Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease.

Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.

An Ancient Harappan Genome Lacks Ancestry from Steppe Pastoralists or Iranian Farmers.

We report an ancient genome from the Indus Valley Civilization (IVC). The individual we sequenced fits as a mixture of people related to ancient Iranians (the largest component) and Southeast Asian hunter-gatherers, a unique profile that matches ancient DNA from 11 genetic outliers from sites in Iran and Turkmenistan in cultural communication with the IVC. These individuals had little if any Steppe pastoralist-derived ancestry, showing that it was not ubiquitous in northwest South Asia during the IVC as it is today. The Iranian-related ancestry in the IVC derives from a lineage leading to early Iranian farmers, herders, and hunter-gatherers before their ancestors separated, contradicting the hypothesis that the shared ancestry between early Iranians and South Asians reflects a large-scale spread of western Iranian farmers east. Instead, sampled ancient genomes from the Iranian plateau and IVC descend from different groups of hunter-gatherers who began farming without being connected by substantial movement of people.

jMorp: Japanese Multi-Omics Reference Panel update report 2023.

Modern medicine is increasingly focused on personalized medicine, and multi-omics data is crucial in understanding biological phenomena and disease mechanisms. Each ethnic group has its unique genetic background with specific genomic variations influencing disease risk and drug response. Therefore, multi-omics data from specific ethnic populations are essential for the effective implementation of personalized medicine. Various prospective cohort studies, such as the UK Biobank, All of Us and Lifelines, have been conducted worldwide. The Tohoku Medical Megabank project was initiated after the Great East Japan Earthquake in 2011. It collects biological specimens and conducts genome and omics analyses to build a basis for personalized medicine. Summary statistical data from these analyses are available in the jMorp web database (https://jmorp.megabank.tohoku.ac.jp), which provides a multidimensional approach to the diversity of the Japanese population. jMorp was launched in 2015 as a public database for plasma metabolome and proteome analyses and has been continuously updated. The current update will significantly expand the scale of the data (metabolome, genome, transcriptome, and metagenome). In addition, the user interface and backend server implementations were rewritten to improve the connectivity between the items stored in jMorp. This paper provides an overview of the new version of the jMorp.

Revealing the recent demographic history of Europe via haplotype sharing in the UK Biobank.

Haplotype-based analyses have recently been leveraged to interrogate the fine-scale structure in specific geographic regions, notably in Europe, although an equivalent haplotype-based understanding across the whole of Europe with these tools is lacking. Furthermore, study of identity-by-descent (IBD) sharing in a large sample of haplotypes across Europe would allow a direct comparison between different demographic histories of different regions. The UK Biobank (UKBB) is a population-scale dataset of genotype and phenotype data collected from the United Kingdom, with established sampling of worldwide ancestries. The exact content of these non-UK ancestries is largely uncharacterized, where study could highlight valuable intracontinental ancestry references with deep phenotyping within the UKBB. In this context, we sought to investigate the sample of European ancestry captured in the UKBB. We studied the haplotypes of 5,500 UKBB individuals with a European birthplace; investigated the population structure and demographic history in Europe, showing in parallel the variety of footprints of demographic history in different genetic regions around Europe; and expand knowledge of the genetic landscape of the east and southeast of Europe. Providing an updated map of European genetics, we leverage IBD-segment sharing to explore the extent of population isolation and size across the continent. In addition to building and expanding upon previous knowledge in Europe, our results show the UKBB as a source of diverse ancestries beyond Britain. These worldwide ancestries sampled in the UKBB may complement and inform researchers interested in specific communities or regions not limited to Britain.

The enigmatic tropical alpine flora on the African sky islands is young, disturbed, and unsaturated.

Tropical alpine floras are renowned for high endemism, spectacular giant rosette plants testifying to convergent adaptation to harsh climates with nightly frosts, and recruitment dominated by long-distance dispersal from remote areas. In contrast to the larger, more recent (late Miocene onward) and contiguous expanses of tropical alpine habitat in South America, the tropical alpine flora in Africa is extremely fragmented across small patches on distant mountains of variable age (Oligocene onward). How this has affected the colonization and diversification history of the highly endemic but species-poor afroalpine flora is not well known. Here we infer phylogenetic relationships of ∼20% of its species using novel genome skimming data and published matrices and infer a timeframe for species origins in the afroalpine region using fossil-calibrated molecular clocks. Although some of the mountains are old, and although stem node ages may substantially predate colonization, most lineages appear to have colonized the afroalpine during the last 5 or 10 My. The accumulation of species increased exponentially toward the present. Taken together with recent reports of extremely low intrapopulation genetic diversity and recent intermountain population divergence, this points to a young, unsaturated, and dynamic island scenario. Habitat disturbance caused by the Pleistocene climate oscillations likely induced cycles of colonization, speciation, extinction, and recolonization. This study contributes to our understanding of differences in the histories of recruitment on different tropical sky islands and on oceanic islands, providing insight into the general processes shaping their remarkable floras.

Creating artificial human genomes using generative neural networks.

Generative models have shown breakthroughs in a wide spectrum of domains due to recent advancements in machine learning algorithms and increased computational power. Despite these impressive achievements, the ability of generative models to create realistic synthetic data is still under-exploited in genetics and absent from population genetics. Yet a known limitation in the field is the reduced access to many genetic databases due to concerns about violations of individual privacy, although they would provide a rich resource for data mining and integration towards advancing genetic studies. In this study, we demonstrated that deep generative adversarial networks (GANs) and restricted Boltzmann machines (RBMs) can be trained to learn the complex distributions of real genomic datasets and generate novel high-quality artificial genomes (AGs) with none to little privacy loss. We show that our generated AGs replicate characteristics of the source dataset such as allele frequencies, linkage disequilibrium, pairwise haplotype distances and population structure. Moreover, they can also inherit complex features such as signals of selection. To illustrate the promising outcomes of our method, we showed that imputation quality for low frequency alleles can be improved by data augmentation to reference panels with AGs and that the RBM latent space provides a relevant encoding of the data, hence allowing further exploration of the reference dataset and features for solving supervised tasks. Generative models and AGs have the potential to become valuable assets in genetic studies by providing a rich yet compact representation of existing genomes and high-quality, easy-access and anonymous alternatives for private databases.

Genome-wide characterization of human minisatellite VNTRs: population-specific alleles and gene expression differences.

Variable Number Tandem Repeats (VNTRs) are tandem repeat (TR) loci that vary in copy number across a population. Using our program, VNTRseek, we analyzed human whole genome sequencing datasets from 2770 individuals in order to detect minisatellite VNTRs, i.e., those with pattern sizes ≥7 bp. We detected 35 638 VNTR loci and classified 5676 as commonly polymorphic (i.e. with non-reference alleles occurring in >5% of the population). Commonly polymorphic VNTR loci were found to be enriched in genomic regions with regulatory function, i.e. transcription start sites and enhancers. Investigation of the commonly polymorphic VNTRs in the context of population ancestry revealed that 1096 loci contained population-specific alleles and that those could be used to classify individuals into super-populations with near-perfect accuracy. Search for quantitative trait loci (eQTLs), among the VNTRs proximal to genes, indicated that in 187 genes expression differences correlated with VNTR genotype. We validated our predictions in several ways, including experimentally, through the identification of predicted alleles in long reads, and by comparisons showing consistency between sequencing platforms. This study is the most comprehensive analysis of minisatellite VNTRs in the human population to date.

Mapping gene flow between ancient hominins through demography-aware inference of the ancestral recombination graph.

The sequencing of Neanderthal and Denisovan genomes has yielded many new insights about interbreeding events between extinct hominins and the ancestors of modern humans. While much attention has been paid to the relatively recent gene flow from Neanderthals and Denisovans into modern humans, other instances of introgression leave more subtle genomic evidence and have received less attention. Here, we present a major extension of the ARGweaver algorithm, called ARGweaver-D, which can infer local genetic relationships under a user-defined demographic model that includes population splits and migration events. This Bayesian algorithm probabilistically samples ancestral recombination graphs (ARGs) that specify not only tree topologies and branch lengths along the genome, but also indicate migrant lineages. The sampled ARGs can therefore be parsed to produce probabilities of introgression along the genome. We show that this method is well powered to detect the archaic migration into modern humans, even with only a few samples. We then show that the method can also detect introgressed regions stemming from older migration events, or from unsampled populations. We apply it to human, Neanderthal, and Denisovan genomes, looking for signatures of older proposed migration events, including ancient humans into Neanderthal, and unknown archaic hominins into Denisovans. We identify 3% of the Neanderthal genome that is putatively introgressed from ancient humans, and estimate that the gene flow occurred between 200-300kya. We find no convincing evidence that negative selection acted against these regions. Finally, we predict that 1% of the Denisovan genome was introgressed from an unsequenced, but highly diverged, archaic hominin ancestor. About 15% of these "super-archaic" regions-comprising at least about 4Mb-were, in turn, introgressed into modern humans and continue to exist in the genomes of people alive today.

Comprehensive Analysis of Human Subtelomeres by Whole Genome Mapping.

Detailed comprehensive knowledge of the structures of individual long-range telomere-terminal haplotypes are needed to understand their impact on telomere function, and to delineate the population structure and evolution of subtelomere regions. However, the abundance of large evolutionarily recent segmental duplications and high levels of large structural variations have complicated both the mapping and sequence characterization of human subtelomere regions. Here, we use high throughput optical mapping of large single DNA molecules in nanochannel arrays for 154 human genomes from 26 populations to present a comprehensive look at human subtelomere structure and variation. The results catalog many novel long-range subtelomere haplotypes and determine the frequencies and contexts of specific subtelomeric duplicons on each chromosome arm, helping to clarify the currently ambiguous nature of many specific subtelomere structures as represented in the current reference sequence (HG38). The organization and content of some duplicons in subtelomeres appear to show both chromosome arm and population-specific trends. Based upon these trends we estimate a timeline for the spread of these duplication blocks.

Differences in spatial versus temporal reaction norms for spring and autumn phenological events.

For species to stay temporally tuned to their environment, they use cues such as the accumulation of degree-days. The relationships between the timing of a phenological event in a population and its environmental cue can be described by a population-level reaction norm. Variation in reaction norms along environmental gradients may either intensify the environmental effects on timing (cogradient variation) or attenuate the effects (countergradient variation). To resolve spatial and seasonal variation in species' response, we use a unique dataset of 91 taxa and 178 phenological events observed across a network of 472 monitoring sites, spread across the nations of the former Soviet Union. We show that compared to local rates of advancement of phenological events with the advancement of temperature-related cues (i.e., variation within site over years), spatial variation in reaction norms tend to accentuate responses in spring (cogradient variation) and attenuate them in autumn (countergradient variation). As a result, among-population variation in the timing of events is greater in spring and less in autumn than if all populations followed the same reaction norm regardless of location. Despite such signs of local adaptation, overall phenotypic plasticity was not sufficient for phenological events to keep exact pace with their cues-the earlier the year, the more did the timing of the phenological event lag behind the timing of the cue. Overall, these patterns suggest that differences in the spatial versus temporal reaction norms will affect species' response to climate change in opposite ways in spring and autumn.

Ancient genomes from present-day France unveil 7,000 years of its demographic history.

Genomic studies conducted on ancient individuals across Europe have revealed how migrations have contributed to its present genetic landscape, but the territory of present-day France has yet to be connected to the broader European picture. We generated a large dataset comprising the complete mitochondrial genomes, Y-chromosome markers, and genotypes of a number of nuclear loci of interest of 243 individuals sampled across present-day France over a period spanning 7,000 y, complemented with a partially overlapping dataset of 58 low-coverage genomes. This panel provides a high-resolution transect of the dynamics of maternal and paternal lineages in France as well as of autosomal genotypes. Parental lineages and genomic data both revealed demographic patterns in France for the Neolithic and Bronze Age transitions consistent with neighboring regions, first with a migration wave of Anatolian farmers followed by varying degrees of admixture with autochthonous hunter-gatherers, and then substantial gene flow from individuals deriving part of their ancestry from the Pontic steppe at the onset of the Bronze Age. Our data have also highlighted the persistence of Magdalenian-associated ancestry in hunter-gatherer populations outside of Spain and thus provide arguments for an expansion of these populations at the end of the Paleolithic Period more northerly than what has been described so far. Finally, no major demographic changes were detected during the transition between the Bronze and Iron Ages.

Population Receptive Field Characteristics in the between- and Within-Digit Dimensions of the Undominant Hand in the Primary Somatosensory Cortex.

Somatotopy is an important guiding principle for sensory fiber organization in the primary somatosensory cortex (S1), which reflects tactile information processing and is associated with disease-related reorganization. However, it is difficult to measure the neuronal encoding scheme in S1 in vivo in normal participants. Here, we investigated the somatotopic map of the undominant hand using a Bayesian population receptive field (pRF) model. The model was established in hand space with between- and within-digit dimensions. In the between-digit dimension, orderly representation was found, which had low variability across participants. The pRF shape tended to be elliptical for digits with high spatial acuity, for which the long axis was along the within-digit dimension. In addition, the pRF width showed different change trends in the 2 dimensions across digits. These results provide new insights into the neural mechanisms in S1, allowing for in-depth investigation of somatosensory information processing and disease-related reorganization.

Maternal Intensive Care Unit Admission as an Indicator of Severe Acute Maternal Morbidity: A Population-Based Study.

BACKGROUND: Severe acute maternal morbidity (SAMM) accounts for any life-threatening complication during pregnancy or after delivery. Measuring and monitoring SAMM seem critical to assessing the quality of maternal health care. The objectives were to explore the validity of intensive care unit (ICU) admission as an indicator of SAMM by characterizing the profile of women admitted to an ICU and of their ICU stay, according to the association with other SAMM criterion. METHODS: We performed a secondary analysis of the 2540 women with SAMM included in the epidemiology of severe acute maternal morbidity (EPIMOMS) multiregional prospective population-based study (2012-2013, n = 182,309 deliveries). The EPIMOMS definition of SAMM, based on national experts' consensus, is a combination of diagnosis, organ dysfunctions, and intervention criteria, including ICU admission. Among women with SAMM, we identified characteristics associated with maternal ICU admission with or with no other SAMM criterion compared with ICU admission, by using multivariable multinomial logistic regression models. RESULTS: Overall, 511 women were admitted to an ICU during or up to 42 days after pregnancy, for a population-based rate of 2.8 of 1000 deliveries (511/182,309; 95% confidence interval [CI], 2.6-3.1); 15.5% of them (79/511; 95% CI, 12.4-18.9) had no other SAMM criterion compared with ICU admission. Among women with SAMM, the odds of ICU admission with no other morbidity criterion were increased in women with preexisting medical conditions (adjusted odds ratio (aOR), 2.13; 95% CI, 1.17-3.86) and cesarean before labor (aOR, 3.12; 95% CI, 1.47-6.64). Women admitted to ICU with no other SAMM criterion had more often decompensation of a preexisting condition, no interventions for organ support, and a shorter length of stay than women admitted with other SAMM criteria. CONCLUSIONS: Among women with SAMM, 1 in 5 is admitted to an ICU; 15.5% of those admitted in ICU have no other SAMM criterion and a less acute condition. These results challenge the use of ICU admission as a criterion of SAMM.

Periodic catastrophes over human evolutionary history are necessary to explain the forager population paradox.

The rapid growth of contemporary human foragers and steady decline of chimpanzees represent puzzling population paradoxes, as any species must exhibit near-stationary growth over much of their evolutionary history. We evaluate the conditions favoring zero population growth (ZPG) among 10 small-scale subsistence human populations and five wild chimpanzee groups according to four demographic scenarios: altered mean vital rates (i.e., fertility and mortality), vital rate stochasticity, vital rate covariance, and periodic catastrophes. Among most human populations, changing mean fertility or survivorship alone requires unprecedented alterations. Stochastic variance and covariance would similarly require major adjustment to achieve ZPG in most populations. Crashes could maintain ZPG in slow-growing populations but must be frequent and severe in fast-growing populations-more extreme than observed in the ethnographic record. A combination of vital rate alteration with catastrophes is the most realistic solution to the forager population paradox. ZPG in declining chimpanzees is more readily obtainable through reducing mortality and altering covariance. While some human populations may have hovered near ZPG under harsher conditions (e.g., violence or food shortage), modern Homo sapiens were equipped with the potential to rapidly colonize new habitats and likely experienced population fluctuations and local extinctions over evolutionary history.

The blood urea nitrogen/creatinine (BUN/cre) ratio was U-shaped associated with all-cause mortality in general population.

OBJECTIVES: This study aimed to explore the relationship between the blood urea nitrogen/creatinine (BUN/Cre) ratio and all-cause or cause-specific mortality in the general population. METHODS: Participants were enrolled from the National Health and Nutrition Examination Survey (NHANES) during 1999 to 2014. Baseline variables were acquired from questionnaires and examinations. Death status were ascertained from National Death Index records. Cox proportional hazards models with cubic spines were used to estimate hazard ratios (HRs) and 95% confidence interval (CI) of all-cause mortality, cardiovascular and cancer mortality. RESULTS: A total of 42038 participants were enrolled in the study with a median 8.13 years of follow-up. Older people and women tend to have a higher BUN/Cre ratio. After multivariable adjustment, BUN/Cre ratio between 11.43 and 14.64 was associated with the lowest all-cause mortality compared with the participants with the lowest quartile (HR 0.83 [0.76, 0.91]; p < 0.001). The highest quartile of BUN/Cre ratio was associated with the lowest risk of cancer mortality (HR 0.64 [0.53, 0.78]; p < 0.001). Restricted cubic splines showed BUN/Cre was nonlinearly associated with all-cause mortality and linearly associated with cancer mortality. CONCLUSIONS: This study confirmed a U-shape relationship between BUN/Cre ratio and all-cause mortality in the general population.

Within family Mendelian randomization studies.

Mendelian randomization (MR) is increasingly used to make causal inferences in a wide range of fields, from drug development to etiologic studies. Causal inference in MR is possible because of the process of genetic inheritance from parents to offspring. Specifically, at gamete formation and conception, meiosis ensures random allocation to the offspring of one allele from each parent at each locus, and these are unrelated to most of the other inherited genetic variants. To date, most MR studies have used data from unrelated individuals. These studies assume that genotypes are independent of the environment across a sample of unrelated individuals, conditional on covariates. Here we describe potential sources of bias, such as transmission ratio distortion, selection bias, population stratification, dynastic effects and assortative mating that can induce spurious or biased SNP-phenotype associations. We explain how studies of related individuals such as sibling pairs or parent-offspring trios can be used to overcome some of these sources of bias, to provide potentially more reliable evidence regarding causal processes. The increasing availability of data from related individuals in large cohort studies presents an opportunity to both overcome some of these biases and also to evaluate familial environmental effects.

Incidence and prevalence of psychogenic nonepileptic seizures in a Norwegian county: A 10-year population-based study.

OBJECTIVE: This study was undertaken to measure the incidence and prevalence of active psychogenic nonepileptic seizures (PNES) in a Norwegian county. METHODS: Using the Norwegian patient registry, we identified patients in Møre and Romsdal County in Norway diagnosed with F44.5 (conversion disorder with seizures or convulsions) or R56.8 (convulsions, not elsewhere classified) in the period January 2010 to January 2020. A review of the patients' medical records and an assessment of diagnostic validity were performed. PNES were diagnosed according to the recommendations by the International League Against Epilepsy Nonepileptic Seizures Task Force. Point prevalence of PNES on January 1, 2020 and incidence rates for the period 2010-2019 were determined. RESULTS: Based on PNES within the past 5 years, we found a PNES prevalence of 23.8/100 000 (95% confidence interval [CI] = 17.9-29.6), including all levels of diagnostic certainty. For the highest level of diagnostic certainty (video-electroencephalographically confirmed), the prevalence was 10.6/100 000 (95% CI = 6.7-14.5). The highest prevalence was found in the age group 15-19 years, at 59.5/100 000 (95% CI = 22.6-96.3). The mean annual incidence rate between 2010 and 2019 was 3.1/100 000/year (95% CI = 2.4-3.7). SIGNIFICANCE: We report for the first time a population-based estimate of the prevalence of PNES. Our findings suggest that the prevalence of PNES is within the range of estimates from non-population-based data. We found a strikingly high prevalence of PNES in the 15-19-year age group.

Population pharmacokinetics of lopinavir/ritonavir in Covid-19 patients.

OBJECTIVE: To develop a population pharmacokinetic model for lopinavir boosted by ritonavir in coronavirus disease 2019 (Covid-19) patients. METHODS: Concentrations of lopinavir/ritonavir were assayed by an accredited LC-MS/MS method. The population pharmacokinetics of lopinavir was described using non-linear mixed-effects modeling (NONMEM version 7.4). After determination of the base model that better described the data set, the influence of covariates (age, body weight, height, body mass index (BMI), gender, creatinine, aspartate aminotransferase (AST), alanine aminotransferase (ALT), C reactive protein (CRP), and trough ritonavir concentrations) was tested on the model. RESULTS: From 13 hospitalized patients (4 females, 9 males, age = 64 ± 16 years), 70 lopinavir/ritonavir plasma concentrations were available for analysis. The data were best described by a one-compartment model with a first-order input (KA). Among the covariates tested on the PK parameters, only the ritonavir trough concentrations had a significant effect on CL/F and improved the fit. Model-based simulations with the final parameter estimates under a regimen lopinavir/ritonavir 400/100 mg b.i.d. showed a high variability with median concentration between 20 and 30 mg/L (Cmin/Cmax) and the 90% prediction intervals within the range 1-100 mg/L. CONCLUSION: According to the estimated 50% effective concentration of lopinavir against SARS-CoV-2 virus in Vero E6 cells (16.7 mg/L), our model showed that at steady state, a dose of 400 mg b.i.d. led to 40% of patients below the minimum effective concentration while a dose of 1200 mg b.i.d. will reduce this proportion to 22%.

Regional Contribution of Previable Infant Deaths to Infant Mortality Rates in the United States.

OBJECTIVE: Lack of standardization of infant mortality rate (IMR) calculation between regions in the United States makes comparisons potentially biased. This study aimed to quantify differences in the contribution of early previable live births (<20 weeks) to U.S. regional IMR. STUDY DESIGN: Population-based cohort study of all U.S. live births and infant deaths recorded between 2007 and 2014 using Centers for Disease Control and Prevention's (CDC's) WONDER database linked birth/infant death records (births from 17-47 weeks). Proportion of infant deaths attributable to births <20 vs. 20 to 47 weeks, and difference (ΔIMR) between reported and modified (births ≥20 weeks) IMRs were compared across four U.S. census regions (North, South, Midwest, and West). RESULTS: Percentages of infant deaths attributable to birth <20 weeks were 6.3, 6.3, 5.3, and 4.1% of total deaths for Northeast, Midwest, South, and West, respectively, p < 0.001. Contribution of < 20-week deaths to each region's IMR was 0.34, 0.42, 0.37, and 0.2 per 1,000 live births. Modified IMR yielded less regional variation with IMRs of 5.1, 6.2, 6.6, and 4.9 per 1,000 live births. CONCLUSION: Live births at <20 weeks contribute significantly to IMR as all result in infant death. Standardization of gestational age cut-off results in more consistent IMRs among U.S. regions and would result in U.S. IMR rates exceeding the healthy people 2020 goal of 6.0 per 1,000 live births.

Alzheimer's team care approach-akin to diabetes-in the development, validation, and population assessment of retinal biomarkers for disease.

There exists a public health imperative to discover and to develop disease-modifying Alzheimer's disease (AD) therapeutics to protect the health of millions of individuals facing AD. Achievement of this goal will be dependent on developing the clinical tools to detect high risk, in the earliest phases of the disease, and at the population level. This article describes the study of retinal biomarkers for the identification of, and tracking of change over time for, individuals in the preclinical stage of AD and substantiates the need for a major cross-disciplinary effort for comparison across labs and clinical sites using diabetes risk monitoring as a perfect analogy. Proposed framework would: (1) support AD working groups across disciplines; (2) establish common imaging platforms to develop and test basic standards, and minimum datasets to embrace and test novel innovations as they emerge; and (3) accelerate AD prevention and quality improvement in real-world care.