Genetic Phenomena Commons^™

Electroacupuncture Ameliorates Inflammatory Response Induced By Retinal Ischemia-Reperfusion Injury And Protects The Retina Through The Dor-Bdnf/Trkb Pathway, Runjie Guo, Yongjie Zhang, Yue Geng, Ping Chen, Tiantian Fu, Yong Xia, Ren Zhang, Yuan Zhu, Jingling Jin, Nange Jin, Hong Xu, Xuesong Tian

Faculty, Staff and Student Publications

Objectives: Retinal ischemia-reperfusion injury (RIRI) is the common pathological basis of many ophthalmic diseases in the later stages, and inflammation is the primary damage mechanism of RIRI. Our study aimed to assess whether electroacupuncture (EA) has a protective effect against RIRI and to elucidate its related mechanisms.

Methods: A high-intraocular pressure (HIOP) model was used to simulate RIRI in Wistar rats. EA was applied to the EA1 group [Jingming (BL1) + Shuigou (GV26)] and the EA2 group [Jingming (BL1) + Hegu (LI4)] respectively for 30 min starting immediately after the onset of reperfusion and repeated (30 min/time) at 12 h …

Doxorubicin-Induced Cardiotoxicity Is Mediated By Neutrophils Through Release Of Neutrophil Elastase, Anchit Bhagat, Pradeep Shrestha, Prince Jeyabal, Zhanglong Peng, Stephanie S Watowich, Eugenie S Kleinerman

Faculty, Staff and Student Publications

The mechanisms by which Doxorubicin (Dox) causes acute and late cardiotoxicity are not completely understood. One understudied area is the innate immune response, and in particular the role of neutrophils in Dox-induced cardiotoxicity. Here, using echocardiography, flow cytometry and immunofluorescence staining, we demonstrated increased infiltration of neutrophils that correlated with decreased heart function, disruption of vascular structures and increased collagen deposition in the heart after Dox treatment. Depleting neutrophils protected the heart from Dox-induced cardiotoxicity and changes in vascular structure. Furthermore, our data using neutrophil elastase (NE) knock-out mice and the NE inhibitor AZD9668 suggest that neutrophils cause this damage …

The Seen And The Unseen: Molecular Classification And Image Based-Analysis Of Gastrointestinal Cancers, Corina-Elena Minciuna, Mihai Tanase, Teodora Ecaterina Manuc, Stefan Tudor, Vlad Herlea, Mihnea P Dragomir, George A Calin, Catalin Vasilescu

Faculty, Staff and Student Publications

Gastrointestinal cancers account for 22.5% of cancer related deaths worldwide and represent circa 20% of all cancers. In the last decades, we have witnessed a shift from histology-based to molecular-based classifications using genomic, epigenomic, and transcriptomic data. The molecular based classification revealed new prognostic markers and may aid the therapy selection. Because of the high-costs to perform a molecular classification, in recent years immunohistochemistry-based surrogate classification were developed which permit the stratification of patients, and in parallel multiple groups developed hematoxylin and eosin whole slide image analysis for sub-classifying these entities. Hence, we are witnessing a return to an image-based …

Translating Mentoring Interventions Research Into Practice: Evaluation Of An Evidence-Based Workshop For Research Mentors On Developing Trainees’ Scientific Communication Skills, Erin K Dahlstrom, Christine Bell, Shine Chang, Hwa Young Lee, Cheryl B Anderson, Annie Pham, Christine Maidl Pribbenow, Carrie A Cameron

Faculty, Staff and Student Publications

A key part of keeping doctoral and postdoctoral trainees in STEM research careers is mentoring. Our previous research indicates that mentoring trainees in scientific communication (SC) skill development increases research career intention through two social-cognitive constructs, self-efficacy in and outcome expectations for acquiring SC skills, as well as science identity. While many mentor training interventions exist, no programs focus on developing SC skills specifically. The "Scientific Communication Advances Research Excellence" (SCOARE) program trains mentors to address trainee scientific communication (SC) skill development as an innovative approach to increase trainee research career persistence. The SCOARE training is a half-day workshop for …

Pathogenic Tau Accelerates Aging-Associated Activation Of Transposable Elements In The Mouse Central Nervous System, Paulino Ramirez, Gabrielle Zuniga, Wenyan Sun, Adrian Beckmann, Elizabeth Ochoa, Sarah L Devos, Bradley Hyman, Gabriel Chiu, Ethan R Roy, Wei Cao, Miranda Orr, Virginie Buggia-Prevot, William J Ray, Bess Frost

Faculty, Staff and Student Publications

Transposable elements comprise almost half of the mammalian genome. A growing body of evidence suggests that transposable element dysregulation accompanies brain aging and neurodegenerative disorders, and that transposable element activation is neurotoxic. Recent studies have identified links between pathogenic forms of tau, a protein that accumulates in Alzheimer's disease and related "tauopathies," and transposable element-induced neurotoxicity. Starting with transcriptomic analyses, we find that age- and tau-induced transposable element activation occurs in the mouse brain. Among transposable elements that are activated at the RNA level in the context of brain aging and tauopathy, we find that the endogenous retrovirus (ERV) class …

Research Career Intentions Among Non-Native English-Speaking Graduate And Postdoctoral Trainees In Stem-Results From Cross-Sectional And Longitudinal Studies, Hwa Young Lee, Shine Chang, Cheryl B Anderson, Erin K Dahlstrom, Carrie Cameron

Faculty, Staff and Student Publications

International graduate trainees, many of whom are non-native English-speaking (L2) trainees, comprise more than half of graduate-level trainees in STEM, but little is known regarding factors that influence their career intentions, especially those that foster their growth as scientists to achieve their full potential in research. Thus, the purpose of our studies was to examine the relationship between L2 status and contextual factors that help shape the learning experiences and plans for research-focused careers. Study 1 collected cross-sectional survey data from doctoral and postdoctoral trainees (N=510) from research institutes in the Texas Medical Center in Houston. We examined which factors …

Transcriptomic-Assisted Immune And Neoantigen Profiling In Premalignancy, Kyle Chang, Florencia Mcallister, Eduardo Vilar

Faculty, Staff and Student Publications

Immune-based cancer therapies such as checkpoint inhibitors (CPI) and vaccines have been increasingly studied across different cancer types. Response to such therapies depends on a number of factors such as mutational burden, neoantigen load, presence of tumor infiltrating lymphocytes, among others. Next-generation sequencing (NGS) technologies are particularly attractive to interrogate the immune response compared to traditional assays such as qRT-PCR and immunohistochemistry (IHC) because they enable the discovery of neoantigens and simultaneous profiling of immune infiltration using gene expression on a large scale. Current approaches in immune profiling utilizes whole-exome sequencing (WES) for human leukocyte allele (HLA) typing and neoantigen …

Cancer Prevention Behaviors In Workers Of A Referral Cancer Center In Mexico City: A Pilot Study On Early Detection Awareness For Cancer, Nancy Reynoso-Noverón, Shine Chang, Luis Alonso Herrera-Montalvo, Abelardo Meneses-García

Faculty, Staff and Student Publications

Background: Prevention strategies for cancer are necessary. Health workers who often serve as role models bear responsibility for prevention counseling and programs. However, whether their habits and behaviors reflect prevention goals are unknown. We describe the prevalence of cancer risk factors and prevention behaviors in health workers of a referral cancer center in Mexico City.

Methods: Cross-sectional study in which workers of the National Cancer Institute were invited to participate in a prevention program, risk factor survey, and nutrition, psychological, and genetic counseling were included. The likelihood of cancer was calculated based on the presence of risk factors. Factors associated …

Surgical Management Of Brain Metastasis: Challenges And Nuances, Chibawanye I Ene, Sherise D Ferguson

Faculty, Staff and Student Publications

Brain metastasis is the most common type of intracranial tumor. The contemporary management of brain metastasis is a challenging issue and traditionally has carried a poor prognosis as these lesions typically occur in the setting of advanced cancer. However, improvement in systemic therapy, advances in radiation techniques and multimodal therapy tailored to the individual patient, has given hope to this patient population. Surgical resection has a well-established role in the management of brain metastasis. Here we discuss the evolving role of surgery in the treatment of this diverse patient population.

High-Resolution Metabolomics Of Exposure To Tobacco Smoke During Pregnancy And Adverse Birth Outcomes In The Atlanta African American Maternal-Child Cohort, Youran Tan, Dana Boyd Barr, P Barry Ryan, Veronika Fedirko, Jeremy A Sarnat, Audrey J Gaskins, Che-Jung Chang, Ziyin Tang, Carmen J Marsit, Elizabeth J Corwin, Dean P Jones, Anne L Dunlop, Donghai Liang

Faculty, Staff and Student Publications

Exposure to tobacco smoke during pregnancy has been associated with a series of adverse reproductive outcomes; however, the underlying molecular mechanisms are not well-established. We conducted an untargeted metabolome-wide association study to identify the metabolic perturbations and molecular mechanisms underlying the association between cotinine, a widely used biomarker of tobacco exposure, and adverse birth outcomes. We collected early and late pregnancy urine samples for cotinine measurement and serum samples for high-resolution metabolomics (HRM) profiling from 105 pregnant women from the Atlanta African American Maternal-Child cohort (2014-2016). Maternal metabolome perturbations mediating prenatal tobacco smoke exposure and adverse birth outcomes were assessed …

Recurrent Hgnet-Mn1 Altered (Astroblastoma Mn1-Altered) Of The Foramen Magnum: Case Report And Molecular Classification, Sricharan Gopakumar, Malcolm F Mcdonald, Himanshu Sharma, Claudio E Tatsui, Gregory N Fuller, Ganesh Rao

Faculty, Staff and Student Publications

Background: Astroblastoma is a rare primary brain tumor of unclear origin, often occurring in young patients less than 30-years-old. It typically arises supratentorially and is diagnosed based on histological features including vascular hyalinization and perivascular pseudorosettes. Recent molecular characterization of primary CNS high-grade neuroepithelial tumors with meningioma I alteration (HGNET-MN1) found that HGNET-MN1 and tumors with morphological signatures of astroblastoma clustered together. Further analysis revealed such astroblastomas have MN1 alteration and the 2021 WHO classification of tumors of the CNS now recognizes astroblastoma MN1-altered as a new entity.

Case description: Here, we present the case of …

Proton Image-Guided Radiation Assignment For Therapeutic Escalation Via Selection Of Locally Advanced Head And Neck Cancer Patients [Pirates]: A Phase I Safety And Feasibility Trial Of Mri-Guided Adaptive Particle Radiotherapy, Lisanne V Van Dijk, Steven J Frank, Ying Yuan, Brandon Gunn, Amy C Moreno, Abdallah S R Mohamed, Kathryn E Preston, Yun Qing, Michael T Spiotto, William H Morrison, Anna Lee, Jack Phan, Adam S Garden, David I Rosenthal, Johannes A Langendijk, Clifton D Fuller

Faculty, Staff and Student Publications

Introduction: Radiation dose-escalation for head and neck cancer (HNC) patients aiming to improve cure rates is challenging due to the increased risk of unacceptable treatment-induced toxicities. With "Proton Image-guided Radiation Assignment for Therapeutic Escalation via Selection of locally advanced head and neck cancer patients" (PIRATES), we present a novel treatment approach that is designed to facilitate dose-escalation while minimizing the risk of dose-limiting toxicities for locally advanced HPV-negative HNC patients. The aim of this Phase I trial is to assess the safety & feasibility of PIRATES approach.

Methods: The PIRATES protocol employs a multi-faceted dose-escalation approach to minimize the risk …

Progression Free Survival Prediction For Head And Neck Cancer Using Deep Learning Based On Clinical And Pet/Ct Imaging Data, Mohamed A Naser, Kareem A Wahid, Abdallah S R Mohamed, Moamen Abobakr Abdelaal, Renjie He, Cem Dede, Lisanne V Van Dijk, Clifton D Fuller

Faculty, Staff and Student Publications

Determining progression-free survival (PFS) for head and neck squamous cell carcinoma (HNSCC) patients is a challenging but pertinent task that could help stratify patients for improved overall outcomes. PET/CT images provide a rich source of anatomical and metabolic data for potential clinical biomarkers that would inform treatment decisions and could help improve PFS. In this study, we participate in the 2021 HECKTOR Challenge to predict PFS in a large dataset of HNSCC PET/CT images using deep learning approaches. We develop a series of deep learning models based on the DenseNet architecture using a negative log-likelihood loss function that utilizes PET/CT …

Combining Tumor Segmentation Masks With Pet/Ct Images And Clinical Data In A Deep Learning Framework For Improved Prognostic Prediction In Head And Neck Squamous Cell Carcinoma, Kareem A Wahid, Renjie He, Cem Dede, Abdallah S R Mohamed, Moamen Abobakr Abdelaal, Lisanne V Van Dijk, Clifton D Fuller, Mohamed A Naser

Faculty, Staff and Student Publications

PET/CT images provide a rich data source for clinical prediction models in head and neck squamous cell carcinoma (HNSCC). Deep learning models often use images in an end-to-end fashion with clinical data or no additional input for predictions. However, in the context of HNSCC, the tumor region of interest may be an informative prior in the generation of improved prediction performance. In this study, we utilize a deep learning framework based on a DenseNet architecture to combine PET images, CT images, primary tumor segmentation masks, and clinical data as separate channels to predict progression-free survival (PFS) in days for HNSCC …

Establishment And Validation Of Pre-Therapy Cervical Vertebrae Muscle Quantification As A Prognostic Marker Of Sarcopenia In Patients With Head And Neck Cancer, Brennan Olson, Jared Edwards, Catherine Degnin, Nicole Santucci, Michelle Buncke, Jeffrey Hu, Yiyi Chen, Clifton D Fuller, Mathew Geltzeiler, Aaron J Grossberg, Daniel Clayburgh

Faculty, Staff and Student Publications

Background: Sarcopenia is prognostic for survival in patients with head and neck cancer (HNC). However, identification of this high-risk feature remains challenging without computed tomography (CT) imaging of the abdomen or thorax. Herein, we establish sarcopenia thresholds at the C3 level and determine if C3 sarcopenia is associated with survival in patients with HNC.

Methods: This retrospective cohort study was conducted in consecutive patients with a squamous cell carcinoma of the head and neck with cross-sectional abdominal or neck imaging within 60 days prior to treatment. Measurement of cross-sectional muscle area at L3 and C3 levels was performed from CT …

Deep-Learning-Based Generation Of Synthetic 6-Minute Mri From 2-Minute Mri For Use In Head And Neck Cancer Radiotherapy, Kareem A Wahid, Jiaofeng Xu, Dina El-Habashy, Yomna Khamis, Moamen Abobakr, Brigid Mcdonald, Nicolette O' Connell, Daniel Thill, Sara Ahmed, Christina Setareh Sharafi, Kathryn Preston, Travis C Salzillo, Abdallah S R Mohamed, Renjie He, Nathan Cho, John Christodouleas, Clifton D Fuller, Mohamed A Naser

Faculty, Staff and Student Publications

Background: Quick magnetic resonance imaging (MRI) scans with low contrast-to-noise ratio are typically acquired for daily MRI-guided radiotherapy setup. However, for patients with head and neck (HN) cancer, these images are often insufficient for discriminating target volumes and organs at risk (OARs). In this study, we investigated a deep learning (DL) approach to generate high-quality synthetic images from low-quality images.

Methods: We used 108 unique HN image sets of paired 2-minute T2-weighted scans (2mMRI) and 6-minute T2-weighted scans (6mMRI). 90 image sets (~20,000 slices) were used to train a 2-dimensional generative adversarial DL model that utilized 2mMRI as input and …

Head And Neck Cancer Primary Tumor Auto Segmentation Using Model Ensembling Of Deep Learning In Pet/Ct Images, Mohamed A Naser, Kareem A Wahid, Lisanne V Van Dijk, Renjie He, Moamen Abobakr Abdelaal, Cem Dede, Abdallah S R Mohamed, Clifton D Fuller

Faculty, Staff and Student Publications

Auto-segmentation of primary tumors in oropharyngeal cancer using PET/CT images is an unmet need that has the potential to improve radiation oncology workflows. In this study, we develop a series of deep learning models based on a 3D Residual Unet (ResUnet) architecture that can segment oropharyngeal tumors with high performance as demonstrated through internal and external validation of large-scale datasets (training size = 224 patients, testing size = 101 patients) as part of the 2021 HECKTOR Challenge. Specifically, we leverage ResUNet models with either 256 or 512 bottleneck layer channels that demonstrate internal validation (10-fold cross-validation) mean Dice similarity coefficient …

Re-Thinking Therapeutic Development For Cns Metastatic Disease, Chantal Saberian, Michael A Davies

Faculty, Staff and Student Publications

There has been unprecedented progress in the development of systemic therapies for patients with metastatic melanoma over the last decade. There is now tremendous potential and momentum to further and markedly reduce the impact of this disease. However, developing more effective treatments for metastases to the CNS remains a critical challenge for patients with melanoma. Melanoma patients with active CNS metastases have largely been excluded from both early-phase and registration trials for all currently approved targeted and immune therapies for this disease. While this exclusion has generally been justified in clinical research due to concerns about poor prognosis, lack of …

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, …