ruixiang Tang, Xiaoqian Jiang, Xia Hu

Faculty, Staff and Student Publications

The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care. However, current approaches face challenges such as data standardization, ethical considerations, and a lack of interoperability between Electronic Health Records (EHRs) and clinical trial criteria. In this paper, we explore the potential of large language models (LLMs) to address these challenges by leveraging their advanced natural language generation capabilities to improve compatibility between EHRs and clinical trial descriptions. We propose an innovative privacy-aware data augmentation approach for LLM-based patient-trial matching (LLM-PTM), which balances the benefits of LLMs while ensuring the security …

Split Learning For Distributed Collaborative Training Of Deep Learning Models In Health Informatics, Zhuohang Li, Chao Yan, Xinmeng Zhang, Gharib Gharibi, Zhijun Yin, Xiaoqian Jiang, Bradley A Malin

Faculty, Staff and Student Publications

Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in part, to the inherent siloed nature of these organizations and patient privacy requirements. To address this problem, we illustrate how split learning can enable collaborative training of deep learning models across disparate and privately maintained health datasets, while keeping the original records and model parameters private. We introduce a new privacy-preserving distributed learning framework that offers a higher level of privacy compared to conventional federated learning. We …

A Scientific Communication Mentoring Intervention Benefits Diverse Mentees With Language Variety Related Discomfort, Carrie A Cameron, Hwa Young Lee, Cheryl B Anderson, Erin K Dahlstrom, Shine Chang

Faculty, Staff and Student Publications

We studied social-psychological effects over time of a faculty-mentor workshop intervention that addressed attitudes associated with language variety and their impact on scientific communication (SC) skill development of PhD and postdoctoral STEM research trainees (N = 274). Six months after their mentors attended the workshop, all mentees had significant gains in productivity in speaking tasks. In particular, mentees with high language discomfort rated their quality of communication with their mentor and their enthusiasm about communicating more highly (p < .05 for both measures), compared to mentees with low language discomfort. In addition, mentees raised speaking nonstandardized varieties of English reported significant reductions in discomfort related to language use (p = .003), compared to mentees raised speaking standardized English. We conclude that training mentors to understand and respond to …

Integrating Comorbidity Knowledge For Alzheimer's Disease Drug Repurposing Using Multi-Task Graph Neural Network, Ko-Hong Lin, Kang-Lin Hsieh, Xiaoqian Jiang, Yejin Kim

Faculty, Staff and Student Publications

Alzheimer's Disease (AD) is a multifactorial disease that shares common etiologies with its multiple comorbidities, especially vascular diseases. To predict repurposable drugs for AD utilizing the relatively well-investigated comorbidities' knowledge, we proposed a multi-task graph neural network (GNN)-based pipeline that incorporates the corresponding biomedical interactome of these diseases with their genetic markers and effective therapeutics. Our pipeline can accurately capture the interactions and disease classification in the network. Next, we predicted drugs that might interact with the AD module by the node embedding similarity. Our candidates are mostly BBB permeable, and literature evidence showed their potential for treating AD pathologies, …

Local Contrastive Learning For Medical Image Recognition, Syed A Rizvi, Ruixiang Tang, Xiaoqian Jiang, Xiaotian Ma, Xia Hu

Faculty, Staff and Student Publications

The proliferation of Deep Learning (DL)-based methods for radiographic image analysis has created a great demand for expert-labeled radiology data. Recent self-supervised frameworks have alleviated the need for expert labeling by obtaining supervision from associated radiology reports. These frameworks, however, struggle to distinguish the subtle differences between different pathologies in medical images. Additionally, many of them do not provide interpretation between image regions and text, making it difficult for radiologists to assess model predictions. In this work, we propose Local Region Contrastive Learning (LRCLR), a flexible fine-tuning framework that adds layers for significant image region selection as well as cross-modality …

School-Based Intervention Impacts Availability Of Vegetables And Beverages In Participants’ Homes, Erin A Hudson, Marissa Burgermaster, Sophia M Isis, Matthew R Jeans, Sarvenaz Vandyousefi, Matthew J Landry, Rebecca Seguin-Fowler, Joya Chandra, Jaimie Davis

Faculty, Staff and Student Publications

As rates of metabolic syndrome rise, children consume too few vegetables and too much added sugar. Because children tend to eat what is available at home, the home environment plays a key role in shaping dietary habits. This secondary analysis evaluated the effects of a school-based gardening, cooking, and nutrition education intervention (TX Sprouts) compared to control on the availability of vegetables, fruit juice, and sugar-sweetened beverages (SSBs) at home. In the TX Sprouts cluster-randomized trial, 16 schools were randomized to TX Sprouts (

Comparison Of Pharmacological Inhibitors Of Lysine-Specific Demethylase 1 In Glioblastoma Stem Cells Reveals Inhibitor-Specific Efficacy Profiles, Lea M Stitzlein, Achintyan Gangadharan, Leslie M Walsh, Deokhwa Nam, Alexsandra B Espejo, Melissa M Singh, Kareena H Patel, Yue Lu, Xiaoping Su, Ravesanker Ezhilarasan, Joy Gumin, Sanjay Singh, Erik Sulman, Frederick F Lang, Joya Chandra

Faculty, Staff and Student Publications

INTRODUCTION: Improved therapies for glioblastoma (GBM) are desperately needed and require preclinical evaluation in models that capture tumor heterogeneity and intrinsic resistance seen in patients. Epigenetic alterations have been well documented in GBM and lysine-specific demethylase 1 (LSD1/KDM1A) is amongst the chromatin modifiers implicated in stem cell maintenance, growth and differentiation. Pharmacological inhibition of LSD1 is clinically relevant, with numerous compounds in various phases of preclinical and clinical development, but an evaluation and comparison of LSD1 inhibitors in patient-derived GBM models is lacking.

METHODS: To assess concordance between knockdown of LSD1 and inhibition of LSD1 using a prototype inhibitor in …

Immune Checkpoint Inhibitors In Advanced Cutaneous Squamous Cell Carcinoma: A Systemic Review And Meta-Analysis, Haoran Zhang, Ai Zhong, Junjie Chen

Faculty, Staff and Student Publications

BACKGROUND: To evaluate the immune checkpoint inhibitors (CPI) for the treatment of patients with advanced cutaneous squamous cell carcinoma (CSCC).

MATERIALS AND METHODS: A meta-analysis was conducted, and the efficacy and safety of CPI were assessed.

RESULTS: A total of 13 studies with 980 patients were included. The pooled objective response rate (ORR) and disease control rate were 47.2% and 64.4%, separately. In addition, patients with primary tumor located in head and neck (odds ratio [OR]: 0.374, 95% confidence interval [CI]: 0.219-0.640, p < 0.001) and positive expression of programmed death ligand 1 (OR: 0.364, 95% CI: 0.158-0.842, P = 0.018) had superior ORR during CPI treatment. The incidence of progression free survival at 6 and 12 months was 59.3% and 52.8%, and 80.6% and 76.4% for overall survival. As for safety, the overall incidence of adverse events with all grades and 3-4 grade was 76.9% and 20.2%.

CONCLUSIONS: Our systematic review confirmed the satisfying efficacy and acceptable toxicity of CPI for advanced CSCC.

Text Classification Of Cancer Clinical Trial Eligibility Criteria, Yumeng Yang, Soumya Jayaraj, Ethan Ludmir, Kirk Roberts

Faculty, Staff and Student Publications

Automatic identification of clinical trials for which a patient is eligible is complicated by the fact that trial eligibility are stated in natural language. A potential solution to this problem is to employ text classification methods for common types of eligibility criteria. In this study, we focus on seven common exclusion criteria in cancer trials: prior malignancy, human immunodeficiency virus, hepatitis B, hepatitis C, psychiatric illness, drug/substance abuse, and autoimmune illness. Our dataset consists of 764 phase III cancer trials with these exclusions annotated at the trial level. We experiment with common transformer models as well as a new pre-trained …

Sart-3 Functions To Regulate Germline Sex Determination In C Elegans, Tokiko Furuta, Swathi Arur

Faculty, Staff and Student Publications

Caenorhabditis elegans gene sart-3 was first identified as the homolog of human SART3 ( S quamous cell carcinoma A ntigen R ecognized by T -cells 3). In humans, expression of SART3 is associated with squamous cell carcinoma, thus most of the studies focus on its potential role as a target of cancer immunotherapy (Shichijo et al. 1998; Yang et al. 1999). Furthermore, SART3 is also known as Tip110 (Liu et al. 2002; Whitmill et al. 2016) in the context of HIV virus host activation pathway. Despite these disease related studies, the molecular function of this protein was not revealed until …

Optimization Of Mesh Generation For Geometric Accuracy, Robustness, And Efficiency Of Biomechanical-Model-Based Deformable Image Registration, Yulun He, Brian M Anderson, Guillaume Cazoulat, Bastien Rigaud, Lusmeralis Almodovar-Abreu, Julianne Pollard-Larkin, Peter Balter, Zhongxing Liao, Radhe Mohan, Bruno Odisio, Stina Svensson, Kristy K Brock

Faculty, Staff and Student Publications

BACKGROUND: Successful generation of biomechanical-model-based deformable image registration (BM-DIR) relies on user-defined parameters that dictate surface mesh quality. The trial-and-error process to determine the optimal parameters can be labor-intensive and hinder DIR efficiency and clinical workflow.

PURPOSE: To identify optimal parameters in surface mesh generation as boundary conditions for a BM-DIR in longitudinal liver and lung CT images to facilitate streamlined image registration processes.

METHODS: Contrast-enhanced CT images of 29 colorectal liver cancer patients and end-exhale four-dimensional CT images of 26 locally advanced non-small cell lung cancer patients were collected. Different combinations of parameters that determine the triangle mesh quality …

Head And Neck Radiation Therapy Patterns Of Practice Variability Identified As A Challenge To Real-World Big Data: Results From The Learning From Analysis Of Multicentre Big Data Aggregation (Lambda) Consortium, Amanda Caissie, Michelle Mierzwa, Clifton David Fuller, Murali Rajaraman, Alex Lin, Andrew Macdonald, Richard Popple, Ying Xiao, Lisanne Vandijk, Peter Balter, Helen Fong, Heping Xu, Matthew Kovoor, Joonsang Lee, Arvind Rao, Mary Martel, Reid Thompson, Brandon Merz, John Yao, Charles Mayo

Faculty, Staff and Student Publications

PURPOSE: Outside of randomized clinical trials, it is difficult to develop clinically relevant evidence-based recommendations for radiation therapy (RT) practice guidelines owing to lack of comprehensive real-world data. To address this knowledge gap, we formed the Learning from Analysis of Multicenter Big Data Aggregation consortium to cooperatively implement RT data standardization, develop software solutions for data analysis, and recommend clinical practice change based on real-world data analyzed. The first phase of this "Big Data" study aimed at characterizing variability in clinical practice patterns of dosimetric data for organs at risk (OARs) that would undermine subsequent use of large-scale, electronically aggregated …

A Novel Nih Research Grant Recommender Using Bert, Jie Zhu, Braja Gopal Patra, Hulin Wu, Ashraf Yaseen

Faculty, Staff and Student Publications

Research grants are important for researchers to sustain a good position in academia. There are many grant opportunities available from different funding agencies. However, finding relevant grant announcements is challenging and time-consuming for researchers. To resolve the problem, we proposed a grant announcements recommendation system for the National Institute of Health (NIH) grants using researchers' publications. We formulated the recommendation as a classification problem and proposed a recommender using state-of-the-art deep learning techniques: i.e. Bidirectional Encoder Representations from Transformers (BERT), to capture intrinsic, non-linear relationship between researchers' publications and grants announcements. Internal and external evaluations were conducted to assess the …

Data Mining Pipeline For Covid-19 Vaccine Safety Analysis Using A Large Electronic Health Record, Yan Huang, Xiaojin Li, Deepa Dongarwar, Hulin Wu, Guo-Qiang Zhang

Faculty, Staff and Student Publications

We developed a novel data mining pipeline that automatically extracts potential COVID-19 vaccine-related adverse events from a large Electronic Health Record (EHR) dataset. We applied this pipeline to Optum® de-identified COVID-19 EHR dataset containing COVID-19 vaccine records between December 11, 2020 and January 20, 2022. We compared post-vaccination diagnoses between the COVID-19 vaccine group and the influenza vaccine group among 553,682 individuals without COVID-19 infection. We extracted 1,414 ICD-10 diagnosis categories (first three ICD10 digits) within 180 days after the first dose of the COVID-19 vaccine. We then ranked the diagnosis codes using the adverse event rates and adjusted odds …

Dose Escalation For Pancreas Sbrt: Potential And Limitations Of Using Daily Online Adaptive Radiation Therapy And An Iterative Isotoxicity Automated Planning Approach, Dong Joo Rhee, Sam Beddar, Joseph Abi Jaoude, Gabriel Sawakuchi, Rachael Martin, Luis Perles, Cenji Yu, Yulun He, Laurence E Court, Ethan B Ludmir, Albert C Koong, Prajnan Das, Eugene J Koay, Cullen Taniguichi, Joshua S Niedzielski

Faculty, Staff and Student Publications

PURPOSE: To determine the dosimetric limitations of daily online adaptive pancreas stereotactic body radiation treatment by using an automated dose escalation approach.

METHODS AND MATERIALS: We collected 108 planning and daily computed tomography (CT) scans from 18 patients (18 patients × 6 CT scans) who received 5-fraction pancreas stereotactic body radiation treatment at MD Anderson Cancer Center. Dose metrics from the original non-dose-escalated clinical plan (non-DE), the dose-escalated plan created on the original planning CT (DE-ORI), and the dose-escalated plan created on daily adaptive radiation therapy CT (DE-ART) were analyzed. We developed a dose-escalation planning algorithm within the radiation treatment …

Transiently Impaired Endothelial Function During Thyroid Hormone Withdrawal In Differentiated Thyroid Cancer Patients, Li-Ying Hou, Xiao Li, Guo-Qiang Zhang, Chuang Xi, Chen-Tian Shen, Hong-Jun Song, Wen-Kun Bai, Zhong-Ling Qiu, Quan-Yong Luo

Faculty, Staff and Student Publications

PURPOSE: Endothelial dysfunction, which was associated with chronic hypothyroidism, was an early event in atherosclerosis. Whether short-term hypothyroidism following thyroxine withdrawal during radioiodine (RAI) therapy was associated with endothelial dysfunction in patients with differentiated thyroid cancer (DTC) was unclear. Aim of the study was to assess whether short-term hypothyroidism could impair endothelial function and the accompanied metabolic changes in the whole process of RAI therapy.

METHODS: We recruited fifty-one patients who underwent total thyroidectomy surgery and would accept RAI therapy for DTC. We analyzed thyroid function, endothelial function and serum lipids levels of the patients at three time points: the …

Revisiting The Type Species Of The Genus Homidia (Collembola, Entomobryidae), Guo-Qiang Zhang, Yu-Xin Zhao, Feng Zhang

Faculty, Staff and Student Publications

Homidiacingula Börner, 1906, the type species of the genus Homidia Börner, 1906, is widespread from India to Southeast Asia, but its detailed morphological characteristics have not yet been described. We examined the morphology of specimens of H.cingula from Indonesia and southwestern China and confirmed their conspecific status by comparing their DNA barcoding sequences. We also compared the morphology of H.cingula with other two closely related species, confirming the valid species status of H.subcingula Denis, 1948. Our study provides new taxonomic and molecular data for the genus Homidia.

Extreme Temperatures And Sickness Absence In The Mediterranean Province Of Barcelona: An Occupational Health Issue, Mireia Utzet, Amaya Ayala-Garcia, Fernando G Benavides, Xavier Basagaña

Faculty, Staff and Student Publications

OBJECTIVES: This study aims to assess the association between daily temperature and sickness absence episodes in the Mediterranean province of Barcelona between 2012 and 2015, according to sociodemographic and occupational characteristics.

METHODS: Ecological study of a sample of salaried workers affiliated to the Spanish social security, resident in Barcelona province between 2012 and 2015. The association between daily mean temperature and risk of new sickness absence episodes was estimated with distributed lag non-linear models. The lag effect up to 1 week was considered. Analyses were repeated separately by sex, age groups, occupational category, economic sector and medical diagnosis groups of …

Selection And Validation Of Optimal Reference Genes For Rt-Qpcr Analyses In Aphidoletes Aphidimyza Rondani (Diptera: Cecidomyiidae), Xiu-Xian Shen, Guo-Qiang Zhang, Yu-Xin Zhao, Xiao-Xiao Zhu, Xiao-Fei Yu, Mao-Fa Yang, Feng Zhang

Faculty, Staff and Student Publications

Aphidoletes aphidimyza is a predator that is an important biological agent used to control agricultural and forestry aphids. Although many studies have investigated its biological and ecological characteristics, few molecular studies have been reported. The current study was performed to identify suitable reference genes to facilitate future gene expression and function analyses via quantitative reverse transcription PCR. Eight reference genes glyceraldehyde-3-phosphate dehydrogenase (GAPDH), RPS13, RPL8, RPS3, α-Tub, β-actin, RPL32, and elongation factor 1 alpha (EF1-α) were selected. Their expression levels were determined under four different experimental conditions (developmental stages, adult …

Workplace Aggression Against Healthcare Workers In A Spanish Healthcare Institution Between 2019 And 2021: The Impact Of The Covid-19 Pandemic, Aitor Díaz, Mireia Utzet, Joan Mirabent, Pilar Diaz, Jose Maria Ramada, Consol Serra, Fernando G Benavides

Faculty, Staff and Student Publications

OBJECTIVES: Describe the incidence of first aggressions among healthcare workers (HCWs) before and during the COVID-19 pandemic in a Spanish healthcare institution, according to workers' socio-occupational characteristics and analyze the impact of the pandemic on it.

METHODS: A cohort involving HCWs who worked in the institution for at least 1 week each year from 1 January 2019 to 31 December 2021. Adjusted relative risks (aRR) were estimated using generalized estimating equations and negative binomial models to calculate the differences in WPA between the different time periods. All analyses were stratified by gender.

RESULTS: Among women, the incidence was 6.8% (6.0; …

Plasma Enzymatic Activity, Proteomics And Peptidomics In Covid-19-Induced Sepsis: A Novel Approach For The Analysis Of Hemostasis, Fernando Dos Santos, Joyce B Li, Nathalia Juocys, Rafi Mazor, Laura Beretta, Nicole G Coufal, Michael T Y Lam, Mazen F Odish, Maria Claudia Irigoyen, Anthony J O'Donoghue, Federico Aletti, Erik B Kistler

Faculty, Staff and Student Publications

Introduction: Infection by SARS-CoV-2 and subsequent COVID-19 can cause viral sepsis. We investigated plasma protease activity patterns in COVID-19-induced sepsis with bacterial superinfection, as well as plasma proteomics and peptidomics in order to assess the possible implications of enhanced proteolysis on major protein systems (e.g., coagulation). Methods: Patients (=4) admitted to the intensive care units (ICUs) at the University of California, San Diego (UCSD) Medical Center with confirmed positive test for COVID-19 by real-time reverse transcription polymerase chain reaction (RT-PCR) were enrolled in a study approved by the UCSD Institutional Review Board (IRB# 190699, Protocol #20-0006). Informed consent was obtained …

Anti-Inflammatory Effects Of Vagus Nerve Stimulation In Pediatric Patients With Epilepsy, Supender Kaur, Nathan R Selden, Alejandro Aballay

Faculty, Staff and Student Publications

INTRODUCTION: The neural control of the immune system by the nervous system is critical to maintaining immune homeostasis, whose disruption may be an underlying cause of several diseases, including cancer, multiple sclerosis, rheumatoid arthritis, and Alzheimer's disease.

METHODS: Here we studied the role of vagus nerve stimulation (VNS) on gene expression in peripheral blood mononuclear cells (PBMCs). Vagus nerve stimulation is widely used as an alternative treatment for drug-resistant epilepsy. Thus, we studied the impact that VNS treatment has on PBMCs isolated from a cohort of existing patients with medically refractory epilepsy. A comparison of genome-wide changes in gene expression …