A multi-ancestry cerebral cortex transcriptome-wide association study identifies genes associated with smoking behaviors.

Transcriptome-wide association studies (TWAS) have provided valuable insight in identifying genes that may impact cigarette smoking. Most of previous studies, however, mainly focused on European ancestry. Limited TWAS studies have been conducted across multiple ancestries to explore genes that may impact smoking behaviors. In this study, we used cis-eQTL data of cerebral cortex from multiple ancestries in MetaBrain, including European, East Asian, and African samples, as reference panels to perform multi-ancestry TWAS analyses on ancestry-matched GWASs of four smoking behaviors including smoking initiation, smoking cessation, age of smoking initiation, and number of cigarettes per day in GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN). Multiple-ancestry fine-mapping approach was conducted to identify credible gene sets associated with these four traits. Enrichment and module network analyses were further performed to explore the potential roles of these identified gene sets. A total of 719 unique genes were identified to be associated with at least one of the four smoking traits across ancestries. Among those, 249 genes were further prioritized as putative causal genes in multiple ancestry-based fine-mapping approach. Several well-known smoking-related genes, including PSMA4, IREB2, and CHRNA3, showed high confidence across ancestries. Some novel genes, e.g., TSPAN3 and ANK2, were also identified in the credible sets. The enrichment analysis identified a series of critical pathways related to smoking such as synaptic transmission and glutamate receptor activity. Leveraging the power of the latest multi-ancestry GWAS and eQTL data sources, this study revealed hundreds of genes and relevant biological processes related to smoking behaviors. These findings provide new insights for future functional studies on smoking behaviors.

Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a frequently diagnosed yet treatable condition, provided it is identified early and managed effectively. This study aims to develop an advanced COPD diagnostic model by integrating deep learning and radiomics features. METHODS: We utilized a dataset comprising CT images from 2,983 participants, of which 2,317 participants also provided epidemiological data through questionnaires. Deep learning features were extracted using a Variational Autoencoder, and radiomics features were obtained using the PyRadiomics package. Multi-Layer Perceptrons were used to construct models based on deep learning and radiomics features independently, as well as a fusion model integrating both. Subsequently, epidemiological questionnaire data were incorporated to establish a more comprehensive model. The diagnostic performance of standalone models, the fusion model and the comprehensive model was evaluated and compared using metrics including accuracy, precision, recall, F1-score, Brier score, receiver operating characteristic curves, and area under the curve (AUC). RESULTS: The fusion model exhibited outstanding performance with an AUC of 0.952, surpassing the standalone models based solely on deep learning features (AUC = 0.844) or radiomics features (AUC = 0.944). Notably, the comprehensive model, incorporating deep learning features, radiomics features, and questionnaire variables demonstrated the highest diagnostic performance among all models, yielding an AUC of 0.971. CONCLUSION: We developed and implemented a data fusion strategy to construct a state-of-the-art COPD diagnostic model integrating deep learning features, radiomics features, and questionnaire variables. Our data fusion strategy proved effective, and the model can be easily deployed in clinical settings. TRIAL REGISTRATION: Not applicable. This study is NOT a clinical trial, it does not report the results of a health care intervention on human participants.

Predicting oral and esophageal cancers by one model in a Chinese prospective cohort study.

OBJECTIVE: Oral and esophageal cancers are both upper gastrointestinal cancers that share a number of risk factors. However, most previous risk prediction models only focused on one of these two types of cancer. There is no single model that could predict both cancers simultaneously. Our objective was to develop a model specifically tailored for oral and esophageal cancers. METHODS: From 1996 to 2007, a total of 431,460 subjects aged 20 and older without a history of cancer at baseline were included and were monitored for an average duration of 7.3 years in Taiwan, China. A total of 704 cases of oral and esophageal cancers were detected. We utilized both univariate and multivariate COX regression for screening predictors and constructing the model. We evaluated the goodness of fit of the model based on discriminatory accuracy, Harrell's C-index, and calibration. RESULTS: Finally, we developed a Cox regression model using the twelve most significant variables: age, gender, alcohol consumption, betel chewing, smoking status, history of oral ulceration, educational level, marital status, oropharynx status, family history of nasopharyngeal carcinoma, volume ratio of blood cell, and gamma-glutamyl transferase. The AUC (area under the curve) for the complete model was 0.82. Additionally, the C-index was 0.807 (with a 95 % confidence interval ranging from 0.789 to 0.824) and internal calibration results demonstrated that the model performed well. CONCLUSIONS: This study identified the twelve most significant common risk factors for oral and esophageal cancers and developed a single prediction model that performs well for both types of cancer.

Validity, feasibility, and effectiveness of a voice-recognition based digital cognitive screener for dementia and mild cognitive impairment in community-dwelling older Chinese adults: A large-scale implementation study.

INTRODUCTION: We investigated the validity, feasibility, and effectiveness of a voice recognition-based digital cognitive screener (DCS), for detecting dementia and mild cognitive impairment (MCI) in a large-scale community of elderly participants. METHODS: Eligible participants completed demographic, cognitive, functional assessments and the DCS. Neuropsychological tests were used to assess domain-specific and global cognition, while the diagnosis of MCI and dementia relied on the Clinical Dementia Rating Scale. RESULTS: Among the 11,186 participants, the DCS showed high completion rates (97.5%) and a short administration time (5.9 min) across gender, age, and education groups. The DCS demonstrated areas under the receiver operating characteristics curve (AUCs) of 0.95 and 0.83 for dementia and MCI detection, respectively, among 328 participants in the validation phase. Furthermore, the DCS resulted in time savings of 16.2% to 36.0% compared to the Mini-Mental State Examination (MMSE) and Montral Cognitive Assessment (MoCA). DISCUSSION: This study suggests that the DCS is an effective and efficient tool for dementia and MCI case-finding in large-scale cognitive screening. HIGHLIGHTS: To our best knowledge, this is the first cognitive screening tool based on voice recognition and utilizing conversational AI that has been assessed in a large population of Chinese community-dwelling elderly. With the upgrading of a new multimodal understanding model, the DCS can accurately assess participants' responses, including different Chinese dialects, and provide automatic scores. The DCS not only exhibited good discriminant ability in detecting dementia and MCI cases, it also demonstrated a high completion rate and efficient administration regardless of gender, age, and education differences. The DCS is economically efficient, scalable, and had a better screening efficacy compared to the MMSE or MoCA, for wider implementation.

Association between plant-based dietary pattern and biological aging trajectory in a large prospective cohort.

BACKGROUND: Aging is a dynamic and heterogeneous process that may better be captured by trajectories of aging biomarkers. Biological age has been advocated as a better biomarker of aging than chronological age, and plant-based dietary patterns have been found to be linked to aging. However, the associations of biological age trajectories with mortality and plant-based dietary patterns remained unclear. METHODS: Using group-based trajectory modeling approach, we identified distinctive aging trajectory groups among 12,784 participants based on a recently developed biological aging measure acquired at four-time points within an 8-year period. We then examined associations between aging trajectories and quintiles of plant-based dietary patterns assessed by overall plant-based diet index (PDI), healthful PDI (hPDI), and unhealthful PDI (uPDI) among 10,191 participants who had complete data on dietary intake, using multivariable multinomial logistics regression adjusting for sociodemographic and lifestyles factors. Cox proportional hazards regression models were applied to investigate the association between aging trajectories and all-cause mortality. RESULTS: We identified three latent classes of accelerated aging trajectories: slow aging, medium-degree, and high-degree accelerated aging trajectories. Participants who had higher PDI or hPDI had lower odds of being in medium-degree (OR = 0.75, 95% CI: 0.65, 0.86 for PDI; OR = 0.73, 95% CI: 0.62, 0.85 for hPDI) or high-degree (OR = 0.63, 95% CI: 0.46, 0.86 for PDI; OR = 0.62, 95% CI: 0.44, 0.88 for hPDI) accelerated aging trajectories. Participants in the highest quintile of uPDI were more likely to be in medium-degree (OR = 1.72, 95% CI: 1.48, 1.99) or high-degree (OR = 1.70, 95% CI: 1.21, 2.38) accelerated aging trajectories. With a mean follow-up time of 8.40 years and 803 (6.28%) participants died by the end of follow-up, we found that participants in medium-degree (HR = 1.56, 95% CI: 1.29, 1.89) or high-degree (HR = 3.72, 95% CI: 2.73, 5.08) accelerated aging trajectory groups had higher risks of death than those in the slow aging trajectory. CONCLUSIONS: We identified three distinctive aging trajectories in a large Asian cohort and found that adopting a plant-based dietary pattern, especially when rich in healthful plant foods, was associated with substantially lowered pace of aging.

Association of frailty with the incidence risk of cardiovascular disease and type 2 diabetes mellitus in long-term cancer survivors: a prospective cohort study.

BACKGROUND: Comorbidities among cancer survivors remain a serious healthcare burden and require appropriate management. Using two widely used frailty indicators, this study aimed to evaluate whether frailty was associated with the incidence risk of cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM) among long-term cancer survivors. METHODS: We included 13,388 long-term cancer survivors (diagnosed with cancer over 5 years before enrolment) free of CVD and 6101 long-term cancer survivors free of T2DM, at the time of recruitment (aged 40-69 years), from the UK Biobank. Frailty was assessed by the frailty phenotype (FP_Frailty, range: 0-5) and the frailty index (FI_Frailty, range: 0-1) at baseline. The incident CVD and T2DM were ascertained through linked hospital data and primary care data, respectively. The associations were examined using Cox proportional hazards regression models. RESULTS: Compared with non-frail participants, those with pre-frailty (FP_Frailty [met 1-2 of the components]: hazard ratio [HR]=1.18, 95% confidence interval [CI]: 1.05, 1.32; FI_Frailty [0.10< FI ≤0.21]: HR=1.51, 95% CI: 1.32, 1.74) and frailty (FP_Frailty [met ≥3 of the components]: HR=2.12, 95% CI: 1.73, 2.60; FI_Frailty [FI >0.21]: HR=2.19, 95% CI: 1.85, 2.59) had a significantly higher risk of CVD in the multivariable-adjusted model. A similar association of FI_Frailty with the risk of incident T2DM was observed. We failed to find such an association for FP_Frailty. Notably, the very early stage of frailty (1 for FP_Frailty and 0.1-0.2 for FI_Frailty) was also positively associated with the risk of CVD and T2DM (FI_Frailty only). A series of sensitivity analyses confirmed the robustness of the findings. CONCLUSIONS: Frailty, even in the very early stage, was positively associated with the incidence risk of CVD and T2DM among long-term cancer survivors, although discrepancies existed between frailty indicators. While the validation of these findings is required, they suggest that routine monitoring, prevention, and interventive programs of frailty among cancer survivors may help to prevent late comorbidities and, eventually, improve their quality of life. Especially, interventions are recommended to target those at an early stage of frailty when healthcare resources are limited.

Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer.

Clinical trial results have recently demonstrated that inhibiting inflammation by targeting the interleukin-1ß pathway can offer a significant reduction in lung cancer incidence and mortality, highlighting a pressing and unmet need to understand the benefits of inflammation-focused lung cancer therapies at the genetic level. While numerous genome-wide association studies (GWAS) have explored the genetic etiology of lung cancer, there remains a large gap between the type of information that may be gleaned from an association study and the depth of understanding necessary to explain and drive translational findings. Thus, in this study we jointly model and integrate extensive multiomics data sources, utilizing a total of 40 genome-wide functional annotations that augment previously published results from the International Lung Cancer Consortium (ILCCO) GWAS, to prioritize and characterize single nucleotide polymorphisms (SNPs) that increase risk of squamous cell lung cancer through the inflammatory and immune responses. Our work bridges the gap between correlative analysis and translational follow-up research, refining GWAS association measures in an interpretable and systematic manner. In particular, reanalysis of the ILCCO data highlights the impact of highly associated SNPs from nuclear factor-κB signaling pathway genes as well as major histocompatibility complex mediated variation in immune responses. One consequence of prioritizing likely functional SNPs is the pruning of variants that might be selected for follow-up work by over an order of magnitude, from potentially tens of thousands to hundreds. The strategies we introduce provide informative and interpretable approaches for incorporating extensive genome-wide annotation data in analysis of genetic association studies.

Inherited variants at 3q13.33 and 3p24.1 are associated with risk of diffuse large B-cell lymphoma and implicate immune pathways.

We previously identified five single nucleotide polymorphisms (SNPs) at four susceptibility loci for diffuse large B-cell lymphoma (DLBCL) in individuals of European ancestry through a large genome-wide association study (GWAS). To further elucidate genetic susceptibility to DLBCL, we sought to validate two loci at 3q13.33 and 3p24.1 that were suggestive in the original GWAS with additional genotyping. In the meta-analysis (5662 cases and 9237 controls) of the four original GWAS discovery scans and three replication studies, the 3q13.33 locus (rs9831894; minor allele frequency [MAF] = 0.40) was associated with DLBCL risk [odds ratio (OR) = 0.83, P = 3.62 × 10-13]. rs9831894 is in linkage disequilibrium (LD) with additional variants that are part of a super-enhancer that physically interacts with promoters of CD86 and ILDR1. In the meta-analysis (5510 cases and 12 817 controls) of the four GWAS discovery scans and four replication studies, the 3p24.1 locus (rs6773363; MAF = 0.45) was also associated with DLBCL risk (OR = 1.20, P = 2.31 × 10-12). This SNP is 29 426-bp upstream of the nearest gene EOMES and in LD with additional SNPs that are part of a highly lineage-specific and tumor-acquired super-enhancer that shows long-range interaction with AZI2 promoter. These loci provide additional evidence for the role of immune function in the etiology of DLBCL, the most common lymphoma subtype.

Population-based targeted sequencing of 54 candidate genes identifies PALB2 as a susceptibility gene for high-grade serous ovarian cancer.

PURPOSE: The known epithelial ovarian cancer (EOC) susceptibility genes account for less than 50% of the heritable risk of ovarian cancer suggesting that other susceptibility genes exist. The aim of this study was to evaluate the contribution to ovarian cancer susceptibility of rare deleterious germline variants in a set of candidate genes. METHODS: We sequenced the coding region of 54 candidate genes in 6385 invasive EOC cases and 6115 controls of broad European ancestry. Genes with an increased frequency of putative deleterious variants in cases versus controls were further examined in an independent set of 14 135 EOC cases and 28 655 controls from the Ovarian Cancer Association Consortium and the UK Biobank. For each gene, we estimated the EOC risks and evaluated associations between germline variant status and clinical characteristics. RESULTS: The ORs associated for high-grade serous ovarian cancer were 3.01 for PALB2 (95% CI 1.59 to 5.68; p=0.00068), 1.99 for POLK (95% CI 1.15 to 3.43; p=0.014) and 4.07 for SLX4 (95% CI 1.34 to 12.4; p=0.013). Deleterious mutations in FBXO10 were associated with a reduced risk of disease (OR 0.27, 95% CI 0.07 to 1.00, p=0.049). However, based on the Bayes false discovery probability, only the association for PALB2 in high-grade serous ovarian cancer is likely to represent a true positive. CONCLUSIONS: We have found strong evidence that carriers of PALB2 deleterious mutations are at increased risk of high-grade serous ovarian cancer. Whether the magnitude of risk is sufficiently high to warrant the inclusion of PALB2 in cancer gene panels for ovarian cancer risk testing is unclear; much larger sample sizes will be needed to provide sufficiently precise estimates for clinical counselling.

Different Transmission Dynamics of Coronavirus Disease 2019 (COVID-19) and Influenza Suggest the Relative Efficiency of Isolation/Quarantine and Social Distancing Against COVID-19 in China.

BACKGROUND: Nonpharmaceutical interventions (NPIs) against coronavirus disease 2019 (COVID-19) are vital to reducing transmission risks. However, the relative efficiency of social distancing against COVID-19 remains controversial, since social distancing and isolation/quarantine were implemented almost at the same time in China. METHODS: In this study, surveillance data of COVID-19 and seasonal influenza in 2018-2020 were used to quantify the relative efficiency of NPIs against COVID-19 in China, since isolation/quarantine was not used for the influenza epidemics. Given that the relative age-dependent susceptibility to influenza and COVID-19 may vary, an age-structured susceptible/infected/recovered model was built to explore the efficiency of social distancing against COVID-19 under different population susceptibility scenarios. RESULTS: The mean effective reproductive number, Rt, of COVID-19 before NPIs was 2.12 (95% confidence interval [CI], 2.02-2.21). By 11 March 2020, the overall reduction in Rt of COVID-19 was 66.1% (95% CI, 60.1-71.2%). In the epidemiological year 2019-20, influenza transmissibility was reduced by 34.6% (95% CI, 31.3-38.2%) compared with transmissibility in epidemiological year 2018-19. Under the observed contact pattern changes in China, social distancing had similar efficiency against COVID-19 in 3 different scenarios. By assuming the same efficiency of social distancing against seasonal influenza and COVID-19 transmission, isolation/quarantine and social distancing could lead to 48.1% (95% CI, 35.4-58.1%) and 34.6% (95% CI, 31.3-38.2%) reductions of the transmissibility of COVID-19, respectively. CONCLUSIONS: Though isolation/quarantine is more effective than social distancing, given that the typical basic reproductive number of COVID-19 is 2-3, isolation/quarantine alone could not contain the COVID-19 pandemic effectively in China.

Circulating adipokine concentrations and risk of five obesity-related cancers: A Mendelian randomization study.

Obesity is considered a chronic inflammatory state characterized by continued secretion of adipokines and cytokines. Experimental and epidemiological evidence indicates that circulating adipokines may be associated with the development of obesity-related cancers, but it is unclear if these associations are causal or confounded. We examined potential causal associations of specific adipokines (adiponectin, leptin, soluble leptin receptor [sOB-R] and plasminogen activator inhibitor-1 [PAI-1]) with five obesity-related cancers (colorectal, pancreatic, renal cell carcinoma [RCC], ovarian and endometrial) using Mendelian randomization (MR) methods. We used summary-level data from large genetic consortia for 114 530 cancer cases and 245 284 controls. We constructed genetic instruments using 18 genetic variants for adiponectin, 2 for leptin and 4 for both sOB-R and PAI-1 (P value for inclusion<5 × 10-8 ). Causal estimates were obtained using two-sample MR methods. In the inverse-variance weighted models, we found an inverse association between adiponectin and risk of colorectal cancer (odds ratio per 1 µg/mL increment in adiponectin concentration: 0.90 [95% confidence interval = 0.84-0.97]; P = .01); but, evidence of horizontal pleiotropy was detected and the association was not present when this was taken into consideration. No association was found for adiponectin and risks of pancreatic cancer, RCC, ovarian cancer and endometrial cancer. Leptin, sOB-R and PAI-1 were also similarly unrelated to risk of obesity-related cancers. Despite the large sample size, our MR analyses do not support causal effects of circulating adiponectin, leptin, sOB-R and PAI-1 concentrations on the development of five obesity-related cancers.

Immune checkpoint-related serum proteins and genetic variants predict outcomes of localized prostate cancer, a cohort study.

BACKGROUND: The clinical predictors and biological mechanisms for localized prostate cancer (PCa) outcomes remain mostly unknown. We aim to evaluate the role of serum immune-checkpoint-related (ICK) proteins and genetic variations in predicting outcomes of localized PCa. METHODS: We profiled the serum levels of 14 ICK-related proteins (BTLA, GITR, HVEM, IDO, LAG-3, PD-1, PD-L1, PD-L2, Tim-3, CD28, CD80, 4-1BB, CD27, and CTLA-4) in 190 patients with localized PCa. The genotypes of 97 single nucleotide polymorphisms (SNPs) from 19 ICK-related genes were analyzed in an extended population (N = 1762). Meta-data from ArrayExpress and TCGA was employed to validate and to probe functional data. Patients were enrolled and tumor aggressiveness, biochemical recurrence (BCR), and progression information were obtained. Statistical analyses were performed analyzing associations between serum biomarkers, genotypes, mRNA and outcomes. RESULTS: We showed that serum (s)BTLA and sTIM3 levels were associated with PCa aggressiveness (P < 0.05). sCD28, sCD80, sCTLA4, sGITR, sHVEM and sIDO correlated with both BCR and progression risks (all P < 0.05). We further identified ICK variants were significantly associated with aggressiveness, BCR and progression. Among them, 4 SNPs located in CD80 (rs7628626, rs12695388, rs491407, rs6804441) were not only associated with BCR and progression risk, but also correlated with sCD80 level (P < 0.01). rs491407 was further validated in an independent cohort. The CD80 mRNA expression was associated with BCR (HR, 1.85, 95% CI 1.06-3.22, P = 0.03) in meta-analysis of validation cohorts. CONCLUSION: We highlight the prognostic value of serum ICK-related proteins for predicting aggressiveness, BCR and progression of PCa. The genetic variations and mRNA expression in CD80 could be predictors and potential targets of localized PCa.

Impact of Wuhan lockdown on the spread of COVID-19 in China: a study based on the data of population mobility.

This study aimed to quantitatively assess the effectiveness of the Wuhan lockdown measure on controlling the spread of coronavirus diesase 2019 (COVID-19). : Firstly，estimate the daily new infection rate in Wuhan before January 23，2020 when the city went into lockdown by consulting the data of Wuhan population mobility and the number of cases imported from Wuhan in 217 cities of Mainland China. Then estimate what the daily new infection rate would have been in Wuhan from January 24 to January 30th if the lockdown measure had been delayed for 7 days，assuming that the daily new infection in Wuhan after January 23 increased in a high，moderate and low trend respectively (using exponential, linear and logarithm growth models). Based on that，calculate the number of infection cases imported from Wuhan during this period. Finally，predict the possible impact of 7-day delayed lockdown in Wuhan on the epidemic situation in China using the susceptible-exposed-infectious-removed (SEIR) model. : The daily new infection rate in Wuhan was estimated to be 0.021%，0.026%，0.029%，0.033% and 0.070% respectively from January 19 to January 23. And there were at least 20 066 infection cases in Wuhan by January 23，2020. If Wuhan lockdown measure had been delayed for 7 days，the daily new infection rate on January 30 would have been 0.335% in the exponential growth model，0.129% in the linear growth model，and 0.070% in the logarithm growth model. Correspondingly，there would have been 32 075，24 819 and 20 334 infection cases travelling from Wuhan to other areas of Mainland China，and the number of cumulative confirmed cases as of March 19 in Mainland China would have been 3.3-3.9 times of the officially reported number. Conclusions: Timely taking city-level lockdown measure in Wuhan in the early stage of COVID-19 outbreak is essential in containing the spread of the disease in China.

Impact of socioeconomic status，population mobility and control measures on COVID-10 development in major cities of China.

:To evaluate the impact of socioeconomic status，population mobility，prevention and control measures on the early-stage coronavirus disease 2019 (COVID-19) development in major cities of China. : The rate of daily new confirmed COVID-19 cases in the 51 cities with the largest number of cumulative confirmed cases as of February 19，2020 (except those in Hubei province) were collected and analyzed using the time series cluster analysis. It was then assessed according to three aspects，that is, socioeconomic status，population mobility，and control measures for the pandemic. : According to the analysis on the 51 cities，4 development patterns of COVID-19 were obtained，including a high-incidence pattern (in Xinyu)，a late high-incidence pattern (in Ganzi)，a moderate incidence pattern (in Wenzhou and other 12 cities)，and a low and stable incidence pattern (in Hangzhou and other 35 cities). Cities with different types and within the same type both had different scores on the three aspects. : There were relatively large difference on the COVID-19 development among different cities in China，possibly affected by socioeconomic status，population mobility and prevention and control measures that were taken. Therefore，a timely public health emergency response and travel restriction measures inside the city can interfere the development of the pandemic. Population flow from high risk area can largely affect the number of cumulative confirmed cases.

Predicting COVID-19 epidemiological trend by applying population mobility data in two-stage modeling.

:To predict the epidemiological trend of coronavirus disease 2019 (COVID-19) by mathematical modeling based on the population mobility and the epidemic prevention and control measures. : As of February 8，2020，the information of 151 confirmed cases in Yueqing，Zhejiang province were obtained，including patients' infection process，population mobility between Yueqing and Wuhan，etc. To simulate and predict the development trend of COVID-19 in Yueqing, the study established two-stage mathematical models，integrating the population mobility data with the date of symptom appearance of confirmed cases and the transmission dynamics of imported and local cases. : It was found that in the early stage of the pandemic，the number of daily imported cases from Wuhan (using the date of symptom appearance) was positively associated with the number of population travelling from Wuhan to Yueqing on the same day and 6 and 9 days before that. The study predicted that the final outbreak size in Yueqing would be 170 according to the number of imported cases estimated by consulting the population number travelling from Wuhan to Yueqing and the susceptible-exposed-infectious-recovered (SEIR) model; while the number would be 165 if using the reported daily number of imported cases. These estimates were close to the 170，the actual monitoring number of cases in Yueqing as of April 27，2020. : The two-stage modeling approach used in this study can accurately predict COVID-19 epidemiological trend.

Genetic variants in epithelial-mesenchymal transition genes as predictors of clinical outcomes in localized prostate cancer.

BACKGROUND: Epithelial-mesenchymal transition (EMT) plays a pivotal role in the progression of prostate cancer (PCa). However, little is known about genetic variants in the EMT pathway as predictors of aggressiveness, biochemical recurrence (BCR) and disease reclassification in localized PCa. PATIENTS AND METHODS: In this multistage study, we evaluated 5186 single nucleotide polymorphisms (SNPs) from 264 genes related to EMT pathway to identify SNPs associated with PCa aggressiveness and BCR in the MD Anderson PCa (MDA-PCa) patient cohort (N = 1762), followed by assessment of the identified SNPs with disease reclassification in the active surveillance (AS) cohort (N = 392). RESULTS: In the MDA-PCa cohort, 312 SNPs were associated with high D'Amico risk (P < 0.05), among which, 14 SNPs in 10 genes were linked to BCR risk. In the AS cohort, 2 of 14 identified SNPs (rs76779889 and rs7083961) in C-terminal Binding Proteins 2 gene were associated with reclassification risk. The associations of rs76779889 with different endpoints were: D'Amico high versus low, odds ratio [95% confidence interval (CI)] = 2.89 (1.32-6.34), P = 0.008; BCR, hazard ratio (HR) (95% CI) = 2.88 (1.42-5.85), P = 0.003; and reclassification, HR (95% CI) = 2.83 (1.40-5.74), P = 0.004. For rs7083961, the corresponding risk estimates were: D'Amico high versus low, odds ratio (95% CI) = 1.69 (1.12-2.57), P = 0.013; BCR, HR (95% CI) = 1.87 (1.15-3.02), P = 0.011 and reclassification, HR (95% CI) = 1.72 (1.09-2.72), P = 0.020. There were cumulative effects of these two SNPs on modulating these endpoints. CONCLUSION: Genetic variants in EMT pathway may influence the risks of localized PCa's aggressiveness, BCR and disease reclassification, suggesting their potential role in the assessment and management of localized PCa.

Perinatal depressive and anxiety symptoms of pregnant women during the coronavirus disease 2019 outbreak in China.

BACKGROUND: On January 20, 2020, a new coronavirus epidemic with human-to-human transmission was officially declared by the Chinese government, which caused significant public panic in China. In light of the coronavirus disease 2019 outbreak, pregnant women may be particularly vulnerable and in special need for preventive mental health strategies. Thus far, no reports exist to investigate the mental health response of pregnant women to the coronavirus disease 2019 outbreak. OBJECTIVE: This study aimed to examine the impact of coronavirus disease 2019 outbreak on the prevalence of depressive and anxiety symptoms and the corresponding risk factors among pregnant women across China. STUDY DESIGN: A multicenter, cross-sectional study was initiated in early December 2019 to identify mental health concerns in pregnancy using the Edinburgh Postnatal Depression Scale. This study provided a unique opportunity to compare the mental status of pregnant women before and after the declaration of the coronavirus disease 2019 epidemic. A total of 4124 pregnant women during their third trimester from 25 hospitals in 10 provinces across China were examined in this cross-sectional study from January 1, 2020, to February 9, 2020. Of these women, 1285 were assessed after January 20, 2020, when the coronavirus epidemic was publicly declared and 2839 were assessed before this pivotal time point. The internationally recommended Edinburgh Postnatal Depression Scale was used to assess maternal depression and anxiety symptoms. Prevalence rates and risk factors were compared between the pre- and poststudy groups. RESULTS: Pregnant women assessed after the declaration of coronavirus disease 2019 epidemic had significantly higher rates of depressive symptoms (26.0% vs 29.6%, P=.02) than women assessed before the epidemic declaration. These women were also more likely to have thoughts of self-harm (P=.005). The depressive rates were positively associated with the number of newly confirmed cases of coronavirus disease 2019 (P=.003), suspected infections (P=.004), and deaths per day (P=.001). Pregnant women who were underweight before pregnancy, primiparous, younger than 35 years, employed full time, in middle income category, and had appropriate living space were at increased risk for developing depressive and anxiety symptoms during the outbreak. CONCLUSION: Major life-threatening public health events such as the coronavirus disease 2019 outbreak may increase the risk for mental illness among pregnant women, including thoughts of self-harm. Strategies targeting maternal stress and isolation such as effective risk communication and the provision of psychological first aid may be particularly useful to prevent negative outcomes for women and their fetuses.

Patterns of racial/ethnic disparities in baseline health-related quality of life and relationship with overall survival in patients with colorectal cancer.

PURPOSE: Racial disparities are evident in colorectal cancer (CRC) prognosis with black patients experiencing worse outcomes than Hispanics and whites, yet mediators of these disparities are not fully known. The aim of this study is to identify variables that contribute to racial/ethnic disparities in health-related quality of life (HR-QoL) and overall survival in CRC. METHODS: Using SF-12 questionnaires, we assessed HR-QoL in 1132 CRC patients by calculating their physical (PCS) and mental composite summary (MCS) scores. Associations between poor PCS/MCS and sociodemographic factors were estimated and survival differences were identified by race/ethnicity. RESULTS: Hispanic patients who never married were at greater risk of poor PCS (OR 2.69; 95% CI 1.11-6.49; P = 0.028) than were currently married patients. College education was associated with a decreased risk of poor PCS in Hispanic and white, but not black, patients. Gender was significantly associated with poor MCS among white patients only. CRC patients who reported a poor PCS or MCS had poor survival, with differences in median survival times (MSTs) by race. The effect of PCS was strongest in white CRC patients with a difference in overall MST of > 116 months between those with favorable versus poor physical HR-QoL. Black patients who reported poor Physical and Mental HR-QoL showed significant risk of a poor outcome. CONCLUSION: These findings suggest that racial/ethnic disparities in CRC survival may be related to differences in HR-QoL. Identified mediators of HR-QoL could supplement current CRC management strategies to improve patients' survival.

Circulating obesity-driven biomarkers are associated with risk of clear cell renal cell carcinoma: a two-stage, case-control study.

Obesity is one of modifiable risk factors for clear cell renal cell cancer (ccRCC). We aim to identify the association between obesity-driven biomarkers and ccRCC risk. This is a retrospective, two-phase, case-control study involving 682 cases and 733 controls. Obesity-driven biomarkers [gastric inhibitory polypeptide (GIP), C-peptide, insulin, resistin, adipsin, peptide YY, pancreatic polypeptide, interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), plasminogen activator inhibitor-1, monocyte chemoattractant protein 1, lipocalin2, leptin, adiponectin] were measured using the Milliplex method. Multivariate logistic regression was used to assess the associations between biomarkers and ccRCC risk. Results revealed that GIP, C-peptide, IL-6 and TNF-α levels were consistently distinct between cases and controls. These markers were significantly associated with ccRCC risk in both phases (except C-peptide). In the combined population, compared with individuals with low levels of the biomarkers, individuals with high level of GIP [odds ratio (OR) = 0.52, 95% confidence interval (CI): 0.40-0.67] had lower risk, whereas individuals with high levels of C-peptide (OR = 1.46, 95% CI: 1.15-1.87), IL-6 (OR = 2.20, 95% CI: 1.50-3.22), TNF-α (OR = 1.90, 95% CI: 1.49-2.43) had significantly higher risk. Stratified analysis showed consistent associations with ccRCC risk in most subgroups (P < 0.05). The risk score based on the IL-6, TNF-α and GIP was positively associated with ccRCC risk in a dose-response manner (P for trend = 2.18E-13). Data from The Cancer Genome Atlas indicate that insulin signaling, IL-6 signaling and TNF-α signaling were enhanced in tumors. Collectively, our study demonstrates the integrative effect of insulin resistance and inflammation in ccRCC development, which may elucidate the basis of association between obesity and carcinogenesis. Further confirmation in prospective cohort studies are warranted for clinical applications in prevention and precision medicine of ccRCC.

A 5-microRNA signature identified from serum microRNA profiling predicts survival in patients with advanced stage non-small cell lung cancer.

Circulating microRNAs (miRNAs) are potential biomarkers for cancer diagnosis, screening and prognosis. This study aimed to identify serum miRNAs as predictors of survival in patients with advanced non-small cell lung cancer (NSCLC). We profiled serum miRNAs in a pilot set of four patients with good survival (>24 months) and four patients with poor survival (<6 months). We selected 140 stably detectable miRNAs and 42 miRNAs reported in literature for further analysis. Expression of these 182 miRNAs was measured using high-throughput polymerase chain reaction assay, and their association with 3-year survival in the discovery (n = 345) and validation (n = 177) cohorts was assessed. Five serum miRNAs (miR-191, miR-28-3p, miR-145, miR-328 and miR-18a) were significantly associated with 3-year overall survival in both cohorts. A combined 5-miRNA risk score was created to assess the cumulative impact of these miRNAs on risk of death. Quartile analysis of the risk score showed significant association with 3-year death risk, with a 4.6-, 6.8- and 9.3-month reduction in median survival time for the second, third and fourth quartiles, respectively. Survival tree analysis also identified distinct risk groups with different 3-year survival durations. Data from The Cancer Genome Atlas revealed all five miRNAs were differentially expressed (P < 0.0001) in paired tumor and normal tissues. Pathway analysis indicated that target genes of these five miRNAs were mainly enriched in inflammatory/immune response pathways and pathways implicated in resistance to chemoradiotherapy and/or targeted therapy. Our results suggested that the 5-miRNA signature could serve as a prognostic predictor in patients with advanced NSCLC.