Digital Commons Network^™

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 …

Application Of Artificial Intelligence To Plasma Metabolomics Profiles To Predict Response To Neoadjuvant Chemotherapy In Triple-Negative Breast Cancer, Ehsan Irajizad, Ranran Wu, Jody Vykoukal, Eunice Murage, Rachelle Spencer, Jennifer B Dennison, Stacy Moulder, Elizabeth Ravenberg, Bora Lim, Jennifer Litton, Debu Tripathym, Vicente Valero, Senthil Damodaran, Gaiane M Rauch, Beatriz Adrada, Rosalind Candelaria, Jason B White, Abenaa Brewster, Banu Arun, James P Long, Kim Anh Do, Sam Hanash, Johannes F Fahrmann

Faculty, Staff and Student Publications

There is a need to identify biomarkers predictive of response to neoadjuvant chemotherapy (NACT) in triple-negative breast cancer (TNBC). We previously obtained evidence that a polyamine signature in the blood is associated with TNBC development and progression. In this study, we evaluated whether plasma polyamines and other metabolites may identify TNBC patients who are less likely to respond to NACT. Pre-treatment plasma levels of acetylated polyamines were elevated in TNBC patients that had moderate to extensive tumor burden (RCB-II/III) following NACT compared to those that achieved a complete pathological response (pCR/RCB-0) or had minimal residual disease (RCB-I). We further applied …

Harmonization Of Multi-Scanner In Vivo Magnetic Resonance Spectroscopy: Enigma Consortium Task Group Considerations, Ashley D Harris, Houshang Amiri, Mariana Bento, Ronald Cohen, Christopher R K Ching, Christina Cudalbu, Emily L Dennis, Arne Doose, Stefan Ehrlich, Ivan I Kirov, Ralf Mekle, Georg Oeltzschner, Eric Porges, Roberto Souza, Friederike I Tam, Brian Taylor, Paul M Thompson, Yann Quidé, Elisabeth A Wilde, John Williamson, Alexander P Lin, Brenda Bartnik-Olson

Faculty, Staff and Student Publications

Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impact, however, has been limited due to small sample sizes and methodological variability in addition to intrinsic limitations of the method itself such as its sensitivity to motion. The lack of standardization from a data acquisition and data processing perspective makes it difficult to pool multiple studies and/or conduct multisite studies that are necessary for supporting clinically relevant findings. Based on the experience of the ENIGMA MRS …

Elastic Shape Analysis Of Brain Structures For Predictive Modeling Of Ptsd, Yuexuan Wu, Suprateek Kundu, Jennifer S Stevens, Negar Fani, Anuj Srivastava

Faculty, Staff and Student Publications

It is well-known that morphological features in the brain undergo changes due to traumatic events and associated disorders such as post-traumatic stress disorder (PTSD). However, existing approaches typically offer group-level comparisons, and there are limited predictive approaches for modeling behavioral outcomes based on brain shape features that can account for heterogeneity in PTSD, which is of paramount interest. We propose a comprehensive shape analysis framework representing brain sub-structures, such as the hippocampus, amygdala, and putamen, as parameterized surfaces and quantifying their shape differences using an elastic shape metric. Under this metric, we compute shape summaries (mean, covariance, PCA) of brain …

Urinary T Cells Are Detected In Patients With Immune Checkpoint Inhibitor-Associated Immune Nephritis That Are Clonotypically Identical To Kidney T Cell Infiltrates, Shailbala Singh, Leticia C Clemente, Edwin R Parra, Amanda Tchakarov, Chao Yang, Yisheng Li, James P Long, Cassian Yee, Jamie S Lin

Faculty, Staff and Student Publications

Acute kidney injury (AKI) occurs in ~20% of patients receiving immune checkpoint inhibitor (ICI) therapy; however, only 2-5% will develop ICI-mediated immune nephritis. Conventional tests are nonspecific in diagnosing disease pathology and invasive procedures (i.e. kidney biopsy) may not be feasible. In other autoimmune renal diseases, urinary immune cells correlated with the pathology or were predictive of disease activity. Corresponding evidence and analysis are absent for ICI-mediated immune nephritis. We report the first investigation analyzing immune cell profiles of matched kidney biopsies and urine of patients with ICI-AKI. We demonstrated the presence of urinary T cells in patients with immune …

Clinical Implementation And Initial Experience With A 15 Tesla Mr-Linac For Mr-Guided Radiation Therapy For Gynecologic Cancer: An R-Ideal Stage 1 And 2a First In Humans Feasibility Study Of New Technology Implementation, David S Lakomy, Jinzhong Yang, Sastry Vedam, Jihong Wang, Belinda Lee, Angela Sobremonte, Pamela Castillo, Neil Hughes, Mustefa Mohammedsaid, Anuja Jhingran, Ann H Klopp, Seungtaek Choi, C David Fuller, Lilie L Lin

Faculty, Staff and Student Publications

PURPOSE: Magnetic resonance imaging-guided linear accelerator systems (MR-linacs) can facilitate the daily adaptation of radiation therapy plans. Here, we report our early clinical experience using a MR-linac for adaptive radiation therapy of gynecologic malignancies.

METHODS AND MATERIALS: Treatments were planned with an Elekta Monaco v5.4.01 and delivered by a 1.5 Tesla Elekta Unity MR-linac. The system offers a choice of daily adaptation based on either position (ATP) or shape (ATS) of the tumor and surrounding normal structures. The ATS approach has the option of manually editing the contours of tumors and surrounding normal structures before the plan is adapted. Here, …

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 …

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 …

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 …

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 …

Multiplexed Engineering And Precision Gene Editing In Cellular Immunotherapy, Alexander Biederstädt, Gohar Shahwar Manzar, May Daher

Faculty, Staff and Student Publications

The advent of cellular immunotherapy in the clinic has entirely redrawn the treatment landscape for a growing number of human cancers. Genetically reprogrammed immune cells, including chimeric antigen receptor (CAR)-modified immune effector cells as well as T cell receptor (TCR) therapy, have demonstrated remarkable responses across different hard-to-treat patient populations. While these novel treatment options have had tremendous success in providing long-term remissions for a considerable fraction of treated patients, a number of challenges remain. Limited

Deep Learning Auto-Segmentation Of Cervical Skeletal Muscle For Sarcopenia Analysis In Patients With Head And Neck Cancer, Mohamed A Naser, Kareem A Wahid, Aaron J Grossberg, Brennan Olson, Rishab Jain, Dina El-Habashy, Cem Dede, Vivian Salama, Moamen Abobakr, Abdallah S R Mohamed, Renjie He, Joel Jaskari, Jaakko Sahlsten, Kimmo Kaski, Clifton D Fuller

Faculty, Staff and Student Publications

Background/purpose: Sarcopenia is a prognostic factor in patients with head and neck cancer (HNC). Sarcopenia can be determined using the skeletal muscle index (SMI) calculated from cervical neck skeletal muscle (SM) segmentations. However, SM segmentation requires manual input, which is time-consuming and variable. Therefore, we developed a fully-automated approach to segment cervical vertebra SM.

Materials/methods: 390 HNC patients with contrast-enhanced CT scans were utilized (300-training, 90-testing). Ground-truth single-slice SM segmentations at the C3 vertebra were manually generated. A multi-stage deep learning pipeline was developed, where a 3D ResUNet auto-segmented the C3 section (33 mm window), the middle slice of the …

Evaluation Of Deep Learning-Based Multiparametric Mri Oropharyngeal Primary Tumor Auto-Segmentation And Investigation Of Input Channel Effects: Results From A Prospective Imaging Registry, Kareem A Wahid, Sara Ahmed, Renjie He, Lisanne V Van Dijk, Jonas Teuwen, Brigid A Mcdonald, Vivian Salama, Abdallah S R Mohamed, Travis Salzillo, Cem Dede, Nicolette Taku, Stephen Y Lai, Clifton D Fuller, Mohamed A Naser

Faculty, Staff and Student Publications

Background/purpose: Oropharyngeal cancer (OPC) primary gross tumor volume (GTVp) segmentation is crucial for radiotherapy. Multiparametric MRI (mpMRI) is increasingly used for OPC adaptive radiotherapy but relies on manual segmentation. Therefore, we constructed mpMRI deep learning (DL) OPC GTVp auto-segmentation models and determined the impact of input channels on segmentation performance.

Materials/methods: GTVp ground truth segmentations were manually generated for 30 OPC patients from a clinical trial. We evaluated five mpMRI input channels (T2, T1, ADC, Ktrans, Ve). 3D Residual U-net models were developed and assessed using leave-one-out cross-validation. A baseline T2 model was compared to mpMRI models (T2 + T1, …

Comparison Of Three Exercise Interventions With And Without Gemcitabine Treatment On Pancreatic Tumor Growth In Mice: No Impact On Tumor Infiltrating Lymphocytes, Priti Gupta, Charles F Hodgman, Claudia Alvarez-Florez, Keri L Schadler, Melissa M Markofski, Daniel P O'Connor, Emily C Lavoy

Faculty, Staff and Student Publications

Exercise has been shown to slow pancreatic tumor growth, but whether exercise interventions of differing volume or intensity yield differential effects on tumor outcomes is unknown. In this study, we compared three exercise training interventions implemented with and without chemotherapy on pancreatic tumor growth in mice. Methods: Male C57BL/6 mice (6-8 weeks old) were subcutaneously inoculated with pancreatic ductal adenocarcinoma tumor cells (PDAC 4662). Upon tumor detection, mice received gemcitabine 15 mg/kg intraperitoneally 3 days/week and were assigned to exercise: high volume continuous exercise (HVCE), low volume continuous exercise (LVCE), high intensity interval training (HIIT), or sedentary (SED). HVCE ran …

Automated Segmentation Of Colorectal Liver Metastasis And Liver Ablation On Contrast-Enhanced Ct Images, Brian M Anderson, Bastien Rigaud, Yuan-Mao Lin, A Kyle Jones, Hynseon Christine Kang, Bruno C Odisio, Kristy K Brock

Faculty, Staff and Student Publications

Objectives: Colorectal cancer (CRC), the third most common cancer in the USA, is a leading cause of cancer-related death worldwide. Up to 60% of patients develop liver metastasis (CRLM). Treatments like radiation and ablation therapies require disease segmentation for planning and therapy delivery. For ablation, ablation-zone segmentation is required to evaluate disease coverage. We hypothesize that fully convolutional (FC) neural networks, trained using novel methods, will provide rapid and accurate identification and segmentation of CRLM and ablation zones.

Methods: Four FC model styles were investigated: Standard 3D-UNet, Residual 3D-UNet, Dense 3D-UNet, and Hybrid-WNet. Models were trained on 92 patients from …

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. …

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 …

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 …

Single-Cell And Single-Nuclei Rna Sequencing As Powerful Tools To Decipher Cellular Heterogeneity And Dysregulation In Neurodegenerative Diseases, Raquel Cuevas-Diaz Duran, Juan Carlos González-Orozco, Iván Velasco, Jia Qian Wu

Faculty, Staff and Student Publications

Neurodegenerative diseases affect millions of people worldwide and there are currently no cures. Two types of common neurodegenerative diseases are Alzheimer's (AD) and Parkinson's disease (PD). Single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq) have become powerful tools to elucidate the inherent complexity and dynamics of the central nervous system at cellular resolution. This technology has allowed the identification of cell types and states, providing new insights into cellular susceptibilities and molecular mechanisms underlying neurodegenerative conditions. Exciting research using high throughput scRNA-seq and snRNA-seq technologies to study AD and PD is emerging. Herein we review the recent progress in understanding …

In- And Out-Group Effects On Social Perception And Empathy In Cocaine Use Disorder, Tatjana Aue, Ann-Kathrin Kexel, Bruno Kluwe-Schiavon, Stephanie Bührer, Markus R Baumgartner, Leila M Soravia, Boris B Quednow

Faculty, Staff and Student Publications

Earlier research revealed that cocaine users display impairments in emotional but not necessarily in cognitive empathy. However, no study to date has tested whether empathy is generally altered or whether impairments are restricted to specific social targets. The current investigation addresses this open question. In addition, we examined whether attributions of warmth and competence as well as personal future expectancies differed between cocaine users and substance-naïve controls. Twenty-two chronic cocaine users and 40 stimulant-naïve controls specified their perceived warmth and competence for four social targets [in-group member, opposite consumption out-group member (cocaine user for controls and non-user for cocaine user), …

Emotion Regulation, Parasympathetic Function, And Psychological Well-Being, Ryan L Brown, Michelle A Chen, Jensine Paoletti, Eva E Dicker, E Lydia Wu-Chung, Angie S Leroy, Marzieh Majd, Robert Suchting, Julian F Thayer, Christopher P Fagundes

Faculty, Staff and Student Publications

The negative emotions generated following stressful life events can increase one's risk of depressive symptoms and promote higher levels of perceived stress. The process model of emotion regulation can help distinguish between adaptive and maladaptive emotion regulation strategies to determine who may be at the greatest risk of worse psychological health across the lifespan. Heart rate variability (HRV) may affect these relationships as it indexes aspects of self-regulation, including emotion and behavioral regulation, that enable an individual to dynamically adapt to the changing demands of both internal and external environments. In this study, we expected individual differences in resting vagally …

Mri Free Water As A Biomarker For Cognitive Performance: Validation In The Markvcid Consortium, Pauline Maillard, Laura J Hillmer, Hanzhang Lu, Konstantinos Arfanakis, Brian T Gold, Christopher E Bauer, Joel H Kramer, Adam M Staffaroni, Lara Stables, Danny J J Wang, Sudha Seshadri, Claudia L Satizabal, Alexa Beiser, Mohamad Habes, Myriam Fornage, Thomas H Mosley, Gary A Rosenberg, Baljeet Singh, Herpreet Singh, Kristin Schwab, Karl G Helmer, Steven M Greenberg, Charles Decarli, Arvind Caprihan

Faculty, Staff and Student Publications

Introduction: To evaluate the clinical validity of free water (FW), a diffusion tensor imaging-based biomarker kit proposed by the MarkVCID consortium, by investigating the association between mean FW (mFW) and executive function.

Methods: Baseline mFW was related to a baseline composite measure of executive function (EFC), adjusting for relevant covariates, in three MarkVCID sub-cohorts, and replicated in five, large, independent legacy cohorts. In addition, we tested whether baseline mFW predicted accelerated EFC score decline (mean follow-up time: 1.29 years).

Results: Higher mFW was found to be associated with lower EFC scores in MarkVCID legacy and sub-cohorts (p-values < 0.05). In addition, higher baseline mFW was associated significantly with accelerated decline in EFC scores (p …