Explore the vast range of titles available.

\"Jennifer Ann Ho introduces readers to a \"typical American\" writer, Gish Jen, the author of four novels, Typical American, Mona in the Promised Land, The Love Wife, and World and Town; a collection of short stories, Who's Irish?; and a collection of lectures, Tiger Writing: Art, Culture, and the Interdependent Self. Jen writes with an engaging, sardonic, and imaginative voice illuminating themes common to the American experience: immigration, assimilation, individualism, the freedom to choose one's path in life, and the complicated relationships that we have with our families and our communities. A second-generation Chinese American, Jen is widely recognized as an important American literary voice, at once accessible, philosophical, and thought-provoking. In addition to her novels, she has published widely in periodicals such as the New Yorker, Atlantic Monthly, and Yale Review. Ho traces the evolution of Jen's career, her themes, and the development of her narrative voice. In the process she shows why Jen's observations about life in the United States, though revealed through the perspectives of her Asian American and Asian immigrant characters, resonate with a variety of audiences who find themselves reflected in Jen's accounts of love, grief, desire, disappointment, and the general domestic experiences that shape all our lives. Following a brief biographical sketch, Ho examines each of Jen's major works, showing how she traces the transformation of immigrant dreams into mundane life, explores the limits of self-identification, and characterizes problems of cross-national communication alongside the universal problems of aging and generational conflict. Looking beyond Jen's fiction work, a final chapter examines her essays and her concerns and stature as a public intellectual, and detailed primary and secondary bibliographies provide a valuable point of departure for both teaching and future scholarship\"-- Provided by publisher.

Although obesity and cardiometabolic traits commonly overlap, underlying pathways remain incompletely defined. The association of metabolite profiles across multiple cardiometabolic traits may lend insights into the interaction of obesity and metabolic health. We sought to investigate metabolic signatures of obesity and related cardiometabolic traits in the community using broad-based metabolomic profiling. We evaluated the association of 217 assayed metabolites and cross-sectional as well as longitudinal changes in cardiometabolic traits among 2,383 Framingham Offspring cohort participants. Body mass index (BMI) was associated with 69 of 217 metabolites (P<0.00023 for all), including aromatic (tyrosine, phenylalanine) and branched chain amino acids (valine, isoleucine, leucine). Additional metabolic pathways associated with BMI included the citric acid cycle (isocitrate, alpha-ketoglutarate, aconitate), the tryptophan pathway (kynurenine, kynurenic acid), and the urea cycle. There was considerable overlap in metabolite profiles between BMI, abdominal adiposity, insulin resistance [IR] and dyslipidemia, modest overlap of metabolite profiles between BMI and hyperglycemia, and little overlap with fasting glucose or elevated blood pressure. Metabolite profiles were associated with longitudinal changes in fasting glucose, but the involved metabolites (ornithine, 5-HIAA, aminoadipic acid, isoleucine, cotinine) were distinct from those associated with baseline glucose or other traits. Obesity status appeared to \"modify\" the association of 9 metabolites with IR. For example, bile acid metabolites were strongly associated with IR among obese but not lean individuals, whereas isoleucine had a stronger association with IR in lean individuals. In this large-scale metabolite profiling study, body mass index was associated with a broad range of metabolic alterations. Metabolite profiling highlighted considerable overlap with abdominal adiposity, insulin resistance, and dyslipidemia, but not with fasting glucose or blood pressure traits.

Salvianolic acids are the most abundant water-soluble compounds extracted from Radix Salvia miltiorrhiza (Danshen). In China, Danshen has been wildly used to treat cardiovascular diseases for hundreds of years. Salvianolic acids, especially salvianolic acid A (Sal A) and salvianolic acid B (Sal B), have been found to have potent anti-oxidative capabilities due to their polyphenolic structure. Recently, intracellular signaling pathways regulated by salvianolic acids in vascular endothelial cells, aortic smooth muscle cells, as well as cardiomyocytes, have been investigated both in vitro and in vivo upon various cardiovascular insults. It is discovered that the cardiovascular protection of salvianolic acids is not only because salvianolic acids act as reactive oxygen species scavengers, but also due to the reduction of leukocyte-endothelial adherence, inhibition of inflammation and metalloproteinases expression from aortic smooth muscle cells, and indirect regulation of immune function. Competitive binding of salvianolic acids to target proteins to interrupt protein-protein interactions has also been found to be a mechanism of cardiovascular protection by salvianolic acids. In this article, we review a variety of studies focusing on the above mentioned mechanisms. Besides, the target proteins of salvianolic acids are also described. These results of recent advances have shed new light to the development of novel therapeutic strategies for salvianolic acids to treat cardiovascular diseases.

Men are at higher risk for serious complications related to COVID-19 infection than women. More robust immune activation in women has been proposed to contribute to decreased disease severity, although systemic inflammation has been associated with worse outcomes in COVID-19 infection. Whether systemic inflammation contributes to sex differences in COVID-19 infection is not known. We examined sex differences in inflammatory markers among 453 men (mean age 61) and 328 women (mean age 62) hospitalized with COVID-19 infection at the Massachusetts General Hospital from March 8 to April 27, 2020. Multivariable linear regression models were used to examine the association of sex with initial and peak inflammatory markers. Exploratory analyses examined the association of sex and inflammatory markers with 28-day clinical outcomes using multivariable logistic regression. Initial and peak CRP were higher in men compared with women after adjustment for baseline differences (initial CRP: ß 0.29, SE 0.07, p = 0.0001; peak CRP: ß 0.31, SE 0.07, p<0.0001) with similar findings for IL-6, PCT, and ferritin (p<0.05 for all). Men had greater than 1.5-greater odds of dying compared with women (OR 1.71, 95% CI 1.04-2.80, p = 0.03). Sex modified the association of peak CRP with both death and ICU admission, with stronger associations observed in men compared with women (death: OR 9.19, 95% CI 4.29-19.7, p <0.0001 in men vs OR 2.81, 95% CI 1.52-5.18, p = 0.009 in women, Pinteraction = 0.02). In a sample of 781 men and women hospitalized with COVID-19 infection, men exhibited more robust inflammatory activation as evidenced by higher initial and peak inflammatory markers, as well as worse clinical outcomes. Better understanding of sex differences in immune responses to COVID-19 infection may shed light on the pathophysiology of COVID-19 infection.

Identifying genetic variants associated with circulating protein concentrations (protein quantitative trait loci; pQTLs) and integrating them with variants from genome-wide association studies (GWAS) may illuminate the proteome’s causal role in disease and bridge a knowledge gap regarding SNP-disease associations. We provide the results of GWAS of 71 high-value cardiovascular disease proteins in 6861 Framingham Heart Study participants and independent external replication. We report the mapping of over 16,000 pQTL variants and their functional relevance. We provide an integrated plasma protein-QTL database. Thirteen proteins harbor pQTL variants that match coronary disease-risk variants from GWAS or test causal for coronary disease by Mendelian randomization. Eight of these proteins predict new-onset cardiovascular disease events in Framingham participants. We demonstrate that identifying pQTLs, integrating them with GWAS results, employing Mendelian randomization, and prospectively testing protein-trait associations holds potential for elucidating causal genes, proteins, and pathways for cardiovascular disease and may identify targets for its prevention and treatment. Genetic variation can influence levels of disease-related plasma proteins and, thus, contribute to the pathogenesis of complex diseases. Here, the authors perform genome-wide QTL analysis for 71 plasma proteins to identify causal proteins for coronary heart disease and provide a molecular QTL browser.

Background: Diabetic retinopathy (DR), whose standard diagnosis is performed by human experts, has high prevalence and requires a more efficient screening method. Although machine learning (ML)–based automated DR diagnosis has gained attention due to recent approval of IDx-DR, performance of this tool has not been examined systematically, and the best ML technique for use in a real-world setting has not been discussed. Objective: The aim of this study was to systematically examine the overall diagnostic accuracy of ML in diagnosing DR of different categories based on color fundus photographs and to determine the state-of-the-art ML approach. Methods: Published studies in PubMed and EMBASE were searched from inception to June 2020. Studies were screened for relevant outcomes, publication types, and data sufficiency, and a total of 60 out of 2128 (2.82%) studies were retrieved after study selection. Extraction of data was performed by 2 authors according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), and the quality assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Meta-analysis of diagnostic accuracy was pooled using a bivariate random effects model. The main outcomes included diagnostic accuracy, sensitivity, and specificity of ML in diagnosing DR based on color fundus photographs, as well as the performances of different major types of ML algorithms. Results: The primary meta-analysis included 60 color fundus photograph studies (445,175 interpretations). Overall, ML demonstrated high accuracy in diagnosing DR of various categories, with a pooled area under the receiver operating characteristic (AUROC) ranging from 0.97 (95% CI 0.96-0.99) to 0.99 (95% CI 0.98-1.00). The performance of ML in detecting more-than-mild DR was robust (sensitivity 0.95; AUROC 0.97), and by subgroup analyses, we observed that robust performance of ML was not limited to benchmark data sets (sensitivity 0.92; AUROC 0.96) but could be generalized to images collected in clinical practice (sensitivity 0.97; AUROC 0.97). Neural network was the most widely used method, and the subgroup analysis revealed a pooled AUROC of 0.98 (95% CI 0.96-0.99) for studies that used neural networks to diagnose more-than-mild DR. Conclusions: This meta-analysis demonstrated high diagnostic accuracy of ML algorithms in detecting DR on color fundus photographs, suggesting that state-of-the-art, ML-based DR screening algorithms are likely ready for clinical applications. However, a significant portion of the earlier published studies had methodology flaws, such as the lack of external validation and presence of spectrum bias. The results of these studies should be interpreted with caution.

Physicians classify patients into those with or without a specific disease. Furthermore, there is often interest in classifying patients according to disease etiology or subtype. Classification trees are frequently used to classify patients according to the presence or absence of a disease. However, classification trees can suffer from limited accuracy. In the data-mining and machine-learning literature, alternate classification schemes have been developed. These include bootstrap aggregation (bagging), boosting, random forests, and support vector machines. We compared the performance of these classification methods with that of conventional classification trees to classify patients with heart failure (HF) according to the following subtypes: HF with preserved ejection fraction (HFPEF) and HF with reduced ejection fraction. We also compared the ability of these methods to predict the probability of the presence of HFPEF with that of conventional logistic regression. We found that modern, flexible tree-based methods from the data-mining literature offer substantial improvement in prediction and classification of HF subtype compared with conventional classification and regression trees. However, conventional logistic regression had superior performance for predicting the probability of the presence of HFPEF compared with the methods proposed in the data-mining literature. The use of tree-based methods offers superior performance over conventional classification and regression trees for predicting and classifying HF subtypes in a population-based sample of patients from Ontario, Canada. However, these methods do not offer substantial improvements over logistic regression for predicting the presence of HFPEF.

Brain metastases are a common and deadly complication of many primary tumors. The progression of these tumors is poorly understood, and treatment options are limited. Two important components of tumor growth are hypoxia and angiogenesis. We conducted a review to look at the possibility of a symbiotic relationship between two transcription factors, Hypoxia-Inducible Factor 1α (HIF1α) and Vascular Endothelial Growth Factor (VEGF), and the role they play in metastasis to the brain. We delve further into this possible relationship by examining commonly used chemotherapeutic agents and their targets. Through an extensive literature review, we identified articles that provided evidence of a strong connection between these transcription factors and the growth of brain metastases, many highlighting a symbiotic relationship. Further supporting this, combinations of chemotherapeutic drugs with varying targets have increased the efficacy of treatment. Angiogenesis and hypoxia have long been known to play a large role in the invasion, growth, and poor outcomes of tumors. However, it is not fully understood how these factors influence one another during metastases. While prior studies have investigated the effects separately, we specifically delve into the synergistic and compounding effects that may exist between them. Our findings underscore the need for greater research allocation to investigate the possible symbiotic relationship between angiogenesis and hypoxia in brain metastasis.

Several forms of pulmonary hypertension (PH) disproportionately affect women. Animal and human studies suggest that estradiol exerts mixed effects on the pulmonary vasculature. Whether premature menopause represents a risk factor for PH is unknown. In this cohort study, women in the UK Biobank aged 40-69 years who were postmenopausal and had complete data available on reproductive history were included. Premature menopause, defined as menopause occurring before age 40 years. Postmenopausal women without premature menopause served as the reference group. The primary outcome was incident PH, ascertained by appearance of a qualifying ICD code in the participant's UK Biobank study record. Of 136,715 postmenopausal women included, 5,201 (3.8%) had premature menopause. Participants were followed up for a median of 11.1 (interquartile range 10.5-11.8) years. The primary outcome occurred in 38 women (0.73%) with premature menopause and 409 (0.31%) without. After adjustment for age, race, ever-smoking, body-mass index, systolic blood pressure, antihypertensive medication use, non-high-density lipoprotein cholesterol, cholesterol-lowering medication use, C-reactive protein, prevalent type 2 diabetes, obstructive sleep apnea, heart failure, mitral regurgitation, aortic stenosis, venous thromboembolism, forced vital capacity (FVC), the forced expiratory volume in 1 second-to-FVC ratio, use of menopausal hormone therapy, and hysterectomy status, premature menopause was independently associated with PH (hazard ratio 2.13, 95% CI 1.31-3.23, P<0.001). In analyses of alternate menopausal age thresholds, risk of PH appeared to increase progressively with younger age at menopause (Ptrend <0.001), with 4.8-fold risk in women with menopause before age 30 years (95% CI 1.82-12.74, P = 0.002). Use of menopausal hormone therapy did not modify the association of premature menopause with PH. Premature menopause may represent an independent risk factor for PH in women. Further investigation of the role of sex hormones in PH is needed in animal and human studies to elucidate pathobiology and identify novel therapeutic targets.

Congenital heart diseases often involve maldevelopment of the evolutionarily recent right heart chamber. To gain insight into right heart structure and function, we fine-tuned deep learning models to recognize the right atrium, right ventricle and pulmonary artery, measuring right heart structures in 40,000 individuals from the UK Biobank with magnetic resonance imaging. Genome-wide association studies identified 130 distinct loci associated with at least one right heart measurement, of which 72 were not associated with left heart structures. Loci were found near genes previously linked with congenital heart disease, including NKX2-5 , TBX5/TBX3 , WNT9B and GATA4 . A genome-wide polygenic predictor of right ventricular ejection fraction was associated with incident dilated cardiomyopathy (hazard ratio, 1.33 per standard deviation; P  = 7.1 × 10 −13 ) and remained significant after accounting for a left ventricular polygenic score. Harnessing deep learning to perform large-scale cardiac phenotyping, our results yield insights into the genetic determinants of right heart structure and function. Genome-wide analyses of cardiac magnetic resonance imaging data identify loci associated with right heart structure and function. A polygenic predictor of right ventricular ejection fraction is associated with dilated cardiomyopathy risk.