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• W2747962007 abstract "The foundation for a new era of data-driven medicine has been set by recent technological advances that enable the assessment and management of human health at an unprecedented level of resolution—what we refer to as high-definition medicine. Our ability to assess human health in high definition is enabled, in part, by advances in DNA sequencing, physiological and environmental monitoring, advanced imaging, and behavioral tracking. Our ability to understand and act upon these observations at equally high precision is driven by advances in genome editing, cellular reprogramming, tissue engineering, and information technologies, especially artificial intelligence. In this review, we will examine the core disciplines that enable high-definition medicine and project how these technologies will alter the future of medicine. The foundation for a new era of data-driven medicine has been set by recent technological advances that enable the assessment and management of human health at an unprecedented level of resolution—what we refer to as high-definition medicine. Our ability to assess human health in high definition is enabled, in part, by advances in DNA sequencing, physiological and environmental monitoring, advanced imaging, and behavioral tracking. Our ability to understand and act upon these observations at equally high precision is driven by advances in genome editing, cellular reprogramming, tissue engineering, and information technologies, especially artificial intelligence. In this review, we will examine the core disciplines that enable high-definition medicine and project how these technologies will alter the future of medicine. We define high-definition medicine as the dynamic assessment, management, and understanding of an individual’s health measured at (or near) its most basic units. It is the data-driven practice of medicine through the utilization of these highly detailed, longitudinal, and multi-parametric measures of the determinants of health to modify disease risk factors, detect disease processes early, drive precise and dynamically adjusted interventions, and determine preventative and therapeutic intervention efficacy from real-world outcomes (Figure 1). In contrast, current medical tests often rely on coarse-grained, static, and often isolated snapshots of an individual’s health state taken months or even years apart. The tools of high-definition medicine operate within four highly interconnected strategies for health management. These strategies are summarized below (Box 1):1.Defining a Personal Baseline of Health: The foundation of high-definition medicine rests on the precise and comprehensive assessment of individual level measures of the determinants of health. Health risks and interventions are tailored and evaluated relative to this personal baseline; comparing you to you and people like you and not to broad population norms.2.High-Definition Prevention: Continuous or frequent assessment of the determinants of health allows for the early detection and response to deviations in health parameters from the personal baseline, before clinically manifest, likely preventing or delaying disease onset.3.High-Precision Treatment: Upon the onset of disease, precision interventions are designed from, and their efficacy informed by, the personal health baseline as well as the precise molecular etiology of the disease. High-definition tools tailored to monitor specific disease processes enable the identification and modification of treatment failures early.4.Billions of High-Resolution People: The direct incorporation of health baseline, health trajectory, and treatment outcomes data collection into the practice of high-definition medicine seamlessly enables a continuously improving, learning health care system, whose collective knowledge can help preserve the health of an individual.Box 1The Pillars of High-Definition Medicine•Defining a Personal Baseline of Health: The foundation of high-definition medicine rests on the precise and comprehensive assessment of individual level measures of the determinants of health. Health risks and interventions are tailored and evaluated relative to this personal baseline; comparing you to you and people like you and not to broad population norms.•High-Definition Prevention: Continuous or frequent assessment of the determinants of health allows for the early detection and response to deviations in health parameters from the personal baseline, before clinically manifest, likely preventing or delaying disease onset.•High-Precision Treatment: Upon the onset of disease, precision interventions are designed from, and their efficacy informed by, the personal health baseline as well as the precise molecular etiology of the disease. High-definition tools tailored to monitor specific disease processes enable the identification and modification of treatment failures early.•Billions of High-Resolution People: The direct incorporation of health baseline, health trajectory, and treatment-outcomes data collection into the practice of high-definition medicine seamlessly enables a continuously improving, learning health care system, whose collective knowledge can help preserve health of an individual. •Defining a Personal Baseline of Health: The foundation of high-definition medicine rests on the precise and comprehensive assessment of individual level measures of the determinants of health. Health risks and interventions are tailored and evaluated relative to this personal baseline; comparing you to you and people like you and not to broad population norms.•High-Definition Prevention: Continuous or frequent assessment of the determinants of health allows for the early detection and response to deviations in health parameters from the personal baseline, before clinically manifest, likely preventing or delaying disease onset.•High-Precision Treatment: Upon the onset of disease, precision interventions are designed from, and their efficacy informed by, the personal health baseline as well as the precise molecular etiology of the disease. High-definition tools tailored to monitor specific disease processes enable the identification and modification of treatment failures early.•Billions of High-Resolution People: The direct incorporation of health baseline, health trajectory, and treatment-outcomes data collection into the practice of high-definition medicine seamlessly enables a continuously improving, learning health care system, whose collective knowledge can help preserve health of an individual. High-definition medicine is emerging from an accelerating coalescence of the biological and medical sciences with computer science and engineering (Sharp et al., 2016Sharp P. Hockfield S. Jacks T. Convergence: The Future of Health. Massachusetts Institute of Technology, Cambridge, Massachusetts2016Google Scholar). The combination of these disciplines has given rise to technologies that produce large volumes of clinically useful information, sometimes as a continuous stream of data, resulting in big-data handling challenges for the effective clinical utilization of these technologies. The basic groundwork for high-definition medicine is already being built, though the infrastructure to support it is still in its early stages and will require significant investment from health care service providers. A 2011 study by McKinsey Global Institute (Manyika et al., 2011Manyika J. Chui M. Brown B. Bughin J. Dobbs R. Roxburgh C. Byers A.H. Big data: The next frontier for innovation, competition, and productivity. McKinsey & Company, 2011Google Scholar) estimated that the effective use of big data by the U.S. health care sector would create an estimated $300 billion in value every year, under the assumption that the required information technology (IT) and analytical investments are made. The health care sector is well poised to capture the value of big data as it is already a data-driven culture, generating the necessary variety and volume of data but lacking the IT assets required to capture, process, and present that data into value-creating insights. The vast majority of the near-term projected value of big data in health care comes from the identification of the most clinically effective and cost-effective treatments from data already being generated by health care providers, all while holding health care outcomes constant. High-definition medicine has similar but more significant implementation hurdles to overcome before its value can be fully realized. Investment in infrastructure suitable for and development of policies governing the capture, storage, privacy, analysis, presentation, and interoperability of large datasets will need to be defined for high-definition medicine technologies not currently used in routine clinical practice. Clinical decision support systems and training programs for medical practitioners must be developed to seamlessly integrate these data and technologies into clinical workflows. The cost effectiveness of these technologies will need to improve before broad clinical implementation is feasible. Financial incentives must be aligned with the health benefits achievable through the use of these technologies to drive adoption. Moreover, policies governing regulatory approval, evidence generation, and reimbursement of the clinical use of these technologies will need to be developed. Overcoming these challenges is a major mandate of the current Food and Drug Administration (FDA), with efforts underway to “have the right policies in place to promote and encourage safe and effective innovation that can benefit consumers, and adopt regulatory approaches to enable the efficient development of these technologies” (Gottlieb, 2017Gottlieb, S. (2017). Fostering Medical Innovation: A Plan for Digital Health Devices. FDA Voice. https://blogs.fda.gov/fdavoice/index.php/2017/06/fostering-medical-innovation-a-plan-for-digital-health-devices/.Google Scholar). Overcoming these barriers has an even greater potential to improve health care outcomes via early detection of disease, precise application of effective therapies, intense monitoring of treatment progress, and rapid identification and correction of treatment failures. Under the assumption that the implementation barriers to high-definition medicine will eventually be overcome, herein we review the progress in the core components of high-definition medicine technologies and envisage how these technologies will develop and interact to alter the future of medicine. At its most basic level, personal risk for disease is comprised of genetic makeup, behavior, and environmental exposures. Currently, an individual’s baseline risk for disease is estimated to be the incidence of disease in the population modified by the measurement of clinical factors known to be associated with disease. Family history is often factored in as a blunt measure of genetic factors as well as shared environment and behaviors. In current practice, deviations from population level norms, both in terms of the measured value of clinical risk factors as well as the level of aggregation of disease in one’s family, are used to identify individuals at elevated risk for disease. In high-definition medicine, baseline risk for disease is defined by more precise measures of genetic makeup, physiologic metrics, behavior, and environmental exposures, and the significance of deviations from expected norms for risk factors are individualized (Figure 2). To establish personal norms and detect deviations early, clinical factors are measured continuously or frequently and in the real-world. This paradigm will result in a more accurate determination of health status as compared to the singular measurements taken in a medical facility, which can be influenced by diurnal variation, state of mind, hydration status, and myriad other factors that vary from moment to moment. High-definition medicine includes the definition of genetic risk for all individuals, at birth, based on complete genome profiling. Although whole-genome sequencing technology is currently available, family history is routinely used to assess genetic risk for disease. Familial aggregation of disease is used to estimate whether an individual is at higher risk for disease, recommend behaviors that might reduce their risk, potentially plan for early screening for disease, and evaluate the significance of early signs of disease. While family history is an effective tool for identifying high-risk individuals, it can be incomplete and inaccurate, and it is only effective at identifying a subset of high-risk individuals when there are multiple close family members affected with early-onset disease (Scheuner et al., 1997Scheuner M.T. Wang S.J. Raffel L.J. Larabell S.K. Rotter J.I. Family history: a comprehensive genetic risk assessment method for the chronic conditions of adulthood.Am. J. Med. Genet. 1997; 71: 315-324Crossref PubMed Scopus (0) Google Scholar). Most common diseases do not fit this profile. Accurately collected family history tends to classify ∼5% of the population as high risk and ∼10% of the population as moderate risk, at a sensitivity and specificity of ∼80% (Berg et al., 2009Berg A.O. Baird M.A. Botkin J.R. Driscoll D.A. Fishman P.A. Guarino P.D. Hiatt R.A. Jarvik G.P. Millon-Underwood S. Morgan T.M. et al.National Institutes of Health State-of-the-Science Conference statement: family history and improving health.Ann. Intern. Med. 2009; 151: 872-877Crossref PubMed Google Scholar, Lu et al., 2014Lu K.H. Wood M.E. Daniels M. Burke C. Ford J. Kauff N.D. Kohlmann W. Lindor N.M. Mulvey T.M. Robinson L. et al.American Society of Clinical OncologyAmerican Society of Clinical Oncology Expert Statement: collection and use of a cancer family history for oncology providers.J. Clin. Oncol. 2014; 32: 833-840Crossref PubMed Scopus (59) Google Scholar, Yoon et al., 2002Yoon P.W. Scheuner M.T. Peterson-Oehlke K.L. Gwinn M. Faucett A. Khoury M.J. Can family history be used as a tool for public health and preventive medicine?.Genet. Med. 2002; 4: 304-310Abstract Full Text Full Text PDF PubMed Scopus (209) Google Scholar). Thus, ∼85% of the population gains no information about their disease risk beyond the incidence rate in the population at large. On the other hand, a full genome sequence is, theoretically, a complete description of an individual’s genetic risk for disease (with the rare exception of potential transgenerational epigenetic inheritance (Heard and Martienssen, 2014Heard E. Martienssen R.A. Transgenerational epigenetic inheritance: myths and mechanisms.Cell. 2014; 157: 95-109Abstract Full Text Full Text PDF PubMed Scopus (300) Google Scholar)). It is a high-definition substitute for family history that is always available, is always complete, and comprehensively captures genetic risk factors that both have and have not manifested themselves as overt disease in family members. Genetic risk for disease can be roughly categorized into single-gene and polygenic traits. Single-gene traits are those where one, two, or a small number of genetic variants in a single gene are sufficient to cause the trait or disease. For disease traits, these genetic variants are usually rare, may be effectively captured by family history, and underlie the majority of the population risk for rare diseases, but they explain a small proportion of the population risk for common diseases (Chong et al., 2015Chong J.X. Buckingham K.J. Jhangiani S.N. Boehm C. Sobreira N. Smith J.D. Harrell T.M. McMillin M.J. Wiszniewski W. Gambin T. et al.Centers for Mendelian GenomicsThe genetic basis of Mendelian phenotypes: discoveries, challenges, and opportunities.Am. J. Hum. Genet. 2015; 97: 199-215Abstract Full Text Full Text PDF PubMed Google Scholar, Manolio et al., 2009Manolio T.A. Collins F.S. Cox N.J. Goldstein D.B. Hindorff L.A. Hunter D.J. McCarthy M.I. Ramos E.M. Cardon L.R. Chakravarti A. et al.Finding the missing heritability of complex diseases.Nature. 2009; 461: 747-753Crossref PubMed Scopus (3778) Google Scholar). The current diagnostic rate of genome-sequencing programs suggests that, for 25%–50% of individuals with a single-gene disorder, genome sequencing can identify the genetic cause (Chong et al., 2015Chong J.X. Buckingham K.J. Jhangiani S.N. Boehm C. Sobreira N. Smith J.D. Harrell T.M. McMillin M.J. Wiszniewski W. Gambin T. et al.Centers for Mendelian GenomicsThe genetic basis of Mendelian phenotypes: discoveries, challenges, and opportunities.Am. J. Hum. Genet. 2015; 97: 199-215Abstract Full Text Full Text PDF PubMed Google Scholar). For those individuals not receiving a genetic diagnosis, technical limitations of current genome-sequencing and analysis technologies, as well as our limited ability to interpret the significance of all detected genetic variants, especially non-coding variants, and the interactions between multiple genetic variants, likely underlie their negative genetic results. Efforts targeted at identifying novel disease genes and understanding the significance of all variants observed in a single medically important gene have demonstrated that the rate of variants of unknown significance can be dramatically reduced through focused genetic and phenotypic data collection (Chong et al., 2015Chong J.X. Buckingham K.J. Jhangiani S.N. Boehm C. Sobreira N. Smith J.D. Harrell T.M. McMillin M.J. Wiszniewski W. Gambin T. et al.Centers for Mendelian GenomicsThe genetic basis of Mendelian phenotypes: discoveries, challenges, and opportunities.Am. J. Hum. Genet. 2015; 97: 199-215Abstract Full Text Full Text PDF PubMed Google Scholar). For example, Myriad Genetics reports that the clinical significance of 97.9% of BRCA1/2 variants has been determined (Eggington et al., 2014Eggington J.M. Bowles K.R. Moyes K. Manley S. Esterling L. Sizemore S. Rosenthal E. Theisen A. Saam J. Arnell C. et al.A comprehensive laboratory-based program for classification of variants of uncertain significance in hereditary cancer genes.Clin. Genet. 2014; 86: 229-237Crossref PubMed Scopus (0) Google Scholar). The continued rapid pace of defining gene variants linked to disease, coupled with efforts to compile combined genotype-phenotype databases, will result in a future where the utility of family history is superseded by whole-genome profiling. Polygenic traits are those where the genetic risk for the trait is comprised of the combined influence of multiple genetic variants of small to moderate effect size. Polygenic genetic risk is the most relevant source of baseline disease risk for the vast majority (∼95%) of common chronic diseases (Manolio et al., 2009Manolio T.A. Collins F.S. Cox N.J. Goldstein D.B. Hindorff L.A. Hunter D.J. McCarthy M.I. Ramos E.M. Cardon L.R. Chakravarti A. et al.Finding the missing heritability of complex diseases.Nature. 2009; 461: 747-753Crossref PubMed Scopus (3778) Google Scholar). Although the genetic variants that explain the entirety, or even the majority, of the heritability of most common chronic diseases have yet to be discovered, clinically useful predictions can still be made with our current, incomplete knowledge (Wray et al., 2010Wray N.R. Yang J. Goddard M.E. Visscher P.M. The genetic interpretation of area under the ROC curve in genomic profiling.PLoS Genet. 2010; 6: e1000864Crossref PubMed Scopus (130) Google Scholar). For example, with our partial knowledge of the polygenic factors underlying disease risk, genetic risk scores can still identify high-risk individuals who benefit most from initiation of lifestyle changes or preventative treatments. Examples include (1) a 27-SNP genetic risk score for coronary artery disease was able to identify high-risk individuals who would benefit most from statin therapy and was projected to lead to a 3-fold reduction in the number of people treated to prevent one heart attack in high- versus low-risk individuals (Mega et al., 2015Mega J.L. Stitziel N.O. Smith J.G. Chasman D.I. Caulfield M. Devlin J.J. Nordio F. Hyde C. Cannon C.P. Sacks F. et al.Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials.Lancet. 2015; 385: 2264-2271Abstract Full Text Full Text PDF PubMed Google Scholar), (2) a 77-SNP genetic risk score for breast cancer was found to be more accurate than standard age-based criteria for guiding decision making on when mammographic screening should be initiated (Mavaddat et al., 2015Mavaddat N. Pharoah P.D. Michailidou K. Tyrer J. Brook M.N. Bolla M.K. Wang Q. Dennis J. Dunning A.M. Shah M. et al.Prediction of breast cancer risk based on profiling with common genetic variants.J. Natl. Cancer Inst. 2015; 107: djv036Crossref PubMed Scopus (79) Google Scholar), and (3) a 54-SNP genetic risk score for prostate cancer was able to identify high-risk individuals and dramatically improve the interpretation and positive predictive value of a positive prostate-specific antigen screen (Seibert et al., 2016Seibert T.M. Fan C.C. Wang Y. Zuber V. Karunamuni R. Parsons J.K. Eeles R.A. Easton D.F. Kote-Jarai Z. Amin Al Olama A. et al.A genetic risk score to guide age-specific, personalized prostate cancer screening.bioRxiv. 2016; https://doi.org/10.1101/089383Crossref Google Scholar) (Box 2). Thus, genetic risk scores for many diseases are already capable of informing health management for high-risk individuals. These scores will need to be validated and shown to have generalizability, especially for non-European individuals, and tested for their ability to change medical decision making and improve outcomes. This capability will only continue to improve as larger and more-refined genetic studies are pursued, but ultimately, large-scale genetic studies performed in the real world, via the merging and continuous analysis of genetic plus health-record data, will likely be necessary to comprehensively capture polygenic genetic risk (Chatterjee et al., 2013Chatterjee N. Wheeler B. Sampson J. Hartge P. Chanock S.J. Park J.H. Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies.Nat Genet. 2013; 45 (405e401–403): 400-405Crossref PubMed Scopus (111) Google Scholar). What needs to be highlighted is that these genetic risk-score studies often provide independent and complementary information to traditional clinical risk factors—the second major component of the definition of a personal health baseline.Box 2Definitions Box•SNP: Single nucleotide polymorphism—a DNA sequence variation occurring when a single nucleotide differs between members of a population.•Epigenome: Chemical changes to DNA and DNA-associated proteins that regulate the activity of functional genomic elements.•Cellular Mosaicism: The presence of two or more disparate populations of cells. Somatic mosaicism refers to cellular populations of cells that differ due to DNA changes.•Immunome: The set of genes and proteins that comprise the immune system.•Microbiome: The collection of microorganisms that populate a particular environment.•Metabolome: The collection of small-molecule chemicals within an organism, cell, or tissue.•Gene Therapy: The insertion of genes into cells, usually to replace missing or defective genes.•Gene Editing: The direct editing of a DNA sequence natively present within a cell. •SNP: Single nucleotide polymorphism—a DNA sequence variation occurring when a single nucleotide differs between members of a population.•Epigenome: Chemical changes to DNA and DNA-associated proteins that regulate the activity of functional genomic elements.•Cellular Mosaicism: The presence of two or more disparate populations of cells. Somatic mosaicism refers to cellular populations of cells that differ due to DNA changes.•Immunome: The set of genes and proteins that comprise the immune system.•Microbiome: The collection of microorganisms that populate a particular environment.•Metabolome: The collection of small-molecule chemicals within an organism, cell, or tissue.•Gene Therapy: The insertion of genes into cells, usually to replace missing or defective genes.•Gene Editing: The direct editing of a DNA sequence natively present within a cell. Traditional clinical risk factors, such as blood pressure or cholesterol levels, are typically measured infrequently and evaluated on the basis of normal and abnormal range cutoffs associated with either optimal health, minimal risk of disease, or reference ranges containing 95% of the reference population (Häggström, 2014Häggström M. Establishment and clinical use of reference ranges.WikiJournal of Medicine. 2014; 1https://doi.org/10.15347/wjm/2014.003Crossref Google Scholar). Optimal health ranges, however, differ per individual based on factors such as age, gender, ethnicity, geography, season, etc. A trivial example is the obvious sexual dimorphism and ethnic differences in body composition, which leads to differences in the optimal body mass index required to minimize years of life lost to metabolic disease (Fontaine et al., 2003Fontaine K.R. Redden D.T. Wang C. Westfall A.O. Allison D.B. Years of life lost due to obesity.JAMA. 2003; 289: 187-193Crossref PubMed Scopus (1428) Google Scholar). More subtle differences in the optimal range of even the most basic nutrients have also been noted. For example, ethnic differences in the relationship between vitamin D levels and cardiovascular disease risk have been observed and likely trace their origins to genetic differences in genes involved in the biological activity of vitamin D (Pilz et al., 2016Pilz S. Verheyen N. Grübler M.R. Tomaschitz A. März W. Vitamin D and cardiovascular disease prevention.Nat. Rev. Cardiol. 2016; 13: 404-417Crossref PubMed Scopus (0) Google Scholar). Even the widely accepted “normal” body temperature of 98.6°F actually displays substantial variability (∼94°F–100°F) across individuals (Sund-Levander et al., 2002Sund-Levander M. Forsberg C. Wahren L.K. Normal oral, rectal, tympanic and axillary body temperature in adult men and women: a systematic literature review.Scand. J. Caring Sci. 2002; 16: 122-128Crossref PubMed Scopus (0) Google Scholar). This inter-individual variability in health parameters suggests that personal baselines for each factor, rather than population norms, are a more effective yardstick for judging the significance of fluctuations in these factors. Some nontraditional but emerging measures of health further exemplify the importance of personal baselines. For example, the human microbiome is tremendously diverse from individual to individual yet is highly stable within each individual over time (Lynch and Pedersen, 2016Lynch S.V. Pedersen O. The human intestinal microbiome in health and disease.N. Engl. J. Med. 2016; 375: 2369-2379Crossref PubMed Scopus (24) Google Scholar). While we do not yet fully understand the significance of microbiome dynamics, many disease states have been linked to perturbations in the microbiome, often leading to reduced microbiome diversity, yet the resultant microbiome profile remains unique from individual to individual despite a shared disease state (Lynch and Pedersen, 2016Lynch S.V. Pedersen O. The human intestinal microbiome in health and disease.N. Engl. J. Med. 2016; 375: 2369-2379Crossref PubMed Scopus (24) Google Scholar). Thus, rather than the detection of specific bacterial species indicative of disease risk, reductions in diversity or deviations in the microbiome profile from an individual’s healthy baseline profile may be a more sensitive indicator of disease risk (see High-Definition Prevention, Microbiome). Of course, an individual’s natural set point for any particular clinical risk factor is not necessarily optimal for health. Rather, this personal health baseline serves as a means to judge progress toward an individualized optimal health target. An examination of even the most broadly applicable population level optimal health ranges for basic clinical risk factors reveals the utility of individualized optimal health ranges. Consider, for example, an individual who has a natural blood pressure set point of 140/90 mmHg. Lower blood pressure is usually associated with improved health span and longevity (Lewington et al., 2002Lewington S. Clarke R. Qizilbash N. Peto R. Collins R. Prospective Studies CollaborationAge-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies.Lancet. 2002; 360: 1903-1913Abstract Full Text Full Text PDF PubMed Scopus (5241) Google Scholar). Efforts to lower the blood pressure of this individual below their natural set point could potentially reduce their absolute risk for disease, and progress toward this goal would be measured against their natural baseline. However, in frail elderly individuals, elevated blood pressure is actually associated with a lower risk of death (Odden et al., 2012Odden M.C. Peralta C.A. Haan M.N. Covinsky K.E. Rethinking the association of high blood pressure with mortality in elderly adults: the impact of frailty.Arch. Intern. Med. 2012; 172: 1162-1168Crossref PubMed Scopus (126) Google Scholar). More generally, the frequent measurement of clinical health factors allows for the ascertainment of health trajectories, as measured by deviations from a personal health baseline, while the interpretation of the clinical significance of these health trajectories is individualized based on health outcomes data collected across a cohort with matched characteristics (Figure 2; see also Billions of High-Resolution People). In summary, the high-definition medicine approach to identifying individuals at risk for disease is based on the direct utilization of genetic risk factors, the comparison of an individual’s personal health baseline relative to other individuals with similar characteristics, and ultimately the definition of risk models that account for" @default.
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• W2747962007 title "High-Definition Medicine" @default.
• W2747962007 cites W1493909984 @default.
• W2747962007 cites W1552143138 @default.
• W2747962007 cites W1604692664 @default.
• W2747962007 cites W1643307576 @default.
• W2747962007 cites W1701211704 @default.
• W2747962007 cites W1846262441 @default.
• W2747962007 cites W1846586690 @default.
• W2747962007 cites W1861083333 @default.
• W2747962007 cites W1902159756 @default.
• W2747962007 cites W1905297489 @default.
• W2747962007 cites W1924463094 @default.
• W2747962007 cites W1964372737 @default.
• W2747962007 cites W1967220865 @default.
• W2747962007 cites W1968380849 @default.
• W2747962007 cites W1972902097 @default.
• W2747962007 cites W1974047233 @default.
• W2747962007 cites W1980991473 @default.
• W2747962007 cites W1983406743 @default.
• W2747962007 cites W1984993302 @default.
• W2747962007 cites W1990602451 @default.
• W2747962007 cites W1990700240 @default.
• W2747962007 cites W1992237817 @default.
• W2747962007 cites W1992462434 @default.
• W2747962007 cites W1999808841 @default.
• W2747962007 cites W2000730994 @default.
• W2747962007 cites W2009195401 @default.
• W2747962007 cites W2013640342 @default.
• W2747962007 cites W2020682484 @default.
• W2747962007 cites W2021459974 @default.
• W2747962007 cites W2021946345 @default.
• W2747962007 cites W2022102389 @default.
• W2747962007 cites W2029053939 @default.
• W2747962007 cites W2029480581 @default.
• W2747962007 cites W2040553259 @default.
• W2747962007 cites W2048479014 @default.
• W2747962007 cites W2048644480 @default.
• W2747962007 cites W2050480410 @default.
• W2747962007 cites W2053369230 @default.
• W2747962007 cites W2056893663 @default.
• W2747962007 cites W2062024333 @default.
• W2747962007 cites W2069967041 @default.
• W2747962007 cites W2078006611 @default.
• W2747962007 cites W2078610240 @default.
• W2747962007 cites W2079297835 @default.
• W2747962007 cites W2088867600 @default.
• W2747962007 cites W2090245272 @default.
• W2747962007 cites W2090657329 @default.
• W2747962007 cites W2097656361 @default.
• W2747962007 cites W2099820553 @default.
• W2747962007 cites W2100632690 @default.
• W2747962007 cites W2104976513 @default.
• W2747962007 cites W2105100844 @default.
• W2747962007 cites W2105735228 @default.
• W2747962007 cites W2112650958 @default.
• W2747962007 cites W2113831855 @default.
• W2747962007 cites W2117315526 @default.
• W2747962007 cites W2118615835 @default.
• W2747962007 cites W2121732950 @default.
• W2747962007 cites W2122178825 @default.
• W2747962007 cites W2125524975 @default.
• W2747962007 cites W2129976097 @default.
• W2747962007 cites W2130830673 @default.
• W2747962007 cites W2132484969 @default.
• W2747962007 cites W2141970008 @default.
• W2747962007 cites W2142550298 @default.
• W2747962007 cites W2143282831 @default.
• W2747962007 cites W2147061679 @default.
• W2747962007 cites W2155766871 @default.
• W2747962007 cites W2159296038 @default.
• W2747962007 cites W2159536887 @default.
• W2747962007 cites W2160784991 @default.
• W2747962007 cites W2161698731 @default.
• W2747962007 cites W2168301815 @default.
• W2747962007 cites W2169769306 @default.
• W2747962007 cites W2169779482 @default.
• W2747962007 cites W2171454102 @default.
• W2747962007 cites W2171705978 @default.
• W2747962007 cites W2191957520 @default.
• W2747962007 cites W2202734965 @default.
• W2747962007 cites W2206370378 @default.
• W2747962007 cites W2213731134 @default.
• W2747962007 cites W2238160795 @default.
• W2747962007 cites W2239118478 @default.
• W2747962007 cites W2258532034 @default.
• W2747962007 cites W2266175888 @default.
• W2747962007 cites W2266982813 @default.
• W2747962007 cites W2267444292 @default.
• W2747962007 cites W2274048411 @default.
• W2747962007 cites W2274066308 @default.

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