Data Science Commons^™

Addressing Ethical Issues In Healthcare Artificial Intelligence Using A Lifecycle-Informed Process, Benjamin X Collins, Jean-Christophe Bélisle-Pipon, Barbara J Evans, Kadija Ferryman, Xiaoqian Jiang, Camille Nebeker, Laurie Novak, Kirk Roberts, Martin Were, Zhijun Yin, Vardit Ravitsky, Joseph Coco, Rachele Hendricks-Sturrup, Ishan Williams, Ellen W Clayton, Bradley A Malin, Bridge2ai Ethics And Trustworthy Ai Working Group

Faculty, Staff and Student Publications

OBJECTIVES: Artificial intelligence (AI) proceeds through an iterative and evaluative process of development, use, and refinement which may be characterized as a lifecycle. Within this context, stakeholders can vary in their interests and perceptions of the ethical issues associated with this rapidly evolving technology in ways that can fail to identify and avert adverse outcomes. Identifying issues throughout the AI lifecycle in a systematic manner can facilitate better-informed ethical deliberation.

MATERIALS AND METHODS: We analyzed existing lifecycles from within the current literature for ethical issues of AI in healthcare to identify themes, which we relied upon to create a lifecycle …

De-Identification Is Not Enough: A Comparison Between De-Identified And Synthetic Clinical Notes, Atiquer Rahman Sarkar, Yao-Shun Chuang, Noman Mohammed, Xiaoqian Jiang

Faculty, Staff and Student Publications

For sharing privacy-sensitive data, de-identification is commonly regarded as adequate for safeguarding privacy. Synthetic data is also being considered as a privacy-preserving alternative. Recent successes with numerical and tabular data generative models and the breakthroughs in large generative language models raise the question of whether synthetically generated clinical notes could be a viable alternative to real notes for research purposes. In this work, we demonstrated that (i) de-identification of real clinical notes does not protect records against a membership inference attack, (ii) proposed a novel approach to generate synthetic clinical notes using the current state-of-the-art large language models, (iii) evaluated …

Question Answering For Electronic Health Records: Scoping Review Of Datasets And Models, Jayetri Bardhan, Kirk Roberts, Daisy Zhe Wang

Faculty, Staff and Student Publications

Background: Question answering (QA) systems for patient-related data can assist both clinicians and patients. They can, for example, assist clinicians in decision-making and enable patients to have a better understanding of their medical history. Substantial amounts of patient data are stored in electronic health records (EHRs), making EHR QA an important research area. Because of the differences in data format and modality, this differs greatly from other medical QA tasks that use medical websites or scientific papers to retrieve answers, making it critical to research EHR QA.

Objective: This study aims to provide a methodological review of existing works on …

Characterizing The Progression From Mild Cognitive Impairment To Dementia: A Network Analysis Of Longitudinal Clinical Visits, Muskan Garg, Sara Hejazi, Sunyang Fu, Maria Vassilaki, Ronald C Petersen, Jennifer St Sauver, Sunghwan Sohn

Faculty, Staff and Student Publications

Background: With the recent surge in the utilization of electronic health records for cognitive decline, the research community has turned its attention to conducting fine-grained analyses of dementia onset using advanced techniques. Previous works have mostly focused on machine learning-based prediction of dementia, lacking the analysis of dementia progression and its associations with risk factors over time. The black box nature of machine learning models has also raised concerns regarding their uncertainty and safety in decision making, particularly in sensitive domains like healthcare.

Objective: We aimed to characterize the progression of health conditions, such as chronic diseases and neuropsychiatric symptoms, …

Meta-Analysis Of Censored Adverse Events, Xinyue Qi, Shouhao Zhou, Christine B Peterson, Yucai Wang, Xinying Fang, Michael L Wang, Chan Shen

Faculty, Staff and Student Publications

Meta-analysis is a powerful tool for assessing drug safety by combining treatment-related toxicological findings across multiple studies, as clinical trials are typically underpowered for detecting adverse drug effects. However, incomplete reporting of adverse events (AEs) in published clinical studies is frequently encountered, especially if the observed number of AEs is below a pre-specified study-dependent threshold. Ignoring the censored AE information, often found in lower frequency, can significantly bias the estimated incidence rate of AEs. Despite its importance, this prevalent issue in meta-analysis has received little statistical or analytic attention in the literature. To address this challenge, we propose a Bayesian …

A Framework For Human Evaluation Of Large Language Models In Healthcare Derived From Literature Review, Thomas Yu Chow Tam, Sonish Sivarajkumar, Sumit Kapoor, Alisa V Stolyar, Katelyn Polanska, Karleigh R Mccarthy, Hunter Osterhoudt, Xizhi Wu, Shyam Visweswaran, Sunyang Fu, Piyush Mathur, Giovanni E Cacciamani, Cong Sun, Yifan Peng, Yanshan Wang

Faculty, Staff and Student Publications

With generative artificial intelligence (GenAI), particularly large language models (LLMs), continuing to make inroads in healthcare, assessing LLMs with human evaluations is essential to assuring safety and effectiveness. This study reviews existing literature on human evaluation methodologies for LLMs in healthcare across various medical specialties and addresses factors such as evaluation dimensions, sample types and sizes, selection, and recruitment of evaluators, frameworks and metrics, evaluation process, and statistical analysis type. Our literature review of 142 studies shows gaps in reliability, generalizability, and applicability of current human evaluation practices. To overcome such significant obstacles to healthcare LLM developments and deployments, we …

A Case Demonstration Of The Open Health Natural Language Processing Toolkit From The National Covid-19 Cohort Collaborative And The Researching Covid To Enhance Recovery Programs For A Natural Language Processing System For Covid-19 Or Postacute Sequelae Of Sars Cov-2 Infection: Algorithm Development And Validation, Andrew Wen, Liwei Wang, Huan He, Sunyang Fu, Sijia Liu, David A Hanauer, Daniel R Harris, Ramakanth Kavuluru, Rui Zhang, Karthik Natarajan, Nishanth P Pavinkurve, Janos Hajagos, Sritha Rajupet, Veena Lingam, Mary Saltz, Corey Elowsky, Richard A Moffitt, Farrukh M Koraishy, Matvey B Palchuk, Jordan Donovan, Lora Lingrey, Garo Stone-Derhagopian, Robert T Miller, Andrew E Williams, Peter J Leese, Paul I Kovach, Emily R Pfaff, Mikhail Zemmel, Robert D Pates, Nick Guthe, Melissa A Haendel, Christopher G Chute, Hongfang Liu, National Covid Cohort Collaborative, Recover Initiative

Faculty, Staff and Student Publications

BACKGROUND: A wealth of clinically relevant information is only obtainable within unstructured clinical narratives, leading to great interest in clinical natural language processing (NLP). While a multitude of approaches to NLP exist, current algorithm development approaches have limitations that can slow the development process. These limitations are exacerbated when the task is emergent, as is the case currently for NLP extraction of signs and symptoms of COVID-19 and postacute sequelae of SARS-CoV-2 infection (PASC).

OBJECTIVE: This study aims to highlight the current limitations of existing NLP algorithm development approaches that are exacerbated by NLP tasks surrounding emergent clinical concepts and …

Improving Large Language Models For Clinical Named Entity Recognition Via Prompt Engineering, Yan Hu, Qingyu Chen, Jingcheng Du, Xueqing Peng, Vipina Kuttichi Keloth, Xu Zuo, Yujia Zhou, Zehan Li, Xiaoqian Jiang, Zhiyong Lu, Kirk Roberts, Hua Xu

Faculty, Staff and Student Publications

IMPORTANCE: The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data and extracting meaningful information with minimal training data. By developing and refining prompt-based strategies, we can significantly enhance the models' performance, making them viable tools for clinical NER tasks and possibly reducing the reliance on extensive annotated datasets.

OBJECTIVES: This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance.

MATERIALS AND METHODS: We evaluated these models on 2 clinical NER tasks: (1) to extract medical problems, treatments, …

Ensemble Pretrained Language Models To Extract Biomedical Knowledge From Literature, Zhao Li, Qiang Wei, Liang-Chin Huang, Jianfu Li, Yan Hu, Yao-Shun Chuang, Jianping He, Avisha Das, Vipina Kuttichi Keloth, Yuntao Yang, Chiamaka S Diala, Kirk E Roberts, Cui Tao, Xiaoqian Jiang, W Jim Zheng, Hua Xu

Faculty, Staff and Student Publications

OBJECTIVES: The rapid expansion of biomedical literature necessitates automated techniques to discern relationships between biomedical concepts from extensive free text. Such techniques facilitate the development of detailed knowledge bases and highlight research deficiencies. The LitCoin Natural Language Processing (NLP) challenge, organized by the National Center for Advancing Translational Science, aims to evaluate such potential and provides a manually annotated corpus for methodology development and benchmarking.

MATERIALS AND METHODS: For the named entity recognition (NER) task, we utilized ensemble learning to merge predictions from three domain-specific models, namely BioBERT, PubMedBERT, and BioM-ELECTRA, devised a rule-driven detection method for cell line and …

Special Supplement Issue On Quality Assurance And Enrichment Of Biological And Biomedical Ontologies And Terminologies, Licong Cui, Ankur Agrawal

Faculty, Staff and Student Publications

Ontologies and terminologies serve as the backbone of knowledge representation in biomedical domains, facilitating data integration, interoperability, and semantic understanding across diverse applications. However, the quality assurance and enrichment of these resources remain an ongoing challenge due to the dynamic nature of biomedical knowledge. In this editorial, we provide an introductory summary of seven articles included in this special supplement issue for quality assurance and enrichment of biological and biomedical ontologies and terminologies. These articles span a spectrum of topics, such as development of automated quality assessment frameworks for Resource Description Framework (RDF) resources, identification of missing concepts in SNOMED …

Perceptions Of Hiv-Related Comorbidities And Usability Of A Virtual Environment For Cardiovascular Disease Prevention Education In Sexual Minority Men With Hiv: Formative Phases Of A Pilot Randomized Controlled Trial, S Raquel Ramos, Harmony Reynolds, Constance Johnson, Gail Melkus, Trace Kershaw, Julian F Thayer, Allison Vorderstrasse

Faculty, Staff and Student Publications

Background: Sexual minority men with HIV are at an increased risk of cardiovascular disease (CVD) and have been underrepresented in behavioral research and clinical trials.

Objective: This study aims to explore perceptions of HIV-related comorbidities and assess the interest in and usability of a virtual environment for CVD prevention education in Black and Latinx sexual minority men with HIV.

Methods: This is a 3-phase pilot behavioral randomized controlled trial. We report on formative phases 1 and 2 that informed virtual environment content and features using qualitative interviews, usability testing, and beta testing with a total of 25 individuals. In phase …

Sexannodb, A Knowledgebase Of Sex-Specific Regulations From Multi-Omics Data Of Human Cancers, Mengyuan Yang, Yuzhou Feng, Jiajia Liu, Hong Wang, Sijia Wu, Weiling Zhao, Pora Kim, Xiaobo Zhou

Faculty, Staff and Student Publications

Background

Sexual differences across molecular levels profoundly impact cancer biology and outcomes. Patient gender significantly influences drug responses, with divergent reactions between men and women to the same drugs. Despite databases on sex differences in human tissues, understanding regulations of sex disparities in cancer is limited. These resources lack detailed mechanistic studies on sex-biased molecules.

Methods

In this study, we conducted a comprehensive examination of molecular distinctions and regulatory networks across 27 cancer types, delving into sex-biased effects. Our analyses encompassed sex-biased competitive endogenous RNA networks, regulatory networks involving sex-biased RNA binding protein-exon skipping events, sex-biased transcription factor-gene regulatory networks, …

Analysis Of Serum Exosome Metabolites Identifies Potential Biomarkers For Human Hepatocellular Carcinoma, Tingting Zhao, Yan Liang, Xiaolan Zhen, Hong Wang, Li Song, Didi Xing, Hui Li

Faculty, Staff and Student Publications

Currently, the clinical cure rate for primary liver cancer remains low. Effective screening and early diagnosis of hepatocellular carcinoma (HCC) remain clinical challenges. Exosomes are intimately associated with tumor development and their contents have the potential to serve as highly sensitive tumor-specific markers. A comprehensive untargeted metabolomics study was conducted using exosome samples extracted from the serum of 48 subjects (36 HCC patients and 12 healthy controls) via a commercial kit. An ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) strategy was used to identify the metabolic compounds. A total of 18 differential metabolites were identified using the non-targeted metabolomics approach of UPLC-QTOF-MS/MS. …

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 …

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 …

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.

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 …

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. …