Data Science Commons^™

Hyperpolarized Magnetic Resonance Imaging, Nuclear Magnetic Resonance Metabolomics, And Artificial Intelligence To Interrogate The Metabolic Evolution Of Glioblastoma, Kang Lin Hsieh, Qing Chen, Travis C Salzillo, Jian Zhang, Xiaoqian Jiang, Pratip K Bhattacharya, Shyan Shams

Faculty, Staff and Student Publications

Glioblastoma (GBM) is a malignant Grade VI cancer type with a median survival duration of only 8-16 months. Earlier detection of GBM could enable more effective treatment. Hyperpolarized magnetic resonance spectroscopy (HPMRS) could detect GBM earlier than conventional anatomical MRI in glioblastoma murine models. We further investigated whether artificial intelligence (A.I.) could detect GBM earlier than HPMRS. We developed a deep learning model that combines multiple modalities of cancer data to predict tumor progression, assess treatment effects, and to reconstruct in vivo metabolomic information from ex vivo data. Our model can detect GBM progression two weeks earlier than conventional MRIs …

Cluster Effect For Snp-Snp Interaction Pairs For Predicting Complex Traits, Hui Yi Lin, Harun Mazumder, Indrani Sarkar, Po Yu Huang, Rosalind A. Eeles, Zsofia Kote-Jarai, Kenneth R. Muir, Johanna Schleutker, Nora Pashayan, Jyotsna Batra, David E. Neal, Sune F. Nielsen, Børge G. Nordestgaard, Henrik Grönberg, Fredrik Wiklund, Robert J. Macinnis, Christopher A. Haiman, Ruth C. Travis, Janet L. Stanford, Adam S. Kibel, Cezary Cybulski, Kay Tee Khaw, Christiane Maier, Stephen N. Thibodeau, Manuel R. Teixeira, Lisa Cannon-Albright, Hermann Brenner, Radka Kaneva, Hardev Pandha, Et Al

School of Public Health Faculty Publications

Single nucleotide polymorphism (SNP) interactions are the key to improving polygenic risk scores. Previous studies reported several significant SNP-SNP interaction pairs that shared a common SNP to form a cluster, but some identified pairs might be false positives. This study aims to identify factors associated with the cluster effect of false positivity and develop strategies to enhance the accuracy of SNP-SNP interactions. The results showed the cluster effect is a major cause of false-positive findings of SNP-SNP interactions. This cluster effect is due to high correlations between a causal pair and null pairs in a cluster. The clusters with a …

Disparities And Protective Factors In Pandemic-Related Mental Health Outcomes: A Louisiana-Based Study, Ariane L. Rung, Evrim Oral, Tyler Prusisz, Edward S. Peters

School of Public Health Faculty Publications

Introduction: The COVID-19 pandemic has had a wide-ranging impact on mental health. Diverse populations experienced the pandemic differently, highlighting pre-existing inequalities and creating new challenges in recovery. Understanding the effects across diverse populations and identifying protective factors is crucial for guiding future pandemic preparedness. The objectives of this study were to (1) describe the specific COVID-19-related impacts associated with general well-being, (2) identify protective factors associated with better mental health outcomes, and (3) assess racial disparities in pandemic impact and protective factors. Methods: A cross-sectional survey of Louisiana residents was conducted in summer 2020, yielding a sample of 986 Black …

Parameter Estimation For Stroke Patients Using Brain Ct Perfusion Imaging With Deep Temporal Convolutional Neural Network, Shake Ibna Abir

Masters Theses & Specialist Projects

Acute ischemic stroke, caused by cerebral artery blockage, is a leading cause of long-term disability and mortality. Effective management relies on accurate, timely assessments from neuroimaging data. Computed tomography perfusion (CTP) imaging is crucial in evaluating stroke patients, offering detailed maps of cerebral perfusion to identify irreversibly damaged tissue and at-risk areas. This detailed assessment is essential for informed therapeutic decisions.

Key perfusion parameters derived from CTP imaging, including cerebral blood volume (CBV), cerebral blood flow (CBF), time to peak (TTP), and mean transit time (MTT), are crucial for understanding the extent and nature of cerebral ischemia, providing valuable insights …

Exploring Healthcare Chatbot Information Presentation: Applying Hierarchical Bayesian Regression And Inductive Thematic Analysis In A Mixed Methods Study, Samuel Nelson Koscelny

All Theses

High blood pressure, also known as hypertension, significantly increases the risk of heart disease and stroke, which are leading causes of death in the United States. While contributing to over 691,000 deaths in 2021 alone in the United States (U.S.), it also imposes immense economic burden on the healthcare system, costing approximately $131 billion annually. One way to address this issue is for increased self-care behaviors and medication adherence, both of which require sufficient health literacy. Despite the importance of health literacy, 90% of U.S. adults struggle with health-related subjects. Overcoming the issues associated with health literacy requires addressing the …

Patient-Centered Clinical Decision Support Challenges And Opportunities Identified From Workflow Execution Models, Dean F Sittig, Aziz Boxwala, Adam Wright, Courtney Zott, Nicole A Gauthreaux, James Swiger, Edwin A Lomotan, Prashila Dullabh

Faculty, Staff and Student Publications

OBJECTIVE: To use workflow execution models to highlight new considerations for patient-centered clinical decision support policies (PC CDS), processes, procedures, technology, and expertise required to support new workflows.

METHODS: To generate and refine models, we used (1) targeted literature reviews; (2) key informant interviews with 6 external PC CDS experts; (3) model refinement based on authors' experience; and (4) validation of the models by a 26-member steering committee.

RESULTS AND DISCUSSION: We identified 7 major issues that provide significant challenges and opportunities for healthcare systems, researchers, administrators, and health IT and app developers. Overcoming these challenges presents opportunities for new …

Automatic Uncovering Of Patient Primary Concerns In Portal Messages Using A Fusion Framework Of Pretrained Language Modelsautomatic Uncovering Of Patient Primary Concerns In Portal Messages Using A Fusion Framework Of Pretrained Language Models, Yang Ren, Yuqi Wu, Jungwei W Fan, Aditya Khurana, Sunyang Fu, Dezhi Wu, Hongfang Liu, Ming Huang

Faculty, Staff and Student Publications

OBJECTIVES: The surge in patient portal messages (PPMs) with increasing needs and workloads for efficient PPM triage in healthcare settings has spurred the exploration of AI-driven solutions to streamline the healthcare workflow processes, ensuring timely responses to patients to satisfy their healthcare needs. However, there has been less focus on isolating and understanding patient primary concerns in PPMs-a practice which holds the potential to yield more nuanced insights and enhances the quality of healthcare delivery and patient-centered care.

MATERIALS AND METHODS: We propose a fusion framework to leverage pretrained language models (LMs) with different language advantages via a Convolution Neural …

Artificial Intelligence In Fusion Protein Three-Dimensional Structure Prediction: Review And Perspective, Himansu Kumar, Pora Kim

Faculty, Staff and Student Publications

Recent advancements in artificial intelligence (AI) have accelerated the prediction of unknown protein structures. However, accurately predicting the three-dimensional (3D) structures of fusion proteins remains a difficult task because the current AI-based protein structure predictions are focused on the WT proteins rather than on the newly fused proteins in nature. Following the central dogma of biology, fusion proteins are translated from fusion transcripts, which are made by transcribing the fusion genes between two different loci through the chromosomal rearrangements in cancer. Accurately predicting the 3D structures of fusion proteins is important for understanding the functional roles and mechanisms of action …

Interpretation Models For Prostate Lesion: Detecting, Explaining, And Understanding., Mehmet Akif Gulum

Electronic Theses and Dissertations

Prostate cancer is a major public health concern, affecting millions of men worldwide. While early detection and treatment of prostate cancer is critical for improving patient outcomes, the detection of prostate lesions is even more important for timely intervention and management of the disease. Prostate lesions are abnormal growths or lumps within the prostate gland, which may or may not be cancerous. The timely detection and accurate diagnosis of prostate lesions is crucial for effective treatment and management of the disease. In recent years, deep learning models have shown promise in accurately detecting and characterizing prostate lesions using advanced imaging …

Influential Factors And Predicting Dose Delivery Accuracy For Imaging And Radiation Oncology Core’S Phantom Program Using Machine Learning, Hunter Mehrens

Dissertations and Theses (Open Access)

IROC’s mission is to help ensure consistent and comparable, high-quality radiotherapy across clinics that participate in national clinical trials. To obtain this mission, IROC’s phantom program provides a third-party end-to-end check of the clinical workflow of a patient receiving radiotherapy. The goal of the phantom audit is to compare the dose delivered to the dose planned by the treatment system ensuring dose delivery accuracy. While IROC’s phantoms are better equipped to catch dose delivery errors compared to a clinic’s QA process, the end-to-end process and reporting of results is time-consuming creating a bottleneck for clinical trial participation. Furthermore, IROC’s passing …

Genomic Data Science Approaches For Understanding Human Diseases, Snehal Shah

All Dissertations

The intricate interplay of genetic predisposition, environmental influences, and lifestyle acts as the multifactorial landscape of diseases. Understanding this complexity presents a significant challenge. Molecular insights into disease mechanisms, particularly the interactions of DNA, RNA, and proteins with environmental and lifestyle factors, have revolutionized disease diagnosis, prognosis, and treatment. High-throughput technologies, such as next-generation sequencing, generate large amounts of molecular data, holding a wealth of knowledge. These datasets unveil the roles of genes and their interactions with various factors through analysis, shedding light on previously unknown molecular mechanisms underlying disease pathogenesis. Furthermore, they facilitate the discovery of biomarkers crucial for …

Safer: Sub-Hypergraph Attention-Based Neural Network For Predicting Effective Responses To Dose Combinations, Yi-Ching Tang, Rongbin Li, Jing Tang, W Jim Zheng, Xiaoqian Jiang

Faculty, Staff and Student Publications

BACKGROUND: The potential benefits of drug combination synergy in cancer medicine are significant, yet the risks must be carefully managed due to the possibility of increased toxicity. Although artificial intelligence applications have demonstrated notable success in predicting drug combination synergy, several key challenges persist: (1) Existing models often predict average synergy values across a restricted range of testing dosages, neglecting crucial dose amounts and the mechanisms of action of the drugs involved. (2) Many graph-based models rely on static protein-protein interactions, failing to adapt to dynamic and higher-order relationships. These limitations constrain the applicability of current methods.

RESULTS: We introduce …

The Significant Role Of Amino Acid Metabolic Reprogramming In Cancer, Xiaohong Liu, Bo Ren, Jie Ren, Minzhi Gu, Lei You, Yupei Zhao

Faculty, Staff and Student Publications

Amino acid metabolism plays a pivotal role in tumor microenvironment, influencing various aspects of cancer progression. The metabolic reprogramming of amino acids in tumor cells is intricately linked to protein synthesis, nucleotide synthesis, modulation of signaling pathways, regulation of tumor cell metabolism, maintenance of oxidative stress homeostasis, and epigenetic modifications. Furthermore, the dysregulation of amino acid metabolism also impacts tumor microenvironment and tumor immunity. Amino acids can act as signaling molecules that modulate immune cell function and immune tolerance within the tumor microenvironment, reshaping the anti-tumor immune response and promoting immune evasion by cancer cells. Moreover, amino acid metabolism can …

Real-World Effectiveness And Tolerability Of Interferon-Free Direct-Acting Antiviral For 15,849 Patients With Chronic Hepatitis C: A Multinational Cohort Study, Fanpu Ji, Sally Tran, Eiichi Ogawa, Chung-Feng Huang, Takanori Suzuki, Yu Jun Wong, Hidenori Toyoda, Dae Won Jun, Liu Li, Haruki Uojima, Akito Nozaki, Makoto Chuma, Cheng-Hao Tseng, Yao-Chun Hsu, Masatoshi Ishigami, Takashi Honda, Masanori Atsukawa, Hiroaki Haga, Masaru Enomoto, Huy Trinh, Carmen Monica Preda, Phillip Vutien, Charles Landis, Dong Hyun Lee, Tsunamasa Watanabe, Hirokazu Takahashi, Hiroshi Abe, Akira Asai, Yuichiro Eguchi, Jie Li, Xiaozhong Wang, Jia Li, Junping Liu, Jing Liang, Carla Pui-Mei Lam, Rui Huang, Qing Ye, Hongying Pan, Jiajie Zhang, Dachuan Cai, Qi Wang, Daniel Q Huang, Grace Wong, Vincent Wai-Sun Wong, Junyi Li, Son Do, Norihiro Furusyo, Makoto Nakamuta, Hideyuki Nomura, Eiji Kajiwara, Eileen L Yoon, Sang Bong Ahn, Koichi Azuma, Kazufumi Dohmen, Jihyun An, Do Seon Song, Hyun Chin Cho, Akira Kawano, Toshimasa Koyanagi, Aritsune Ooho, Takeaki Satoh, Kazuhiro Takahashi, Ming-Lun Yeh, Pei-Chien Tsai, Satoshi Yasuda, Yunyu Zhao, Yishan Liu, Tomomi Okubo, Norio Itokawa, Mi Jung Jun, Toru Ishikawa, Koichi Takaguchi, Tomonori Senoh, Mingyuan Zhang, Changqing Zhao, Raluca Ioana Alecu, Wei Xuan Tay, Pooja Devan, Joanne Kimiko Liu, Ritsuzo Kozuka, Elena Vargas-Accarino, Ai-Thien Do, Mayumi Maeda, Wan-Long Chuang, Jee-Fu Huang, Chia-Yen Dai, Ramsey Cheung, Maria Buti, Junqi Niu, Wen Xie, Hong Ren, Seng Gee Lim, Chao Wu, Man-Fung Yuen, Jia Shang, Qiang Zhu, Yoshiyuki Ueno, Yasuhito Tanaka, Jun Hayashi, Ming-Lung Yu, Mindie H Nguyen

Faculty, Staff and Student Publications

BACKGROUND AND AIMS: As practice patterns and hepatitis C virus (HCV) genotypes (GT) vary geographically, a global real-world study from both East and West covering all GTs can help inform practice policy toward the 2030 HCV elimination goal. This study aimed to assess the effectiveness and tolerability of DAA treatment in routine clinical practice in a multinational cohort for patients infected with all HCV GTs, focusing on GT3 and GT6.

METHODS: We analyzed the sustained virological response (SVR12) of 15,849 chronic hepatitis C patients from 39 Real-World Evidence from the Asia Liver Consortium for HCV clinical sites in Asia Pacific, …

Effect Of Esketamine On Hypotension In Women With Preoperative Anxiety Undergoing Elective Cesarean Section: A Randomized, Double-Blind, Controlled Trial, Yu Qi, Meiyan Zhou, Yaqi Dong, Wenting Zheng, Qinyu Jiang, Yanyu Li, Xinghe Wang, Jia Sun, Hai Zhou, Zhengquan Hu, Liwei Wang

Faculty, Staff and Student Publications

To investigate the effect of low-doses esketamine on spinal anesthesia-induced hypotension in women with preoperative anxiety undergoing elective cesarean section, the randomized controlled trial enrolled 120 women aged 18-35 years who preoperative State-Trait Anxiety Inventory State scores > 40, conducted from September 2022 to August 2023 in Xuzhou Central Hospital, China. Women in the esketamine group received a single intravenous injection of 0.2 mg/kg esketamine after sensory block level achieved. The incidence of hypotension in the esketamine group was significantly lower than the control group at T2 (10% [6 of 60]; P < 0.001), T3 (5.0% [3 of 60]; P = 0.007) and T4(5.0% [3 of 60]; P = 0.004). Despite being higher in the esketamine group, the overall rates of hypertension (11.7% [7 of 60]; P = 0.186), tachycardia (23.3% [14 of 60]; P = 0.246), and bradycardia (0.0% [0 of 60]; P = 0.079) were no significantly difference between the two groups. STAI-S scores was significantly lower in the esketamine group (mean [SD] 37.52[7.14]) than in the control group (mean [SD] 41.03[9.66], P = 0.39) in postoperative day 1. Spinal anesthesia combined with intravenous low-doses esketamine infusion can significantly reduce the incidence of hypotension in women with preoperative anxiety undergoing elective cesarean section.

Innovation Path At Institute For Protein Design Of Washington University And Its Enlightenment For Construction Of New Life Sciences R&D Institutions, Runzhou Zhao, Ming Ni, Yunzhi Fa, Xiaochen Bo, Jian Jiao

Bulletin of Chinese Academy of Sciences (Chinese Version)

The Institute for Protein Design (IPD) at the University of Washington is a pioneering local and state-supported non-profit scientific research institution. Since its establishment in 2012, IPD has seized the opportunity of AI for Science and open science, and continuously enhanced its capabilities of fundamental innovations, breakthrough technologies, and industrial impact. We summarized five factors contributing to IPD’s development, including focusing on the cutting-edge issues of basic scientific research to gain a first-mover advantage and then further expand, integrating AI-enhanced digital tools and solid experimental validations, facilitating the integrated development of innovation and industrial chains, giving full play to the …

Low Skeletal Muscle Mass Is Associated With Inferior Preoperative And Postoperative Shoulder Function In Elderly Rotator Cuff Tear Patients, Yang Yang, Binbin Zheng, Xiaofang Lin, Mengqin Zhang, Yongzhi Ye, Haixiao Chen, Xiaobo Zhou

Faculty, Staff and Student Publications

BACKGROUND: The age-related loss of skeletal muscle mass is an important characteristic of sarcopenia, an increasingly recognized condition with systemic implications. However, its association with shoulder function in elderly patients with rotator cuff tears (RCT) remains unknown. This study aimed to investigate the relationship between low skeletal muscle mass and shoulder function in elderly RCT patients.

METHODS: A retrospective analysis was conducted on RCT patients who underwent chest computed tomography (CT) scans for clinical evaluation. Preoperative CT scan images of the chest were used to calculate the cross-sectional area (CSA) of thoracic muscle at the T4 level. The medical records …

Prediction Of Mild Cognitive Impairment Status: Pilot Study Of Machine Learning Models Based On Longitudinal Data From Fitness Trackers, Qidi Xu, Yejin Kim, Karen Chung, Paul Schulz, Assaf Gottlieb

Faculty, Staff and Student Publications

BACKGROUND: Early signs of Alzheimer disease (AD) are difficult to detect, causing diagnoses to be significantly delayed to time points when brain damage has already occurred and current experimental treatments have little effect on slowing disease progression. Tracking cognitive decline at early stages is critical for patients to make lifestyle changes and consider new and experimental therapies. Frequently studied biomarkers are invasive and costly and are limited for predicting conversion from normal to mild cognitive impairment (MCI).

OBJECTIVE: This study aimed to use data collected from fitness trackers to predict MCI status.

METHODS: In this pilot study, fitness trackers were …

Deepface: Deep-Learning-Based Framework To Contextualize Orofacial-Cleft-Related Variants During Human Embryonic Craniofacial Development, Yulin Dai, Toshiyuki Itai, Guangsheng Pei, Fangfang Yan, Yan Chu, Xiaoqian Jiang, Seth M Weinberg, Nandita Mukhopadhyay, Mary L Marazita, Lukas M Simon, Peilin Jia, Zhongming Zhao

Faculty, Staff and Student Publications

Orofacial clefts (OFCs) are among the most common human congenital birth defects. Previous multiethnic studies have identified dozens of associated loci for both cleft lip with or without cleft palate (CL/P) and cleft palate alone (CP). Although several nearby genes have been highlighted, the "casual" variants are largely unknown. Here, we developed DeepFace, a convolutional neural network model, to assess the functional impact of variants by SNP activity difference (SAD) scores. The DeepFace model is trained with 204 epigenomic assays from crucial human embryonic craniofacial developmental stages of post-conception week (pcw) 4 to pcw 10. The Pearson correlation coefficient between …

Gender-Specific Mental Health Outcomes In Central America: A Natural Experiment, Thea Nagasuru

Computer Science Summer Fellows

While COVID lockdown measures have had varying effects on the mental health of different demographics, several bodies of research have noted their disparate effect on women. Why is women's mental health more negatively impacted by lockdown measures, and how much more are they impacted than men? How can we predict and mitigate these negative effects on women? This paper aims to contribute to answering those questions by comparing COVID stringency measures and their effect on the gap in depression rates between men and women in two neighboring countries: Nicaragua and Honduras.

Epigenome-Wide Association Study Of Lung Cancer Among Never Smokers In Two Prospective Cohorts In Shanghai, China, Mohammad L Rahman, Charles E Breeze, Xiao-Ou Shu, Jason Y Y Wong, Batel Blechter, Andres Cardenas, Xuting Wang, Bu-Tian Ji, Wei Hu, Qiuyin Cai, H Dean Hosgood, Gong Yang, Jianxin Shi, Jirong Long, Yu-Tang Gao, Douglas A Bell, Wei Zheng, Nathaniel Rothman, Qing Lan

Faculty, Staff and Student Publications

BACKGROUND: The aetiology of lung cancer among individuals who never smoked remains elusive, despite 15% of lung cancer cases in men and 53% in women worldwide being unrelated to smoking. Epigenetic alterations, particularly DNA methylation (DNAm) changes, have emerged as potential drivers. Yet, few prospective epigenome-wide association studies (EWAS), primarily focusing on peripheral blood DNAm with limited representation of never smokers, have been conducted.

METHODS: We conducted a nested case-control study of 80 never-smoking incident lung cancer cases and 83 never-smoking controls within the Shanghai Women's Health Study and Shanghai Men's Health Study. DNAm was measured in prediagnostic oral rinse …

Context-Dependent T-Box Transcription Factor Family: From Biology To Targeted Therapy, Siwen Li, Xiangyuan Luo, Mengyu Sun, Yijun Wang, Zerui Zhang, Junqing Jiang, Dian Hu, Jiaqian Zhang, Zhangfan Wu, Yufei Wang, Wenjie Huang, Limin Xia

Faculty, Staff and Student Publications

T-BOX factors belong to an evolutionarily conserved family of transcription factors. T-BOX factors not only play key roles in growth and development but are also involved in immunity, cancer initiation, and progression. Moreover, the same T-BOX molecule exhibits different or even opposite effects in various developmental processes and tumor microenvironments. Understanding the multiple roles of context-dependent T-BOX factors in malignancies is vital for uncovering the potential of T-BOX-targeted cancer therapy. We summarize the physiological roles of T-BOX factors in different developmental processes and their pathological roles observed when their expression is dysregulated. We also discuss their regulatory roles in tumor …

The Protective Efficacy Of A Sars-Cov-2 Vaccine Candidate B1351v Against Several Variant Challenges In K18-Hace2 Mice, Jie Yang, Huifen Fan, Anna Yang, Wenhui Wang, Xin Wan, Fengjie Lin, Dongsheng Yang, Jie Wu, Kaiwen Wang, Wei Li, Qian Cai, Lei You, Deqin Pang, Jia Lu, Changfu Guo, Jinrong Shi, Yan Sun, Xinguo Li, Kai Duan, Shuo Shen, Shengli Meng, Jing Guo, Zejun Wang

Faculty, Staff and Student Publications

The emergence of SARS-CoV-2 variants of concern (VOCs) with increased transmissibility and partial resistance to neutralization by antibodies has been observed globally. There is an urgent need for an effective vaccine to combat these variants. Our study demonstrated that the B.1.351 variant inactivated vaccine candidate (B.1.351V) generated strong binding and neutralizing antibody responses in BALB/c mice against the B.1.351 virus and other SARS-CoV-2 variants after two doses within 28 days. Immunized K18-hACE2 mice also exhibited elevated levels of live virus-neutralizing antibodies against various SARS-CoV-2 viruses. Following infection with these viruses, K18-hACE2 mice displayed a stable body weight, a high survival …

Influence Of Polypyrrole On Phosphorus- And Tio2-Based Anode Nanomaterials For Li-Ion Batteries, Chiwon Kang, Kibum Song, Seungho Ha, Yujin Sung, Yejin Kim, Keun-Young Shin, Byung Hyo Kim

Faculty, Staff and Student Publications

Phosphorus (P) and TiO2 have been extensively studied as anode materials for lithium-ion batteries (LIBs) due to their high specific capacities. However, P is limited by low electrical conductivity and significant volume changes during charge and discharge cycles, while TiO2 is hindered by low electrical conductivity and slow Li-ion diffusion. To address these issues, we synthesized organic–inorganic hybrid anode materials of P–polypyrrole (PPy) and TiO2–PPy, through in situ polymerization of pyrrole monomer in the presence of the nanoscale inorganic materials. These hybrid anode materials showed higher cycling stability and capacity compared to pure P and TiO2. The enhancements are attributed …

A/B Testing Of User Enrollment Forms To Enhance Diversity In The Biomedical Workforce Via The National Research Mentoring Network: User-Centered Design Case Study, Toufeeq Ahmed Syed, Erika L Thompson, Jason Johnson, Zainab Latif, Nan Kennedy, Damaris Javier, Katie Stinson, Jamboor K Vishwanatha

Faculty, Staff and Student Publications

BACKGROUND: The National Research Mentoring Network (NRMN) is a National Institutes of Health-funded program for diversifying the science, technology, engineering, math, and medicine research workforce through the provision of mentoring, networking, and professional development resources. The NRMN provides mentoring resources to members through its online platform-MyNRMN.

OBJECTIVE: MyNRMN helps members build a network of mentors. Our goal was to expand enrollment and mentoring connections, especially among those who have been historically underrepresented in biomedical training and the biomedical workforce.

METHODS: To improve the ease of enrollment, we implemented the split testing of iterations of our user interface for platform registration. …

Genetic Analysis Of Seven Patients With Inherited Ichthyosis And Nagashima-Type Palmoplantar Keratoderma, Jing Zhang, Yue Yao, Ya Tan, Hua-Ying Hu, Lin-Xi Zeng, Guo-Qiang Zhang

Faculty, Staff and Student Publications

Inherited ichthyosis comprises a series of heterogeneous dermal conditions; it mainly manifests as widespread hyperkeratosis, xerosis and scaling of the skin. At times, overlapping symptoms require differential diagnosis between ichthyosis and several other similar disorders. The present study reports seven patients with confirmed or suspected to be associated with ichthyosis by conducting a thorough clinical and genetic investigation. Genetic testing was conducted using whole-exome sequencing, with Sanger sequencing as the validation method. The MEGA7 program was used to analyze the conservation of amino acid residues affected by the detected missense variants. The enrolled patients exhibited ichthyosis-like but distinct clinical manifestations. …

Fedgmmat: Federated Generalized Linear Mixed Model Association Tests, Wentao Li, Han Chen, Xiaoqian Jiang, Arif Harmanci

Faculty, Staff and Student Publications

Increasing genetic and phenotypic data size is critical for understanding the genetic determinants of diseases. Evidently, establishing practical means for collaboration and data sharing among institutions is a fundamental methodological barrier for performing high-powered studies. As the sample sizes become more heterogeneous, complex statistical approaches, such as generalized linear mixed effects models, must be used to correct for the confounders that may bias results. On another front, due to the privacy concerns around Protected Health Information (PHI), genetic information is restrictively protected by sharing according to regulations such as Health Insurance Portability and Accountability Act (HIPAA). This limits data sharing …

Ghcu, A Molecular Chaperone, Regulates Leaf Curling By Modulating The Distribution Of Kngh1 In Cotton, Yihao Zang, Chenyu Xu, Lishan Yu, Longen Ma, Lisha Xuan, Sunyi Yan, Yayao Zhang, Yiwen Cao, Xiaoran Li, Zhanfeng Si, Jieqiong Deng, Tianzhen Zhang, Yan Hu

Faculty, Staff and Student Publications

Leaf shape is considered to be one of the most significant agronomic traits in crop breeding. However, the molecular basis underlying leaf morphogenesis in cotton is still largely unknown. In this study, through genetic mapping and molecular investigation using a natural cotton mutant cu with leaves curling upward, the causal gene GHCU is successfully identified as the key regulator of leaf flattening. Knockout of GHCU or its homolog in cotton and tobacco using CRISPR results in abnormal leaf shape. It is further discovered that GHCU facilitates the transport of the HD protein KNOTTED1-like (KNGH1) from the adaxial to the abaxial …

Identification Of Immune-Associated Biomarkers Of Diabetes Nephropathy Tubulointerstitial Injury Based On Machine Learning: A Bioinformatics Multi-Chip Integrated Analysis, Lin Wang, Jiaming Su, Zhongjie Liu, Shaowei Ding, Yaotan Li, Baoluo Hou, Yuxin Hu, Zhaoxi Dong, Jingyi Tang, Hongfang Liu, Weijing Liu

Faculty, Staff and Student Publications

BACKGROUND: Diabetic nephropathy (DN) is a major microvascular complication of diabetes and has become the leading cause of end-stage renal disease worldwide. A considerable number of DN patients have experienced irreversible end-stage renal disease progression due to the inability to diagnose the disease early. Therefore, reliable biomarkers that are helpful for early diagnosis and treatment are identified. The migration of immune cells to the kidney is considered to be a key step in the progression of DN-related vascular injury. Therefore, finding markers in this process may be more helpful for the early diagnosis and progression prediction of DN.

METHODS: The …

Metabolomics Analysis Reveals Metabolite Diversity Of The Rare Cliff Plant Oresitrophe Rupifraga Unge, Hao Wang, Jinjun Cao, Sheng Chang, Caifeng Yan, Guangming Zhang

Faculty, Staff and Student Publications

Oresitrophe is monotypic, with the only species, Oresitrophe rupifraga Bunge, which is exclusive to China, having special growth and developmental traits due to its habitat. Furthermore, it has bright flowers and medicinal benefits. This study investigated the metabolites present in various tissues of Oresitrophe rupifraga Bunge. Using a widely targeted metabolomics approach, 1965 different metabolites were identified in Oresitrophe rupifraga Bunge. Based on principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), the aboveground and underground metabolites of Oresitrophe rupifraga differed significantly. The comparison between bulblets and leaves revealed the differential expression of 461 metabolites, whereas the …