Extracting Information on Dietary Supplements from Clinical Notes in Electronic Health Record Systems Through Natural Language Processing Techniques

University of Minnesota Ph.D. dissertation. February 2020. Major: Health Informatics. Advisor: Rui Zhang. 1 computer file (PDF); xii, 115 pages.Patient safety has been linked with increasing importance to the growing popularity and consumption of dietary supplements (DS). DS are promoted and regulated as food without rigorous pre-marketing tests and Food and Drug Administration (FDA) approval, thus propagating concerns surrounding their safety and efficacy. The current post-marketing surveillance primarily relies on voluntarily submitted reports of suspected adverse events (AEs) caused by DS. A reporting schema such as this inherently suffers from underestimation and reporting bias. Additionally, there remains a paucity of clinical trials conducted to evaluate the pharmaceutical mechanisms and the safety of DS. The limitations mentioned above have created a critical need to use alternative data sources for active pharmacovigilance on DS safety, which can be addressed through leveraging clinical notes in the electronic health records (EHR), a valuable data source documenting comprehensive real-world information with respects to patient safety in the course of care visits. Therefore, the essential and fundamental step for advancing potential DS pharmacovigilance study is to automatically extract DS usage and safety information embedded in unstructured clinical notes. Natural language processing (NLP), and more precisely information extraction (IE), offers a set of enabling techniques and tools that can facilitate the automatic information extraction process. In this dissertation, IE methods have been developed and evaluated with the aim of extracting DS information from clinical notes. First, a study was conducted to demonstrate the feasibility of using word embeddings to expand the terminology of DS in clinical notes. Through the extrinsic evaluation, 14 commonly used DS semantic variants, brand names, and misspellings were expanded. Expanded terms have been shown to be valuable in notes/patients identification tasks, with more notes and patients retrieved compared with two sets of baseline terms. Second, to detect and extract the named entities of DS as well as their relations with events (i.e., indications or AEs), named entity recognition (NER) and relation extraction (RE) tasks have been performed. Both machine learning and deep learning methods were evaluated and compared in these two tasks. Deep learning models are found to be more efficient and scalable compared with machine learning models. Finally, machine learning-based and rule-based classifiers were built to automatically classify the use status of DS into four categories (i.e., Continuing, Discontinued, Started, Unclassified). The machine learning-based classifier performs better when the sample size doubles. The techniques and methods developed in this dissertation can be further integrated into existing EHR or NLP systems for automatic DS IE, which can potentially advance DS active surveillance and improve patient safety via clinical decision support

Using natural language processing methods to classify use status of dietary supplements in clinical notes

Abstract Background Despite widespread use, the safety of dietary supplements is open to doubt due to the fact that they can interact with prescribed medications, leading to dangerous clinical outcomes. Electronic health records (EHRs) provide a potential way for active pharmacovigilance on dietary supplements since a fair amount of dietary supplement information, especially those on use status, can be found in clinical notes. Extracting such information is extremely significant for subsequent supplement safety research. Methods In this study, we collected 2500 sentences for 25 commonly used dietary supplements and annotated into four classes: Continuing (C), Discontinued (D), Started (S) and Unclassified (U). Both rule-based and machine learning-based classifiers were developed on the same training set and evaluated using the hold-out test set. The performances of the two classifiers were also compared. Results The rule-based classifier achieved F-measure of 0.90, 0.85, 0.90, and 0.86 in C, D, S, and U status, respectively. The optimal machine learning-based classifier (Maximum Entropy) achieved F-measure of 0.90, 0.92, 0.91 and 0.88 in C, D, S, and U status, respectively. The comparison result shows that the machine learning-based classifier has a better performance, which is more efficient and scalable especially when the sample size doubles. Conclusions Machine learning-based classifier outperforms rule-based classifier in categorization of the use status of dietary supplements in clinical notes. Future work includes applying deep learning methods and developing a hybrid system to approach use status classification task

Secrecy and Throughput Performance of Cooperative Cognitive Decode-and-Forward Relaying Vehicular Networks with Direct Links and Poisson Distributed Eavesdroppers

Cooperative communication and cognitive radio can effectively improve spectrum utilization, coverage range, and system throughput of vehicular networks, whereas they also incur several security issues and wiretapping attacks. Thus, security and threat detection are vitally important for such networks. This paper investigates the secrecy and throughput performance of an underlay cooperative cognitive vehicular network, where a pair of secondary vehicles communicate through a direct link and the assistance of a decode-and-forward (DF) secondary relay in the presence of Poisson-distributed colluding eavesdroppers and under an interference constraint set by the primary receiver. Considering mixed Rayleigh and double-Rayleigh fading channels, we design a realistic relaying transmission scheme and derive the closed-form expressions of secrecy and throughput performance, such as the secrecy outage probability (SOP), the connection outage probability (COP), the secrecy and connection outage probability (SCOP), and the overall secrecy throughput, for traditional and proposed schemes, respectively. An asymptotic analysis is further presented in the high signal-to-noise ratio (SNR) regime. Numerical results illustrate the impacts of network parameters on secrecy and throughput and reveal that the advantages of the proposed scheme are closely related to the channel gain of the relay link compared to the direct link

Controls on seasonal erosion behavior and potential increase in sediment evacuation in the warming Tibetan Plateau

Global warming and intensified climate variability have greatly affected Earth's surface processes and continental sediment evacuation. River suspended sediment is a sensitive indicator for tracing seasonal surface erosion, but details of the rates of sediment generation and evacuation, and their connections with nowadays warming climate are not entirely clear, particularly in Tibet and other high-altitude areas where field observations remain scarce. Here, we investigate daily to seasonal river sediment transport dynamics between the cold, permafrost-dominated northeastern Tibetan Plateau and warm, non-permafrost Sichuan and Taiwan regions. Our results show that at a given river water discharge, greater river suspended sediment was evacuated during the pre-monsoon season (April-Mid June) relative to other seasons in the cold NE Tibetan catchments. In contrast, no such phenomenon was observed in the warm, non-permafrost regions. These comparisons likely indicate a center role of freeze-thaw processes on loose sediment generation, which enhanced sediment output. Hydmmeteorological records show up to similar to 2 degrees C warming in the NE Tibetan Plateau since the past 30 years, coupled with an 8-fold increase in sediment transport. We suggest that continuous warming climate may further accelerate sediment and soil carbon release in the Tibetan Plateau and other global permafrost-dominated areas, which in turn influences climate feedback

Fast and Fine Location of Total Lightning from Low Frequency Signals Based on Deep-Learning Encoding Features

Lightning location provides an important means for the study of lightning discharge process and thunderstorms activity. The fine positioning capability of total lightning based on low-frequency signals has been improved in many aspects, but most of them are based on post waveform processing, and the positioning speed is slow. In this study, artificial intelligence technology is introduced for the first time to lightning positioning, based on low-frequency electric-field detection array (LFEDA). A new method based on deep-learning encoding features matching is also proposed, which provides a means for fast and fine location of total lightning. Compared to other LFEDA positioning methods, the new method greatly improves the matching efficiency, up to more than 50%, thereby considerably improving the positioning speed. Moreover, the new algorithm has greater fine-positioning and anti-interference abilities, and maintains high-quality positioning under low signal-to-noise ratio conditions. The positioning efficiency for return strokes of triggered lightning was 99.17%, and the standard deviation of the positioning accuracy in the X and Y directions was approximately 70 m

Development of two potential diagnostic monoclonal antibodies against human cytomegalovirus glycoprotein B

Human cytomegalovirus glycoprotein B (gB) represents a target for diagnosis and treatment in view of the role it plays in virus entry and spread. Nevertheless, to our knowledge, rare detection of a gB antigen has been reported in transplant patients and limited information is available about diagnostic gB monoclonal antibodies (mAbs). Our aim was to develop gB mAbs with diagnostic potential. Hydrophilic gB peptides (ST: amino acids 27-40, SH: amino acids 81-94) of favorable immunogenicity were synthesized and used to immunize BALB/c mice. Two mAbs, named ZJU-FH6 and ZJU-FE6, were generated by the hybridoma technique and limited serial dilution and then characterized by indirect ELISA, Western blotting, immunoprecipitation, and immunohistochemical staining. The mAbs displayed high titers of specific binding affinities for the ST and SH synthetic peptides at an mAb dilution of 1:60,000 and 1:240,000, respectively. Western blotting and immunoprecipitation indicated that these mAbs recognized both denatured and native gB of the Towne and AD169 strains. The mAbs, when used as the primary antibody, showed positive staining in cells infected with both Towne and AD169 strains. The mAbs were then tested on patients submitted to allogeneic hematopoietic stem cell transplantation. The gB antigen positivity rates of the patients tested using ZJU-FH6 and ZJU-FE6 were 62.0 and 63.0%, respectively. The gB antigen showed a significant correlation with the level of pp65 antigen in peripheral blood leukocytes. In conclusion, two potential diagnostic gB mAbs were developed and were shown to be capable of recognizing gB in peripheral blood leukocytes in a reliable manner

Crystal structure of the type IV secretion system component CagX from Helicobacter pylori

Helicobacter pylori, a Gram-negative bacterial pathogen prevalent in the human population, is the causative agent of severe gastric diseases. An H. pylori type IV secretion (T4S) system encoded by the cytotoxin-associated gene pathogenicity island (cagPAI) is responsible for communication with host cells. As a component of the cagPAI T4S system core complex, CagX plays an important role in virulence-protein translocation into the host cells. In this work, the crystal structure of the C-terminal domain of CagX (CagXct), which is a homologue of the VirB9 protein from the VirB/D4 T4S system, is presented. CagXct is only the second three-dimensional structure to be elucidated of a VirB9-like protein. Another homologue, TraO, which is encoded on the Escherichia coli conjugative plasmid pKM101, shares only 19% sequence identity with CagXct; however, there is a remarkable similarity in tertiary structure between these two β-sandwich protein domains. Most of the residues that are conserved between CagXct and TraO are located within the protein core and appear to be responsible for the preservation of this domain fold. The studies presented here will contribute to our understanding of different bacterial T4S systems

Acupuncture for Parkinson’s Disease: Efficacy Evaluation and Mechanisms in the Dopaminergic Neural Circuit

Parkinson’s disease (PD) is a chronic and progressive neurodegenerative disease caused by degeneration of dopaminergic neurons in the substantia nigra. Existing pharmaceutical treatments offer alleviation of symptoms but cannot delay disease progression and are often associated with significant side effects. Clinical studies have demonstrated that acupuncture may be beneficial for PD treatment, particularly in terms of ameliorating PD symptoms when combined with anti-PD medication, reducing the required dose of medication and associated side effects. During early stages of PD, acupuncture may even be used to replace medication. It has also been found that acupuncture can protect dopaminergic neurons from degeneration via antioxidative stress, anti-inflammatory, and antiapoptotic pathways as well as modulating the neurotransmitter balance in the basal ganglia circuit. Here, we review current studies and reflect on the potential of acupuncture as a novel and effective treatment strategy for PD. We found that particularly during the early stages, acupuncture may reduce neurodegeneration of dopaminergic neurons and regulate the balance of the dopaminergic circuit, thus delaying the progression of the disease. The benefits of acupuncture will need to be further verified through basic and clinical studies

The role of graphene oxide in dramatically enhancing the mechanical and photoresponsive self-healing properties of poly(N, N-dimethylacrylamide) hybrid hydrogels

A fast self-healing hydrogel system with high strength and excellent response to the near infrared light (NIR) was proposed. The effect of graphene oxide (GO) on the mechanical and self-healing properties of poly(N,N-Dimethylacrylamide) (PDMA) hydrogels was studied. The results indicated that the self-healing efficiency of pure PDMA hydrogels was lower than that of PDMA-GO hydrogels. Especially, when the content of GO was more than or equal to 3 mg ml ^−1 , the self-healing efficiency can reach to approximately 100% after 2 h. It is due to that the increase in the content of GO could induce a rapid temperature rise of the hydrogel, which was benefit for improving the fast self-healing capacity. Most notably, PDMA-GO hydrogels still exhibited the excellent mechanical properties and the stable loading-unload behavior after the damage parts were healed. It can be contributed that GO forms hydrogen bonds with the three-dimensional network structure of the hydrogel matrix

BIRC5 facilitates cisplatin‐chemoresistance in a m6A‐dependent manner in ovarian cancer

Abstract Cisplatin‐based chemotherapy is the standard treatment for metastatic ovarian cancer (OC). However, chemoresistance continues to pose significant clinical challenges. Recent research has highlighted the baculoviral inhibitor of the apoptosis protein repeat‐containing 5 (BIRC5) as a member of the inhibitor of the apoptosis protein (IAP) family. Notably, BIRC5, which has robust anti‐apoptotic capabilities, is overexpressed in numerous cancers. Its dysfunction has been linked to challenges in cancer treatment. Yet, the role of BIRC5 in the chemoresistance of OC remains elusive. In our present study, we observed an upregulation of BIRC5 in cisplatin‐resistant cell lines. This upregulation was associated with enhanced chemoresistance, which was diminished when the expression of BIRC5 was silenced. Intriguingly, BIRC5 exhibited a high number of N6‐methyladenosine (m6A) binding sites. The modification of m6A was found to enhance the expression of BIRC5 by recognizing and binding to the 3′‐UTR of mRNA. Additionally, the insulin‐like growth factor 2 mRNA‐binding protein 1 (IGF2BP1) was shown to stabilize BIRC5 mRNA, synergizing with METTL3 and intensifying chemoresistance. Supporting these in vitro findings, our in vivo experiments revealed that tumors were significantly smaller in size and volume when BIRC5 was silenced. This reduction was notably counteracted by co‐silencing BIRC5 and overexpressing IGF2BP1. Our results underscored the pivotal role of BIRC5 in chemoresistance. The regulation of its expression and the stability of its mRNA were influenced by m6A modifications involving both METTL3 and IGF2BP1. These insights presented BIRC5 as a promising potential therapeutic target for addressing cisplatin resistance in OC