Biomedical Informatics Commons^™

Leptomeningeal Disease In Melanoma: An Update On The Developments In Pathophysiology And Clinical Care, Inna Smalley, Adrienne Boire, Priscilla Brastianos, Harriet M Kluger, Eva Hernando-Monge, Peter A Forsyth, Kamran A Ahmed, Keiran S M Smalley, Sherise Ferguson, Michael A Davies, Isabella C Glitza Oliva

Faculty, Staff and Student Publications

Leptomeningeal disease (LMD) remains a major challenge in the clinical management of metastatic melanoma patients. Outcomes for patient remain poor, and patients with LMD continue to be excluded from almost all clinical trials. However, recent trials have demonstrated the feasibility of conducting prospective clinical trials in these patients. Further, new insights into the pathophysiology of LMD are identifying rational new therapeutic strategies. Here we present recent advances in the understanding of, and treatment options for, LMD from metastatic melanoma. We also annotate key areas of future focus to accelerate progress for this challenging but emerging field.

Myeloid Lineage Switch In Kmt2a- Rearranged Acute Lymphoblastic Leukemia Treated With Lymphoid Lineagedirected Therapies, Alex Bataller, Tareq Abuasab, David Mccall, Wei Wang, Branko Cuglievan, Ghayas C Issa, Elias Jabbour, Nicholas Short, Courtney D Dinardo, Guilin Tang, Guillermo Garcia-Manero, Hagop M Kantarjian, Koji Sasaki

Faculty, Staff and Student Publications

No abstract provided.

Editorial: Mathematical Modeling And Computational Predictions In Oncoimmunology, Vladimir A Kuznetsov, Heiko Enderling, Mark Chaplain

Faculty, Staff and Student Publications

No abstract provided.

Spatial Multiplexing And Omics, Julienne L Carstens, Santhoshi N Krishnan, Arvind Rao, Anna G Sorace, Erin H Seeley, Sammy Ferri-Borgogno, Jared K Burks

Faculty, Staff and Student Publications

Much like solving a game of Clue, understanding physiological mysteries involves answering who, what, when, and where. Multi-omics approaches delve into cellular and molecular identities (the who and what), while longitudinal data collection addresses the when. Spatial dimensions address the where. This Primer discusses current technologies enabling quantification across biological scales, emphasizing the importance of retaining the spatial dimension of that data. We outline experimental design considerations, including targeted versus untargeted approaches, sample types, biological scale, and four main classes of molecule detection. Spatial analytics are explored, covering questioning approaches, analytical platforms, image segmentation and sampling. Example applications, reproducibility considerations, …

Weak Supervision, Strong Results: Achieving High Performance In Intracranial Hemorrhage Detection With Fewer Annotation Labels, Kareem A Wahid, David Fuentes

Faculty, Staff and Student Publications

No abstract provided.

The Impact Of Psychological Strengths On Veteran Populations' Mental Health Trajectories During The Covid-19 Pandemic, Amanda Mccleery, Jonathan K Wynn, Derek M Novacek, Eric A Reavis, Damla Senturk, Catherine A Sugar, Jack Tsai, Michael F Green

Faculty, Staff and Student Publications

Purpose: Mental health trajectories during the COVID-19 pandemic have been examined in Veterans with tenuous social connections, i.e., those with recent homelessness (RHV) or a psychotic disorder (PSY), and in control Veterans (CTL). We test potential moderating effects on these trajectories by psychological factors that may help individuals weather the socio-emotional challenges associated with the pandemic (i.e., 'psychological strengths').

Methods: We assessed 81 PSY, 76 RHV, and 74 CTL over 5 periods between 05/2020 and 07/2021. Mental health outcomes (i.e., symptoms of depression, anxiety, contamination concerns, loneliness) were assessed at each period, and psychological strengths (i.e., a composite score based …

Fueling Cardiac Myocyte Proliferation, Manisha Deogharia, Priyatansh Gurha

Faculty, Staff and Student Publications

No abstract provided.

Metformin In Conjunction With Stereotactic Radiotherapy For Early-Stage Non-Small Cell Lung Cancer: Long-Term Results Of A Prospective Phase Ii Clinical Trial, Molly K Tate, Mike Hernandez, Joe Y Chang, Steven H Lin, Zhongxing Liao, Suja M Koshy, Heath D Skinner, Stephen G Chun

Faculty, Staff and Student Publications

Background/aim: Non-small cell lung cancer (NSCLC) is increasingly detected in early stages and there is interest in improving outcomes with stereotactic body radiotherapy (SBRT). As metformin affects NSCLC signaling pathways, it might alter the metabolism of NSCLC treated with SBRT. This study investigated the long-term outcomes of a phase II clinical trial evaluating metformin in conjunction with SBRT for early-stage NSCLC.

Patients and methods: The trial evaluated patients with American Joint Commission on Cancer (AJCC) 7th edition Stage I-II, cT1-T2N0M0 NSCLC who were randomized 6:1 to receive metformin versus placebo in conjunction with SBRT. The outcomes analyzed included local failure …

Predicting Suicidality In Late-Life Depression By 3d Convolutional Neural Network And Cross-Sample Entropy Analysis Of Resting-State Fmri, Chemin Lin, Chih-Mao Huang, Wei Chang, You-Xun Chang, Ho-Ling Liu, Shu-Hang Ng, Huang-Li Lin, Tatia Mei-Chun Lee, Shwu-Hua Lee, Shun-Chi Wu

Faculty, Staff and Student Publications

Background: Predicting suicide is a pressing issue among older adults; however, predicting its risk is difficult. Capitalizing on the recent development of machine learning, considerable progress has been made in predicting complex behavior such as suicide. As depression remained the strongest risk for suicide, we aimed to apply deep learning algorithms to identify suicidality in a group with late-life depression (LLD).

Methods: We enrolled 83 patients with LLD, 35 of which were non-suicidal and 48 were suicidal, including 26 with only suicidal ideation and 22 with past suicide attempts, for resting-state functional magnetic resonance imaging (MRI). Cross-sample entropy (CSE) analysis …

A Milestone In Epithelial-Mesenchymal Transition, Yutong Sun, Li Ma

Faculty, Staff and Student Publications

No abstract provided.

A Reinforcement Learning (Rl)-Motivated Simulation Framework For Evaluating Vancomycin Dosing Strategies, Bingyu Mao, Ziqian Xie, Laila Rasmy, Masayuki Nigo, Degui Zhi

Faculty, Staff and Student Publications

Achieving and maintaining the therapeutic range in vancomycin treatment is important for optimal outcomes. While guidelines and best practices based on empirical studies exist, the theoretical best dosing strategies under various conditions remain illusive. We developed an RL-based simulation framework using a deep learning two-compartment pharmacokinetic model (PK-RNN-2CM) and introduced the area under the time-concentration curve (AUC) reward score, which translates clinical guidelines into an RL reward. Ground truth time-concentration curves were generated from patient-specific data, and simulated curves were produced under different dosing strategies with optional noise perturbations to mimic real-world settings. Evaluation metrics included 24-hour AUC assessments and …

Correction: Preclinical Development Of 1b7/Cd3, A Novel Anti-Tslpr Bispecific Antibody That Targets Crlf2-Rearranged Ph-Like B-All, Ze Tian, Chunhua Shi, Guojun Yang, Jason K Allen, Qing Shi, Amin Al-Shami, Jill Wardell Olson, Melinda G Smith, Qing Chang, Jasbir Kaur, Junping You, Timothy E Lofton, Michelle A Gonzalez, Qi Zhang, Dongxing Zha, Sarah K Tasian, Nitin Jain, Marina Y Konopleva, Timothy Heffernan, Jeffrey J Molldrem

Faculty, Staff and Student Publications

No abstract provided.

Real-World Analysis Of Safety And Efficacy Of Car T-Cell Therapy In Lymphoma Patients With Decreased Kidney Function, Omar Mamlouk, Paolo Strati, Lei Feng, Ryan Sun, Amy Ayers, Raphael E Steiner, Ranjit Nair, Christopher Flowers, Jeremy L Ramdial, Neeraj Saini, Samer A Srour, Richard E Champlin, Partow Kebriaei, Loretta J Nastoupil, Maria A Rodriguez, Elizabeth J Shpall, Yago Nieto, Jason Westin, Sattva S Neelapu, Sreedhar Mandayam, Sairah Ahmed

Faculty, Staff and Student Publications

No abstract provided.

Multicenter Randomized Controlled Trial Of Exercise In Aortic Dissection Survivors: Rationale, Design, And Initial Hemodynamic Data, Yasmin A Toy, Kayla N House, Leslie M Boyer, Jennifer L Mcnamara, Marion A Hofmann-Bowman, Kim A Eagle, Michelle S Lim, Alan C Braverman, Siddharth K Prakash

Faculty, Staff and Student Publications

Aims: There are currently no evidence-based guidelines for exercise after thoracic aortic dissection (TAD), leading to highly variable recommendations that frequently lead patients to restrict their physical activities. This multicenter randomized controlled trial was intended to evaluate the safety and efficacy of a moderate intensity guided exercise program for TAD survivors.

Methods: Participants were eligible if they had a Type A or Type B dissection at least 90 days before enrollment and could attend two in-person study visits. The guided exercise circuit consisted of six aerobic, isotonic, or isometric exercises that participants continued at home with virtual follow-up sessions. The …

Epigallocatechin Gallate Regulates The Myeloid-Specific Transcription Factor Pu1 In Macrophages, Manjula Karpurapu, Kavita Kumari Kakarala, Sangwoon Chung, Yunjuan Nie, Amritendu Koley, Patrick Dougherty, John W Christman

Faculty, Staff and Student Publications

Our previous research demonstrated that PU.1 regulates expression of the genes involved in inflammation in macrophages. Selective knockdown of PU.1 in macrophages ameliorated LPS-induced acute lung injury (ALI) in bone marrow chimera mice. Inhibitors that block the transcriptional activity of PU.1 in macrophages have the potential to mitigate the pathophysiology of LPS-induced ALI. However, complete inactivation of PU.1 gene disrupts normal myelopoiesis. Although the green tea polyphenol Epigallocatechin gallate (EGCG) has been shown to regulate inflammatory genes in various cell types, it is not known if EGCG alters the transcriptional activity of PU.1 protein. Using Schrodinger Glide docking, we have …

Federated Multiple Imputation For Variables That Are Missing Not At Random In Distributed Electronic Health Records, Yi Lian, Xiaoqian Jiang, Qi Long

Faculty, Staff and Student Publications

Large electronic health records (EHR) have been widely implemented and are available for research activities. The magnitude of such databases often requires storage and computing infrastructure that are distributed at different sites. Restrictions on data-sharing due to privacy concerns have been another driving force behind the development of a large class of distributed and/or federated machine learning methods. While missing data problem is also present in distributed EHRs, albeit potentially more complex, distributed multiple imputation (MI) methods have not received as much attention. An important advantage of distributed MI, as well as distributed analysis, is that it allows researchers to …

Suppression Of Il-1Β Promotes Beneficial Accumulation Of Fibroblast-Like Cells In Atherosclerotic Plaques In Clonal Hematopoiesis, Trevor P Fidler, Andrew Dunbar, Eunyoung Kim, Brian Hardaway, Jessica Pauli, Chenyi Xue, Sandra Abramowicz, Tong Xiao, Kavi O'Connor, Nadja Sachs, Nan Wang, Lars Maegdefessel, Ross Levine, Muredach Reilly, Alan R Tall

Faculty, Staff and Student Publications

Clonal hematopoiesis (CH) is an independent risk factor for atherosclerotic cardiovascular disease. Murine models of CH suggest a central role of inflammasomes and IL-1β in accelerated atherosclerosis and plaque destabilization. Here we show using single-cell RNA sequencing in human carotid plaques that inflammasome components are enriched in macrophages, while the receptor for IL-1β is enriched in fibroblasts and smooth muscle cells (SMCs). To address the role of inflammatory crosstalk in features of plaque destabilization, we conducted SMC fate mapping in Ldlr-/- mice modeling Jak2VF or Tet2 CH treated with IL-1β antibodies. Unexpectedly, this treatment minimally affected SMC differentiation, …

Microbiota Restoration Therapies For Recurrent Clostridioides Difficile Infection Reach An Important New Milestone, Herbert L Dupont, Andrew W Dupont, Glenn S Tillotson

Faculty, Staff and Student Publications

Microbiota restoration therapy has become a standard treatment for recurrent Clostridioides difficile infection (rCDI). In this article, we review the studies supporting the licensure of two live biotherapeutic products (LBPs) designed to prevent rCDI and to provide clinicians with a perspective on their differences. PubMed was reviewed on 1 October 2023, for all papers published concerning the current Food and Drug Administration allowance of the use of fecal microbiota transplantation (FMT) and the studies that led to the licensure of RBX2660 (REBYOTA™), generic name, fecal microbiota, live-jslm, and SER-109 (VOWST™), generic name, fecal microbiota spores, live-brpk. OpenBiome continues to produce …

Large Language Models Struggle In Token-Level Clinical Named Entity Recognition, Qiuhao Lu, Rui Li, Andrew Wen, Jinlian Wang, Liwei Wang, Hongfang Liu

Faculty, Staff and Student Publications

Large Language Models (LLMs) have revolutionized various sectors, including healthcare where they are employed in diverse applications. Their utility is particularly significant in the context of rare diseases, where data scarcity, complexity, and specificity pose considerable challenges. In the clinical domain, Named Entity Recognition (NER) stands out as an essential task and it plays a crucial role in extracting relevant information from clinical texts. Despite the promise of LLMs, current research mostly concentrates on document-level NER, identifying entities in a more general context across entire documents, without extracting their precise location. Additionally, efforts have been directed towards adapting ChatGPTfor token-level …

Loss Of The Auxiliary Α 2Δ1 Voltage-Sensitive Calcium Channel Subunit Impairs Bone Formation And Anabolic Responses To Mechanical Loading, Madison M Kelly, Karan Sharma, Christian S Wright, Xin Yi, Perla C Reyes Fernandez, Aaron T Gegg, Taylor A Gorrell, Megan L Noonan, Ahmed Baghdady, Jacob A Sieger, Annette C Dolphin, Stuart J Warden, Padmini Deosthale, Lilian I Plotkin, Uma Sankar, Julia M Hum, Alexander G Robling, Mary C Farach-Carson, William R Thompson

Faculty, Staff and Student Publications

Voltage-sensitive calcium channels (VSCCs) influence bone structure and function, including anabolic responses to mechanical loading. While the pore-forming (α1) subunit of VSCCs allows Ca2+ influx, auxiliary subunits regulate the biophysical properties of the pore. The α2δ1 subunit influences gating kinetics of the α1 pore and enables mechanically induced signaling in osteocytes; however, the skeletal function of α2δ1 in vivo remains unknown. In this work, we examined the skeletal consequences of deleting Cacna2d1, the gene encoding α2δ1. Dual-energy X-ray absorptiometry and microcomputed tomography imaging demonstrated that deletion of α2δ1 diminished bone mineral content and density in both male and female …

Human Minor Salivary Glands: A Readily Available Source Of Salivary Stem/Progenitor Cells For Regenerative Applications, Caitlynn M L Barrows, Danielle Wu, Simon Young, Mary C Farach-Carson

Faculty, Staff and Student Publications

Resident stem/progenitor cells within the secretory salivary glands offer a potential therapeutic resource for use in the regeneration of salivary glands needed to restore saliva production in patients with chronic xerostomia, or dry mouth. Methods were developed previously to isolate human stem/progenitor cells (hS/PCs) from major salivary glands (parotid/submandibular). Abundant minor salivary glands located in readily accessible locations in the oral cavity and lip could provide an additional valuable therapeutic resource. An advantage of this cell resource is that these minor glands about the size of grape seeds can be harvested from healthy donors using minimally invasive surgical procedures. The …

Barriers And Facilitators Of Digital Health Use For Self-Management Of Hypertensive Disorders By Black Pregnant Women, Morgan A Foreman, Angela Ross, Angela P H Burgess, Sahiti Myneni, Amy Franklin

Faculty, Staff and Student Publications

Although digital health tools are increasingly common for managing health conditions, these applications are often developed without consideration of differences across user populations. A reproducible framework is needed to support tailoring applications to include cultural considerations, potentially leading to better adoption and more effective use. As a first step, this study captures a snapshot of Black women's barriers and facilitators in using digital health products for self-management of hypertensive disorders of pregnancy (HDP). One-on-one semi-structured interviews were conducted with 17 Black pregnant women with HDP. We established a unique model for cultural tailoring with these experiences using Black feminist theory …

Enhancing Patient Medication Safety At Home: A Patient-Facing Technology Architecture Integrating Redcap, Visualization Dashboards, And An Ai- Driven Chatbot, Yue Yu, Yuheng Shi, Eric Yang, Katie Gahn, Heidi Mason, Yun Jiang, Yang Gong

Faculty, Staff and Student Publications

This work demonstrates a novel architecture for a patient-facing technology (PFT) that supports patients with cancer in self-managing medication concerns and symptoms after care transitions back home. Patient-generated data are collected and stored using the framework of Research Electronic Data Capture (REDCap), which serves as a key component of the architecture. Individual patient and administrator dashboards are integrated with REDCap to visualize medication or symptom-related data and generate customized report summaries. Additionally, a Q&A chatbot, using a retrieval augmented generation (RAG) framework, is intended to integrate into the architecture to further enhance the interactivity of the PFT. The proposed architecture …

Biomedical Big Data Technologies, Applications, And Challenges For Precision Medicine: A Review, Xue Yang, Kexin Huang, Dewei Yang, Weiling Zhao, Xiaobo Zhou

Faculty, Staff and Student Publications

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent …

Harnessing Uncertainty In Radiotherapy Auto-Segmentation Quality Assurance, Kareem A Wahid, Jaakko Sahlsten, Joel Jaskari, Michael J Dohopolski, Kimmo Kaski, Renjie He, Enrico Glerean, Benjamin H Kann, Antti Mäkitie, Clifton D Fuller, Mohamed A Naser, David Fuentes

Faculty, Staff and Student Publications

One of the key contributions of this study is the reappropriation of standard DL outputs as a quality indicator to identify cases that clinicians should review further. The authors achieve this by applying an empirically derived threshold to the softmax output of their DL network, computing the mean of the thresholded score map (termed the HiS metric), and correlating it with standard geometric quality indices. When juxtaposed with a mean entropy — a commonly used measure of model output uncertainty — HiS consistently demonstrated a stronger correlation with the geometric indices, suggesting its superior ability to stratify cases needing additional …

Microstructural Organization Of Distributed White Matter Associated With Fine Motor Control In Us Service Members With Mild Traumatic Brain Injury, Benjamin S C Wade, David F Tate, Eamonn Kennedy, Erin D Bigler, Gerald E York, Brian A Taylor, Maya Troyanskaya, Elizabeth S Hovenden, Naomi Goodrich-Hunsaker, Mary R Newsome, Emily L Dennis, Tracy Abildskov, Mary Jo Pugh, William C Walker, Kimbra Kenney, Aaron Betts, Robert Shih, Robert C Welsh, Elisabeth A Wilde

Faculty, Staff and Student Publications

Mild traumatic brain injury (mTBI) is the most common form of brain injury. While most individuals recover from mTBI, roughly 20% experience persistent symptoms, potentially including reduced fine motor control. We investigate relationships between regional white matter organization and subcortical volumes associated with performance on the Grooved Pegboard (GPB) test in a large cohort of military Service Members and Veterans (SM&Vs) with and without a history of mTBI(s). Participants were enrolled in the Long-term Impact of Military-relevant Brain Injury Consortium-Chronic Effects of Neurotrauma Consortium. SM&Vs with a history of mTBI(s) (n = 847) and without mTBI (n = …

Editorial: Methods And Applications Of Tumour Metabolic Imaging In The Preclinical And Clinical Setting, Liangjie Lin, Zhiliang Wei, Jing Wang, Nian Wang

Faculty, Staff and Student Publications

No abstract provided.

Deep Filtered Back Projection For Ct Reconstruction, X I Tan, Xuan Liu, Kai Xiang, Jing Wang, Shan Tan

Faculty, Staff and Student Publications

Filtered back projection (FBP) is a classic analytical algorithm for computed tomography (CT) reconstruction, with high computational efficiency. However, images reconstructed by FBP often suffer from excessive noise and artifacts. The original FBP algorithm uses a window function to smooth signals and a linear interpolation to estimate projection values at un-sampled locations. In this study, we propose a novel framework named DeepFBP in which an optimized filter and an optimized nonlinear interpolation operator are learned with neural networks. Specifically, the learned filter can be considered as the product of an optimized window function and the ramp filter, and the learned …

Registration Of Hyperspectral Images And Mass Spectrometry Data For The Correlation Of Tissue Optical Spectra And Molecular Profiles, Angie T Fei, Douglas W Strand, Jing Wang

Faculty, Staff and Student Publications

Hyperspectral imaging (HSI) is a label-free imaging modality that is emerging for non-invasive detection of various diseases including cancers. HSI provides high-resolution spatial images where each pixel has a spectral curve with numerous wavelength bands from the visible to infrared ranges. The rich spatial and spectral information can be used to discriminate various types of tissues and pathophysiological conditions. However, it can be difficult to explain spectral data with respect to the underline cellular and molecular mechanism. In this study, we developed an approach that registers hyperspectral images and mass spectrometry (MS) data where MS provides tissue molecular profiles. Human …

Refinement Of An Emergency Department-Based, Advance Care Planning Intervention For Patients With Cognitive Impairment And Their Caregivers, Kei Ouchi, Christopher Joshi, Jenson Kaithamattam, Seth A Gale, Gad A Marshall, Alison Pietras, Wei Wang, Edward W Boyer, James A Tulsky, Susan D Block, Dorene Rentz, Mara A Schonberg

Faculty, Staff and Student Publications

Background and objectives: Advance care planning (ACP) conversations are important to provide goal-concordant care (i.e., the care that matches the patient's previously stated goals) near end of life. While 31% of older adults presenting to the emergency department (ED) have dementia, only 39% have previously had ACP conversations. We refined and piloted an ED-based, motivational interview designed to stimulate ACP conversations (ED GOAL) for patients living with cognitive impairment and their caregivers.

Research design and methods: We systematically refined ED GOAL and then conducted an acceptability study in an urban, academic medical center. We prospectively enrolled adults aged 50+ with …