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Introduction Acute heart failure is a serious condition. Atrial fibrillation is the most frequent arrhythmia in patients with acute heart failure. The occurrence of atrial fibrillation in heart failure patients worsens their prognosis and leads to a substantial increase in treatment costs. There is no tool that can effectively predict the onset of atrial fibrillation in patients with acute heart failure in the ICU currently. Materials and methods We retrospectively analyzed the MIMIC-IV database of patients admitted to the intensive care unit (ICU) for acute heart failure and who were initially sinus rhythm. Data on demographics, comorbidities, laboratory findings, vital signs, and treatment were extracted. The cohort was divided into a training set and a validation set. Variables selected by LASSO regression and multivariate logistic regression in the training set were used to develop a model for predicting the occurrence of atrial fibrillation in acute heart failure in the ICU. A nomogram was drawn and an online calculator was developed. The discrimination and calibration of the model was evaluated. The performance of the model was tested using the validation set. Results This study included 2342 patients with acute heart failure, 646 of whom developed atrial fibrillation during their ICU stay. Using LASSO and multiple logistic regression, we selected six significant variables: age, prothrombin time, heart rate, use of vasoactive drugs within 24 h, Sequential Organ Failure Assessment (SOFA) score, and Acute Physiology Score (APS) III. The C-index of the model was 0.700 (95% CI 0.672–0.727) and 0.682 (95% CI 0.639–0.725) in the training and validation sets, respectively. The calibration curves also performed well in both sets. Conclusion We developed a simple and effective model for predicting atrial fibrillation in patients with acute heart failure in the ICU.

Nowadays, a chip-scale Global Navigation Satellite System (GNSS) receiver is ubiquitous in smartphones. In a smartphone GNSS receiver, the least square (LS) or Kalman Filter (KF) is implemented to estimate the position. With the aim to improve the smartphone GNSS position accuracy, we propose a position-smoothing method considering more historical information than the traditional methods, i.e., LS and KF. More past states are regarded as unknowns, and a cost function is constructed to optimize these states. An open-source smartphone dataset from Google was used for testing the proposed method. The experimental results indicate that the proposed method outperforms the other conventional methods in position errors. In addition, we open the source code. We expect that the optimization method implemented in the smartphone GNSS position smoothing application can be an illustrative example to clearly introduce such an optimization method and a reference for its implementation, which might inspire some other meaningful and exciting applications in GNSS.

Pedestrian dead reckoning (PDR) is an effective technology for pedestrian navigation. In PDR, the steps are detected with the measurements of self-contained sensors, such as accelerometers, and the position is updated with additional heading angles. A smartphone is usually equipped with a low-cost microelectromechanical system accelerometer, which can be utilized to implement PDR for pedestrian navigation. Since the PDR position errors diverge with the walking distance, the global navigation satellite system (GNSS) is usually integrated with PDR for more reliable position results. This paper implemented a smartphone PDR/GNSS via a Kalman filter and factor graph optimization (FGO). In the FGO, the PDR factor is modeled, and the states are correlated with a dead reckoning algorithm. The GNSS position is modeled as the “GNSS” factor to constrain the states at each step. With a graphic model representing the states and measurements, the state estimation is converted to a nonlinear least square problem, and we utilize the Georgia Tech Smoothing and Mapping graph optimization library to implement the optimization. We tested the proposed method on a Huawei Mate 40 Pro handset with a standard playground field test, and the field test results showed that the FGO effectively improved the smartphone position accuracy. We have released the source codes and hope that they will inspire other works on pedestrian navigation, i.e., constructing an adaptive multi-sensor integration system using FGO on a smartphone.

Purpose The present study aims to demonstrate the correlation between estrogen-related receptor α (ERRα) and G protein-coupled estrogen receptor (GPER) expression and its predictive role in the prognosis of patients with triple-negative breast cancer (TNBC). Methods A retrospective review of 199 cases of TNBC was conducted to assess the GPER and ERRα expression, and its clinicopathologic and prognostic implications. Subsequently, the effects of ERRα and GPER on cell viability, migration, and invasion induced by estrogen were also investigated in vitro. Results Compared to TNBCs with ERRα low expression, ERRα-high patients exhibited higher nuclear grade, more frequent lymph nodal metastasis, a higher rate of local recurrence, and distant metastasis. Survival analyses revealed that ERRα-high patients had decreased overall survival (OS), local recurrence-free survival (LRFS), and distant disease-free survival (DDFS) than ERRα-low patients. The GPER expression level positively correlated with ERRα (R=0.167, P=0.18), and TNBCs with ERRα-low/GPER-low demonstrated the best survival outcomes among groups. In vitro, E2 significantly enhanced cell viability, migration, and invasion in BT-549 and MDA-MB-231 cell lines, which was associated with the increased expression of ERRα. Moreover, the overexpression of ERRα induced by estrogen and G1 (GPER agonist) was reversed by knocking down of GPER and blocking the MAPK signaling with PD98059 in both cell lines. Conclusion Our findings suggest that ERRα and GPER synergistically predict unfavorable prognosis in TNBCs. Mechanically, GPER mediates the upregulation expression of ERRα induced by estrogen and promotes cell viability, migration, and invasion.

Xiaoyu Wang,1 Yan Li,2 Jianchang Fu,3 Kewen Zhou,1 Tinghuai Wang1 1Department of Physiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, People’s Republic of China; 2Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, People’s Republic of China; 3Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People’s Republic of ChinaCorrespondence: Tinghuai Wang; Kewen ZhouDepartment of Physiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, People’s Republic of ChinaEmail wangth@mail.sysu.edu.cn; zhoukw6@mail.sysu.edu.cnBackground: Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype and is associated with poor prognosis. The aberrant expression of circadian genes contributes to the origin and progression of breast cancer. The present study was designed to explore the potential function and prognosis value of circadian genes in TNBC.Methods: The transcriptome data of circadian genes were downloaded from The Cancer Genomic Atlas (TCGA), GSE25066 and GSE31448 datasets. The differential expressed circadian genes between non-TNBC and TNBC patients were analysed by Wilcoxon test. Univariate and multivariate Cox regression analyses were employed to identify the prognostic circadian genes. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) were performed to study the biological functions of ARNTL2. The composition of 22 immune cells in the tumour samples was estimated with CIBERSORT algorithm. The correlations between ARNTL2 expression and tumour-infiltrating immune cells were evaluated by Spearman correlation coefficient.Results: A total of 8 circadian genes were found to be differentially expressed between non-TNBC and TNBC, but only ARNTL2 has prognostic value. Multivariate Cox analysis identified that ARNTL2 was an independent prognosis factor for overall survival and relapse-free survival in TNBC patients. Functionally, ARNTL2 was mainly involved in immune response processes such as positive regulation of cytokine production, regulation of innate immune response, and cellular responses to molecules of bacterial origin. High expression of ARNTL2 was positively correlated with activated CD4 memory T cells, activated mast cells, and neutrophil infiltration and the expression of markers of neutrophils (ITGAM), dendritic cells (HLA-DRA, HLA-DPA1, ITGAM), Th1 (IL1B, STAT1), Th2 (IL13), Th17 (STAT3) and mast cells (TPSB2, TPSAB1).Conclusion: ARNTL2 may be linked with the functional modulation of the tumour immune microenvironment and serve as a potential biomarker for predicting the prognosis of TNBC patients.Keywords: ARNTL2, immune cell infiltration, triple-negative breast cancer, biomarker, circadian rhythm

The role of G protein-coupled estrogen receptor 1 (GPER) signaling, including promotion of Ezrin phosphorylation (which could be activated by estrogen), has not yet been clearly identified in triple-negative breast cancer (TNBC). This study aimed to evaluate the prognostic value of GPER and Ezrin in TNBC patients. Clinicopathologic features including age, menopausal status, tumor size, nuclear grade, lymph node metastasis, AJCC TNM stage, and ER, PR and HER-2 expression were evaluated from 249 TNBC cases. Immunohistochemical staining of GPER and Ezrin was performed on TNBC pathological sections. Kaplan–Meier analyses, as well as logistic regressive and Cox regression model tests were applied to evaluate the prognostic significance between different subgroups. Compared to the GPER-low group, the GPER-high group exhibited higher TNM staging (P = 0.021), more death (P P P P P P P P = 0.8617; PFS, P = 0.1905; LRFS, P = 0.4378; DDFS, P = 0.2538). There was a significant positive correlation between GPER and Ezrin expression level (R = 0.508, P P = 0.001; HR: 0.320, 95% CI 0.162–0.631, P = 0.001). Thus, overexpression of GPER and Ezrin may contribute to aggressive behavior and indicate unfavorable prognosis in TNBC; this may correspond to an individual’s estrogen levels.

Cardiovascular and metabolic diseases remain the leading cause of morbidity and mortality worldwide. Endothelial dysfunction is a key player in the initiation and progression of cardiovascular and metabolic diseases. Current evidence suggests that the anti-diabetic drug metformin improves insulin resistance and protects against endothelial dysfunction in the vasculature. Hereby, we provide a timely review on the protective effects and molecular mechanisms of metformin in preventing endothelial dysfunction and cardiovascular and metabolic diseases.

Hydrogen (H2) is colorless, odorless, and the lightest of gas molecules. Studies in the past ten years have indicated that H2 is extremely important in regulating the homeostasis of the cardiovascular system and metabolic activity. Delivery of H2 by various strategies improves cardiometabolic diseases, including atherosclerosis, vascular injury, ischemic or hypertrophic ventricular remodeling, intermittent hypoxia- or heart transplantation-induced heart injury, obesity and diabetes in animal models or in clinical trials. The purpose of this review is to summarize the physical and chemical properties of H2, and then, the functions of H2 with an emphasis on the therapeutic potential and molecular mechanisms involved in the diseases above. We hope this review will provide the future outlook of H2-based therapies for cardiometabolic disease.

Triple-negative breast cancer (TNBC) is the most malignant type of breast cancer with ample vascularisation and high vascular endothelial growth factor (VEGF) expression. The sex steroid hormone oestrogen is involved in several cellular activities associated with TNBC regulation. However, the role of oestrogen in VEGF expression and angiogenesis in TNBC remains unclear. In this study, we found that treatment with 17β-oestradiol (E2) inhibited VEGF mRNA and protein expression in the TNBC cell lines MDA-MB-468 and MDA-MB-436. To further elaborate on the phenomenon of E2-regulated angiogenesis, we showed that conditioned medium from the TNBC cell line MDA-MB-468 treated with E2 inhibits the tube formation ability of human umbilical vein endothelial cells (HUVECs). Additionally, the G-protein-coupled oestrogen receptor-1 (GPER-1)-specific agonist G-1 has a function similar to that of E2. While G-15, the selective antagonist of GPER-1, notably reversed the inhibitory effects of E2 and G-1 on VEGF expression and tube formation, suggesting that GPER-1 is involved in the E2-induced angiogenesis suppression in TNBC cells. Moreover, E2 inhibited in vivo tumour growth and angiogenesis and reduced the expression levels of VEGF, NF-κB/p65, STAT3, and the endothelial marker CD34 in MDA-MB-468 xenograft tumours. Our findings provide important evidence that E2 can inhibit VEGF expression and angiogenesis in TNBC by activating GPER-1, offering additional insight into tumour angiogenesis and targets for drug intervention in TNBC.

Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.

Accurate and efficient portrait instance segmentation has become a crucial enabler for many multimedia applications on mobile devices. We present a novel convolutional neural network (CNN) architecture to explicitly address the long standing problems in portrait segmentation, i.e., semantic coherence and boundary localization. Specifically, we propose a cross-granularity categorical attention mechanism leveraging the deep supervisions to close the semantic gap of CNN feature hierarchy by imposing consistent category-oriented information across layers. Furthermore, a cross-granularity boundary enhancement module is proposed to boost the boundary awareness of deep layers by integrating the shape context cues from shallow layers of the network. We further propose a novel and efficient non-parametric affinity model to achieve efficient instance segmentation on mobile devices. We present a portrait image dataset with instance level annotations dedicated to evaluating portrait instance segmentation algorithms. We evaluate our approach on challenging datasets which obtains state-of-the-art results.