First Affiliated Hospital of Xi'an Jiaotong University

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.

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.

OBJECTIVE: To determine the relation between overweight and obesity in mothers and preterm birth and low birth weight in singleton pregnancies in developed and developing countries. DESIGN: Systematic review and meta-analyses. DATA SOURCES: Medline and Embase from their inceptions, and reference lists of identified articles. STUDY SELECTION: Studies including a reference group of women with normal body mass index that assessed the effect of overweight and obesity on two primary outcomes: preterm birth (before 37 weeks) and low birth weight (<2500 g). DATA EXTRACTION: Two assessors independently reviewed titles, abstracts, and full articles, extracted data using a piloted data collection form, and assessed quality. DATA SYNTHESIS: 84 studies (64 cohort and 20 case-control) were included, totalling 1 095 834 women. Although the overall risk of preterm birth was similar in overweight and obese women and women of normal weight, the risk of induced preterm birth was increased in overweight and obese women (relative risk 1.30, 95% confidence interval 1.23 to 1.37). Although overall the risk of having an infant of low birth weight was decreased in overweight and obese women (0.84, 0.75 to 0.95), the decrease was greater in developing countries than in developed countries (0.58, 0.47 to 0.71 v 0.90, 0.79 to 1.01). After accounting for publication bias, the apparent protective effect of overweight and obesity on low birth weight disappeared with the addition of imputed "missing" studies (0.95, 0.85 to 1.07), whereas the risk of preterm birth appeared significantly higher in overweight and obese women (1.24, 1.13 to 1.37). CONCLUSIONS: Overweight and obese women have increased risks of preterm birth and induced preterm birth and, after accounting for publication bias, appeared to have increased risks of preterm birth overall. The beneficial effects of maternal overweight and obesity on low birth weight were greater in developing countries and disappeared after accounting for publication bias.

Importance: Anlotinib is a novel multitarget tyrosine kinase inhibitor for tumor angiogenesis and proliferative signaling. A phase 2 trial showed anlotinib to improve progression-free survival with a potential benefit of overall survival, leading to the phase 3 trial to confirm the drug's efficacy in advanced non-small cell lung cancer (NSCLC). Objective: To investigate the efficacy of anlotinib on overall survival of patients with advanced NSCLC progressing after second-line or further treatment. Design, Setting, and Participants: The ALTER 0303 trial was a multicenter, double-blind, phase 3 randomized clinical trial designed to evaluate the efficacy and safety of anlotinib in patients with advanced NSCLC. Patients from 31 grade-A tertiary hospitals in China were enrolled between March 1, 2015, and August 31, 2016. Those aged 18 to 75 years who had histologically or cytologically confirmed NSCLC were eligible (n = 606), and those who had centrally located squamous cell carcinoma with cavitary features or brain metastases that were uncontrolled or controlled for less than 2 months were excluded. Patients (n = 440) were randomly assigned in a 2-to-1 ratio to receive either 12 mg/d of anlotinib or a matched placebo. All cases were treated with study drugs at least once in accordance with the intention-to-treat principle. Main Outcomes and Measures: The primary end point was overall survival. The secondary end points were progression-free survival, objective response rate, disease control rate, quality of life, and safety. Results: In total, 439 patients were randomized, 296 to the anlotinib group (106 [36.1%] were female and 188 [64.0%] were male, with a mean [SD] age of 57.9 [9.1] years) and 143 to the placebo group (46 [32.2%] were female and 97 [67.8%] were male, with a mean [SD] age of 56.8 [9.1] years). Overall survival was significantly longer in the anlotinib group (median, 9.6 months; 95% CI, 8.2-10.6) than the placebo group (median, 6.3 months; 95% CI, 5.0-8.1), with a hazard ratio (HR) of 0.68 (95% CI, 0.54-0.87; P = .002). A substantial increase in progression-free survival was noted in the anlotinib group compared with the placebo group (median, 5.4 months [95% CI, 4.4-5.6] vs 1.4 months [95% CI, 1.1-1.5]; HR, 0.25 [95% CI, 0.19-0.31]; P < .001). Considerable improvement in objective response rate and disease control rate was observed in the anlotinib group over the placebo group. The most common grade 3 or higher adverse events in the anlotinib arm were hypertension and hyponatremia. Conclusions and Relevance: Among the Chinese patients in this trial, anlotinib appears to lead to prolonged overall survival and progression-free survival. This finding suggests that anlotinib is well tolerated and is a potential third-line or further therapy for patients with advanced NSCLC. Trial Registration: ClinicalTrials.gov identifier: NCT02388919.

BACKGROUND: A novel coronavirus (CoV), severe acute respiratory syndrome (SARS)-CoV-2, has infected >75 000 individuals and spread to >20 countries. It is still unclear how fast the virus evolved and how it interacts with other microorganisms in the lung. METHODS: We have conducted metatranscriptome sequencing for bronchoalveolar lavage fluid samples from 8 patients with SARS-CoV-2, and also analyzed data from 25 patients with community-acquired pneumonia (CAP), and 20 healthy controls for comparison. RESULTS: The median number of intrahost variants was 1-4 in SARS-CoV-2-infected patients, ranged from 0 to 51 in different samples. The distribution of variants on genes was similar to those observed in the population data. However, very few intrahost variants were observed in the population as polymorphisms, implying either a bottleneck or purifying selection involved in the transmission of the virus, or a consequence of the limited diversity represented in the current polymorphism data. Although current evidence did not support the transmission of intrahost variants in a possible person-to-person spread, the risk should not be overlooked. Microbiotas in SARS-CoV-2-infected patients were similar to those in CAP, either dominated by the pathogens or with elevated levels of oral and upper respiratory commensal bacteria. CONCLUSION: SARS-CoV-2 evolves in vivo after infection, which may affect its virulence, infectivity, and transmissibility. Although how the intrahost variant spreads in the population is still elusive, it is necessary to strengthen the surveillance of the viral evolution in the population and associated clinical changes.

PURPOSE: We assessed the efficacy and safety of camrelizumab [an anti-programmed death (PD-1) mAb] plus apatinib (a VEGFR-2 tyrosine kinase inhibitor) in patients with advanced hepatocellular carcinoma (HCC). PATIENTS AND METHODS: This nonrandomized, open-label, multicenter, phase II study enrolled patients with advanced HCC who were treatment-naïve or refractory/intolerant to first-line targeted therapy. Patients received intravenous camrelizumab 200 mg (for bodyweight ≥50 kg) or 3 mg/kg (for bodyweight <50 kg) every 2 weeks plus oral apatinib 250 mg daily. The primary endpoint was objective response rate (ORR) assessed by an independent review committee (IRC) per RECIST v1.1. RESULTS: Seventy patients in the first-line setting and 120 patients in the second-line setting were enrolled. As of January 10, 2020, the ORR was 34.3% [24/70; 95% confidence interval (CI), 23.3-46.6] in the first-line and 22.5% (27/120; 95% CI, 15.4-31.0) in the second-line cohort per IRC. Median progression-free survival in both cohorts was 5.7 months (95% CI, 5.4-7.4) and 5.5 months (95% CI, 3.7-5.6), respectively. The 12-month survival rate was 74.7% (95% CI, 62.5-83.5) and 68.2% (95% CI, 59.0-75.7), respectively. Grade ≥3 treatment-related adverse events (TRAE) were reported in 147 (77.4%) of 190 patients, with the most common being hypertension (34.2%). Serious TRAEs occurred in 55 (28.9%) patients. Two (1.1%) treatment-related deaths occurred. CONCLUSIONS: .