Publications by authors named "Siqin Feng"

Background: Coronary thrombosis events continue to be the leading cause of morbidity and mortality worldwide. Recently, emerging evidence has highlighted the role of gut microbiota in cardiovascular disease, but few studies have systematically investigated the gut microbiota variation associated with atherothrombosis.

Methods: We conducted multi-omics analysis (metagenomics sequencing and serum metabolomics) on 146 subjects from Peking Union Medical College Hospital-Coronary Artery Disease (PUMCH-CAD) cohort.

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Aims: We aimed to assess the association between serum 25-hydroxyvitamin D (25(OH)D) levels with all-cause and cardiovascular mortality in patients with nonalcoholic fatty liver disease (NAFLD).

Methods: We performed a retrospective cohort study based on the US National Health and Nutrition Examination Survey 2001-2016 on adults aged ≥20 years. NAFLD was determined as a US Fatty Liver Index score ≥ 30 in the absence of other liver conditions.

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To explore the impacts of household solid fuel use for cooking and heating on diabetes and fasting blood glucose (FBG) levels, we used data from the China Health and Retirement Longitudinal Study, a national survey including middle-aged and older adults. Multivariable logistic and linear regression models were used to explore the relationship between household solid fuel use (coal, crop residue, and wood) for cooking and heating with diabetes and FBG levels. Subgroup analyses were also performed based on age, sex, region of residence, smoking status, and body mass index to examine potential interactions between the variables and household solid fuel use.

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Hyperhomocysteine (HHcy) is known as a risk factor for coronary artery disease (CAD). Despite the knowledge that gut microbiota related metabolism pathway shares metabolites with that of Hcy, little has been shown concerning the association between HHcy and gut microbiota. To explore their relationship in the context of CAD, 105 patients and 14 healthy controls were recruited from one single medical center located in Beijing, China.

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Background: Autosomal recessive familial hypercholesterolemia (ARH) is a very rare lipid metabolic monogenic disorder caused by homozygosity or compound heterozygosity for mutations in the low-density lipoprotein receptor adapter protein 1 () gene. It is a life-threatening disease characterized by markedly elevated low-density lipoprotein cholesterol (LDL-C), xanthomas, and premature coronary artery disease. Membranous nephropathy (MN) is less commonly observed in children.

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Background And Aims: Host-microbiota interactions involving metabolic pathways have been linked to the pathogenesis of atherosclerotic disease and type 2 diabetes. As stable coronary artery disease (SCAD) patients combined with type 2 diabetes have significantly increased risk for cardiac event, we focused on elucidating the role of microbiota affecting cardiometabolic disease development.

Methods And Results: We used multi-omics analyses (metagenomics and metabolomics) of fecal and serum samples from a prospective cohort including stable coronary artery disease combined with diabetes mellitus (SCAD + T2DM, n = 38), SCAD (n = 71), and healthy control (HC, n = 55).

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Familial hypercholesterolemia (FH) is an inherited rare disease leading to markedly elevated low-density lipoprotein cholesterol (LDL-C) levels and increased risk for cardiovascular event. Gut microbiota has been implicated as a pivotal contributing factor in hyperlipidemia, however, its role in FH remains elusive. We performed whole-exome and metagenomics sequencing on a family with 22 members in which myocardial infarctions occurred at a young age with unclear etiology.

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Background: The gut microbiota was shown to play a crucial role in the development of vascular dysfunction, and the bacterial composition differed between healthy controls and coronary artery disease patients. The goal of this study was to investigate how the gut microbiota affects host metabolic homeostasis at the organism scale.

Methods: We colonized germ-free C57BL/6 J mice with faeces from healthy control donors (Con) and coronary artery disease (CAD) patients and fed both groups a high fat diet for 12 weeks.

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The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is a current global threat for which there is an urgent need to search for an effective therapy. The transmembrane spike (S) glycoprotein of SARS-CoV-2 directly binds to the host angiotensin-converting enzyme 2 (ACE2) and mediates viral entrance, which is therefore considered as a promising drug target. Considering that new drug development is a time-consuming process, drug repositioning may facilitate rapid drug discovery dealing with sudden infectious diseases.

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The performance of convolutional neural network- (CNN-) based object detection has achieved incredible success. Howbeit, existing CNN-based algorithms suffer from a problem that small-scale objects are difficult to detect because it may have lost its response when the feature map has reached a certain depth, and it is common that the scale of objects (such as cars, buses, and pedestrians) contained in traffic images and videos varies greatly. In this paper, we present a 32-layer multibranch convolutional neural network named MBNet for fast detecting objects in traffic scenes.

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Background: Coronary artery disease (CAD) is associated with gut microbiota alterations in different populations. Gut microbe-derived metabolites have been proposed as markers of major adverse cardiac events. However, the relationship between the gut microbiome and the different stages of CAD pathophysiology remains to be established by a systematic study.

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