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Item Systems biology approach to stage-wise characterization of epigenetic genes in lung adenocarcinoma(Springer Nature, 2013-12-26) Pradhan, Meeta P.; Desai, Akshay; Palakal, Mathew J.; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and EngineeringBackground: Epigenetics refers to the reversible functional modifications of the genome that do not correlate to changes in the DNA sequence. The aim of this study is to understand DNA methylation patterns across different stages of lung adenocarcinoma (LUAD). Results: Our study identified 72, 93 and 170 significant DNA methylated genes in Stages I, II and III respectively. A set of common 34 significant DNA methylated genes located in the promoter section of the true CpG islands were found across stages, and these were: HOX genes, FOXG1, GRIK3, HAND2, PRKCB, etc. Of the total significant DNA methylated genes, 65 correlated with transcription function. The epigenetic analysis identified the following novel genes across all stages: PTGDR, TLX3, and POU4F2. The stage-wise analysis observed the appearance of NEUROG1 gene in Stage I and its re-appearance in Stage III. The analysis showed similar epigenetic pattern across Stage I and Stage III. Pathway analysis revealed important signaling and metabolic pathways of LUAD to correlate with epigenetics. Epigenetic subnetwork analysis identified a set of seven conserved genes across all stages: UBC, KRAS, PIK3CA, PIK3R3, RAF1, BRAF, and RAP1A. A detailed literature analysis elucidated epigenetic genes like FOXG1, HLA-G, and NKX6-2 to be known as prognostic targets. Conclusion: Integrating epigenetic information for genes with expression data can be useful for comprehending in-depth disease mechanism and for the ultimate goal of better target identification.Item Self-sovereign identity empowered non-fungible patient tokenization for health information exchange using blockchain technology(Elsevier, 2023-05) Zhuang, Yan; Shyu, Chi-Ren; Hong, Shenda; Li, Pengfei; Zhang, Luxia; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and EngineeringBackground: Patient tokenization is a novel approach that allows anonymous patient-level linkage across healthcare facilities, minimizing the risk of breaching protected health information in health information exchange (HIE). Most patient tokenization is the centralized approach that is unable to address data security concerns fundamentally. Non-Fungible Tokens (NFT), which are non-transferable cryptographic assets on the blockchain, have the potential to provide secure, decentralized, and trustworthy patient tokenization. Self-Sovereign Identity (SSI) is a user-centric approach to verify the ownership of NFTs in a decentralized manner. Methods We have developed a blockchain architecture that contains four modules: (1) Creation module for NFTs creation, (2) Linkage module to link the local patients' accounts to their NFTs, (3) Authentication module that allows patients to permit healthcare providers to access their token, and (4) Exchange module, which involves the HIE process and the validation of the legitimacy of the token through SSI. Results A case study has been conducted on the proposed architecture. Over 3 million transactions have been completed successfully with a blockchain validation and written time of 1.17 s on average. A stability test has also been conducted with a higher throughput of 200 transactions per second running for an hour with an average transaction processing time of 1.42 s. Conclusions This study proposed a blockchain architecture that achieves SSI-enabled NFT-based patient tokenization. Our architecture design, implementation, and case studies have demonstrated the feasibility and potential of NFT with SSI to establish a secure, transparent, and patient-centric identity management and HIE.Item Spectral probabilities of top-down tandem mass spectra(Springer Nature, 2014) Liu, Xiaowen; Segar, Matthew W.; Li, Shuai Cheng; Kim, Sangtae; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and EngineeringBackground: In mass spectrometry-based proteomics, the statistical significance of a peptide-spectrum or protein-spectrum match is an important indicator of the correctness of the peptide or protein identification. In bottom-up mass spectrometry, probabilistic models, such as the generating function method, have been successfully applied to compute the statistical significance of peptide-spectrum matches for short peptides containing no post-translational modifications. As top-down mass spectrometry, which often identifies intact proteins with post-translational modifications, becomes available in many laboratories, the estimation of statistical significance of top-down protein identification results has come into great demand. Results: In this paper, we study an extended generating function method for accurately computing the statistical significance of protein-spectrum matches with post-translational modifications. Experiments show that the extended generating function method achieves high accuracy in computing spectral probabilities and false discovery rates. Conclusions: The extended generating function method is a non-trivial extension of the generating function method for bottom-up mass spectrometry. It can be used to choose the correct protein-spectrum match from several candidate protein-spectrum matches for a spectrum, as well as separate correct protein-spectrum matches from incorrect ones identified from a large number of tandem mass spectra.Item Measuring agreement between decision support reminders: the cloud vs. the local expert(Springer Nature, 2014-04-10) Dixon, Brian Edward; Simonaitis, Linas; Perkins, Susan M.; Wright, Adam; Middleton, Blackford; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and EngineeringBackground: A cloud-based clinical decision support system (CDSS) was implemented to remotely provide evidence-based guideline reminders in support of preventative health. Following implementation, we measured the agreement between preventive care reminders generated by an existing, local CDSS and the new, cloud-based CDSS operating on the same patient visit data. Methods: Electronic health record data for the same set of patients seen in primary care were sent to both the cloud-based web service and local CDSS. The clinical reminders returned by both services were captured for analysis. Cohen's Kappa coefficient was calculated to compare the two sets of reminders. Kappa statistics were further adjusted for prevalence and bias due to the potential effects of bias in the CDS logic and prevalence in the relative small sample of patients. Results: The cloud-based CDSS generated 965 clinical reminders for 405 patient visits over 3 months. The local CDSS returned 889 reminders for the same patient visit data. When adjusted for prevalence and bias, observed agreement varied by reminder from 0.33 (95% CI 0.24 - 0.42) to 0.99 (95% CI 0.97 - 1.00) and demonstrated almost perfect agreement for 7 of the 11 reminders. Conclusions: Preventive care reminders delivered by two disparate CDS systems show substantial agreement. Subtle differences in rule logic and terminology mapping appear to account for much of the discordance. Cloud-based CDSS therefore show promise, opening the door for future development and implementation in support of health care providers with limited resources for knowledge management of complex logic and rules.Item Predicting DNA-Binding Proteins and Binding Residues by Complex Structure Prediction and Application to Human Proteome(Public Library of Science, 2014-05-02) Zhao, Huiying; Wang, Jihua; Zhou, Yaoqi; Yang, Yuedong; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and EngineeringAs more and more protein sequences are uncovered from increasingly inexpensive sequencing techniques, an urgent task is to find their functions. This work presents a highly reliable computational technique for predicting DNA-binding function at the level of protein-DNA complex structures, rather than low-resolution two-state prediction of DNA-binding as most existing techniques do. The method first predicts protein-DNA complex structure by utilizing the template-based structure prediction technique HHblits, followed by binding affinity prediction based on a knowledge-based energy function (Distance-scaled finite ideal-gas reference state for protein-DNA interactions). A leave-one-out cross validation of the method based on 179 DNA-binding and 3797 non-binding protein domains achieves a Matthews correlation coefficient (MCC) of 0.77 with high precision (94%) and high sensitivity (65%). We further found 51% sensitivity for 82 newly determined structures of DNA-binding proteins and 56% sensitivity for the human proteome. In addition, the method provides a reasonably accurate prediction of DNA-binding residues in proteins based on predicted DNA-binding complex structures. Its application to human proteome leads to more than 300 novel DNA-binding proteins; some of these predicted structures were validated by known structures of homologous proteins in APO forms. The method [SPOT-Seq (DNA)] is available as an on-line server at http://sparks-lab.org.Item Genetic variation among 481 diverse soybean accessions, inferred from genomic re-sequencing(Springer Nature, 2021-02-08) Valliyodan, Babu; Brown, Anne V.; Wang, Juexin; Patil, Gunvant; Liu, Yang; Otyama, Paul I.; Nelson, Rex T.; Vuong, Tri; Song, Qijian; Musket, Theresa A.; Wagner, Ruth; Marri, Pradeep; Reddy, Sam; Sessions, Allen; Wu, Xiaolei; Grant, David; Bayer, Philipp E.; Roorkiwal, Manish; Varshney, Rajeev K.; Liu, Xin; Edwards, David; Xu, Dong; Joshi, Trupti; Cannon, Steven B.; Nguyen, Henry T .; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and EngineeringWe report characteristics of soybean genetic diversity and structure from the resequencing of 481 diverse soybean accessions, comprising 52 wild (Glycine soja) selections and 429 cultivated (Glycine max) varieties (landraces and elites). This data was used to identify 7.8 million SNPs, to predict SNP effects relative to genic regions, and to identify the genetic structure, relationships, and linkage disequilibrium. We found evidence of distinct, mostly independent selection of lineages by particular geographic location. Among cultivated varieties, we identified numerous highly conserved regions, suggesting selection during domestication. Comparisons of these accessions against the whole U.S. germplasm genotyped with the SoySNP50K iSelect BeadChip revealed that over 95% of the re-sequenced accessions have a high similarity to their SoySNP50K counterparts. Probable errors in seed source or genotype tracking were also identified in approximately 5% of the accessions.Item Effects of Novel Raloxifene Analogs Alone or in Combination with Mechanical Loading in the Col1a2G610c/+ Murine Model of Osteogenesis Imperfecta(Elsevier, 2024) Kohler, Rachel; Creecy, Amy; Williams, David R.; Allen, Matthew R.; Wallace, Joseph M.; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and EngineeringOsteogenesis imperfecta (OI) is a hereditary bone disease in which gene mutations affect collagen formation, leading to a weak, brittle bone phenotype that can cause severe skeletal deformity and increased fracture risk. OI interventions typically repurpose osteoporosis medications to increase bone mass, but this approach does not address compromised tissue-level material properties. Raloxifene (RAL) is a mild anti-resorptive used to treat osteoporosis that has also been shown to increase bone strength by a-cellularly increasing bone bound water content, but RAL cannot be administered to children due to its hormonal activity. The goal of this study was to test a RAL analog with no estrogen receptor (ER) signaling but maintained ability to reduce fracture risk. The best performing analog from a previous analog characterization project, named RAL-ADM, was tested in an in vivo study. Female wildtype (WT) and Col1a2G610C/+ (G610C) mice were randomly assigned to treated or untreated groups, for a total of 4 groups (n = 15). Starting at 10 weeks of age, all mice underwent compressive tibial loading 3×/week to induce an anabolic bone formation response in conjunction with RAL-ADM treatment (0.5 mg/kg; 5×/week) for 6 weeks. Tibiae were scanned via microcomputed tomography then tested to failure in four-point bending. RAL-ADM had reduced ER affinity, and increased post-yield properties, but did not improve bone strength in OI animals, suggesting some properties can be improved by RAL analogs but further development is needed to create an analog with decidedly positive impacts to OI bone.Item Lessons Learned from OpenEMR Implementation in Graduate Health Informatics Curriculum(American Medical Informatics Association, 2024-11-10) Sunchu, Keerthika; Moncy, Megha; Purkayastha, Saptarshi; Fulton, CathyThis study examines the integration of OpenEMR, a Meaningful Use-certified open-source electronic health record (EHR) system, into a Health Informatics curriculum. The primary objective was to address the disparity between theoretical knowledge and practical application in health informatics education. The implementation process revealed several significant challenges, including unintended system modifications that compromised functionality, data entry errors that impacted usability, and technical issues that impeded accessibility. To mitigate these challenges, a series of interventions were implemented. These included backend modifications to enhance data entry accuracy, usability improvements such as limiting open tabs to facilitate navigation, and the implementation of proactive measures to expedite the resolution of technical issues. The experiences gained from this integration process highlight three critical aspects of health informatics education: the significance of practical proficiency in EHR systems, the necessity for user-centric interface design, and the importance of adaptability and problem-solving skills. The study proposes several future directions for research and practice. These include fostering global collaboration, developing standardized curricula for EHR education, and establishing robust mechanisms for continuous assessment and improvement. The findings underscore the pivotal role of integrating hands-on EHR experience into health informatics education, emphasizing its potential to equip students with the essential competencies required to navigate the complex and dynamic healthcare landscape.Item Chikamatsu, Mori, and the uncanny valley(Sage, 2025-02-06) MacDorman, Karl F.; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and EngineeringIn Japan, robotics projects like Geminoid, modeled after Hiroshi Ishiguro, exhibit a fascination with creating human doubles. Yet, warnings against this also thread through Japanese thought, from the Edo-period playwright Chikamatsu Monzaemon (1653-1724) to the robotics professor Mori Masahiro (1927-2025). Though centuries apart, they describe the same uncanny valley phenomenon-eerie, cold, repellent feelings that arise when confronting the imperfectly human. In an interview with Hozumi Ikan, translated here, Chikamatsu presents a theory of realism exemplified through puppet theater and kabuki. He divides realism into four zones: the unreal, conceptual realism, surface realism, and the real. The unreal lacks authenticity, surface realism lacks soul, and the real lacks expressiveness. For Chikamatsu, it is conceptual realism that captivates an audience. A play's unfolding events evoke empathy and emotion through their meaning for the characters. Similarly, Mori divides realism into four zones: industrial, humanoid, and android robots, and real people. Industrial robots evoke little affinity, and androids risk appearing eerie. Though real people evoke the most affinity, androids cannot become indistinguishable from them. For Mori, only humanoid robots evoke affinity without risking uncanniness. By exploring anthropomorphism, both Chikamatsu and Mori illuminate principles for designing robots that do not unsettle but delight.Item The Impact of Low Serum Magnesium Levels on COVID-19 Severity and Potential Therapeutic Benefits of Magnesium Supplementation: A Systematic Review(Springer Nature, 2025-01-08) Majumder, Mehrab Hasan; Sazzad, Sadman; Hasin, Rabeya; Brishti, Tasnim Jabbar; Tabassum, Fateha Nadia; Ahamed, Tanvir; Masud, Abdullah A.; Akter, Fahima; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and EngineeringIn this review, our objective was to analyze the association between serum magnesium (Mg) levels, Mg supplementation, and coronavirus disease 2019 (COVID-19) outcomes. This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, searching major databases until February 2023. Twenty-six studies (11,363 patients) were included: 22 examining serum Mg levels (8474 patients) and four investigating Mg supplementations (2889 patients). Most studies indicated an association between lower serum Mg levels and increased COVID-19 severity, including higher mortality rates and prolonged recovery periods. Critical patients demonstrated significantly lower Mg levels compared to moderate/severe cases. However, some studies reported conflicting findings, with hypermagnesemia also associated with poor outcomes in specific patient populations. Regarding supplementation, higher dietary Mg intake correlated with shorter hospitalization duration and faster recovery. Mg supplementation exceeding 450 mg showed potential benefits, including increased antibody titers in pregnant women and reduced oxygen support requirements in elderly patients when combined with vitamins D and B12. While evidence suggests a potential relationship between Mg status and COVID-19 outcomes, findings are heterogeneous. Further investigation through well-designed clinical trials is required to gain deeper insights into the role of Mg in COVID-19 pathophysiology and the therapeutic potential of Mg supplementation.