Perspectives on the role of industry in informatics research and authorship

OBJECTIVES

Advances in informatics research come from academic, nonprofit, and for-profit industry organizations, and from academic-industry partnerships. While scientific studies of commercial products may offer critical lessons for the field, manuscripts authored by industry scientists are sometimes categorically rejected. We review historical context, community perceptions, and guidelines on informatics authorship.

PROCESS

We convened an expert panel at the American Medical Informatics Association 2022 Annual Symposium to explore the role of industry in informatics research and authorship with community input. The panel summarized session themes and prepared recommendations.

CONCLUSIONS

Authorship for informatics research, regardless of affiliation, should be determined by International Committee of Medical Journal Editors uniform requirements for authorship. All authors meeting criteria should be included, and categorical rejection based on author affiliation is unethical. Informatics research should be evaluated based on its scientific rigor; all sources of bias and conflicts of interest should be addressed through disclosure and, when possible, methodological mitigation.

Whole-cycle management of women with epilepsy of child-bearing age: ontology construction and application

BACKGROUND

The effective management of epilepsy in women of child-bearing age necessitates a concerted effort from multidisciplinary teams. Nevertheless, there exists an inadequacy in the seamless exchange of knowledge among healthcare providers within this context. Consequently, it is imperative to enhance the availability of informatics resources and the development of decision support tools to address this issue comprehensively.

MATERIALS AND METHODS

The development of the Women with Epilepsy of Child-Bearing Age Ontology (WWECA) adhered to established ontology construction principles. The ontology's scope and universal terminology were initially established by the development team and subsequently subjected to external evaluation through a rapid Delphi consensus exercise involving domain experts. Additional entities and attribute annotation data were sourced from authoritative guideline documents and specialized terminology databases within the respective field. Furthermore, the ontology has played a pivotal role in steering the creation of an online question-and-answer system, which is actively employed and assessed by a diverse group of multidisciplinary healthcare providers.

RESULTS

WWECA successfully integrated a total of 609 entities encompassing various facets related to the diagnosis and medication for women of child-bearing age afflicted with epilepsy. The ontology exhibited a maximum depth of 8 within its hierarchical structure. Each of these entities featured three fundamental attributes, namely Chinese labels, definitions, and synonyms. The evaluation of WWECA involved 35 experts from 10 different hospitals across China, resulting in a favorable consensus among the experts. Furthermore, the ontology-driven online question and answer system underwent evaluation by a panel of 10 experts, including neurologists, obstetricians, and gynecologists. This evaluation yielded an average rating of 4.2, signifying a positive reception and endorsement of the system's utility and effectiveness.

CONCLUSIONS

Our ontology and the associated online question and answer system hold the potential to serve as a scalable assistant for healthcare providers engaged in the management of women with epilepsy (WWE). In the future, this developmental framework has the potential for broader application in the context of long-term management of more intricate chronic health conditions.

Pan-genome mediated therapeutic target mining in Kingella kingae and inhibition assessment using traditional Chinese medicinal compounds: an informatics approach

Kingella kingae causes bacteremia, endocarditis, osteomyelitis, septic arthritis, meningitis, spondylodiscitis, and lower respiratory tract infections in pediatric patients. Usually it demonstrates disease after inflammation of mouth, lips or infections of the upper respiratory tract. To date, therapeutic targets in this bacterium remain unexplored. We have utilized a battery of bioinformatics tools to mine these targets in this study. Core genes were initially inferred from 55 genomes of K. kingae and 39 therapeutic targets were mined using an in-house pipeline. We selected aroG product (KDPG aldolase) involved in chorismate pathway, for inhibition analysis of this bacterium using lead-like metabolites from traditional Chinese medicinal plants. Pharmacophore generation was done using control ZINC36444158 (1,16-bis[(dihydroxyphosphinyl)oxy]hexadecane), followed by molecular docking of top hits from a library of 36,000 compounds. Top prioritized compounds were ZINC95914016, ZINC33833283 and ZINC95914219. ADME profiling and simulation of compound dosing (100 mg tablet) was done to infer compartmental pharmacokinetics in a population of 300 individuals in fasting state. PkCSM based toxicity analysis revealed the compounds ZINC95914016 and ZINC95914219 as safe and with almost similar bioavailability. However, ZINC95914016 takes less time to reach maximum concentration in the plasma and shows several optimal parameters compared to other leads. In light of obtained data, we recommend this compound for further testing and induction in experimental drug design pipeline.Communicated by Ramaswamy H. Sarma.

Validating core therapeutic targets for osteoporosis treatment based on integrating network pharmacology and informatics

OBJECTIVE

Our goal was to find metabolism-related lncRNAs that were associated with osteoporosis (OP) and construct a model for predicting OP progression using these lncRNAs.

METHODS

The GEO database was employed to obtain gene expression profiles. The WGCNA technique and differential expression analysis were used to identify hypoxia-related lncRNAs. A Lasso regression model was applied to select 25 hypoxia-related genes, from which a classification model was created. Its robust classification performance was confirmed with an area under the ROC curve close to 1, as verified on the validation set. Concurrently, we constructed a ceRNA network based on these genes to unveil potential regulatory processes. Biologically active compounds of STZYD were identified using the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP) database. BATMAN was used to identify its targets, and we obtained OP-related genes from Malacards and DisGeNET, followed by identifying intersection genes with metabolism-related genes. A pharmacological network was then constructed based on the intersecting genes. The pharmacological network was further integrated with the ceRNA network, resulting in the creation of a comprehensive network that encompasses herb-active components, pathways, lncRNAs, miRNAs, and targets. Expression levels of hypoxia-related lncRNAs in mononuclear cells isolated from peripheral blood of OP and normal patients were subsequently validated using quantitative real-time PCR (qRT-PCR). Protein levels of RUNX2 were determined through a western blot assay.

RESULTS

CBFB, GLO1, NFKB2 and PIK3CA were identified as central therapeutic targets, and ADD3-AS1, DTX2P1-UPK3BP1-PMS2P11, TTTY1B, ZNNT1 and LINC00623 were identified as core lncRNAs.

CONCLUSIONS

Our work uncovers a possible therapeutic mechanism for STZYD, providing a potential therapeutic target for OP. In addition, a prediction model of metabolism-related lncRNAs of OP progression was constructed to provide a reference for the diagnosis of OP patients.

Characterization and genome-informatic analysis of a novel lytic mendocina phage vB_PmeS_STP12 suitable for phage therapy pseudomonas or biocontrol

BACKGROUND

A novel lytic bacteriophage (phage) was isolated with Pseudomonas mendocina strain STP12 (P. mendocina) from the untreated site of Sewage Treatment Plant of Lovely Professional University, India. P. mendocina is a Gram-negative, rod-shaped, aerobic bacterium belonging to the family Pseudomonadaceae and has been reported in fifteen (15) cases of economically important diseases worldwide.

METHODS AND RESULTS

Here, a novel phage specifically infecting and killing P. mendocina strain STP12 was isolated from sewage sample using enrichment, spot test and double agar overlay (DAOL) method and was designated as vB_PmeS_STP12. The phage vB-PmeS-STP12 was viable at wide range of pH and temperature ranging from 4 to10 and - 20 to 70 °C respectively. Host range and efficiency of plating (EOP) analysis indicated that phage vB-PmeS-STP12 was capable of infecting and killing P. mendocina strain STP6 with EOP of 0.34. Phage vB_PmeS_STP12 was found to have a significant bacterial reduction (p < 0.005) at all the doses administered, particularly at optimal MOI of 1 PFU/CFU, compared to the control. Morphological analysis using high resolution transmission electron microscopy (HR-TEM) revealed an icosahedral capsid of ~ 55 nm in diameter on average with a short, non-contractile tail. The genome of vB_PmeS_STP12 is a linear, dsDNA containing 36,212 bp in size with a GC content of 58.87% harbouring 46 open reading frames (ORFs). The 46 predicted ORFs encode proteins with functional information categorized as lysis, replication, packaging, regulation, assembly, infection, immune, and hypothetical. However, the genome of vB_PmeS_STP12 appeared to be devoid of tRNAs, integrase gene, toxins genes, virulence factors, antimicrobial resistance genes (ARGs) and CRISPR arrays. The blast analysis with phylogeny revealed that vB_PmeS_STP12 is genetically similar to Pseudomonas phage PMBT14, Pseudomonas phage Almagne and Serratia phage Serbin with a highest identity of 74.00%, 74.93% and 59.48% respectively.

CONCLUSIONS

Taken together, characterization, morphological analysis and genome-informatics indicated that vB_PmeS_STP12 is podovirus morphotype belonging to the class Caudoviticetes, family Zobellviridae which appeared to be devoid of integrase gene, ARGs, CRISPR arrays, virulence factors and toxins genes, exhibiting stability and infectivity at wide range of pH (4 to10) and temperature (-20 to 70 °C), thereby making vB_PmeS_STP12 suitable for phage therapy or biocontrol. Based on the bibliometric analysis and data availability with respect to sequences deposited in GenBank, this is the first report of a phage infecting Pseudomonas mendocina.

Overview of the Knowledge Management Center for Illuminating the Druggable Genome

The Knowledge Management Center (KMC) for the Illuminating the Druggable Genome (IDG) project aims to aggregate, update, and articulate protein-centric data knowledge for the entire human proteome, with emphasis on the understudied proteins from the three IDG protein families. KMC collates and analyzes data from over 70 resources to compile the Target Central Resource Database (TCRD), which is the web-based informatics platform (Pharos). These data include experimental, computational, and text-mined information on protein structures, compound interactions, and disease and phenotype associations. Based on this knowledge, proteins are classified into different Target Development Levels (TDLs) for identification of understudied targets. Additional work by the KMC focuses on enriching target knowledge and producing DrugCentral and other data visualization tools for expanding investigation of understudied targets.

Informatic challenges and advances in illuminating the druggable proteome

The understudied members of the druggable proteomes offer promising prospects for drug discovery efforts. While large-scale initiatives have generated valuable functional information on understudied members of the druggable gene families, translating this information into actionable knowledge for drug discovery requires specialized informatics tools and resources. Here, we review the unique informatics challenges and advances in annotating understudied members of the druggable proteome. We demonstrate the application of statistical evolutionary inference tools, knowledge graph mining approaches, and protein language models in illuminating understudied protein kinases, pseudokinases, and ion channels.

Informatics and Computational Approaches for the Discovery and Optimization of Natural Product-Inspired Inhibitors of the SARS-CoV-2 2'-O-Methyltransferase

The urgent need for new classes of orally available, safe, and effective antivirals─covering a breadth of emerging viruses─is evidenced by the loss of life and economic challenges created by the HIV-1 and SARS-CoV-2 pandemics. As frontline interventions, small-molecule antivirals can be deployed prophylactically or postinfection to control the initial spread of outbreaks by reducing transmissibility and symptom severity. Natural products have an impressive track record of success as prototypic antivirals and continue to provide new drugs through synthesis, medicinal chemistry, and optimization decades after discovery. Here, we demonstrate an approach using computational analysis typically used for rational drug design to identify and develop natural product-inspired antivirals. This was done with the goal of identifying natural product prototypes to aid the effort of progressing toward safe, effective, and affordable broad-spectrum inhibitors of Betacoronavirus replication by targeting the highly conserved RNA 2'-O-methyltransferase (2'-O-MTase). Machaeriols RS-1 (7) and RS-2 (8) were identified using a previously outlined informatics approach to first screen for natural product prototypes, followed by in silico-guided synthesis. Both molecules are based on a rare natural product group. The machaeriols (3-6), isolated from the genus Machaerium, endemic to Amazonia, inhibited the SARS-CoV-2 2'-O-MTase more potently than the positive control, Sinefungin (2), and in silico modeling suggests distinct molecular interactions. This report highlights the potential of computationally driven screening to leverage natural product libraries and improve the efficiency of isolation or synthetic analog development.

Public Health Informatics and the Perioperative Physician: Looking to the Future

The role of informatics in public health has increased over the past few decades, and the coronavirus disease 2019 (COVID-19) pandemic has underscored the critical importance of aggregated, multicenter, high-quality, near-real-time data to inform decision-making by physicians, hospital systems, and governments. Given the impact of the pandemic on perioperative and critical care services (eg, elective procedure delays; information sharing related to interventions in critically ill patients; regional bed-management under crisis conditions), anesthesiologists must recognize and advocate for improved informatic frameworks in their local environments. Most anesthesiologists receive little formal training in public health informatics (PHI) during clinical residency or through continuing medical education. The COVID-19 pandemic demonstrated that this knowledge gap represents a missed opportunity for our specialty to participate in informatics-related, public health-oriented clinical care and policy decision-making. This article briefly outlines the background of PHI, its relevance to perioperative care, and conceives intersections with PHI that could evolve over the next quarter century.

A Pragmatic Informatics Approach to Develop Knowledge Tools for Supporting Cardiac Surgical Patients' Mental Health Needs

A pragmatic informatics approach was developed to create knowledge tools for co-design of a new model of mental healthcare in cardiac surgery The real-world evidence generation leverages existing technological platforms and routine data collections plus tailored brief tools, surveys and qualitative data.

A FHIR Native Radiology Informatics Platform

FHIR is a new standard that is rapidly being adopted in healthcare. We describe and implement a Radiology informatics platform (RIS) that is FHIR native and incorporates the ability to execute AI algorithms to aid with the interpretation of scans. Our design utilises the FHIR workflow pattern as an application programming interface with functionality provided by independent micro services thus granting flexibility and expandability.

Designing an Informatics Infrastructure for a National Aged Care Medication Roundtable

In the residential aged care sector medication management has been identified as a major area of concern contributing to poor outcomes and quality of life for residents. Monitoring medication management in residential aged care in Australia has been highly reliant on small, internal audits. The introduction of electronic medication administration systems provides new opportunities to establish improved methods for ongoing, timely and efficient monitoring of a range of medication indicators, made more meaningful by linking medication data with resident characteristics and outcomes. Benchmarking contemporary medication indicators provides a further opportunity for improvement and is most effective when indicator data are adjusted to take account of confounding factors, such as residents' characteristics and health conditions. Roundtables provide a structure for sharing and discussing indicator data in a trusted and supportive environment and encourage the identification of strategies which may be effective in improving medication management. This paper describes a new project to establish, implement and evaluate a National Aged Care Medication Roundtable.

Evaluating Informatics Competencies Requirements for Hospital Nurse Managers in Japan

This study classifies the nursing informatics competency requirements for nurses and healthcare leaders in the United States according to each of the four levels listed in the "Management Ladder for Hospital Nurse Managers (JNA version)" published by the Japanese Nursing Association. Computer skills were included in Level I. Fifteen informatics knowledge items and four informatic competency items that could not be classified for the levels of the management ladder for nurse managers in Japan. This list of nursing informatics competencies, categorized according to the management levels of hospital nurse managers, can be used to provide nursing informatics training to them.

An Informatics Platform for the Management of Data for Australian Dementia Network (ADNeT) Initiative

Alzheimer's disease and other dementias are becoming more prevalent and placing increasing burdens on the community. The ADNeT Screening and Trials initiative aims to improve research outcomes by identifying people with an increased risk of developing these diseases and directing them to suitable clinical trials. To support the initiative, we have developed a modular informatics platform utilizing private cloud deployment to securely manage operational and research data across six clinical sites.

Resilience Informatics for Public Health

Resilience research is attracting increasing attention as stressors such as pandemics and climate change impact normal life worldwide. Informatics tools can play an important role in enhancing resilience of people, communities, and organizations. We present Resilience Informatics as a sub-discipline of resilience research and propose a conceptual framework for Resilience Informatics to aid in the development and effective deployment of informatics systems for resilience.

Computational Phenotyping of OMOP CDM Normalized EHR for Prenatal and Postpartum Episodes: An Informatics Framework and Clinical Implementation on All of Us

The use of Electronic Health Records (EHR) in pregnancy care and obstetrics-gynecology (OB/GYN) research has increased in recent years. In pregnancy, timing is important because clinical characteristics, risks, and patient management are different in each stage of pregnancy. However, the difficulty of accurately differentiating pregnancy episodes and temporal information of clinical events presents unique challenges for EHR phenotyping. In this work, we introduced the concept of time relativity and proposed a comprehensive framework of computational phenotyping for prenatal and postpartum episodes based on the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). We implemented it on the All of Us national EHR database and identified 6,280 pregnancies with accurate start and end dates among 5,399 female patients. With the ability to identify different episodes in pregnancy care, this framework provides new opportunities for phenotyping complex clinical events and gestational morbidities for pregnant women, thus improving maternal and infant health.

Multidimensional Cross-Linking and Real-Time Informatics for Multiprotein Interaction Studies

Chemical cross-linking combined with mass spectrometry is a technique used to study protein structures and identify protein complexes. Traditionally, chemical cross-linkers contain two reactive groups, allowing them to covalently bond a pair of proximal residues, either within a protein or between two proteins. The output of a cross-linking experiment is a list of interacting site pairs that provide structural constraints for modeling of new structures and complexes. Due to the binary reactive nature of cross-linking reagents, only pairs of interacting sites can be directly observed, and assembly of higher-order structures typically requires prior knowledge of complex composition or iterative docking to produce a putative model. Here, we describe a new tetrameric cross-linker bearing four amine-reactive groups, allowing it to covalently link up to four proteins simultaneously and a real-time instrument method to facilitate the identification of these tetrameric cross-links. We applied this new cross-linker to isolated mitochondria and identified a number of higher-order cross-links in various OXPHOS complexes and ATP synthase, demonstrating its utility in characterizing complex interfaces. We also show that higher-order cross-links can be used to effectively filter models of large protein assemblies generated by using Alphafold. Higher-dimensional cross-linking provides a new avenue for characterizing multiple protein interfaces, even in complex samples such as intact mitochondria.

Integrative Research of Neuropharmacology and Informatics Pharmacology for Mental Disorder

Mental illness poses a huge social burden, accounting for approximately 14% of all deaths. Depression, a major component of mental illness, affects approximately 300 million people worldwide, mainly in developed countries, and is not only a major social burden but also a cause of suicide. The social burden of depression is estimated to increase further in developing countries, and overcoming it is a pressing issue for all countries, including Japan. Although clinical evidence has demonstrated the efficacy of serotonergic neurotransmission enhancers in the treatment of depression, the full picture of their therapeutic effects has not yet been fully elucidated. In this review, we show that the hyperactivity of serotonin neurons, especially those in the dorsal raphe nucleus, is commonly induced by various antidepressants within a period corresponding to the onset of their clinical efficacy. We established quantitative prediction methods for pharmacological activity using only chemical structures to translate the biological understanding of mental disorders, including major depressive disorders, into clinically effective therapeutics. Our method exhibited better performance than the previously reported methods of quantitative prediction, while targeting a larger number of proteins. Our article suggests the importance of integrative neuropharmacology and informatics-based pharmacology studies to understand the biological basis of mental disorders and facilitate drug development for these disorders.

Improving Vaccine Equity: How Community Engagement and Informatics Facilitate Health System Outreach to Underrepresented Groups

BACKGROUND

Given the inequities in access to health care resources like COVID-19 vaccination, health systems should carefully consider how to reach underrepresented groups. Reflecting on vaccine rollout efforts holds insight on the role of community engagement and informatics support in promoting health equity.

OBJECTIVES

This study aimed to assess the effectiveness of four outreach strategies deployed by University of Washington (UW) Medicine in improving vaccine equity over traditional vaccine scheduling online or by phone, we report on appointment scheduling and completion of appointments (i.e., vaccine administration) through (1) automated outreach to individuals from underrepresented groups, (2) temporary "pop-up" clinics in neighborhoods highly impacted by COVID-19, (3) vulnerable population clinics, and (4) mobile vaccine vans.

METHODS

We conducted a 6-month retrospective analysis of electronic health records (EHR) to describe the sociodemographic characteristics of individuals who scheduled appointments using the outreach strategies and characteristics associated with a greater likelihood of vaccine administration based on appointment completion. To help explain trends in the EHR data, we engaged 10 health system leaders and staff who spearheaded the outreach strategies in follow-up conversations to identify qualitative insights into what worked and why.

RESULTS

Compared with traditional scheduling, all outreach strategies except vulnerable population clinics had higher vaccine appointment completion rates, including automated outreach (N = 3,734 [94.7%], p < 0.001), pop-up clinics (N = 4,391 [96.0%], p < 0.001), and mobile vans (N = 4,198 [99.1%], p < 0.001); and lower cancellation rates, including automated outreach (N = 166 [1.1%], p < 0.001), pop-up clinics (N = 155 [0.6%], p < 0.001), and mobile vans (N = 0 [0%], p < 0.001). Qualitative insights emphasized ongoing community partnerships and information resources in successful outreach.

CONCLUSION

Vaccine equity outreach strategies improved the proportion of patients who scheduled and completed vaccination appointments among populations disproportionately impacted by COVID-19. Engaging community partners and equity-focused informatics tools can facilitate outreach. Lessons from these outreach strategies carry practical implications for health systems to amplify their health equity efforts.

Informatics guided FE design of bioactive titanium alloy/composite multi-layered dental implants

A dental implant with three distinct layers, of titanium alloy at core, porous titanium alloy at the intermediate layer and titanium alloy hydroxyapatite composite at the outer layer, is designed to achieve low elastic modulus and adequate strength with bioactive surface. Artificial Neural Network (ANN) along with Rule of Mixture (ROM) is used to generate the objective functions for the Genetic Algorithm (GA) based multi-objective optimization for achieving the optimal designs, which are validated using Finite Element Analysis (FEA) simulations. The composition and processing parameters are correlated with the yield strength and elastic modulus of titanium alloy using ANN. The ANN models are generated to express the strength and effective modulus of the implant using ROM. To determine the optimal composition of titanium alloys, porous layers, and composite layers for a three-layer dental implant, multi-objective genetic algorithm is employed. The Pareto optimal solutions provide the guidelines for designing the implant. A few selected non-dominated solutions are used for studying the actual stress distribution at the bone-implant interface using FEA, and showed significant improvements compared to conventional implants.

Application of novel PACS-based informatics platform to identify imaging based predictors of CDKN2A allelic status in glioblastomas

Gliomas with CDKN2A mutations are known to have worse prognosis but imaging features of these gliomas are unknown. Our goal is to identify CDKN2A specific qualitative imaging biomarkers in glioblastomas using a new informatics workflow that enables rapid analysis of qualitative imaging features with Visually AcceSAble Rembrandtr Images (VASARI) for large datasets in PACS. Sixty nine patients undergoing GBM resection with CDKN2A status determined by whole-exome sequencing were included. GBMs on magnetic resonance images were automatically 3D segmented using deep learning algorithms incorporated within PACS. VASARI features were assessed using FHIR forms integrated within PACS. GBMs without CDKN2A alterations were significantly larger (64 vs. 30%, p = 0.007) compared to tumors with homozygous deletion (HOMDEL) and heterozygous loss (HETLOSS). Lesions larger than 8 cm were four times more likely to have no CDKN2A alteration (OR: 4.3; 95% CI 1.5-12.1; p < 0.001). We developed a novel integrated PACS informatics platform for the assessment of GBM molecular subtypes and show that tumors with HOMDEL are more likely to have radiographic evidence of pial invasion and less likely to have deep white matter invasion or subependymal invasion. These imaging features may allow noninvasive identification of CDKN2A allele status.

Informatics approaches to improve the quality of dental care

Despite technological advances, challenges exist in US dental care, including variations in quality of care, access and untreated dental needs. The implementation of learning health systems (LHSs) in dentistry can help to address these challenges. LHSs use robust informatics infrastructure including data and technology to continuously measure and improve the quality and safety of care and can help to reduce costs and improve patient outcomes. The use of EHRs and standardized diagnostic terminologies are highlighted, as they allow for the storage and sharing of patient data, providing a comprehensive view of a patient's medical and dental history, and can be used to identify patterns and trends to improve the delivery of care. The BigMouth Dental Data Repository is an example of an informatic platform that aggregates patient data from multiple institutions and is being used to for scientific inquiry to improve oral health.

Frontiers in nonviral delivery of small molecule and genetic drugs, driven by polymer chemistry and machine learning for materials informatics

Materials informatics (MI) has immense potential to accelerate the pace of innovation and new product development in biotechnology. Close collaborations between skilled physical and life scientists with data scientists are being established in pursuit of leveraging MI tools in automation and artificial intelligence (AI) to predict material properties in vitro and in vivo. However, the scarcity of large, standardized, and labeled materials data for connecting structure-function relationships represents one of the largest hurdles to overcome. In this Highlight, focus is brought to emerging developments in polymer-based therapeutic delivery platforms, where teams generate large experimental datasets around specific therapeutics and successfully establish a design-to-deployment cycle of specialized nanocarriers. Three select collaborations demonstrate how custom-built polymers protect and deliver small molecules, nucleic acids, and proteins, representing ideal use-cases for machine learning to understand how molecular-level interactions impact drug stabilization and release. We conclude with our perspectives on how MI innovations in automation efficiencies and digitalization of data-coupled with fundamental insight and creativity from the polymer science community-can accelerate translation of more gene therapies into lifesaving medicines.

Formalizing Invertebrate Morphological Data: A Descriptive Model for Cuticle-Based Skeleto-Muscular Systems, an Ontology for Insect Anatomy, and their Potential Applications in Biodiversity Research and Informatics

The spectacular radiation of insects has produced a stunning diversity of phenotypes. During the past 250 years, research on insect systematics has generated hundreds of terms for naming and comparing them. In its current form, this terminological diversity is presented in natural language and lacks formalization, which prohibits computer-assisted comparison using semantic web technologies. Here we propose a Model for Describing Cuticular Anatomical Structures (MoDCAS) which incorporates structural properties and positional relationships for standardized, consistent, and reproducible descriptions of arthropod phenotypes. We applied the MoDCAS framework in creating the ontology for the Anatomy of the Insect Skeleto-Muscular system (AISM). The AISM is the first general insect ontology that aims to cover all taxa by providing generalized, fully logical, and queryable, definitions for each term. It was built using the Ontology Development Kit (ODK), which maximizes interoperability with Uberon (Uberon multispecies anatomy ontology) and other basic ontologies, enhancing the integration of insect anatomy into the broader biological sciences. A template system for adding new terms, extending, and linking the AISM to additional anatomical, phenotypic, genetic, and chemical ontologies is also introduced. The AISM is proposed as the backbone for taxon-specific insect ontologies and has potential applications spanning systematic biology and biodiversity informatics, allowing users to: 1) use controlled vocabularies and create semiautomated computer-parsable insect morphological descriptions; 2) integrate insect morphology into broader fields of research, including ontology-informed phylogenetic methods, logical homology hypothesis testing, evo-devo studies, and genotype to phenotype mapping; and 3) automate the extraction of morphological data from the literature, enabling the generation of large-scale phenomic data, by facilitating the production and testing of informatic tools able to extract, link, annotate, and process morphological data. This descriptive model and its ontological applications will allow for clear and semantically interoperable integration of arthropod phenotypes in biodiversity studies.

Operational Ontology for Oncology (O3): A Professional Society-Based, Multistakeholder, Consensus-Driven Informatics Standard Supporting Clinical and Research Use of Real-World Data From Patients Treated for Cancer

PURPOSE

The ongoing lack of data standardization severely undermines the potential for automated learning from the vast amount of information routinely archived in electronic health records (EHRs), radiation oncology information systems, treatment planning systems, and other cancer care and outcomes databases. We sought to create a standardized ontology for clinical data, social determinants of health, and other radiation oncology concepts and interrelationships.

METHODS AND MATERIALS

The American Association of Physicists in Medicine's Big Data Science Committee was initiated in July 2019 to explore common ground from the stakeholders' collective experience of issues that typically compromise the formation of large inter- and intra-institutional databases from EHRs. The Big Data Science Committee adopted an iterative, cyclical approach to engaging stakeholders beyond its membership to optimize the integration of diverse perspectives from the community.

RESULTS

We developed the Operational Ontology for Oncology (O3), which identified 42 key elements, 359 attributes, 144 value sets, and 155 relationships ranked in relative importance of clinical significance, likelihood of availability in EHRs, and the ability to modify routine clinical processes to permit aggregation. Recommendations are provided for best use and development of the O3 to 4 constituencies: device manufacturers, centers of clinical care, researchers, and professional societies.

CONCLUSIONS

O3 is designed to extend and interoperate with existing global infrastructure and data science standards. The implementation of these recommendations will lower the barriers for aggregation of information that could be used to create large, representative, findable, accessible, interoperable, and reusable data sets to support the scientific objectives of grant programs. The construction of comprehensive "real-world" data sets and application of advanced analytical techniques, including artificial intelligence, holds the potential to revolutionize patient management and improve outcomes by leveraging increased access to information derived from larger, more representative data sets.

Cannabis for Healing in a Native Community Clinic: Development and Results from an Informatics Research Tool

This paper describes how the Puyallup Tribe created a clinic specializing in cannabis-based treatments and partnered with a university research team to assess the impacts of cannabis on patient outcomes. Clinic leaders and research team co-developed an informatics research tool that included survey questions about patient demographics, cannabis use, and measures of pain, depression, anxiety, other substance use, and trauma. Over the first 2.5 years of operations, 69 patients completed a survey. Participants were an average age of 50 years old (SD = 16.7), female (77.6%) and American Indian/Alaska Native (61.5%) with more than 12 years of education (66.7%). Over 77% of the participants used either cannabidiol-dominant (CBD) alone or both CBD and Tetrahydrocannabinol-dominant (THC) products, nearly 23% used neither CBD nor THC products. Most came to the clinic for a pain relief appointment (70.3%). Compared to the general population, participants experienced more pain-related comorbidities, such as anxiety, fatigue, sleep, and pain, and fewer physical functioning capabilities. Over half reported symptoms consistent with depressive or post-traumatic stress disorder. The informatics research tool was successfully integrated into a unique Tribally owned medical clinic.

Nurses' Informatics Competency Assessment of Health Information System Usage: A Cross-sectional Survey

Nurses' informatics competencies are nurses' professional requirements to guarantee the quality of patient care and affect nurses' use of health information systems. The purpose of this survey was to describe nurses' perceptions of their informatics competencies regarding health information system usage. A previously tested web-based questionnaire with multiple-choice questions was sent to nurses whose e-mail address was available through three Finnish Nursing Associations (N = 58 276). A total of 3610 nurses working in Finland responded. Both descriptive and explanatory statistics were used to analyze the data. The three dependent variables "nursing documentation," "digital environment," and "ethics and data protection" were formulated from the data. Nurses' overall informatics competency was good. The "ethics and data protection" competency score was higher than that of "nursing documentation" or "digital environment." Recently graduated nurses and nurses working in outpatient care, virtual hospital, examination, or operation had highest "digital environment" competency score. Health information system experience was associated with "nursing documentation." Nurses are highly qualified health information systems users. However, the competency requirements generated by rapidly expanding digitalization have challenged nurses. It is important to increase educational programs for nurses of how to use digital devices, and how to support patients to use digital services.

Structural informatics approach for designing an epitope-based vaccine against the brain-eating Naegleria fowleri

Primary Amoebic Meningoencephalitis (PAM), a severe lethal brain disease, is caused by a parasite, Naegleria fowleri, also known as the "brain-eating amoeba". The chances of a patient's recovery after being affected by this parasite are very low. Only 5% of people are known to survive this life-threatening infection. Despite the fact that N. fowleri causes a severe, fatal infection, there is no proper treatment available to prevent or cure it. In this context, it is necessary to formulate a potential vaccine that could be able to combat N. fowleri infection. The current study aimed at developing a multi-epitope subunit vaccine against N. fowleri by utilizing immunoinformatics techniques and reverse vaccinology approaches. The T- and B-cell epitopes were predicted by various tools. In order to choose epitopes with the ability to trigger both T- and B-cell-mediated immune responses, the epitopes were put through a screening pipeline including toxicity, antigenicity, cytokine-inductivity, and allergenicity analysis. Three vaccine constructs were designed from the generated epitopes linked with linkers and adjuvants. The modeled vaccines were docked with the immune receptors, where vaccine-1 showed the highest binding affinity. Binding affinity and stability of the docked complex were confirmed through normal mode analysis and molecular dynamic simulations. Immune simulations developed the immune profile, and in silico cloning affirmed the expression probability of the vaccine construct in Escherichia coli (E. coli) strain K12. This study demonstrates an innovative preventative strategy for the brain-eating amoeba by developing a potential vaccine through immunoinformatics and reverse vaccinology approaches. This study has great preventive potential for Primary Amoebic Meningoencephalitis, and further research is required to assess the efficacy of the designed vaccine.

Role of Individual Clinician Authority in the Implementation of Informatics Tools for Population-Based Medication Management: Qualitative Semistructured Interview Study

BACKGROUND

Direct oral anticoagulant (DOAC) medications are frequently associated with inappropriate prescribing and adverse events. To improve the safe use of DOACs, health systems are implementing population health tools within their electronic health record (EHR). While EHR informatics tools can help increase awareness of inappropriate prescribing of medications, a lack of empowerment (or insufficient empowerment) of nonphysicians to implement change is a key barrier.

OBJECTIVE

This study examined how the individual authority of clinical pharmacists and anticoagulation nurses is impacted by and changes the implementation success of an EHR DOAC Dashboard for safe DOAC medication prescribing.

METHODS

We conducted semistructured interviews with pharmacists and nurses following the implementation of the EHR DOAC Dashboard at 3 clinical sites. Interview transcripts were coded according to the key determinants of implementation success. The intersections between individual clinician authority and other determinants were examined to identify themes.

RESULTS

A high level of individual clinician authority was associated with high levels of key facilitators for effective use of the DOAC Dashboard (communication, staffing and work schedule, job satisfaction, and EHR integration). Conversely, a lack of individual authority was often associated with key barriers to effective DOAC Dashboard use. Positive individual authority was sometimes present with a negative example of another determinant, but no evidence was found of individual authority co-occurring with a positive instance of another determinant.

CONCLUSIONS

Increased individual clinician authority is a necessary antecedent to the effective implementation of an EHR DOAC Population Management Dashboard and positively affects other aspects of implementation.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1186/s13012-020-01044-5.

A - 138 Using the Federal Interagency Traumatic Brain Injury Research (FITBIR) Informatics System to Understand Complex Associations between Traumatic Brain Injury and Alcohol or Substance Use

OBJECTIVE

Associations between traumatic brain injury (TBI) and alcohol or substance use are complex and likely bidirectional. The purpose of this project was to conduct a proof-of-concept study demonstrating our ability to compile patient-level TBI data from shared studies in the Federal Interagency Traumatic Brain Injury Research (FITBIR) Informatics System to improve our understanding of key TBI outcomes.

METHOD

We searched the FITBIR database for shared studies reporting alcohol or substance use among participants with TBI. We merged and harmonized data across the relevant studies to determine rates of alcohol or substance use by TBI severity.

RESULTS

In the alcohol use sample (N = 1539), 82% of participants had a history of mild TBI and 46% met criteria for alcohol use disorder (AUD). Participants with a history of mild TBI had 1.34 greater odds of screening positive for AUD (95% CI: 0.98, 1.82) and males had 2.48 increased odds of screening positive for AUD (95% CI: 1.67, 3.68). Unfortunately, due to limited data on substance use we were unable to conduct the intended analyses for this outcome.

CONCLUSIONS

Data support research and theory suggesting that rates of AUD are higher among individuals with mild TBI versus those without, particularly among males. Additionally, this proof-of-concept study established methods, created data harmonization and analysis code, and provided the TBI-SUD meta dataset back to FITBIR for dissemination. Further, numerous additional datasets have been shared with the FITBIR platform since the time of these analyses, which will allow our team and others to extend these analyses over time.

A - 10 Comorbid Traumatic Brain Injury and Posttraumatic Stress Disorder: Evidence from the Federal Interagency Traumatic Brain Injury Research (FITBIR) Informatics System

OBJECTIVE

Although posttraumatic stress disorder (PTSD) is common following traumatic brain injury (TBI; Greer et al., 2020), the complex association between these conditions requires further explication. Data repositories, such as FITBIR, which is the product of a national effort to compile individual participant-level TBI data from multiple studies into a unified database, provide a useful avenue for exploring the PTSD-TBI relationship. The present project is a proof-of-concept study demonstrating the ability to harmonize data from numerous shared studies to better understand comorbid PTSD following TBI.

METHOD

We searched for, merged, and harmonized data from studies with TBI and PTSD variables to analyze rates of probable PTSD across TBI severity categories. The methods and code used to extract, clean, standardize, and harmonize the data have been publicly shared and as additional studies are contributed to FITBIR, they will be added to these meta-datasets.

RESULTS

After harmonizing key variables across FITBIR datasets, the final sample consisted of 1633 participants. Approximately 79% of participants across studies had a history of mild TBI (mTBI) and 32-37% screened positive for PTSD. Those with mTBI had 2.8 greater odds of screening positive for PTSD compared to those with no TBI (95% CI: 1.90, 3.90).

CONCLUSIONS

Study findings show that unifying patient-level data is possible and can contribute to knowledge of complex medical and psychiatric comorbidity. These methods allow for nuanced analyses addressing diversity, equity, and inclusion issues often left out of single studies due to small sample sizes of minoritized groups within individual studies.

The case for expressing nursing theories using ontologies

Nursing and informatics share a common strength in their use of structured representations of domains, specifically the underlying notion of 'things' (ie, concepts, constructs, or named entities) and the relationships among those things. Accurate representation of nursing knowledge in machine-interpretable formats is a necessary next step for leveraging contemporary technologies. Expressing validated nursing theories in ontologies, and in particular formal ontologies, would serve not only nursing, but also investigators from other domains, clinical information system developers, and the users of advanced technologies such as artificial intelligence that seek to learn from the real-world data and evidence generated by nurses and others. Such efforts will enable sharing knowledge and conceptualizations about phenomena across the domains of nursing and generating, testing, revising, and providing theoretically-based perspectives when leveraging contemporary technologies. Nursing is well situated for this work, leveraging intentional and focused collaborations among nurse informaticists, scientists, and theorists.

Lessons Learned in Change Management in Deploying Novel Informatics Solutions: Experience Implementing a Point-of-Care Patient Photography System with Radiography

We describe implementation of a point-of-care system for simultaneous acquisition of patient photographs along with portable radiographs at a large academic hospital. During the implementation process, we observed several technical challenges in the areas of (1) hardware-automatic triggering for photograph acquisition, camera hardware enclosure, networking, and system server hardware and (2) software-post-processing of photographs. Additionally, we also faced cultural challenges involving workflow issues, communication with technologists and users, and system maintenance. We describe our solutions to address these challenges. We anticipate that these experiences will provide useful insights into deploying and iterating new technologies in imaging informatics.

Augmenting Prediction of Intracranial Aneurysms' Risk Status Using Velocity-Informatics: Initial Experience

Our primary goal here is to demonstrate that innovative analytics of aneurismal velocities, named velocity-informatics, enhances intracranial aneurysm (IA) rupture status prediction. 3D computer models were generated using imaging data from 112 subjects harboring anterior IAs (4-25 mm; 44 ruptured and 68 unruptured). Computational fluid dynamics simulations and geometrical analyses were performed. Then, computed 3D velocity vector fields within the IA dome were processed for velocity-informatics. Four machine learning methods (support vector machine, random forest, generalized linear model, and GLM with Lasso or elastic net regularization) were employed to assess the merits of the proposed velocity-informatics. All 4 ML methods consistently showed that, with velocity-informatics metrics, the area under the curve and prediction accuracy both improved by approximately 0.03. Overall, with velocity-informatics, the support vector machine's prediction was most promising: an AUC of 0.86 and total accuracy of 77%, with 60% and 88% of ruptured and unruptured IAs being correctly identified, respectively.

Applying anti-racist approaches to informatics: a new lens on traditional frames

Health organizations and systems rely on increasingly sophisticated informatics infrastructure. Without anti-racist expertise, the field risks reifying and entrenching racism in information systems. We consider ways the informatics field can recognize institutional, systemic, and structural racism and propose the use of the Public Health Critical Race Praxis (PHCRP) to mitigate and dismantle racism in digital forms. We enumerate guiding questions for stakeholders along with a PHCRP-Informatics framework. By focusing on (1) critical self-reflection, (2) following the expertise of well-established scholars of racism, (3) centering the voices of affected individuals and communities, and (4) critically evaluating practice resulting from informatics systems, stakeholders can work to minimize the impacts of racism. Informatics, informed and guided by this proposed framework, will help realize the vision of health systems that are more fair, just, and equitable.

Development and validation of PolyScan, an information technology triage tool for older adults with polypharmacy: a healthcare informatics study

Introduction Polypharmacy is associated with potentially inappropriate medicine prescribing and avoidable medicine-related harm. Polypharmacy should not be perceived as inherently harmful. Instead, priority should be placed on reducing inappropriate prescribing. Aim The study aimed to develop and validate PolyScan, a primary care information technology tool, to triage older adults with polypharmacy who are prescribed potentially inappropriate medicines. Methods Twenty-one indicators from a New Zealand criteria of potentially inappropriate medicines to correct for older adults with polypharmacy were developed into a set of implementable definitions. The definitions were applied as algorithmic logic statements used to interrogate hospital and emergency department records and pharmaceutical collection data to classify whether each indicator was present at an individual patient level, and then triage individuals based on the number of indicators met. Validity was evaluated by comparing PolyScan's accuracy against a manual review of healthcare records for 300 older adults. Results PolyScan was successfully implemented as a tool that can be used to identify potentially inappropriate prescribing in older adults with polypharmacy at different levels of aggregation. The tool has utility for individual practitioners delivering patient care, primary care organisations undertaking quality improvement programmes, and policymakers considering system-level interventions for medicines-related safety. During the validity assessment, PolyScan identified nine individuals (3%) with polypharmacy and indicators of potentially inappropriate medicine. Five unique indicators were detected. PolyScan achieved 100% sensitivity, specificity, and positive and negative predictive values. Discussion PolyScan can support clinicians, clinics, and policymakers with allocation of resources, rational medicine campaigns, and identifying individuals prescribed potentially inappropriate medicines for review.

Cell Therapy Informatics: Updates on the Integration of HCT/IEC Functionalities into an Electronic Medical Record System in the US to Promote Efficiency, Patient Safety, Research, and Data Interoperability

The use of electronic health/medical record (EMR) systems has streamlined medical practice and improved efficiency of clinical care in recent years. However, EMR systems are not generally well designed to support research and tracking of longitudinal outcomes across populations, which are particularly important in hematopoietic stem cell transplantation (HCT) and immune effector cell therapy (IEC), where data reporting to registries and regulatory agencies are often required. Since its formation in 2014, the HCT EMR user group has worked with a large EMR vendor (Epic) to develop many functionalities within the EMR to improve the care of HCT/IEC patients and facilitate the capture of HCT/IEC data in an easily interoperable format. Awareness and the widespread adoption of these new tools among transplant centers remains a challenge, however. In this report, we aim to increase awareness and adoption of these new features in the Epic EMR across the transplantation community, advocate for the use of data standards, and promote future collaboration with other commercial EMRs to develop standardized HCT/IEC content to improve patient care and facilitate interoperable data exchange.

Cancer Informatics for Cancer Centers: Sharing Ideas on How to Build an Artificial Intelligence-Ready Informatics Ecosystem for Radiation Oncology

In August 2022, the Cancer Informatics for Cancer Centers brought together cancer informatics leaders for its biannual symposium, Precision Medicine Applications in Radiation Oncology, co-chaired by Quynh-Thu Le, MD (Stanford University), and Walter J. Curran, MD (GenesisCare). Over the course of 3 days, presenters discussed a range of topics relevant to radiation oncology and the cancer informatics community more broadly, including biomarker development, decision support algorithms, novel imaging tools, theranostics, and artificial intelligence (AI) for the radiotherapy workflow. Since the symposium, there has been an impressive shift in the promise and potential for integration of AI in clinical care, accelerated in large part by major advances in generative AI. AI is now poised more than ever to revolutionize cancer care. Radiation oncology is a field that uses and generates a large amount of digital data and is therefore likely to be one of the first fields to be transformed by AI. As experts in the collection, management, and analysis of these data, the informatics community will take a leading role in ensuring that radiation oncology is prepared to take full advantage of these technological advances. In this report, we provide highlights from the symposium, which took place in Santa Barbara, California, from August 29 to 31, 2022. We discuss lessons learned from the symposium for data acquisition, management, representation, and sharing, and put these themes into context to prepare radiation oncology for the successful and safe integration of AI and informatics technologies.

Identification of Zip8-correlated hub genes in pulmonary hypertension by informatic analysis

Background

Pulmonary hypertension (PH) is a syndrome characterized by marked remodeling of the pulmonary vasculature and increased pulmonary vascular resistance, ultimately leading to right heart failure and even death. The localization of Zrt/Irt-like Protein 8 (ZIP8, a metal ion transporter, encoded by SLC39A8) was abundantly in microvasculature endothelium and its pivotal role in the lung has been demonstrated. However, the role of Zip8 in PH remains unclear.

Methods

Bioinformatics analysis was employed to identify SLC39A8 expression patterns and differentially expressed genes (DEGs) between PH patients and normal controls (NC), based on four datasets (GSE24988, GSE113439, GSE117261, and GSE15197) from the Biotechnology Gene Expression Omnibus (NCBI GEO) database. Gene set enrichment analysis (GSEA) was performed to analyze signaling pathways enriched for DEGs. Hub genes were identified by cytoHubba analysis in Cytoscape. Reverse transcriptase-polymerase chain reaction was used to validate SLC39A8 and its correlated metabolic DEGs expression in PH (SU5416/Hypoxia) mice.

Results

SLC39A8 expression was downregulated in PH patients, and this expression pattern was validated in PH (SU5416/Hypoxia) mouse lung tissue. SLC39A8-correlated genes were mainly enriched in the metabolic pathways. Within these SLC39A8-correlated genes, 202 SLC39A8-correlated metabolic genes were screened out, and seven genes were identified as SLC39A8-correlated metabolic hub genes. The expression patterns of hub genes were analyzed between PH patients and controls and further validated in PH mice. Finally, four genes (Fasn, Nsdhl, Acat2, and Acly) were downregulated in PH mice. However, there were no significant differences in the expression of the other three hub genes between PH mice and controls. Of the four genes, Fasn and Acly are key enzymes in fatty acids synthesis, Nsdhl is involved in cholesterol synthesis, and Acat2 is implicated in cholesterol metabolic transformation. Taken together, these results provide novel insight into the role of Zip8 in PH.

Genome-Wide Analysis of Haemonchus contortus Proteases and Protease Inhibitors Using Advanced Informatics Provides Insights into Parasite Biology and Host-Parasite Interactions

Biodiversity within the animal kingdom is associated with extensive molecular diversity. The expansion of genomic, transcriptomic and proteomic data sets for invertebrate groups and species with unique biological traits necessitates reliable in silico tools for the accurate identification and annotation of molecules and molecular groups. However, conventional tools are inadequate for lesser-known organismal groups, such as eukaryotic pathogens (parasites), so that improved approaches are urgently needed. Here, we established a combined sequence- and structure-based workflow system to harness well-curated publicly available data sets and resources to identify, classify and annotate proteases and protease inhibitors of a highly pathogenic parasitic roundworm (nematode) of global relevance, called Haemonchus contortus (barber's pole worm). This workflow performed markedly better than conventional, sequence-based classification and annotation alone and allowed the first genome-wide characterisation of protease and protease inhibitor genes and gene products in this worm. In total, we identified 790 genes encoding 860 proteases and protease inhibitors representing 83 gene families. The proteins inferred included 280 metallo-, 145 cysteine, 142 serine, 121 aspartic and 81 "mixed" proteases as well as 91 protease inhibitors, all of which had marked physicochemical diversity and inferred involvements in >400 biological processes or pathways. A detailed investigation revealed a remarkable expansion of some protease or inhibitor gene families, which are likely linked to parasitism (e.g., host-parasite interactions, immunomodulation and blood-feeding) and exhibit stage- or sex-specific transcription profiles. This investigation provides a solid foundation for detailed explorations of the structures and functions of proteases and protease inhibitors of H. contortus and related nematodes, and it could assist in the discovery of new drug or vaccine targets against infections or diseases.

quickBAM: a parallelized BAM file access API for high-throughput sequence analysis informatics

MOTIVATION

In time-critical clinical settings, such as precision medicine, genomic data needs to be processed as fast as possible to arrive at data-informed treatment decisions in a timely fashion. While sequencing throughput has dramatically increased over the past decade, bioinformatics analysis throughput has not been able to keep up with the pace of computer hardware improvement, and consequently has now turned into the primary bottleneck. Modern computer hardware today is capable of much higher performance than current genomic informatics algorithms can typically utilize, therefore presenting opportunities for significant improvement of performance. Accessing the raw sequencing data from BAM files, e.g. is a necessary and time-consuming step in nearly all sequence analysis tools, however existing programming libraries for BAM access do not take full advantage of the parallel input/output capabilities of storage devices.

RESULTS

In an effort to stimulate the development of a new generation of faster sequence analysis tools, we developed quickBAM, a software library to accelerate sequencing data access by exploiting the parallelism in commodity storage hardware currently widely available. We demonstrate that analysis software ported to quickBAM consistently outperforms their current versions, in some cases finishing an analysis in under 3 min while the original version took 1.5 h, using the same storage solution.

AVAILABILITY AND IMPLEMENTATION

Open source and freely available at https://gitlab.com/yiq/quickbam/, we envision that quickBAM will enable a new generation of high-performance informatics tools, either directly boosting their performance if they are currently data-access bottlenecked, or allow data-access to keep up with further optimizations in algorithms and compute techniques.

Informatics for your Gut: at the Interface of Nutrition, the Microbiome, and Technology

BACKGROUND

A significant portion of individuals in the United States and worldwide experience diseases related to or driven by diet. As research surrounding user-centered design and the microbiome grows, movement of the spectrum of translational science from bench to bedside for improvement of human health through nutrition becomes more accessible. In this literature survey, we examined recent literature examining informatics research at the interface of nutrition and the microbiome.

OBJECTIVES

The objective of this survey was to synthesize recent literature describing how technology is being applied to understand health at the interface of nutrition and the microbiome focusing on the perspective of the consumer.

METHODS

A survey of the literature published between January 1, 2021 and October 10, 2022 was performed using the PubMed database and resulting literature was evaluated against inclusion and exclusion criteria.

RESULTS

A total of 139 papers were retrieved and evaluated against inclusion and exclusion criteria. After evaluation, 45 papers were reviewed in depth revealing four major themes: (1) microbiome and diet, (2) usability,(3) reproducibility and rigor, and (4) precision medicine and precision nutrition.

CONCLUSIONS

A review of the relationships between current literature on technology, nutrition and the microbiome, and self-management of dietary patterns was performed. Major themes that emerged from this survey revealed exciting new horizons for consumer management of diet and disease, as well as progress towards elucidating the relationship between diet, the microbiome, and health outcomes. The survey revealed continuing interest in the study of diet-related disease and the microbiome and acknowledgement of needs for data re-use, sharing, and unbiased and rigorous measurement of the microbiome. The literature also showed trends toward enhancing the usability of digital interventions to support consumer health and home management, and consensus building around how precision medicine and precision nutrition may be applied in the future to improve human health outcomes and prevent diet-related disease.