Digital Commons Network^™

Machine Learning Predicting Acute Pain And Opioid Dose In Radiation Treated Oropharyngeal Cancer Patients, Vivian Salama, Laia Humbert-Vidan, Brandon Godinich, Kareem A Wahid, Dina M Elhabashy, Mohamed A Naser, Renjie He, Abdallah S R Mohamed, Ariana J Sahli, Katherine A Hutcheson, Gary Brandon Gunn, David I Rosenthal, Clifton D Fuller, Amy C Moreno

Faculty, Staff and Student Publications

Introduction: Acute pain is common among oral cavity/oropharyngeal cancer (OCC/OPC) patients undergoing radiation therapy (RT). This study aimed to predict acute pain severity and opioid doses during RT using machine learning (ML), facilitating risk-stratification models for clinical trials.

Methods: A retrospective study examined 900 OCC/OPC patients treated with RT during 2017-2023. Pain intensity was assessed using NRS (0-none, 10-worst) and total opioid doses were calculated using morphine equivalent daily dose (MEDD) conversion factors. Analgesics efficacy was assessed using combined pain intensity and total MEDD. ML predictive models were developed and validated, including Logistic Regression (LR), Support Vector Machine (SVM), Random …

Psychometric Properties And Meaningful Change Thresholds For The Qol-E Instrument In Patients With Myelodysplastic Neoplasms, Esther Natalie Oliva, Shien Guo, Jennifer Lord-Bessen, Aylin Yucel, Roberto Latagliata, Massimo Breccia, Giuseppe A Palumbo, Grazia Sanpaolo, Marta Riva, Valeria Santini, Uwe Platzbecker, Guillermo Garcia-Manero, Pierre Fenaux, Christopher G Pelligra

Faculty, Staff and Student Publications

peripheral blood cytopenias, and an increased risk of progression to acute myeloid leukemia. One of the main treatment goals is improving quality of life (QoL), particularly for patients with lower-risk MDS (LR-MDS) who may live longer with compromised QoL. The QOL-E© is a patient-reported outcome (PRO) measure specifically developed to address the lack of a health-related QoL questionnaire for patients with MDS. The objective of this study was to evaluate the psychometric performance of the QOL-E in patients with LR-MDS.

Methods: Data from four clinical trials in MDS (MEDALIST, DARB-MDS, EQoL-MDS, and RevMDS trials) were used to assess construct validity, …

Depletion Of Adipose Stroma-Like Cancer-Associated Fibroblasts Potentiates Pancreatic Cancer Immunotherapy, Joseph Rupert, Alexes Daquinag, Yongmei Yu, Yulin Dai, Zhongming Zhao, Mikhail G Kolonin

Faculty, Staff and Student Publications

This study shows that populations of CAFs have distinct effects on pancreatic cancer progression and shows that depletion of CAFs expressing adipose markers potentiates tumor/metastasis suppression effects of immune checkpoint blockade.

Quantitatively Assessing The Impact Of The Quality Of Snomed Ct Subtype Hierarchy On Cohort Queries, Xubing Hao, Xiaojin Li, Yan Huang, Jay Shi, Rashmie Abeysinghe, Cui Tao, Kirk Roberts, Guo-Qiang Zhang, Licong Cui

Faculty, Staff and Student Publications

Objective: SNOMED CT provides a standardized terminology for clinical concepts, allowing cohort queries over heterogeneous clinical data including Electronic Health Records (EHRs). While it is intuitive that missing and inaccurate subtype (or is-a) relations in SNOMED CT reduce the recall and precision of cohort queries, the extent of these impacts has not been formally assessed. This study fills this gap by developing quantitative metrics to measure these impacts and performing statistical analysis on their significance.

Material and methods: We used the Optum de-identified COVID-19 Electronic Health Record dataset. We defined micro-averaged and macro-averaged recall and precision metrics to assess the …

Building Patient-Facing Technology: A Redcap-Based Approach, Yuheng Shi, Eric Yang, Katie Gahn, Heidi Mason, Yun Jiang, Yang Gong

Faculty, Staff and Student Publications

This work describes the architecture design of a patient-facing technology (PFT) based on the Research Electronic Data Capture (REDCap) platform and other tools to support cancer patients in self-tracking and managing medication concerns and symptoms during transitions of care. The design is guided by the Chronic Care Model (CCM) and User-Centered Design (UCD) principles for a personalized application to inform, engage, and empower patients. We describe the evolutional details of four major versions, which represent milestones of our PFT, highlighting how specific objectives were achieved and the barriers encountered. Additionally, patient representatives were involved in the evaluation of prototypes, and …

Generalizable Self-Supervised Learning For Brain Cta In Acute Stroke, Yingjun Dong, Samiksha Pachade, Kirk Roberts, Xiaoqian Jiang, Sunil A Sheth, Luca Giancardo

Faculty, Staff and Student Publications

Acute stroke management involves rapid and accurate interpretation of CTA imaging data. However, generalizable models for multiple acute stroke tasks able to learn from unlabeled data do not exist. We propose a linear probed self-supervised contrastive learning utilizing 3D CTA images and the findings section of radiologists' reports for pretraining. Subsequently, the pretrained model was applied to four disparate tasks: large vessel occlusion (LVO) detection, acute ischemic stroke detection, acute ischemic stroke, intracerebral hemorrhage classification, and ischemic core volume prediction. The tasks chosen are particularly challenging as they cannot be directly extracted from the radiology reports findings with keywords. The …

Reusable Generic Clinical Decision Support System Module For Immunization Recommendations In Resource-Constraint Settings, Samuil Orlioglu, Akash Shanmugan Boobalan, Kojo Abanyie, Richard D Boyce, Hua Min, Yang Gong, Dean F Sittig, Paul Biondich, Adam Wright, Christian Nøhr, Timothy Law, David Robinson, Arild Faxvaag, Nina Hubig, Ronald Gimbel, Lior Rennert, Xia Jing

Faculty, Staff and Student Publications

Clinical decision support systems (CDSS) are routinely employed in clinical settings to improve quality of care, ensure patient safety, and deliver consistent medical care. However, rule-based CDSS, currently available, do not feature reusable rules. In this study, we present CDSS with reusable rules. Our solution includes a common CDSS module, electronic medical record (EMR) specific adapters, CDSS rules written in the clinical quality language (CQL) (derived from CDC immunization recommendations), and patient records in fast healthcare interoperability resources (FHIR) format. The proposed CDSS is entirely browser-based and reachable within the user's EMR interface at the client-side. This helps to avoid …

Temporal Ensemble Logic For Integrative Representation Of The Entirety Of Clinical Trials, Xiaojin Li, Yan Huang, Rashmie Abeysinghe, Zenan Sun, Hongyu Chen, Pengze Li, Xing He, Shiqiang Tao, Cui Tao, Jiang Bian, Licong Cui, Guo-Qiang Zhang

Faculty, Staff and Student Publications

Clinical trials are typically specified with protocols that define eligibility criteria, treatment regimens, follow-up schedules, and outcome assessments. Temporality is a hallmark of all clinical trials, reflected within and across trial components, with complex dependencies unfolding across multiple time points. Despite their importance, clinical trial protocols are described in free-text format, limiting their semantic precision and the ability to support automated reasoning, leverage data across studies and sites, or simulate trial execution under varying assumptions using Real-World Data. This paper introduces a formalized representation of clinical trials using Temporal Ensemble Logic (TEL). TEL incorporates metricized modal operators, such as “always …

Rank Aggregation Methods And Tools In Genomic Data Analysis, Wenping Zou, Savannah Mwesigwa, Sayed-Rzgar Hosseini, Zhongming Zhao

Faculty, Staff and Student Publications

Rank aggregation (RA) is the process of consolidating disparate rankings into a single unified ranking. It holds immense potential in the field of genomics. RA has applications in diverse research areas, such as gene expression analysis, meta-analysis, gene prioritization, and biomarker discovery. However, there are many challenges in the application of the RA approach to biological data, such as dealing with heterogeneous data sources, rankings of mixed quality, and evaluating the consolidated rankings. In this review, we present an overview of the existing RA methods with an emphasis on those that have been tailored to the complexities of genomics research. …

Disposition Outcomes Following Prehospital Use Of Naloxone In A Large Metropolitan City In The United States, James R Langabeer, Christine Bakos-Block, A Sarah Cohen, Ishmam Alam, Bhanumathi Gopal, Marylou Cardenas-Turanzas, Arlo F Weltge, David Persse, Tiffany Champagne-Langabeer

Faculty, Staff and Student Publications

Objectives: During a drug overdose, research suggests individuals may not call 9-1-1 out of fear of criminal justice concerns. Of those that call, research is inconclusive about the disposition of the emergency transport. We evaluated transport outcomes for adults with opioid-related overdose in the Emergency Medical Services (EMS) of a large metropolitan city in the United States.

Methods: We reviewed the EMS incident report database from the patient care record system for years 2018 to 2022. We queried all records, searching for relevant terms, and two reviewers cross-checked the database to identify cases that did not result in death at …

Introducing Advances In Health Information Science And Practice, Susan Fenton

Faculty, Staff and Student Publications

No abstract provided.

Privacy-Preserving Collaborative Population Stratification With Dynamic Algorithm And Hyperparameter Selection, Maryam Ghasemian, Lynette Hammond Gerido, Erman Ayday

Faculty, Staff and Student Publications

We present a privacy-preserving selection layer for collaborative population stratification under 𝜖-local differential privacy (LDP). Rather than fixing a single pipeline (e.g., PCA+K-Means with preset 𝐾), our framework lets parties choose among three DP pipelines: PCA→Noise, Noise→PCA, and Noise-Only, according to their resources, and has an honest-but-curious server aggregate only DP shares to automatically select the clustering algorithm (K-Means, GMM, or Hierarchical) and 𝐾 that maximize internal metrics (Silhouette, Calinski–Harabasz, Davies–Bouldin). Because selection operates on DP data, it adds no further privacy loss. On openSNP (942 samples, 28,396 SNPs), the PCA-augmented pipelines yield higher utility …

Privacy-Preserving Verification Of Ml Preprocessing Via Model Behavior Indicators, Wenbiao Li, Anisa Halimi, Jaideep Vaidya, Xiaoqian Jiang, Erman Ayday

Faculty, Staff and Student Publications

We present a privacy-preserving framework to verify whether a declared data preprocessing pipeline was correctly applied before training a machine learning model on sensitive data. The verifier has only black-box query access to the model and combines three behavior indicators: shift in prediction accuracy, Kullback-Leibler (KL) divergence between output distributions, and explanation vectors from Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). The method requires neither the original training records nor ground-truth labels. It supports two tasks: (i) a binary decision on correctness and (ii) a multi-class diagnosis identifying which step is missing. Experiments on three tabular datasets …

Safeguarding Privacy In Genome Research: A Comprehensive Framework For Authors, Maryam Ghasemian, Lynette Hammond Gerido, Erman Ayday

Faculty, Staff and Student Publications

As genomic research continues to advance, sharing of genomic data and research outcomes has become increasingly important for fostering collaboration and accelerating scientific discovery. However, such data sharing must be balanced with the need to protect the privacy of individuals whose genetic information is being utilized. This paper presents a bidirectional framework for evaluating privacy risks associated with data shared (both in terms of summary statistics and research datasets) in genomic research papers, particularly focusing on re-identification risks such as membership inference attacks (MIA). The framework consists of a structured workflow that begins with a questionnaire designed to capture researchers' …

Not Fully Synthetic: Llm-Based Hybrid Approaches Towards Privacy-Preserving Clinical Note Sharing, Atiquer Rahman Sarkar, Yao-Shun Chuang, Xiaoqian Jiang, Noman Mohammed

Faculty, Staff and Student Publications

The publication and sharing of clinical notes are crucial for healthcare research and innovation. However, privacy regulations such as HIPAA and GDPR pose significant challenges. While de-identification techniques aim to remove protected health information, they often fall short of achieving complete privacy protection. Similarly, the current state of synthetic clinical note generation can lack nuance and content coverage. To address these limitations, we propose an approach that combines de-identification, filtration, and synthetic clinical note generation. Variations of this approach currently retain 36%-61% of the original note's content and fill the remaining gaps using an LLM, ensuring high information coverage. We …

Using Digitally Delivered Measurement-Based Care In Substance Use Disorder Treatment: Qualitative Analysis Of Patients’ Perspectives, Tessa Frohe, Eliza B Cohn, Madeline C Frost, Tascha R Johnson, Kevin A Hallgren

Faculty, Staff and Student Publications

Background: Measurement-based care (MBC) is a clinical practice where patients complete standardized outcome measures throughout treatment to monitor clinical progress and inform clinical decision-making. However, MBC is rarely adopted in routine substance use disorder (SUD) treatment. We developed a digital MBC system and pilot tested it in an outpatient adult community SUD treatment setting.

Objectives:The current study aims to characterize qualitative feedback from the pilot participants about their experiences using the MBC system in SUD treatment, focusing on perceived benefits, drawbacks, and suggestions for improvement.

Methods: Participants (N=30; n=11 female 37%) completed weekly MBC questionnaires via smartphone for 6 …

Medication Prescribing Checklists And Their Impact On Patient Safety: A Scoping Review, Yilei Liu, Tate W Miner, Kawtar Zouaidi, Anika N Roy, Muhammad F Walji, Kristin N Ray, Donald B Rindal, Elsbeth Kalenderian, Katie J Suda

Faculty, Staff and Student Publications

Background: Medication prescribing checklists and the impact on patient safety remain unexplored.

Objectives: This scoping review aimed to identify key elements of these checklists and evaluate their impact on patient safety outcomes.

Methods: We searched PubMed and Embase for studies reporting checklists in medication prescribing practices and the impact on patient safety outcomes as of October 23, 2024. We applied Fisher's exact test to evaluate the association between checklist effectiveness (based on patient safety outcomes) with study design (randomized controlled trials [RCTs] vs non-RCTs) and intervention type (bundled intervention vs checklist-only intervention).

Results: After full-text review, 53 articles met inclusion …

Gene Expression Changes In Human Cerebral Arteries Following Hemoglobin Exposure: Implications For Vascular Responses In Sah, Chathathayil M Shafeeque, Arif O Harmanci, Sithara Thomas, Ari C Dienel, Devin W Mcbride, Kumar T Peeyush, Spiros L Blackburn

Faculty, Staff and Student Publications

Subarachnoid hemorrhage (SAH), characterized by the presence of hemoglobin (Hb) in the subarachnoid space, significantly impacts cerebral vessels, leading to various pathological outcomes. The toxicity of cell-free Hb released from erythrocytes and its metabolites after SAH causes vasoconstriction and neuronal damage, and correlates with delayed ischemic neurological deficits (DIND). While animal models have provided substantial and invaluable data in the research of aneurysmal SAH, the specific effects of subarachnoid blood on cerebral arteries remain greatly understudied. Here, we describe the changes in the genetic profile of human cerebral arteries exposed to free Hb for 48 h. We performed an ex …

A Bayesian Deep Segmentation Framework For Glioblastoma Tumor Segmentation Using Follow-Up Mris, Tanjida Kabir, Kang-Lin Hsieh, Luis Nunez, Yu-Chun Hsu, Juan C Rodriguez Quintero, Octavio Arevalo, Kangyi Zhao, Jay-Jiguang Zhu, Roy F Riascos, Mahboubeh Madadi, Xiaoqian Jiang, Shayan Shams

Faculty, Staff and Student Publications

Background: Glioblastoma (GBM) is the most common malignant brain tumor with an abysmal prognosis. Since complete tumor cell removal is impossible due to the infiltrative nature of GBM, accurate measurement is paramount for GBM assessment. Preoperative magnetic resonance images (MRIs) are crucial for initial diagnosis and surgical planning, while follow-up MRIs are vital for evaluating treatment response. The structural changes in the brain caused by surgical and therapeutic measures create significant differences between preoperative and follow-up MRIs. In clinical research, advanced deep learning models trained on preoperative MRIs are often applied to assess follow-up scans, but their effectiveness in this …

Examining Educational And Career Transition Points Among A Diverse, Virtual Mentoring Network, Erika L Thompson, Toufeeq Ahmed Syed, Zainab Latif, Katie Stinson, Damaris Javier, Gabrielle Saleh, Jamboor K Vishwanatha

Faculty, Staff and Student Publications

Given the differences in trajectory for under-represented minorities in biomedical careers, we sought to explore how a virtual mentoring program, the National Research Mentoring Network (NRMN), and its platform (MyNRMN), may facilitate transitions in the science, technology, engineering, mathematics, and medicine (STEMM) pipeline. The purpose of this study was to describe how the size of an MyNRMN member’s mentoring network and level of engagement correlate with academic and career transitions. We examined MyNRMN platform user data from March 2020 to May 2021 (n = 2993). Logistic regression estimated the odds of a career or academic transition related to NRMN …

Deep5mc: Predicting 5-Methylcytosine (5mc) Methylation Status Using A Deep Learning Transformer Approach, Evan Kinnear, Houssemeddine Derbel, Zhongming Zhao, Qian Liu

Faculty, Staff and Student Publications

DNA methylations, such as 5-methylcytosine (5mC), are crucial in biological processes, and aberrant methylations are strongly linked to various human diseases. Genomic 5mC is not randomly distributed but exhibits a strong association with genomic sequences. Thus, various computational methods were developed to predict 5mC status based on DNA sequences. These methods generated promising achievements and overcome the limitations of experimental approaches. However, few studies have comprehensively investigated the dependency of 5mC on genomic sequences, and most existing methods focus on specific genomic regions. In this work, we introduce Deep5mC, a deep learning transformer-based method designed to predict 5mC methylations. Deep5mC …

Mmrt: Multimut Recursive Tree For Predicting Functional Effects Of High-Order Protein Variants From Low-Order Variants, Bryce Forrest, Houssemeddine Derbel, Zhongming Zhao, Qian Liu

Faculty, Staff and Student Publications

Protein sequences primarily determine their stability and functions. Mutations may occur at one, two, or three positions at the same time (low-order variants) or at multiple positions simultaneously (high-order variants), which affect protein functions. So far, low-order variants, such as single variants, double variants, and triple variants, have been well-studied through high-throughput experimental scanning techniques and computational prediction methods. However, research on high-order variants remains limited because of the difficulty of scanning an exponentially large number of potential variant combinations. Nonetheless, studying higher-order variants is crucial for understanding the pathogenesis of complex diseases, advancing protein engineering, and driving precision medicine. …

Enhancing The Utility Of Polygenic Scores In Alzheimer’S Disease Through Systematic Curation And Annotation, Savannah Mwesigwa, Yulin Dai, Nitesh Enduru, Zhongming Zhao

Faculty, Staff and Student Publications

Introduction: Polygenic Scores (PGSs) assess cumulative genetic risk variants that contribute to the association with complex diseases like Alzheimer's Disease (AD). The PGS Catalog is a valuable repository of PGSs of various complex diseases, but it lacks standardized annotations and harmonization, making the information difficult to integrate for a specific disease.

Methods: In this study, we curated 44 PGS datasets for AD from the PGS Catalog, categorized them into five methodological groups, and annotated 813,257 variants to nearby genes. We aligned the scores based on the "GWAS significant variants" (GWAS-SV) method with the GWAS Catalog and flagged redundant files and …

Development And Validation Of Natural Language Processing Algorithms In The National Enact Network, Yanshan Wang, Jordan Hilsman, Chenyu Li, Michele Morris, Paul M Heider, Sunyang Fu, Min Ji Kwak, Andrew Wen, Joseph R Applegate, Liwei Wang, Elmer Bernstam, Hongfang Liu, Jack Chang, Daniel R Harris, Alexandria Corbeau, Darren Henderson, John Osborne, Richard E Kennedy, Nelly-Estefanie Garduno-Rapp, Justin F Rousseau, Chao Yan, You Chen, Mayur B Patel, Tyler J Murphy, Bradley A Malin, Chan Mi Park, Jungwei W Fan, Sunghwan Sohn, Sandeep Pagali, Yifan Peng, Aman Pathak, Yonghui Wu, Zongqi Xia, Salvatore Loguercio, Steven E Reis, Shyam Visweswaran

Faculty, Staff and Student Publications

Objective: Electronic Health Record (EHR) data are critical for advancing translational research and AI technologies. The ENACT network offers access to structured EHR data across 57 CTSA hubs. However, substantial information is contained in clinical narratives, requiring natural language processing (NLP) for research. The ENACT NLP Working Group was formed to make NLP-derived clinical information accessible and queryable across the network.

Methods: We established the ENACT NLP Working Group with 13 sites selected based on criteria including clinical notes access, IT infrastructure, NLP expertise, and institutional support. We divided sites into five focus groups targeting clinical tasks within disease contexts. …

Epidemiology Of Patient Record Duplication, Onur Sahin, Audrey Zhao, Reuben Joseph Applegate, Todd R Johnson, Elmer V Bernstam

Faculty, Staff and Student Publications

Objectives: Duplicate patient records can increase costs and medical errors. We assessed the association between demographic factors, comorbidities, health care usage, and duplicate electronic health records.

Methods: We analyzed the association between duplicate patient records and multiple demographic variables (race, Hispanic ethnicity, sex, and age) as well as the Charlson Comorbidity Index (CCI), number of diagnoses, and number of health care encounters. The study population included 3,018,413 patients seen at a large urban academic medical center with at least one recorded diagnosis. Duplication of patient medical records was determined by using a previously validated enterprise Master Person Index.

Results: Unknown …

Tagraxofusp Maintenance Post-Hematopoietic Stem Cell Transplantation Provides Long-Term Survival And Manageable Safety For A Patient With Blastic Plasmacytoid Dendritic Cell Neoplasm, Qaiser Bashir, Marina Konopleva, Glorette Abueg, Jeremy Ramdial, Chitra Hosing, Samer A Srour, Amin Alousi, Uday R Popat, Yago Nieto, Gheath Alatrash, Richard E Champlin, Elizabeth J Shpall, Muzaffar Qazilbash, Naveen Pemmaraju

Faculty, Staff and Student Publications

Presented here is the case of a 68-year-old woman with blastic plasmacytoid dendritic cell neoplasm (BPDCN) treated with tagraxofusp (TAG) maintenance therapy post-allogeneic hematopoietic stem cell transplantation (allo-HCT). Prior to allo-HCT, the patient was treated with hydroxyurea and mini-CVD (cyclophosphamide, vincristine, and dexamethasone alternating with methotrexate (Methotrexate) and cytarabine) + venetoclax + TAG for 5 cycles, which induced morphologic complete remission with minimal residual disease. After allo-HCT, the patient had persistent cytogenic abnormalities 45,XX,der(7)add(7)(p13)del(7)(q11.2q22)add(7)(q32),add(12)(p13),-15,del(16)(q23),-17,+22,+2mar[1]/46,XX[19], and was then treated with TAG maintenance therapy at 9 mg/kg on a 28-day cycle for 16 cycles. At mid-treatment (cycle 6 of 16 cycles of …

Co-Morbid Indomethacin-Responsive Headaches In A Woman In Her Late 60s With Paroxysmal Hemicrania And Hypnic Headache: A Case Report, Ashlyn Brown, Randolph W Evans, Claudia Carrizo, Mark Burish

Faculty, Staff and Student Publications

Paroxysmal hemicrania (PH) and hypnic headache (HH) are rare indomethacin-responsive headache syndromes. This case report details the new onset of both disorders in a woman in her late 60s. One headache type presented as severe pain centered on the right eyebrow, lasting 30 minutes, occurring more than 8 times daily, and associated with ipsilateral lacrimation and rhinorrhea. The second type was a right frontal severe pain, with onset at 4 a.m., occurring only during sleep, lasting 30 minutes, and with no associated factors. The patient's response to indomethacin for both headache types was confirmed through an unblinded ABAB study design: …

Microbial Imprints On Colorectal Cancer: The Epigenetic Silencing Of Phlpp1 As A Prognostic Nexus, Xiangsheng Huang, Faraz Bishehsari

Faculty, Staff and Student Publications

No abstract provided.

Bismuth-Infused Perivascular Wrap Facilitates Delivery Of Mesenchymal Stem Cells And Attenuation Of Neointimal Hyperplasia In Rat Arteriovenous Fistulas, Allan John R Barcena, Joy Vanessa D Perez, Marvin R Bernardino, Jossana A Damasco, Erin Marie D San Valentin, Carleigh Klusman, Benjamin Martin, Gino Martin Canlas, Francisco M Heralde, Natalie Fowlkes, Richard R Bouchard, Jizhong Cheng, Steven Y Huang, Marites P Melancon

Faculty, Staff and Student Publications

BACKGROUND: Mesenchymal stem cells (MSCs) have emerged as novel therapies for supporting arteriovenous fistula (AVF) maturation, and bioresorbable polymeric scaffolds have enabled sustained MSC delivery into maturing AVFs. However, the radiolucency of biopolymeric wraps prevents in vivo monitoring of their integrity and location, hindering long-term preclinical investigations.

METHODS: We infused bismuth nanoparticles (BiNPs) into polycaprolactone (PCL) to fabricate an electrospun perivascular wrap capable of MSC delivery and conducive to longitudinal monitoring using conventional imaging. We tested the wraps' effects on the attenuation of markers of neointimal hyperplasia (i.e., endothelial dysfunction, hypoxia, and inflammation), the leading cause of AVF failure, in …

Editorial: Protein Regulation By Lipids, Mikhail V Bogdanov, Elena G Govorunova

Faculty, Staff and Student Publications

No abstract provided.