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The 2019 coronavirus disease (COVID-19) has become a global pandemic. To date, although many studies have reported on the computed tomography (CT) manifestations of COVID-19, the vascular enlargement sign (VES) of COVID-19 has not been deeply examined, with the few available studies reporting an inconsistent prevalence. We thus performed a systematic review and meta-analysis based on the best available studies to estimate the prevalence and identify the underlying differential diagnostic value of VES.

We searched nine English and Chinese language databases up to April 23, 2020. Studies that evaluated CT features of COVID-19 patients and reported VES, with or without comparison with other pneumonia were included. The methodologic quality was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Meta-analyses with random effects models were performed to calculate the aggregate prevalence and pooled odds ratios (ORs) of VES. We also conducted meta-regression and subgroup analyses to distribution, and sample size. No significant publication bias was seen (P=0.29).

VES on thoracic CT was found in almost two-thirds of COVID-19 patients, and was more prevalent compared with that of the non-COVID-19 patients, supporting a promising role for VES in identifying pneumonia caused by coronavirus.
VES on thoracic CT was found in almost two-thirds of COVID-19 patients, and was more prevalent compared with that of the non-COVID-19 patients, supporting a promising role for VES in identifying pneumonia caused by coronavirus.
Cancer-associated fibroblasts (CAFs), as the activated stroma cells, contribute to tumor progression via the release of cytokines, growth factors, and hormones. However, neither the factors produced by CAFs nor the molecular mechanisms were illuminated very well in gastric cancer (GC).

Immunohistochemical staining of alpha-smooth muscle actin (α-SMA) was applied to examine the number of CAFs in GC samples from 227 patients. ELISA and qRT-PCR were performed to detect the expression of interleukin 17a (IL-17a). The migration and invasion of GC cells were determined by the Transwell assay. The expressions of JAK2, STAT3, MMP-2, MMP-9, TIMP-1, and TIMP-2 were measured by western blotting. IL-17a was blocked with a polyclonal antibody, and JAK2/STAT3 signaling pathway was blocked by a specific inhibitor AG490.

High CAFs in GC tissues were positively correlated with advanced TNM stage and perineural invasion. Furthermore, GC patients with high CAFs in tumor tissues had an obvious worse disease-free survival (tients. CAFs secreted IL-17a, which promoted the migration and invasion of GC cells through activating JAK2/STAT3 signaling. These results may identify IL-17a as a promising prognostic marker and therapeutic target of GC.
Hypoxia following ischemic stroke is a common cause of brain insults. Mounting evidence suggests that long non-coding RNAs (lncRNAs) play a vital role in regulating certain physiological and pathological processes including ischemic stroke. For the first time, the present study investigated the effects and mechanism of LncRNA MACC1-AS1 on hypoxia-induced human brain microvascular endothelial cells (HBMECs).

LncRNA MACC1-AS1 levels in HBMECs were detected via reverse transcription quantitative polymerase chain reaction (RT-qPCR) assay. Reactive oxygen species (ROS), malondialdehyde (MDA), superoxide dismutase (SOD), and catalase (CAT), were detected using their respective kits. selleck kinase inhibitor Flow cytometry and clone formation assay were performed to evaluate the effects of lncRNA MACC1-AS1 on cell apoptosis and cell proliferation respectively. Angiogenesis capacity was evaluated via tube formation assay. Transwell migration assay was performed for assessment of cell migration, Western blot assay was performed for measurhis study, we found that LncRNA MACC1-AS1 exerted a protective role in hypoxia-induced HBMECs via regulating miR-6867-5p/TWIST1, indicating a new therapeutic strategy for future ischemic stroke therapy.
The effects of
(
) infection on metabolic syndrome (MetS) in pregnant women are unclear to date. This study was designed to explore the relationship between
infection and MetS during pregnancy.

Pregnant women were enrolled in the prospective cohort study, and their demographic data and metabolic parameters were collected.
infection was measured using the C13 urea breath test. All enrolled patients were followed up until the last baby was born. Metabolic disorders, including elevated levels of serum triglycerides (TG), high-density lipoprotein (HDL) cholesterol and blood glucose (BG), and adverse pregnancy outcomes, including gestational diabetes mellitus (GDM), preeclampsia, spontaneous preterm birth (SPB), fetal growth restriction (FGR), and uncomplicated pregnancy, were recorded during follow up.

There were 320 pregnant women enrolled in this study. They were divided into two groups according to
infection, and each group was then divided into two subgroups on whether their BMI was more than 24 or not. The results showed that
infection significantly increased the incidence of MetS as well as other metabolic disorders, especially in pregnant women with high BMI. Multivariable logistic regression analysis showed that risk factors of MetS were high BMI and
infection. Besides,
infection increased the incidence of GDM and preeclampsia and potentially reduced the incidence of uncomplicated pregnancy.

infection in pregnant women acts as a crucial risk factor of Mets and affects the incidence of several adverse pregnancy outcomes.
H. pylori infection in pregnant women acts as a crucial risk factor of Mets and affects the incidence of several adverse pregnancy outcomes.
Parkinson's disease (PD) gradually degrades the functionality of the brain. Because of its relevance to the abnormality of the brain, electroencephalogram (EEG) signal is used for the early detection of this disease. This paper introduces a novel computer-aided diagnosis method to detect PD, which is an efficient deep learning method based on a pooling-based deep recurrent neural network (PDRNN). Therefore, the purpose of this study is to detect Parkinson's disease based on deep recurrent neural network of EEG signal.

The EEG signals of 20 patients with Parkinson's disease and 20 healthy people in Henan Provincial People's Hospital (People's Hospital of Zhengzhou University) were examined, and a PDRNN learning method was applied on the dataset for managing the demand of the traditional feature presentation step.

The suggested DPRNN network gives the precision, sensitivity and specificity of 88.31%, 84.84% and 91.81%, respectively. Nevertheless, 11.28% of the healthy cases are wrongly categorized in Parkinson class.
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