First Affiliated Hospital of Xi'an Jiaotong University

Abstract Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature 1 . Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium 2 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we characterized mutational signatures using 84,729,690 somatic mutations from 4,645 whole-genome and 19,184 exome sequences that encompass most types of cancer. We identified 49 single-base-substitution, 11 doublet-base-substitution, 4 clustered-base-substitution and 17 small insertion-and-deletion signatures. The substantial size of our dataset, compared with previous analyses 3–15 , enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated—but distinct—DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes that contribute to the development of human cancer.

Abstract Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale 1–3 . Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter 4 ; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation 5,6 ; analyses timings and patterns of tumour evolution 7 ; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity 8,9 ; and evaluates a range of more-specialized features of cancer genomes 8,10–18 .

Schizophrenia has a heritability of 60–80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies. A genome-wide association study including over 76,000 individuals with schizophrenia and over 243,000 control individuals identifies common variant associations at 287 genomic loci, and further fine-mapping analyses highlight the importance of genes involved in synaptic processes.

autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

BACKGROUND: Recently, the potential role of gut microbiome in metabolic diseases has been revealed, especially in cardiovascular diseases. Hypertension is one of the most prevalent cardiovascular diseases worldwide, yet whether gut microbiota dysbiosis participates in the development of hypertension remains largely unknown. To investigate this issue, we carried out comprehensive metagenomic and metabolomic analyses in a cohort of 41 healthy controls, 56 subjects with pre-hypertension, 99 individuals with primary hypertension, and performed fecal microbiota transplantation from patients to germ-free mice. RESULTS: Compared to the healthy controls, we found dramatically decreased microbial richness and diversity, Prevotella-dominated gut enterotype, distinct metagenomic composition with reduced bacteria associated with healthy status and overgrowth of bacteria such as Prevotella and Klebsiella, and disease-linked microbial function in both pre-hypertensive and hypertensive populations. Unexpectedly, the microbiome characteristic in pre-hypertension group was quite similar to that in hypertension. The metabolism changes of host with pre-hypertension or hypertension were identified to be closely linked to gut microbiome dysbiosis. And a disease classifier based on microbiota and metabolites was constructed to discriminate pre-hypertensive and hypertensive individuals from controls accurately. Furthermore, by fecal transplantation from hypertensive human donors to germ-free mice, elevated blood pressure was observed to be transferrable through microbiota, and the direct influence of gut microbiota on blood pressure of the host was demonstrated. CONCLUSIONS: Overall, our results describe a novel causal role of aberrant gut microbiota in contributing to the pathogenesis of hypertension. And the significance of early intervention for pre-hypertension was emphasized.

Abstract Objective To assess the prevalence of diabetes and its risk factors. Design Population based, cross sectional study. Setting 31 provinces in mainland China with nationally representative cross sectional data from 2015 to 2017. Participants 75 880 participants aged 18 and older—a nationally representative sample of the mainland Chinese population. Main outcome measures Prevalence of diabetes among adults living in China, and the prevalence by sex, regions, and ethnic groups, estimated by the 2018 American Diabetes Association (ADA) and the World Health Organization diagnostic criteria. Demographic characteristics, lifestyle, and history of disease were recorded by participants on a questionnaire. Anthropometric and clinical assessments were made of serum concentrations of fasting plasma glucose (one measurement), two hour plasma glucose, and glycated haemoglobin (HbA 1c ). Results The weighted prevalence of total diabetes (n=9772), self-reported diabetes (n=4464), newly diagnosed diabetes (n=5308), and prediabetes (n=27 230) diagnosed by the ADA criteria were 12.8% (95% confidence interval 12.0% to 13.6%), 6.0% (5.4% to 6.7%), 6.8% (6.1% to 7.4%), and 35.2% (33.5% to 37.0%), respectively, among adults living in China. The weighted prevalence of total diabetes was higher among adults aged 50 and older and among men. The prevalence of total diabetes in 31 provinces ranged from 6.2% in Guizhou to 19.9% in Inner Mongolia. Han ethnicity had the highest prevalence of diabetes (12.8%) and Hui ethnicity had the lowest (6.3%) among five investigated ethnicities. The weighted prevalence of total diabetes (n=8385) using the WHO criteria was 11.2% (95% confidence interval 10.5% to 11.9%). Conclusion The prevalence of diabetes has increased slightly from 2007 to 2017 among adults living in China. The findings indicate that diabetes is an important public health problem in China.

OBJECTIVE: Depression is the most common mental illness worldwide. It has become an important public health problem. This study aimed to determine the global burden of depression and how it has changed between 1990 and 2017. METHODS: We used information on depression obtained by the Global Burden of Disease (GBD) study from 1990 to 2017. The age-standardized incidence rate (ASR) and estimated annual percentage change (EAPC) were used to assess the global burden of depression. RESULTS: The number of incident cases of depression worldwide increased from 172 million in 1990 to 25,8 million in 2017, representing an increase of 49.86%. The ASR of depression varied widely between the 195 analyzed countries and regions in 2017, being highest in Lesotho (6.59 per 1000) and lowest in Myanmar (1.28 per 1000). The ASR increased the most between 1990 and 2017 in Belgium (EAPC = 0.88, 95% confidence interval [CI] = 0.78 to 0.97), and decreased the most in Cuba (EAPC = -1.26, 95% CI = -1.36 to -1.14). The ASR increased in regions with a high sociodemographic index, such as high-income North America (EAPC = 0.41, 95% CI = 0.31 to 0.51), and decreased significantly in South Asia (EAPC = -0.63, 95% CI = -0.85 to -0.41). The proportions of the population with major depressive disorder and dysthymia were essentially stable both globally and in various countries, with a much larger proportion having major depressive disorder. CONCLUSION: Depression remains a major public health issue, and governments should support the research necessary to develop better prevention and treatment interventions.

Abstract Developing injectable nanocomposite conductive hydrogel dressings with multifunctions including adhesiveness, antibacterial, and radical scavenging ability and good mechanical property to enhance full‐thickness skin wound regeneration is highly desirable in clinical application. Herein, a series of adhesive hemostatic antioxidant conductive photothermal antibacterial hydrogels based on hyaluronic acid‐graft‐dopamine and reduced graphene oxide (rGO) using a H 2 O 2 /HPR (horseradish peroxidase) system are prepared for wound dressing. These hydrogels exhibit high swelling, degradability, tunable rheological property, and similar or superior mechanical properties to human skin. The polydopamine endowed antioxidant activity, tissue adhesiveness and hemostatic ability, self‐healing ability, conductivity, and NIR irradiation enhanced in vivo antibacterial behavior of the hydrogels are investigated. Moreover, drug release and zone of inhibition tests confirm sustained drug release capacity of the hydrogels. Furthermore, the hydrogel dressings significantly enhance vascularization by upregulating growth factor expression of CD31 and improve the granulation tissue thickness and collagen deposition, all of which promote wound closure and contribute to a better therapeutic effect than the commercial Tegaderm films group in a mouse full‐thickness wounds model. In summary, these adhesive hemostatic antioxidative conductive hydrogels with sustained drug release property to promote complete skin regeneration are an excellent wound dressing for full‐thickness skin repair.

Abstract Developing injectable antibacterial and conductive shape memory hemostatic with high blood absorption and fast recovery for irregularly shaped and noncompressible hemorrhage remains a challenge. Here we report injectable antibacterial conductive cryogels based on carbon nanotube (CNT) and glycidyl methacrylate functionalized quaternized chitosan for lethal noncompressible hemorrhage hemostasis and wound healing. These cryogels present robust mechanical strength, rapid blood-triggered shape recovery and absorption speed, and high blood uptake capacity. Moreover, cryogels show better blood-clotting ability, higher blood cell and platelet adhesion and activation than gelatin sponge and gauze. Cryogel with 4 mg/mL CNT (QCSG/CNT4) shows better hemostatic capability than gauze and gelatin hemostatic sponge in mouse-liver injury model and mouse-tail amputation model, and better wound healing performance than Tegaderm™ film. Importantly, QCSG/CNT4 presents excellent hemostatic performance in rabbit liver defect lethal noncompressible hemorrhage model and even better hemostatic ability than Combat Gauze in standardized circular liver bleeding model.

OBJECTIVE: The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. METHODS: We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. RESULTS: The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. CONCLUSION: These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. KEY POINTS: • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.

The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (<50 bp) and 27,622 SVs (≥50 bp) per genome. We also discover 156 inversions per genome and 58 of the inversions intersect with the critical regions of recurrent microdeletion and microduplication syndromes. Taken together, our SV callsets represent a three to sevenfold increase in SV detection compared to most standard high-throughput sequencing studies, including those from the 1000 Genomes Project. The methods and the dataset presented serve as a gold standard for the scientific community allowing us to make recommendations for maximizing structural variation sensitivity for future genome sequencing studies.

Abstract Background The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 2.5 million cases of Corona Virus Disease (COVID-19) in the world so far, with that number continuing to grow. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment is a priority. Pathogenic laboratory testing is the gold standard but is time-consuming with significant false negative results. Therefore, alternative diagnostic methods are urgently needed to combat the disease. Based on COVID-19 radiographical changes in CT images, we hypothesized that Artificial Intelligence’s deep learning methods might be able to extract COVID-19’s specific graphical features and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. Methods and Findings We collected 1,065 CT images of pathogen-confirmed COVID-19 cases (325 images) along with those previously diagnosed with typical viral pneumonia (740 images). We modified the Inception transfer-learning model to establish the algorithm, followed by internal and external validation. The internal validation achieved a total accuracy of 89.5% with specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images that first two nucleic acid test results were negative, 46 were predicted as COVID-19 positive by the algorithm, with the accuracy of 85.2%. Conclusion These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. Author summary To control the spread of the COVID-19, screening large numbers of suspected cases for appropriate quarantine and treatment measures is a priority. Pathogenic laboratory testing is the gold standard but is time-consuming with significant false negative results. Therefore, alternative diagnostic methods are urgently needed to combat the disease. We hypothesized that Artificial Intelligence’s deep learning methods might be able to extract COVID-19’s specific graphical features and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time. We collected 1,065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the Inception transfer-learning model to establish the algorithm. The internal validation achieved a total accuracy of 89.5% with specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images that first two nucleic acid test results were negative, 46 were predicted as COVID-19 positive by the algorithm, with the accuracy of 85.2%. Our study represents the first study to apply artificial intelligence to CT images for effectively screening for COVID-19.

Importance: Recent data on prevalence, awareness, treatment, and risk factors of diabetes in China is necessary for interventional efforts. Objective: To estimate trends in prevalence, awareness, treatment, and risk factors of diabetes in China based on national data. Design, Setting, and Participants: Cross-sectional nationally representative survey data collected in adults aged 18 years or older in mainland China from 170 287 participants in the 2013-2014 years and 173 642 participants in the 2018-2019 years. Exposures: Fasting plasma glucose and hemoglobin A1c levels were measured for all participants. A 2-hour oral glucose tolerance test was conducted for all participants without diagnosed diabetes. Main Outcomes and Measures: Primary outcomes were diabetes and prediabetes defined according to American Diabetes Association criteria. Secondary outcomes were awareness, treatment, and control of diabetes and prevalence of risk factors. A hemoglobin A1c level of less than 7.0% (53 mmol/mol) among treated patients with diabetes was considered adequate glycemic control. Results: In 2013, the median age was 55.8 years (IQR, 46.4-65.2 years) and the weighted proportion of women was 50.0%; in 2018, the median age was 51.3 years (IQR, 42.1-61.6 years), and the weighted proportion of women was 49.5%. The estimated prevalence of diabetes increased from 10.9% (95% CI, 10.4%-11.5%) in 2013 to 12.4% (95% CI, 11.8%-13.0%) in 2018 (P < .001). The estimated prevalence of prediabetes was 35.7% (95% CI, 34.2%-37.3%) in 2013 and 38.1% (95% CI, 36.4%-39.7%) in 2018 (P = .07). In 2018, among adults with diabetes, 36.7% (95% CI, 34.7%-38.6%) reported being aware of their condition, and 32.9% (95% CI, 30.9%-34.8%) reported being treated; 50.1% (95% CI, 47.5%-52.6%) of patients receiving treatment were controlled adequately. These rates did not change significantly from 2013. From 2013 to 2018, low physical activity, high intake of red meat, overweight, and obesity significantly increased in prevalence. Conclusions and Relevance: In this survey study, the estimated diabetes prevalence was high and increased from 2013 to 2018. There was no significant improvement in the estimated prevalence of adequate treatment.

Major depressive disorder (MDD) is common and disabling, but its neuropathophysiology remains unclear. Most studies of functional brain networks in MDD have had limited statistical power and data analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues. Twenty-five research groups in China established the REST-meta-MDD Consortium by contributing R-fMRI data from 1,300 patients with MDD and 1,128 normal controls (NCs). Data were preprocessed locally with a standardized protocol before aggregated group analyses. We focused on functional connectivity (FC) within the default mode network (DMN), frequently reported to be increased in MDD. Instead, we found decreased DMN FC when we compared 848 patients with MDD to 794 NCs from 17 sites after data exclusion. We found FC reduction only in recurrent MDD, not in first-episode drug-naïve MDD. Decreased DMN FC was associated with medication usage but not with MDD duration. DMN FC was also positively related to symptom severity but only in recurrent MDD. Exploratory analyses also revealed alterations in FC of visual, sensory-motor, and dorsal attention networks in MDD. We confirmed the key role of DMN in MDD but found reduced rather than increased FC within the DMN. Future studies should test whether decreased DMN FC mediates response to treatment. All R-fMRI indices of data contributed by the REST-meta-MDD consortium are being shared publicly via the R-fMRI Maps Project.

Long-read and strand-specific sequencing technologies together facilitate the de novo assembly of high-quality haplotype-resolved human genomes without parent-child trio data. We present 64 assembled haplotypes from 32 diverse human genomes. These highly contiguous haplotype assemblies (average minimum contig length needed to cover 50% of the genome: 26 million base pairs) integrate all forms of genetic variation, even across complex loci. We identified 107,590 structural variants (SVs), of which 68% were not discovered with short-read sequencing, and 278 SV hotspots (spanning megabases of gene-rich sequence). We characterized 130 of the most active mobile element source elements and found that 63% of all SVs arise through homology-mediated mechanisms. This resource enables reliable graph-based genotyping from short reads of up to 50,340 SVs, resulting in the identification of 1526 expression quantitative trait loci as well as SV candidates for adaptive selection within the human population.

Chromothripsis is a mutational phenomenon characterized by massive, clustered genomic rearrangements that occurs in cancer and other diseases. Recent studies in selected cancer types have suggested that chromothripsis may be more common than initially inferred from low-resolution copy-number data. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we analyze patterns of chromothripsis across 2,658 tumors from 38 cancer types using whole-genome sequencing data. We find that chromothripsis events are pervasive across cancers, with a frequency of more than 50% in several cancer types. Whereas canonical chromothripsis profiles display oscillations between two copy-number states, a considerable fraction of events involve multiple chromosomes and additional structural alterations. In addition to non-homologous end joining, we detect signatures of replication-associated processes and templated insertions. Chromothripsis contributes to oncogene amplification and to inactivation of genes such as mismatch-repair-related genes. These findings show that chromothripsis is a major process that drives genome evolution in human cancer.

Abstract Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. A comprehensive thematic survey on medical image segmentation using deep learning techniques is presented. This paper makes two original contributions. Firstly, compared to traditional surveys that directly divide literatures of deep learning on medical image segmentation into many groups and introduce literatures in detail for each group, we classify currently popular literatures according to a multi‐level structure from coarse to fine. Secondly, this paper focuses on supervised and weakly supervised learning approaches, without including unsupervised approaches since they have been introduced in many old surveys and they are not popular currently. For supervised learning approaches, we analyse literatures in three aspects: the selection of backbone networks, the design of network blocks, and the improvement of loss functions. For weakly supervised learning approaches, we investigate literature according to data augmentation, transfer learning, and interactive segmentation, separately. Compared to existing surveys, this survey classifies the literatures very differently from before and is more convenient for readers to understand the relevant rationale and will guide them to think of appropriate improvements in medical image segmentation based on deep learning approaches.

INTRODUCTION: The socioeconomic costs of Alzheimer's disease (AD) in China and its impact on global economic burden remain uncertain. METHODS: We collected data from 3098 patients with AD in 81 representative centers across China and estimated AD costs for individual patient and total patients in China in 2015. Based on this data, we re-estimated the worldwide costs of AD. RESULTS: The annual socioeconomic cost per patient was US $19,144.36, and total costs were US $167.74 billion in 2015. The annual total costs are predicted to reach US $507.49 billion in 2030 and US $1.89 trillion in 2050. Based on our results, the global estimates of costs for dementia were US $957.56 billion in 2015, and will be US $2.54 trillion in 2030, and US $9.12 trillion in 2050, much more than the predictions by the World Alzheimer Report 2015. DISCUSSION: China bears a heavy burden of AD costs, which greatly change the estimates of AD cost worldwide.

BACKGROUND: The appropriate target for systolic blood pressure to reduce cardiovascular risk in older patients with hypertension remains unclear. METHODS: In this multicenter, randomized, controlled trial, we assigned Chinese patients 60 to 80 years of age with hypertension to a systolic blood-pressure target of 110 to less than 130 mm Hg (intensive treatment) or a target of 130 to less than 150 mm Hg (standard treatment). The primary outcome was a composite of stroke, acute coronary syndrome (acute myocardial infarction and hospitalization for unstable angina), acute decompensated heart failure, coronary revascularization, atrial fibrillation, or death from cardiovascular causes. RESULTS: Of the 9624 patients screened for eligibility, 8511 were enrolled in the trial; 4243 were randomly assigned to the intensive-treatment group and 4268 to the standard-treatment group. At 1 year of follow-up, the mean systolic blood pressure was 127.5 mm Hg in the intensive-treatment group and 135.3 mm Hg in the standard-treatment group. During a median follow-up period of 3.34 years, primary-outcome events occurred in 147 patients (3.5%) in the intensive-treatment group, as compared with 196 patients (4.6%) in the standard-treatment group (hazard ratio, 0.74; 95% confidence interval [CI], 0.60 to 0.92; P = 0.007). The results for most of the individual components of the primary outcome also favored intensive treatment: the hazard ratio for stroke was 0.67 (95% CI, 0.47 to 0.97), acute coronary syndrome 0.67 (95% CI, 0.47 to 0.94), acute decompensated heart failure 0.27 (95% CI, 0.08 to 0.98), coronary revascularization 0.69 (95% CI, 0.40 to 1.18), atrial fibrillation 0.96 (95% CI, 0.55 to 1.68), and death from cardiovascular causes 0.72 (95% CI, 0.39 to 1.32). The results for safety and renal outcomes did not differ significantly between the two groups, except for the incidence of hypotension, which was higher in the intensive-treatment group. CONCLUSIONS: In older patients with hypertension, intensive treatment with a systolic blood-pressure target of 110 to less than 130 mm Hg resulted in a lower incidence of cardiovascular events than standard treatment with a target of 130 to less than 150 mm Hg. (Funded by the Chinese Academy of Medical Sciences and others; STEP ClinicalTrials.gov number, NCT03015311.).

Tumor heterogeneity of high-grade glioma (HGG) is recognized by four clinically relevant subtypes based on core gene signatures. However, molecular signaling in glioma stem cells (GSCs) in individual HGG subtypes is poorly characterized. Here we identified and characterized two mutually exclusive GSC subtypes with distinct dysregulated signaling pathways. Analysis of mRNA profiles distinguished proneural (PN) from mesenchymal (Mes) GSCs and revealed a pronounced correlation with the corresponding PN or Mes HGGs. Mes GSCs displayed more aggressive phenotypes in vitro and as intracranial xenografts in mice. Further, Mes GSCs were markedly resistant to radiation compared with PN GSCs. The glycolytic pathway, comprising aldehyde dehydrogenase (ALDH) family genes and in particular ALDH1A3, were enriched in Mes GSCs. Glycolytic activity and ALDH activity were significantly elevated in Mes GSCs but not in PN GSCs. Expression of ALDH1A3 was also increased in clinical HGG compared with low-grade glioma or normal brain tissue. Moreover, inhibition of ALDH1A3 attenuated the growth of Mes but not PN GSCs. Last, radiation treatment of PN GSCs up-regulated Mes-associated markers and down-regulated PN-associated markers, whereas inhibition of ALDH1A3 attenuated an irradiation-induced gain of Mes identity in PN GSCs. Taken together, our data suggest that two subtypes of GSCs, harboring distinct metabolic signaling pathways, represent intertumoral glioma heterogeneity and highlight previously unidentified roles of ALDH1A3-associated signaling that promotes aberrant proliferation of Mes HGGs and GSCs. Inhibition of ALDH1A3-mediated pathways therefore might provide a promising therapeutic approach for a subset of HGGs with the Mes signature.