P2 receptor mRNA expression profiles in human lymphocytes, monocytes and CD34+ stem and progenitor cells

BACKGROUND: Extracellular nucleotides (ATP, ADP, UTP and UDP) exert a wide range of biological effects in blood cells mediated by multiple ionotropic P2X receptors and G protein-coupled P2Y receptors. Although pharmacological experiments have suggested the presence of several P2 receptor subtypes on monocytes and lymphocytes, some results are contradictory. Few physiological functions have been firmly established to a specific receptor subtype, partly because of a lack of truly selective agonists and antagonists. This stimulated us to investigate the expression of P2X and P2Y receptors in human lymphocytes and monocytes with a newly established quantitative mRNA assay for P2 receptors. In addition, we describe for the first time the expression of P2 receptors in CD34(+ )stem and progenitor cells implicating a potential role of P2 receptors in hematopoietic lineage and progenitor/stem cell function. RESULTS: Using a quantitative mRNA assay, we assessed the hypothesis that there are specific P2 receptor profiles in inflammatory cells. The P2X(4 )receptor had the highest expression in lymphocytes and monocytes. Among the P2Y receptors, P2Y(12 )and P2Y(2 )had highest expression in lymphocytes, while the P2Y(2 )and P2Y(13 )had highest expression in monocytes. Several P2 receptors were expressed (P2Y(2), P2Y(1), P2Y(12), P2Y(13), P2Y(11), P2X(1), P2X(4)) in CD34+ stem and progenitor cells. CONCLUSIONS: The most interesting findings were the high mRNA expression of P2Y(12 )receptors in lymphocytes potentially explaining the anti-inflammatory effects of clopidogrel, P2Y(13 )receptors in monocytes and a previously unrecognised expression of P2X(4 )in lymphocytes and monocytes. In addition, for the first time P2 receptor mRNA expression patterns was studied in CD34(+ )stem and progenitor cells. Several P2 receptors were expressed (P2Y(2), P2Y(1), P2Y(12), P2Y(13), P2Y(11), P2X(1), P2X(4)), indicating a role in differentiation and proliferation. Thus, it is possible that specific antibodies to P2 receptors could be used to identify progenitors for monocytes, lymphocytes and megakaryocytes

PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting

When predicting PM2.5 concentrations, it is necessary to consider complex information sources since the concentrations are influenced by various factors within a long period. In this paper, we identify a set of critical domain knowledge for PM2.5 forecasting and develop a novel graph based model, PM2.5-GNN, being capable of capturing long-term dependencies. On a real-world dataset, we validate the effectiveness of the proposed model and examine its abilities of capturing both fine-grained and long-term influences in PM2.5 process. The proposed PM2.5-GNN has also been deployed online to provide free forecasting service.Comment: Pre-print version of a ACM SIGSPATIAL 2020 poster [paper](https://dl.acm.org/doi/10.1145/3397536.3422208). The code is available at [Github](https://github.com/shawnwang-tech/PM2.5-GNN), and the talk is available at [YouTube](https://www.youtube.com/watch?v=VX93vMthkGM

GWO-BP neural network based OP performance prediction for mobile multiuser communication networks

The complexity and variability of wireless channels makes reliable mobile multiuser communications challenging. As a consequence, research on mobile multiuser communication networks has increased significantly in recent years. The outage probability (OP) is commonly employed to evaluate the performance of these networks. In this paper, exact closed-form OP expressions are derived and an OP prediction algorithm is presented. Monte-Carlo simulation is used to evaluate the OP performance and verify the analysis. Then, a grey wolf optimization back-propagation (GWO-BP) neural network based OP performance prediction algorithm is proposed. Theoretical results are used to generate training data. We also examine the extreme learning machine (ELM), locally weighted linear regression (LWLR), support vector machine (SVM), BP neural network, and wavelet neural network methods. Compared to the wavelet neural network, LWLR, SVM, BP, and ELM methods, the results obtained show that the GWO-BP method provides the best OP performance prediction

A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer

Purpose: To evaluate the potential of machine learning (ML)-based radiomics approach for predicting tumor mutation burden (TMB) in gastric cancer (GC).Methods: The contrast enhanced CT (CECT) images with corresponding clinical information of 256 GC patients were retrospectively collected. Patients were separated into training set (n = 180) and validation set (n = 76). A total of 3,390 radiomics features were extracted from three phases images of CECT. The least absolute shrinkage and selection operator (LASSO) model was used for feature screening. Seven machine learning (ML) algorithms were employed to find the optimal classifier. The predictive ability of radiomics model (RM) was evaluated with receiver operating characteristic. The correlation between RM and TMB values was evaluated using Spearman’s correlation coefficient. The explainability of RM was assessed by the Shapley Additive explanations (SHAP) method.Results: Logistic regression algorithm was chosen for model construction. The RM showed good predictive ability of TMB status with AUCs of 0.89 [95% confidence interval (CI): 0.85–0.94] and 0.86 (95% CI: 0.74–0.98) in the training and validation sets. The correlation analysis revealed a good correlation between RM and TMB levels (correlation coefficient: 0.62, p < 0.001). The RM also showed favorable and stable predictive accuracy within the cutoff value range 6–16 mut/Mb in both sets.Conclusion: The ML-based RM offered a promising image biomarker for predicting TMB status in GC patients

Patient-assessed short-term positive response to cardiac resynchronization therapy is an independent predictor of long-term mortality.

Cardiac resynchronization therapy (CRT) has a well-documented positive effect on mortality and heart failure morbidity. The aim of this study was to assess the long-term survival and the predictive value of self-assessed functional status on the long-term prognosis of patients treated with CRT-pacemaker (CRT-P).METHODS AND RESULTS: Data were retrospectively collected from medical records of 446 consecutive patients implanted with CRT-P at a large-volume Swedish tertiary care centre. Primary outcome was all-cause mortality, predictive variables were assessed by log-rank test and univariate cox regression. Three hundred and nine patients had reliable information available on early improvement after implantation and were included in the multivariate analyses. The cohort was followed for a median of 79 months and was similar in baseline characteristics compared with major controlled trials. During follow-up 204 patients died, yearly mortality was 11.7%. Early improvement of self-assessed functional status was a strong independent predictor of survival [hazard ratio, HR 0.59, confidence interval (CI) 0.40-0.87, P = 0.007], together with well-known predictors; NYHA III-IV vs I-II (HR 1.66, CI 1.09-2.536, P = 0.018), age (HR 1.05, CI 1.03-1.08, P < 0.001), male gender (HR 2.0, CI 1.11-3.45, P = 0.021), and loop diuretic use (HR 4.41, CI 1.08-18.02). Patients with early improvement of self-assessed functional status had better 2-year and 5-year survival (P < 0.001).CONCLUSIONS: Real-life patient characteristics and predictors of outcome compare well with those in published prospective trials. Self-assessed functional status is a strong predictor of long-term survival, which may have implications for a more active follow-up of patients without spontaneous improvement

Zwitterionic coating assisted by dopamine with metal-phenolic networks loaded on titanium with improved biocompatibility and antibacterial property for artificial heart

Introduction: Titanium (Ti) and Ti-based alloy materials are commonly used to develop artificial hearts. To prevent bacterial infections and thrombus in patients with implanted artificial hearts, long-term prophylactic antibiotics and anti-thrombotic drugs are required, and this may lead to health complications. Therefore, the development of optimized antibacterial and antifouling surfaces for Ti-based substrate is especially critical when designing artificial heart implants.Methods: In this study, polydopamine and poly-(sulfobetaine methacrylate) polymers were co-deposited to form a coating on the surface of Ti substrate, a process initiated by Cu2+ metal ions. The mechanism for the fabrication of the coating was investigated by coating thickness measurements as well as Ultraviolet–visible and X-ray Photoelectron (XPS) spectroscopy. Characterization of the coating was observed by optical imaging, scanning electron microscope (SEM), XPS, atomic force microscope (AFM), water contact angle and film thickness. In addition, antibacterial property of the coating was tested using Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) as model strains, while the material biocompatibility was assessed by the antiplatelet adhesion test using platelet-rich plasma and in vitro cytotoxicity tests using human umbilical vein endothelial cells and red blood cells.Results and discussion: Optical imaging, SEM, XPS, AFM, water contact angle, and film thickness tests demonstrated that the coating was successfully deposited on the Ti substrate surface. The biocompatibility and antibacterial assays showed that the developed surface holds great potential for improving the antibacterial and antiplatelet adhesion properties of Ti-based heart implants