Digital Commons Network^™

Mutant P53 Gains Oncogenic Functions Through A Chromosomal Instability-Induced Cytosolic Dna Response, Mei Zhao, Tianxiao Wang, Frederico O Gleber-Netto, Zhen Chen, Daniel J Mcgrail, Javier A Gomez, Wutong Ju, Mayur A Gadhikar, Wencai Ma, Li Shen, Qi Wang, Ximing Tang, Sen Pathak, Maria Gabriela Raso, Jared K Burks, Shiaw-Yih Lin, Jing Wang, Asha S Multani, Curtis R Pickering, Junjie Chen, Jeffrey N Myers, Ge Zhou

Faculty, Staff and Student Publications

Inactivating TP53 mutations leads to a loss of function of p53, but can also often result in oncogenic gain-of-function (GOF) of mutant p53 (mutp53) proteins which promotes tumor development and progression. The GOF activities of TP53 mutations are well documented, but the mechanisms involved remain poorly understood. Here, we study the mutp53 interactome and find that by targeting minichromosome maintenance complex components (MCMs), GOF mutp53 predisposes cells to replication stress and chromosomal instability (CIN), leading to a tumor cell-autonomous and cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING)-dependent cytosolic DNA response that activates downstream non-canonical nuclear factor kappa light chain …

Cell Cycle Arrest Induces Lipid Droplet Formation And Confers Ferroptosis Resistance, Hyemin Lee, Amber Horbath, Lavanya Kondiparthi, Jitendra Kumar Meena, Guang Lei, Shayani Dasgupta, Xiaoguang Liu, Li Zhuang, Pranavi Koppula, Mi Li, Iqbal Mahmud, Bo Wei, Philip L Lorenzi, Khandan Keyomarsi, Masha V Poyurovsky, Kellen Olszewski, Boyi Gan

Faculty, Staff and Student Publications

How cells coordinate cell cycling with cell survival and death remains incompletely understood. Here, we show that cell cycle arrest has a potent suppressive effect on ferroptosis, a form of regulated cell death induced by overwhelming lipid peroxidation at cellular membranes. Mechanistically, cell cycle arrest induces diacylglycerol acyltransferase (DGAT)-dependent lipid droplet formation to sequester excessive polyunsaturated fatty acids (PUFAs) that accumulate in arrested cells in triacylglycerols (TAGs), resulting in ferroptosis suppression. Consequently, DGAT inhibition orchestrates a reshuffling of PUFAs from TAGs to phospholipids and re-sensitizes arrested cells to ferroptosis. We show that some slow-cycling antimitotic drug-resistant cancer cells, such as …

Clonal Hematopoiesis And Incident Heart Failure With Preserved Ejection Fraction, Art Schuermans, Michael C Honigberg, Laura M Raffield, Bing Yu, Mary B Roberts, Charles Kooperberg, Pinkal Desai, April P Carson, Amil M Shah, Christie M Ballantyne, Alexander G Bick, Pradeep Natarajan, Joann E Manson, Eric A Whitsel, Charles B Eaton, Alexander P Reiner

Faculty, Staff and Student Publications

IMPORTANCE: Clonal hematopoiesis of indeterminate potential (CHIP), the age-related clonal expansion of hematopoietic stem cells with leukemogenic acquired genetic variants, is associated with incident heart failure (HF).

OBJECTIVE: To evaluate the associations of CHIP and key gene-specific CHIP subtypes with incident HF with preserved ejection fraction (HFpEF) and reduced ejection fraction (HFrEF).

DESIGN, SETTING, AND PARTICIPANTS: This population-based cohort study included participants from 2 racially diverse prospective cohort studies with uniform HF subtype adjudication: the Jackson Heart Study (JHS) and Women's Health Initiative (WHI). JHS participants were enrolled during 2000 to 2004 and followed up through 2016. WHI participants were …

Crispr-Cas9 Base Editing Of Pathogenic Camkiiδ Improves Cardiac Function In A Humanized Mouse Model, Simon Lebek, Xurde M Caravia, Leon G Straub, Damir Alzhanov, Wei Tan, Hui Li, John R Mcanally, Kenian Chen, Lin Xu, Philipp E Scherer, Ning Liu, Rhonda Bassel-Duby, Eric N Olson

Faculty, Staff and Student Publications

Cardiovascular diseases are the most common cause of worldwide morbidity and mortality, highlighting the necessity for advanced therapeutic strategies. Ca2+/calmodulin-dependent protein kinase IIδ (CaMKIIδ) is a prominent inducer of various cardiac disorders, which is mediated by 2 oxidation-sensitive methionine residues within the regulatory domain. We have previously shown that ablation of CaMKIIδ oxidation by CRISPR-Cas9 base editing enables the heart to recover function from otherwise severe damage following ischemia/reperfusion (IR) injury. Here, we extended this therapeutic concept toward potential clinical translation. We generated a humanized CAMK2D knockin mouse model in which the genomic sequence encoding the entire regulatory domain was …

Unraveling The Intercellular Communication Disruption And Key Pathways In Alzheimer’S Disease: An Integrative Study Of Single-Nucleus Transcriptomes And Genetic Association, Andi Liu, Brisa S Fernandes, Citu Citu, Zhongming Zhao

Faculty, Staff and Student Publications

BACKGROUND: Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer's disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication mediated by dysregulated ligand-receptor (LR) interactions remains very limited in AD brains.

METHODS: We systematically assessed the intercellular communication networks by using a discovery snRNA-seq dataset comprising 69,499 nuclei from 48 human postmortem prefrontal cortex (PFC) samples. We replicated the findings using an independent snRNA-seq dataset of 56,440 nuclei from 18 PFC samples. By integrating genetic signals from AD genome-wide association studies (GWAS) summary statistics and whole genome sequencing (WGS) data, we prioritized …

Effect Of Acupuncture Vs Sham Acupuncture On Patients With Poststroke Motor Aphasia: A Randomized Clinical Trial, Boxuan Li, Shizhe Deng, Bifang Zhuo, Bomo Sang, Junjie Chen, Menglong Zhang, Guang Tian, Lili Zhang, Yuzheng Du, Peng Zheng, Gonglei Yue, Zhihong Meng

Faculty, Staff and Student Publications

IMPORTANCE: Motor aphasia is common among patients with stroke. Acupuncture is recommended as an alternative therapy for poststroke aphasia, but its efficacy remains uncertain.

OBJECTIVE: To investigate the effects of acupuncture on language function, neurological function, and quality of life in patients with poststroke motor aphasia.

DESIGN, SETTING, AND PARTICIPANTS: This multicenter, sham-controlled, randomized clinical trial was conducted in 3 tertiary hospitals in China from October 21, 2019, to November 13, 2021. Adult patients with poststroke motor aphasia were enrolled. Data analysis was performed from February to April 2023.

INTERVENTIONS: Eligible participants were randomly allocated (1:1) to manual acupuncture (MA) …

Metarnn: Differentiating Rare Pathogenic And Rare Benign Missense Snvs And Indels Using Deep Learning, Fang Li, Zenan Sun, Ahmed Abdelhameed, Tiehang Duan, Laila Rasmy, Xinyue Hu, Jianping He, Yifang Dang, Jingna Feng, Jianfu Li, Yichen Wang, Tianchen Lyu, Naomi Braun, Si Pham, Michael Gharacholou, Delisa Fairweather, Degui Zhi, Jiang Bian, Cui Tao

Faculty, Staff and Student Publications

BACKGROUND: Effective management of dual antiplatelet therapy (DAPT) following drug-eluting stent (DES) implantation is crucial for preventing adverse events. Traditional prognostic tools, such as rule-based methods or Cox regression, despite their widespread use and ease, tend to yield moderate predictive accuracy within predetermined timeframes. This study introduces a new contrastive learning-based approach to enhance prediction efficacy over multiple time intervals.

METHODS: We utilized retrospective, real-world data from the OneFlorida + Clinical Research Consortium. Our study focused on two primary endpoints: ischemic and bleeding events, with prediction windows of 1, 2, 3, 6, and 12 months post-DES implantation. Our approach first …

Perspectives On Enhancing Clinical Informatics Education In The Artificial Intelligence Era, Hua Min, Xia Jing, Yang Gong, Ping Yu

Faculty, Staff and Student Publications

Objectives: This paper aims to analyze clinical informatic (CI) - a subfield of biomedical and health informatics (BHI) - programs to identify challenges and provide solutions for CI education. Using an online clinical decision support system (CDSS) course as a case study, we demonstrate how these challenges can be addressed. In addition, we discuss the potential impact of generative artificial intelligence (AI), along with the opportunities and risks it presents for CI education.

Methods: This is a perspective paper. The viewpoint analysis is based on a review of formal academic and training programs offered by the American Medical Informatics Association …

Federated Node Classification Over Distributed Ego-Networks With Secure Contrastive Embedding Sharing, Han Xie, Li Xiong, Carl Yang

Faculty, Staff and Student Publications

Federated learning on graphs (a.k.a., federated graph learning- FGL) has recently received increasing attention due to its capacity to enable collaborative learning over distributed graph datasets without compromising local clients' data privacy. In previous works, clients of FGL typically represent institutes or organizations that possess sets of entire graphs (e.g., molecule graphs in biochemical research) or parts of a larger graph (e.g., sub-user networks of e-commerce platforms). However, another natural paradigm exists where clients act as remote devices retaining the graph structures of local neighborhoods centered around the device owners (i.e., ego-networks), which can be modeled for specific graph applications …

Descriptor: Synthetic Genomic Dataset With Diverse Ancestry (Syngen6), Xinyue Wang, Sitao Min, Jaideep Vaidya

Faculty, Staff and Student Publications

Advancements in genomic analysis techniques and data-driven research are driving precision medicine. However, in many cases, these advances are not equitable and do not help all subpopulations, since many existing genomic datasets lack diversity, limiting their applicability for studying populations beyond those of European ancestry. Thus, to advance genomic analysis and to allow for a fair benchmarking of novel proposed approaches, there is a significant demand for balanced and representative datasets. To address this issue, we developed,

Narrative Feature Or Structured Feature? A Study Of Large Language Models To Identify Cancer Patients At Risk Of Heart Failure, Ziyi Chen, Mengyuan Zhang, Mustafa Mohammed Ahmed, Yi Guo, Thomas J George, Jiang Bian, Yonghui Wu

Faculty, Staff and Student Publications

Cancer treatments are known to introduce cardiotoxicity, negatively impacting outcomes and survivorship. Identifying cancer patients at risk of heart failure (HF) is critical to improving cancer treatment outcomes and safety. This study examined machine learning (ML) models to identify cancer patients at risk of HF using electronic health records (EHRs), including traditional ML, Time-Aware long short-term memory (T-LSTM), and large language models (LLMs) using novel narrative features derived from the structured medical codes. We identified a cancer cohort of 12,806 patients from the University of Florida Health, diagnosed with lung, breast, and colorectal cancers, among which 1,602 individuals developed HF …

Bridging The Gap: Rademacher Complexity In Robust And Standard Generalization, Jiancong Xiao, Ruoyu Sun, Qi Long, Weijie J Su

Faculty, Staff and Student Publications

Training Deep Neural Networks (DNNs) with adversarial examples often results in poor generalization to test-time adversarial data. This paper investigates this issue, known as adversarially robust generalization, through the lens of Rademacher complexity. Building upon the studies by Khim and Loh (2018); Yin et al. (2019), numerous works have been dedicated to this problem, yet achieving a satisfactory bound remains an elusive goal. Existing works on DNNs either apply to a surrogate loss instead of the robust loss or yield bounds that are notably looser compared to their standard counterparts. In the latter case, the bounds have a …

Disentangling Accelerated Cognitive Decline From The Normal Aging Process And Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach, Yulin Dai, Yu-Chun Hsu, Brisa S Fernandes, Kai Zhang, Xiaoyang Li, Nitesh Enduru, Andi Liu, Astrid M Manuel, Xiaoqian Jiang, Zhongming Zhao, Alzheimer’S Disease Neuroimaging Initiative

Faculty, Staff and Student Publications

BACKGROUND: The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points.

OBJECTIVE: To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach.

METHODS: We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. …

Preparing Healthcare Education For An Ai-Augmented Future, Jiajie Zhang, Susan H Fenton

Faculty, Staff and Student Publications

Artificial intelligence (AI) fundamentally transforms healthcare education as a knowledge enterprise, creating a distributed cognitive system composed of the human brain, which remains relatively unchanged, and AI-based knowledge and cognitive functions, which have accelerated exponentially in scale and power. Education must focus on developing skills to collaborate with AI and on achieving outcomes like problems solved and discoveries made. Curriculum and education policies also need to adapt to this transformation.

Prioritizing Clinically Significant Lung Cancer Somatic Mutations For Targeted Therapy Through Efficient Ngs Data Filtering System, Jinlian Wang, Hui Li, Hongfang Liu

Faculty, Staff and Student Publications

In the realm of lung cancer treatment, where genetic heterogeneity presents formidable challenges, precision oncology demands an exacting approach to identify and hierarchically sort clinically significant somatic mutations. Current Next-Generation Sequencing (NGS) data filtering pipelines, while utilizing various external databases for mutation screening, often fall short in comprehensive integration and flexibility needed to keep pace with the evolving landscape of clinical data. Our study introduces a sophisticated NGS data filtering system, which not only aggregates but effectively synergizes diverse data sources, encompassing genetic variants, gene functions, clinical evidence, and an extensive body of literature. This system is distinguished by a …

Human Equilibrative Nucleoside Transporter 1: Novel Biomarker And Prognostic Indicator For Patients With Gemcitabine-Treated Pancreatic Cancer, Jianchun Xiao, Fangyu Zhao, Wenhao Luo, Gang Yang, Yicheng Wang, Jiangdong Qiu, Yueze Liu, Lei You, Lianfang Zheng, Taiping Zhang

Faculty, Staff and Student Publications

AIM: This article aimed to find appropriate pancreatic cancer (PC) patients to treat with Gemcitabine with better survival outcomes by detecting hENT1 levels.

METHODS: We collected surgical pathological tissues from PC patients who received radical surgery in our hospital from September 2004 to December 2014. A total of 375 PC tissues and paired adjacent nontumor tissues were employed for the construction of 4 tissue microarrays (TMAs). The quality of the 4 TMAs was examined by HE staining. We performed immunohistochemistry analysis to evaluate hENT1 expression in the TMAs. Moreover, we detected hENT1 expression level and proved the role of hENT1 …

Predicting Endothelium-Dependent Diastolic Function (Fmd)And Its Correlation With The Degree Of Coronary Artery Disease (Cad) And Plaque Vulnerability For Cardiovascular Events, Guangming Zhang, Jing Yang, Hanghang Xing, Hongning Yin, Guoqing Gu

Faculty, Staff and Student Publications

OBJECTIVE: This study aims to investigate the correlation between vascular endothelium-dependent diastolic function (FMD) and the degree of coronary artery disease (CAD), plaque vulnerability, and its predictive value for cardiovascular events.

METHODS: Initially, patients (n=100) who were admitted from January 2020 to January 2021 and intended to undergo percutaneous coronary intervention (PCI) were selected. Further, FMD in all patients was determined before the procedure and divided into a high-FMD group (≥4.2%) and a low-FMD group (

RESULTS: No significant differences were observed concerning general information, number of coronary arteries-associated branches, lesion type, involvement of the left main stem (LM), the …

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 …

Engravings, Secrets, And Interpretability Of Neural Networks, Nathaniel Hobbs, Periklis A Papakonstantinou, Jaideep Vaidya

Faculty, Staff and Student Publications

This work proposes a definition and examines the problem of undetectably engraving special input/output information into a Neural Network (NN). Investigation of this problem is significant given the ubiquity of neural networks and society's reliance on their proper training and use. We systematically study this question and provide (1) definitions of security for secret engravings, (2) machine learning methods for the construction of an engraved network, (3) a threat model that is instantiated with state-of-the-art interpretability methods to devise distinguishers/attackers. In this work, there are two kinds of algorithms. First, the constructions of engravings through machine learning training methods. Second, …

Igamt: Privacy-Preserving Electronic Health Record Synthesization With Heterogeneity And Irregularity, Wenjie Wang, Pengfei Tang, Jian Lou, Yuanming Shao, Lance Waller, Yi-An Ko, Li Xiong

Faculty, Staff and Student Publications

Utilizing electronic health records (EHR) for machine learning-driven clinical research has great potential to enhance outcome predictions and treatment personalization. Nonetheless, due to privacy and security concerns, the secondary use of EHR data is regulated, constraining researchers’ access to EHR data. Generating synthetic EHR data with deep learning methods is a viable and promising approach to mitigate privacy concerns, offering not only a supplementary resource for downstream applications but also sidestepping the privacy risks associated with real patient data. While prior efforts have concentrated on EHR data synthesis, significant challenges persist: addressing the heterogeneity of features including temporal and non-temporal …

A Real-World Disproportionality Analysis Of Everolimus: Data Mining Of The Public Version Of Fda Adverse Event Reporting System, Bin Zhao, Yumei Fu, Shichao Cui, Xiangning Chen, Shu Liu, Lan Luo

Faculty, Staff and Student Publications

Background: Everolimus is an inhibitor of the mammalian target of rapamycin and is used to treat various tumors. The presented study aimed to evaluate the Everolimus-associated adverse events (AEs) through data mining of the US Food and Drug Administration Adverse Event Reporting System (FAERS).

Methods: The AE records were selected by searching the FDA Adverse Event Reporting System database from the first quarter of 2009 to the first quarter of 2022. Potential adverse event signals were mined using the disproportionality analysis, including reporting odds ratio the proportional reporting ratio the Bayesian confidence propagation neural network and the empirical Bayes geometric …

An Automated Approach For Identifying Erroneous Is-A Relations In Snomed Ct, Ran Hu, Jay Shi, Licong Cui, Rashmie Abeysinghe

Faculty, Staff and Student Publications

SNOMED CT is the most comprehensive clinical terminology employed worldwide and enhancing its accuracy is of utmost importance. In this work, we introduce an automated approach to identifying erroneous IS-A relations in SNOMED CT. We first extract linked concept-pairs from which we generate Term Difference Pairs (TDPs) that contain differences between the concepts. Given a TDP, if the reversed TDP also exists and the number of linked-pairs generating this TDP is less than those generating the reversed TDP, then we suggest the former linked-pairs as potentially erroneous IS-A relations. We applied this approach to the Clinical finding and Procedure subhierarchies …

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 …

Unified Resource Browser: An Interactive And Reconfigurable Web Framework For Biomedical Metadata Exploration, Visualization, And Management, Shiwei Lin, Ling Tong, Kimberly A Smith, Lydia Ng, Tyler Mollenkopf, Yan Huang, Rashmie Abeysinghe, Wei-Chun Chou, Antarr T Byrd, Licong Cui, Guo-Qiang Zhang, Xiaojin Li, Shiqiang Tao

Faculty, Staff and Student Publications

We present Unified Resource Browser, a web-based framework designed to optimize the configuration, exploration, visualization, and dissemination of complex biomedical metadata. As a core component of Neuroanatomy-Anchored Information Management Platform (NIMP), Unified Resource Browser enables researchers to efficiently access donor information, brain tissue samples, and sequencing data through structured resource tables and advanced search and filtering capabilities. The platform allows users to customize data views, share configurations via direct links, and seamlessly export data for further analysis. Integrated visualization tools offer immediate insights through customizable charts, enhancing data interpretation. By improving the accessibility and usability of biomedical data resources, Unified …

Networks Extracted From Nonlinear Fmri Connectivity Exhibit Unique Spatial Variation And Enhanced Sensitivity To Differences Between Individuals With Schizophrenia And Controls, Spencer Kinsey, Katarzyna Kazimierczak, Pablo Andrés Camazón, Jiayu Chen, Tülay Adali, Peter Kochunov, Bhim M Adhikari, Judith Ford, Theo G M Van Erp, Mukesh Dhamala, Vince D Calhoun, Armin Iraji

Faculty, Staff and Student Publications

Schizophrenia is a chronic brain disorder associated with widespread alterations in functional brain connectivity. Although data-driven approaches such as independent component analysis are often used to study how schizophrenia impacts linearly connected networks, alterations within the underlying nonlinear functional connectivity structure remain largely unknown. Here we report the analysis of networks from explicitly nonlinear functional magnetic resonance imaging connectivity in a case-control dataset. We found systematic spatial variation, with higher nonlinear weight within core regions, suggesting that linear analyses underestimate functional connectivity within network centers. We also found that a unique nonlinear network incorporating default-mode, cingulo-opercular and central executive regions …

‘Door-To-Prophylaxis’ As A Novel Quality Improvement Metric In Prevention Of Venous Thromboembolism Following Traumatic Injury, Jan-Michael Van Gent, Thomas W Clements, David E Lubkin, Carter W Kaminski, Jonathan K Bates, Mariela Sandoval, Thaddeus J Puzio, Bryan A Cotton

Faculty, Staff and Student Publications

OBJECTIVE: Venous thromboembolism (VTE) risk reduction strategies include early initiation of chemoprophylaxis, reducing missed doses, weight-based dosing and dose adjustment using anti-Xa levels. We hypothesized that time to initiation of chemoprophylaxis would be the strongest modifiable risk for VTE, even after adjusting for competing risk factors.

METHODS: A prospectively maintained trauma registry was queried for patients admitted July 2017-October 2021 who were 18 years and older and received emergency release blood products. Patients with deep vein thrombosis or pulmonary embolism (VTE) were compared to those without (no VTE). Door-to-prophylaxis was defined as time from hospital arrival to first dose of …

Unveiling The Complex World Of Extracellular Vesicles: Novel Characterization Techniques And Manufacturing Considerations, James J Lai, John J Hill, Casey Y Huang, Gino C Lee, Karol W Mai, Maggie Y Shen, Simon K Wang

Faculty, Staff and Student Publications

Extracellular vesicles (EVs) function as potent mediators of intercellular communication for many in vivo processes, contributing to both health and disease related conditions. Given their biological origins and diverse functionality from correspondingly unique "cargo" compositions, both endogenous and modified EVs are garnering attention as promising therapeutic modalities and vehicles for targeted therapeutic delivery applications. Their diversity in composition, however, has revealed a significant need for more comprehensive analytical-based characterization methods, and manufacturing processes that are consistent and scalable. In this review, we explore the dynamic landscape of EV research and development efforts, ranging from novel isolation approaches, to their analytical …

The Efficacy Of Botulinum Toxin Use In Service Members And Veterans With Migraine And Post-Traumatic Headache Disorders: A Scoping Review, Qing Zhao Ruan, Daniel J Pak, David E Reece, Rohan Jotwani, Sean Li, Moises Dominguez, Alan David Kaye, R Jason Yong, Abdulrazaq Albilali, Sadiq Bhayani, Jamal Hasoon, Michael E Schatman, Jun Beom Ku, Thomas T Simopoulos, Sait Ashina, Christopher L Robinson

Faculty, Staff and Student Publications

Objective: To delineate the trend of use of botulinum toxin, including onabotulinum toxinA (OTA), in active military personnel and veterans with the diagnoses of migraine and post-traumatic headache (PTH) and describe the efficacy of botulinum toxin administration.

Background: Service members and veterans represent a unique population in the medical management of headache disorders, particularly migraine. They exhibit higher susceptibility to pain of greater intensity and longer durations, possibly due to their history of exposure to combat, trauma, and the associated psychological stresses. Given the burden and morbid nature of these headache disorders, prophylactic measures to reduce migraine attacks and disability …

Phase 1 Trial Of Tpi 287, A Microtubule Stabilizing Agent, In Combination With Bevacizumab In Adults With Recurrent Glioblastoma, Samuel A Goldlust, Louis B Nabors, Sigmund Hsu, Nimish Mohile, Paul J Duic, Tara Benkers, Samuel Singer, Mayank Rao, Lori Cappello, Sandra L Silberman, George Farmer

Faculty, Staff and Student Publications

BACKGROUND: Recurrent glioblastoma (rGBM) has limited treatment options. This phase 1 protocol was designed to study the safety and preliminary efficacy of TPI 287, a central nervous system penetrant microtubule stabilizer, in combination with bevacizumab (BEV) for the treatment of rGBM.

METHODS: GBM patients with up to 2 prior relapses without prior exposure to anti-angiogenic therapy were eligible. A standard 3 + 3 design was utilized to determine the maximum tolerated dose (MTD) of TPI 287. Cohorts received TPI 287 at 140-220 mg/m

RESULTS: Twenty-four patients were enrolled at 6 centers. Treatment was generally well tolerated. Fatigue, myelosuppression, and peripheral …

Case Report: Nocturnal Low-Frequency Stimulation Of The Centromedian Thalamic Nucleus Improves Sleep Quality And Seizure Control, Surya Suresh, Ganne Chaitanya, Ajay Deep Kachhvah, Vladimir Vashin, Manojkumar Saranathan, Sandipan Pati

Faculty, Staff and Student Publications

Sleep disturbances and drug-resistant seizures significantly impact people with idiopathic generalized epilepsy (IGE). Thalamic deep brain stimulation (DBS) offers potential treatment, but its effect on sleep and seizure control needs clarification. In this study, we combined wearable sleep monitoring with electroencephalogram (EEG) confirmation to investigate the impact of nocturnal centromedian nucleus (CM) DBS parameters in a patient with drug-resistant IGE. We found that high-frequency (125 Hz) CM stimulation during sleep severely disrupted sleep macro architecture and exacerbated seizures. Conversely, switching to low-frequency (10 Hz) stimulation enhanced both sleep quality and seizure control. This study underscores the critical need to personalize …