The Scope of Big Data in One Medicine: Unprecedented Opportunities and Challenges

Socio-Technical Analysis of the Benefits and Barriers to Using a Digital Representation of the Global Horse Population in Equine Veterinary Medicine

There is a consensus that future medicine will benefit from a comprehensive analysis of harmonized, interconnected, and interoperable health data. These data can originate from a variety of sources. In particular, data from veterinary diagnostics and the monitoring of health-related life parameters using the Internet of Medical Things are considered here. To foster the usage of collected data in this way, not only do technical aspects need to be addressed but so do organizational ones, and to this end, a socio-technical matrix is first presented that complements the literature. It is used in an exemplary analysis of the system. Such a socio-technical matrix is an interesting tool for analyzing the process of data sharing between actors in the system dependent on their social relations. With the help of such a socio-technical tool and using equine veterinary medicine as an example, the social system of veterinarians and owners as actors is explored in terms of barriers and enablers of an effective digital representation of the global equine population.

From big data to better patient outcomes

Among medical specialties, laboratory medicine is the largest producer of structured data and must play a crucial role for the efficient and safe implementation of big data and artificial intelligence in healthcare. The area of personalized therapies and precision medicine has now arrived, with huge data sets not only used for experimental and research approaches, but also in the "real world". Analysis of real world data requires development of legal, procedural and technical infrastructure. The integration of all clinical data sets for any given patient is important and necessary in order to develop a patient-centered treatment approach. Data-driven research comes with its own challenges and solutions. The Findability, Accessibility, Interoperability, and Reusability (FAIR) Guiding Principles provide guidelines to make data findable, accessible, interoperable and reusable to the research community. Federated learning, standards and ontologies are useful to improve robustness of artificial intelligence algorithms working on big data and to increase trust in these algorithms. When dealing with big data, the univariate statistical approach changes to multivariate statistical methods significantly shifting the potential of big data. Combining multiple omics gives previously unsuspected information and provides understanding of scientific questions, an approach which is also called the systems biology approach. Big data and artificial intelligence also offer opportunities for laboratories and the In Vitro Diagnostic industry to optimize the productivity of the laboratory, the quality of laboratory results and ultimately patient outcomes, through tools such as predictive maintenance and "moving average" based on the aggregate of patient results.

Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy

How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

Identifying correlations driven by influential observations in large datasets

Although high-throughput data allow researchers to interrogate thousands of variables simultaneously, it can also introduce a significant number of spurious results. Here we demonstrate that correlation analysis of large datasets can yield numerous false positives due to the presence of outliers that canonical methods fail to identify. We present Correlations Under The InfluencE (CUTIE), an open-source jackknifing-based method to detect such cases with both parametric and non-parametric correlation measures, and which can also uniquely rescue correlations not originally deemed significant or with incorrect sign. Our approach can additionally be used to identify variables or samples that induce these false correlations in high proportion. A meta-analysis of various omics datasets using CUTIE reveals that this issue is pervasive across different domains, although microbiome data are particularly susceptible to it. Although the significance of a correlation eventually depends on the thresholds used, our approach provides an efficient way to automatically identify those that warrant closer examination in very large datasets.

Great future or greedy venture: Precision medicine needs philosophy

INTRODUCTION

Over the past decade, we have witnessed the initiation and implementation of precision medicine (PM), a discipline that promises to individualize and personalize medical management and treatment, rendering them ultimately more precise and effective. Despite of the continuing advances and numerous clinical applications, the potential of PM remains highly controversial, sparking heated debates about its future.

METHOD

The present article reviews the philosophical issues and practical challenges that are critical to the feasibility and implementation of PM.

OUTCOME

The explanation and argument about the relations between PM and computability, uncertainty as well as complexity, show that key foundational assumptions of PM might not be fully validated.

CONCLUSION

The present analysis suggests that our current understanding of PM is probably oversimplified and too superficial. More efforts are needed to realize the hope that PM has elicited, rather than make the term just as a hype.

Sparse HJ Biplot: A New Methodology via Elastic Net

The HJ biplot is a multivariate analysis technique that allows us to represent both individuals and variables in a space of reduced dimensions. To adapt this approach to massive datasets, it is necessary to implement new techniques that are capable of reducing the dimensionality of the data and improving interpretation. Because of this, we propose a modern approach to obtaining the HJ biplot called the elastic net HJ biplot, which applies the elastic net penalty to improve the interpretation of the results. It is a novel algorithm in the sense that it is the first attempt within the biplot family in which regularisation methods are used to obtain modified loadings to optimise the results. As a complement to the proposed method, and to give practical support to it, a package has been developed in the R language called SparseBiplots. This package fills a gap that exists in the context of the HJ biplot through penalized techniques since in addition to the elastic net, it also includes the ridge and lasso to obtain the HJ biplot. To complete the study, a practical comparison is made with the standard HJ biplot and the disjoint biplot, and some results common to these methods are analysed.

Data-Driven Surveillance: Effective Collection, Integration, and Interpretation of Data to Support Decision Making

The biggest change brought about by the “era of big data” to health in general, and epidemiology in particular, relates arguably not to the volume of data encountered, but to its variety. An increasing number of new data sources, including many not originally collected for health purposes, are now being used for epidemiological inference and contextualization. Combining evidence from multiple data sources presents significant challenges, but discussions around this subject often confuse issues of data access and privacy, with the actual technical challenges of data integration and interoperability. We review some of the opportunities for connecting data, generating information, and supporting decision-making across the increasingly complex “variety” dimension of data in population health, to enable data-driven surveillance to go beyond simple signal detection and support an expanded set of surveillance goals.

Initiatives, Concepts, and Implementation Practices of FAIR (Findable, Accessible, Interoperable, and Reusable) Data Principles in Health Data Stewardship Practice: Protocol for a Scoping Review

Background

Data stewardship is an essential driver of research and clinical practice. Data collection, storage, access, sharing, and analytics are dependent on the proper and consistent use of data management principles among the investigators. Since 2016, the FAIR (findable, accessible, interoperable, and reusable) guiding principles for research data management have been resonating in scientific communities. Enabling data to be findable, accessible, interoperable, and reusable is currently believed to strengthen data sharing, reduce duplicated efforts, and move toward harmonization of data from heterogeneous unconnected data silos. FAIR initiatives and implementation trends are rising in different facets of scientific domains. It is important to understand the concepts and implementation practices of the FAIR data principles as applied to human health data by studying the flourishing initiatives and implementation lessons relevant to improved health research, particularly for data sharing during the coronavirus pandemic.

Objective

This paper aims to conduct a scoping review to identify concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in the health data domain.

Methods

The Arksey and O’Malley stage-based methodological framework for scoping reviews will be used for this review. PubMed, Web of Science, and Google Scholar will be searched to access relevant primary and grey publications. Articles written in English and published from 2014 onwards with FAIR principle concepts or practices in the health domain will be included. Duplication among the 3 data sources will be removed using a reference management software. The articles will then be exported to a systematic review management software. At least two independent authors will review the eligibility of each article based on defined inclusion and exclusion criteria. A pretested charting tool will be used to extract relevant information from the full-text papers. Qualitative thematic synthesis analysis methods will be employed by coding and developing themes. Themes will be derived from the research questions and contents in the included papers.

Results

The results will be reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews) reporting guidelines. We anticipate finalizing the manuscript for this work in 2021.

Conclusions

We believe comprehensive information about the FAIR data principles, initiatives, implementation practices, and lessons learned in the FAIRification process in the health domain is paramount to supporting both evidence-based clinical practice and research transparency in the era of big data and open research publishing.

International Registered Report Identifier (IRRID)

PRR1-10.2196/22505

Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review

Background

Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research.

Objective

This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions.

Methods

A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines.

Results

A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance.

Conclusions

Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.

Mouse Quantitative Proteomics Knowledgebase: reference protein concentration ranges in 20 mouse tissues using 5000 quantitative proteomics assays

Laboratory mouse is the most used animal model in biological research, largely due to its high conserved synteny with human. Researchers use mice to answer various questions ranging from determining a pathological effect of knocked out/in gene to understanding drug metabolism. Our group developed >5000 quantitative targeted proteomics assays for 20 mouse tissues and determined the concentration ranges of a total of more than 1600 proteins using heavy labelled internal standards. We describe here MouseQuaPro; a knowledgebase that hosts this collection of carefully curated experimental data. The Web-based application includes protein concentrations from >700 mouse tissue samples from three common research strains, corresponding to more than 200k experimentally determined concentrations. The knowledgebase integrates the assay and protein concentration information with their human orthologs, functional and molecular annotations, biological pathways, related human diseases, and known gene expressions. At its core are the protein concentration ranges, which provide insights into (dis)similarities between tissues, strains, and sexes. MouseQuaPro implements advanced search as well as filtering functionalities with a simple interface and interactive visualization. This information-rich resource provides an initial map of protein absolute concentration in mouse tissues and allows guided design of proteomics phenotyping experiments. The knowledgebase is available at mousequapro.proteincentre.com. (Reviewer access username and password: mousequapro_reviewer1234567).

Individual differences in the encoding of contextual details following acute stress: An explorative study

Information processing under stressful circumstances depends on many experimental conditions, like the information valence or the point in time at which brain function is probed. This also holds true for memorizing contextual details (or ‘memory contextualization’). Moreover, large interindividual differences appear to exist in (context‐dependent) memory formation after stress, but it is mostly unknown which individual characteristics are essential. Various characteristics were explored from a theory‐driven and data‐driven perspective, in 120 healthy men. In the theory‐driven model, we postulated that life adversity and trait anxiety shape the stress response, which impacts memory contextualization following acute stress. This was indeed largely supported by linear regression analyses, showing significant interactions depending on valence and time point after stress. Thus, during the acutephase of the stress response, reduced neutral memory contextualization was related to salivary cortisol level; moreover, certain individual characteristics correlated with memory contextualization of negatively valenced material: (a) life adversity, (b) α‐amylase reactivity in those with low life adversity and (c) cortisol reactivity in those with low trait anxiety. Better neutral memory contextualization during the recoveryphase of the stress response was associated with (a) cortisol in individuals with low life adversity and (b) α‐amylase in individuals with high life adversity. The data‐driven Random Forest‐based variable selection also pointed to (early) life adversity—during the acutephase—and (moderate) α‐amylase reactivity—during the recoveryphase—as individual characteristics related to better memory contextualization. Newly identified characteristics sparked novel hypotheses about non‐anxious personality traits, age, mood and states during retrieval of context‐related information.

Fine-scale tracking of wild waterfowl and their impact on highly pathogenic avian influenza outbreaks in the Republic of Korea, 2014-2015

Wild migratory waterfowl are considered one of the most important reservoirs and long-distance carriers of highly pathogenic avian influenza (HPAI). Our study aimed to explore the spatial and temporal characteristics of wild migratory waterfowl’s wintering habitat in the Republic of Korea (ROK) and to evaluate the impact of these habitats on the risk of HPAI outbreaks in commercial poultry farms. The habitat use of 344 wild migratory waterfowl over four migration cycles was estimated based on tracking records. The association of habitat use with HPAI H5N8 outbreaks in poultry farms was evaluated using a multilevel logistic regression model. We found that a poultry farm within a wild waterfowl habitat had a 3–8 times higher risk of HPAI outbreak than poultry farms located outside of the habitat. The range of wild waterfowl habitats increased during autumn migration, and was associated with the epidemic peak of HPAI outbreaks on domestic poultry farms in the ROK. Our findings provide a better understanding of the dynamics of HPAI infection in the wildlife–domestic poultry interface and may help to establish early detection, and cost-effective preventive measures.

Big Data Applications in Medical Field: A Literature Review

Digital imaging and medical reporting have acquired an essential role in healthcare, but the main challenge is the storage of a high volume of patient data. Although newer technologies are already introduced in the medical sciences to save records size, Big Data provides advancements by storing a large amount of data to improve the efficiency and quality of patient treatment with better care. It provides intelligent automation capabilities to reduce errors than manual inputs. Large numbers of research papers on big data in the medical field are studied and analyzed for their impacts, benefits, and applications. Big data has great potential to support the digitalization of all medical and clinical records and then save the entire data regarding the medical history of an individual or a group. This paper discusses big data usage for various industries and sectors. Finally, 12 significant applications for the medical field by the implementation of big data are identified and studied with a brief description. This technology can be gainfully used to extract useful information from the available data by analyzing and managing them through a combination of hardware and software. With technological advancement, big data provides health-related information for millions of patient-related to life issues such as lab tests reporting, clinical narratives, demographics, prescription, medical diagnosis, and related documentation. Thus, Big Data is essential in developing a better yet efficient analysis and storage healthcare services. The demand for big data applications is increasing due to its capability of handling and analyzing massive data. Not only in the future but even now, Big Data is proving itself as an axiom of storing, developing, analyzing, and providing overall health information to the physicians.

Analysis of puerperal metritis treatment records in a grazing dairy farm in Argentina

OBJECTIVE

To assess the efficacy of antibiotic usage for the treatment of puerperal metritis (PM) and its association with reproductive performance, a retrospective cohort study including a total of 9168 records of cows from a dairy farm in Argentina was run.

MATERIAL AND METHODS

Cows having a PM3 (metricheck, scale 0-3) and treated with ceftiofur (ceftiofur crystalline free acid, 6.6 mg/kg) at 0-21 days postpartum (p. p.) (n = 2688), and cows having a PM 1-2 and not treated with an antibiotic at 0-21 days p. p. (n = 6480) were included in the study. All cows were reexamined with metricheck to assess the clinical cure (vaginal discharge [VD] score 0), partial cure (VD score similar or lower than previous), no cure (VD score higher than previous). Cows with a metricheck VD1-3 after 0-21 days p. p. were diagnosed as clinical endometritis (CE) 1-3. The occurrence of PM1-3, cure rate, calving to conception interval, the hazard of pregnancy, odds for non-pregnancy, and odds for CE were analyzed using SAS software.

RESULTS

A total of 8876 PM1-3 records were included, 2435 records of PM3 treatments with ceftiofur (27.43 %), and 6441 records of PM1-2 (72.57 %) with no treatment. Cows having PM1 and PM2 became pregnant 14 and 12 days earlier than cows with PM3 (p < 0.001). The PM3 ceftiofur treated cows had a clinical cure of 24.85 % (PM0); 53.63 % had a partially cure; and 18.52 % no cure. Conversely, cows with PM1-2 had a 51.96 %, 20.70 %, and 24.53 % cure rate, respectively (p < 0.001). Cows having complete cure became pregnant 13 and 11 days earlier than cows having partial cure and no cure (p < 0.001). Cows that had PM3 during the first 21 days p. p. had twice the chances of developing CE compared to cows having PM1-2 (41.28 % vs. 24.14 %, p < 0.001). After 21 days p. p., less than 1 % of cows with clinical cure developed CE compared to 63.32 % that developed CE with partial cure, and 38.21 % with no cure (p < 0.001).

CONCLUSION AND CLINICAL RELEVANCE

After ceftiofur treatment, 78 % of cows were cured when measured by disappearance of fetid VD but only 25 % of cows had clinical cure when measured by appearance of a clear VD. The cows that remained with clinical metritis had more chances of having CE after 21 days p. p. and had more days open than cows with clear normal VD.

The Influence of Physical Activity and Epigenomics On Cognitive Function and Brain Health in Breast Cancer

The risk of breast cancer increases with age, with the majority of women diagnosed with breast cancer being postmenopausal. It has been estimated that 25-75% of women with breast cancer experience changes in cognitive function (CF) related to disease and treatment, which compromises psychological well-being, decision making, ability to perform daily activities, and adherence to cancer therapy. Unfortunately, the mechanisms that underlie neurocognitive changes in women with breast cancer remain poorly understood, which in turn limits the development of effective treatments and prevention strategies. Exercise has great potential as a non-pharmaceutical intervention to mitigate the decline in CF in women with breast cancer. Evidence suggests that DNA methylation, an epigenetic mechanism for gene regulation, impacts CF and brain health (BH), that exercise influences DNA methylation, and that exercise impacts CF and BH. Although investigating DNA methylation has the potential to uncover the biologic foundations for understanding neurocognitive changes within the context of breast cancer and its treatment as well as the ability to understand how exercise mitigates these changes, there is a dearth of research on this topic. The purpose of this review article is to compile the research in these areas and to recommend potential areas of opportunity for investigation.

A Vision for Development and Utilization of High-Throughput Phenotyping and Big Data Analytics in Livestock

Automated high-throughput phenotyping with sensors, imaging, and other on-farm technologies has resulted in a flood of data that are largely under-utilized. Drastic cost reductions in sequencing and other omics technology have also facilitated the ability for deep phenotyping of livestock at the molecular level. These advances have brought the animal sciences to a cross-roads in data science where increased training is needed to manage, record, and analyze data to generate knowledge and advances in Agriscience related disciplines. This paper describes the opportunities and challenges in using high-throughput phenotyping, “big data,” analytics, and related technologies in the livestock industry based on discussions at the Livestock High-Throughput Phenotyping and Big Data Analytics meeting, held in November 2017 (see: https://www.animalgenome.org/bioinfo/community/workshops/2017/). Critical needs for investments in infrastructure for people (e.g., “big data” training), data (e.g., data transfer, management, and analytics), and technology (e.g., development of low cost sensors) were defined by this group. Though some subgroups of animal science have extensive experience in predictive modeling, cross-training in computer science, statistics, and related disciplines are needed to use big data for diverse applications in the field. Extensive opportunities exist for public and private entities to harness big data to develop valuable research knowledge and products to the benefit of society under the increased demands for food in a rapidly growing population.

Early Detection of Bacteraemia Using Ten Clinical Variables with an Artificial Neural Network Approach

An adequate model for predicting bacteraemia has not yet been developed. This study aimed to evaluate the performance of an artificial neural network (ANN)-based prediction model in comparison with previous statistical models. The performance of multi-layer perceptron (MLP), a representative ANN model, was verified via comparison with a non-neural network model. A total of 1260 bacteraemia episodes were identified in 13,402 patients. In MLP with 128 hidden layer nodes, the area under the receiver operating characteristic curve (AUC) of the prediction performance was 0.729 (95% confidence interval [CI]; 0.712–0.728), while in MLP with 256 hidden layer nodes, it was 0.727 (95% CI; 0.713–0.727). In a conventional Bayesian statistical method, the AUC was 0.7. The aforementioned two MLP models exhibited the highest sensitivity (0.810). The ranking of clinical variables was used to describe the influential power of the prediction. Serum alkaline phosphatase was one of the most influential clinical variables, and one-out search was the best ranking method for measuring the influence of the clinical variables. Furthermore, adding variables beyond the 10 top-ranking ones did not significantly affect the prediction of bacteraemia. The ANN model is not inferior to conventional statistical approaches. Bacteraemia can be predicted using only the top 10 clinical variables determined by a ranking method, and the model can be used in clinical practice by applying real-time monitoring.

Metabolomics in genetic testing

Metabolomics is an intriguing field of study providing a new readout of the biochemical activities taking place at the moment of sampling within a subject's biofluid or tissue. Metabolite concentrations are influenced by several factors including disease, environment, drugs, diet and, importantly, genetics. Metabolomics signatures, which describe a subject's phenotype, are useful for disease diagnosis and prognosis, as well as for predicting and monitoring the effectiveness of treatments. Metabolomics is conventionally divided into targeted (i.e., the quantitative analysis of a predetermined group of metabolites) and untargeted studies (i.e., analysis of the complete set of small-molecule metabolites contained in a biofluid without a pre-imposed metabolites-selection). Both approaches have demonstrated high value in the investigation and understanding of several monogenic and multigenic conditions. Due to low costs per sample and relatively short analysis times, metabolomics can be a useful and robust complement to genetic sequencing.

A Transdisciplinary Approach Supporting the Implementation of a Big Data Project in Livestock Production: An Example From the Swiss Pig Production Industry

Big Data approaches offer potential benefits for improving animal health, but they have not been broadly implemented in livestock production systems. Privacy issues, the large number of stakeholders, and the competitive environment all make data sharing, and integration a challenge in livestock production systems. The Swiss pig production industry illustrates these and other Big Data issues. It is a highly decentralized and fragmented complex network made up of a large number of small independent actors collecting a large amount of heterogeneous data. Transdisciplinary approaches hold promise for overcoming some of the barriers to implementing Big Data approaches in livestock production systems. The purpose of our paper is to describe the use of a transdisciplinary approach in a Big Data research project in the Swiss pig industry. We provide a brief overview of the research project named “Pig Data,” describing the structure of the project, the tools developed for collaboration and knowledge transfer, the data received, and some of the challenges. Our experience provides insight and direction for researchers looking to use similar approaches in livestock production system research.

Systematic review of the use of big data to improve surgery in low- and middle-income countries

Background

Technological advances have led to the generation of large amounts of data, both in surgical research and practice. Despite this, it is unclear how much originates in low‐ and middle‐income countries (LMICs) and what barriers exist to the use of such data in improving surgical care. The aim of this review was to capture the extent and impact of programmes that use large volumes of patient data on surgical care in LMICs.

Methods

A PRISMA‐compliant systematic literature review of PubMed, Embase and Google Scholar was performed in August 2018. Prospective studies collecting large volumes of patient‐level data within LMIC settings were included and evaluated qualitatively.

Results

A total of 68 studies were included from 71 LMICs, involving 708 032 patients. The number of patients in included studies varied widely (from 335 to 428 346), with 25 reporting data on 3000 or more LMIC patients. Patient inclusion in large‐data studies in LMICs has increased dramatically since 2015. Studies predominantly involved Brazil, China, India and Thailand, with low patient numbers from Africa and Latin America. Outcomes after surgery were commonly the focus (33 studies); very few large studies looked at access to surgical care or patient expenditure. The use of large data sets specifically to improve surgical outcomes in LMICs is currently limited.

Conclusion

Large volumes of data are becoming more common and provide a strong foundation for continuing investigation. Future studies should address questions more specific to surgery.

Precision medicine: a call for increased pharmacogenomic education

Precision medicine is an emerging model of care where providers consider patients' genetic profiles, lifestyles and environments to offer more precise therapy. The potential of precision medicine is boundless as interdisciplinary teams utilize genetic technologies to improve patient outcomes. The integration of precision medicine into healthcare faces many barriers, including a lack of standardization and reimbursement concerns. This article argues that increased pharmacogenetics education and system-wide implementation is necessary to overcome some of these challenges. Extensive expansion of pharmacogenomics education is a step toward producing knowledgeable clinicians who are poised to apply its methodology and champion for patient-centered care.

The study of interactions between genome and exposome in the development of systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a systemic inflammatory autoimmune disease characterized by a broad spectrum of clinical and serological manifestations. This may reflect a complex and multifactorial etiology involving several identified genetic and environmental factors, though not explaining the full risk of SLE. Established SLE risk genotypes are either very rare or with modest effect sizes and twin studies indicate that other factors besides genetics must be operative in SLE etiology. The exposome comprises the cumulative environmental influences on an individual and associated biological responses through the lifespan. It has been demonstrated that exposure to silica, smoking and exogenous hormones candidate as environmental risk factors in SLE, while alcohol consumption seems to be protective. Very few studies have investigated potential gene-environment interactions to determine if some of the unexplained SLE risk is attributable hereto. Even less have focused on interactions between specific risk genotypes and environmental exposures relevant to SLE pathogenesis. Cohort and case-control studies may provide data to suggest such biological interactions and various statistical measures of interaction can indicate the magnitude of such. However, such studies do often have very large sample-size requirements and we suggest that the rarity of SLE to some extent can be compensated by increasing the ratio of controls. This review summarizes the current body of knowledge on gene-environment interactions in SLE. We argue for the prioritization of studies that comprise the increasing details available of the genome and exposome relevant to SLE as they have the potential to disclose new aspects of SLE pathogenesis including phenotype heterogeneity.

Beyond the Helix: Ethical, Legal, and Social Implications in Genomics

OBJECTIVE

To review ethical, legal, and social implications of genomics, a ground-breaking science that when applied improves cancer care outcomes.

DATA SOURCES

PubMed, Cumulative Index to Nursing and Allied Health (CINAHL), Cochrane Library, consensus statements, and professional guidelines.

CONCLUSION

Ethical, legal, and social domains of genomics are not fully delineated. Areas needing further discussion and policies include return of findings, informed consent, electronic health records, and data resources and sharing.

IMPLICATIONS FOR NURSING PRACTICE

All nurses need a basic understanding of the ethical, legal, and social implications of genomics.

Artificial intelligence for aging and longevity research: Recent advances and perspectives

The applications of modern artificial intelligence (AI) algorithms within the field of aging research offer tremendous opportunities. Aging is an almost universal unifying feature possessed by all living organisms, tissues, and cells. Modern deep learning techniques used to develop age predictors offer new possibilities for formerly incompatible dynamic and static data types. AI biomarkers of aging enable a holistic view of biological processes and allow for novel methods for building causal models-extracting the most important features and identifying biological targets and mechanisms. Recent developments in generative adversarial networks (GANs) and reinforcement learning (RL) permit the generation of diverse synthetic molecular and patient data, identification of novel biological targets, and generation of novel molecular compounds with desired properties and geroprotectors. These novel techniques can be combined into a unified, seamless end-to-end biomarker development, target identification, drug discovery and real world evidence pipeline that may help accelerate and improve pharmaceutical research and development practices. Modern AI is therefore expected to contribute to the credibility and prominence of longevity biotechnology in the healthcare and pharmaceutical industry, and to the convergence of countless areas of research.

Big Data and the Study of Social Inequalities in Health: Expectations and Issues

Understanding the construction of the social gradient in health is a major challenge in the field of social epidemiology, a branch of epidemiology that seeks to understand how society and its different forms of organization influence health at a population level. Attempting to answer these questions involves large datasets of varied heterogeneous data suggesting that Big Data approaches could be then particularly relevant to the study of social inequalities in health. Nevertheless, real challenges have to be addressed in order to make the best use of the development of Big Data in health for the benefit of all. The main purpose of this perspective is to discuss some of these challenges, in particular: (i) the perimeter and the particularity of Big Data in health, which must be broader than a vision centerd solely on care, the individual and his or her biological characteristics; (ii) the need for clarification regarding the notion of data, the validity of data and the question of causal inference for various actors involved in health, such data as researchers, health professionals and the civilian population; (iii) the need for regulation and control of data and their uses by public authorities for the common good and the fight against social inequalities in health. To face these issues, it seems essential to integrate different approaches into a close dialog, integrating methodological, societal, and ethical issues. This question cannot escape an interdisciplinary approach, including users or patients.

Applications of RNA Indexes for Precision Oncology in Breast Cancer

Precision oncology aims to offer the most appropriate treatments to cancer patients mainly based on their individual genetic information. Genomics has provided numerous valuable data on driver mutations and risk loci; however, it remains a formidable challenge to transform these data into therapeutic agents. Transcriptomics describes the multifarious expression patterns of both mRNAs and non-coding RNAs (ncRNAs), which facilitates the deciphering of genomic codes. In this review, we take breast cancer as an example to demonstrate the applications of these rich RNA resources in precision medicine exploration. These include the use of mRNA profiles in triple-negative breast cancer (TNBC) subtyping to inform corresponding candidate targeted therapies; current advancements and achievements of high-throughput RNA interference (RNAi) screening technologies in breast cancer; and microRNAs as functional signatures for defining cell identities and regulating the biological activities of breast cancer cells. We summarize the benefits of transcriptomic analyses in breast cancer management and propose that unscrambling the core signaling networks of cancer may be an important task of multiple-omic data integration for precision oncology.

A Smartphone-Based Application Improves the Accuracy, Completeness, and Timeliness of Cattle Disease Reporting and Surveillance in Ethiopia

Accurate disease reporting, ideally in near real time, is a prerequisite to detecting disease outbreaks and implementing appropriate measures for their control. This study compared the performance of the traditional paper-based approach to animal disease reporting in Ethiopia to one using an application running on smartphones. In the traditional approach, the total number of cases for each disease or syndrome was aggregated by animal species and reported to each administrative level at monthly intervals; while in the case of the smartphone application demographic information, a detailed list of presenting signs, in addition to the putative disease diagnosis were immediately available to all administrative levels via a Cloud-based server. While the smartphone-based approach resulted in much more timely reporting, there were delays due to limited connectivity; these ranged on average from 2 days (in well-connected areas) up to 13 days (in more rural locations). We outline the challenges that would likely be associated with any widespread rollout of a smartphone-based approach such as the one described in this study but demonstrate that in the long run the approach offers significant benefits in terms of timeliness of disease reporting, improved data integrity and greatly improved animal disease surveillance.

Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets

Statistical and machine learning (ML)-based methods have recently advanced in construction of gene regulatory network (GRNs) based on high-throughput biological datasets. GRNs underlie almost all cellular phenomena; hence, comprehensive GRN maps are essential tools to elucidate gene function, thereby facilitating the identification and prioritization of candidate genes for functional analysis. High-throughput gene expression datasets have yielded various statistical and ML-based algorithms to infer causal relationship between genes and decipher GRNs. This review summarizes the recent advancements in the computational inference of GRNs, based on large-scale transcriptome sequencing datasets of model plants and crops. We highlight strategies to select contextual genes for GRN inference, and statistical and ML-based methods for inferring GRNs based on transcriptome datasets from plants. Furthermore, we discuss the challenges and opportunities for the elucidation of GRNs based on large-scale datasets obtained from emerging transcriptomic applications, such as from population-scale, single-cell level, and life-course transcriptome analyses.