Echinacoside attenuates lipopolysaccharide-induced acute lung injury in newborn mice via inactivation of NF- κB/NLRP3 signaling pathway

Purpose: To investigate the effect of echinacoside (ECH) on acute lung injury (ALI) and the underlying mechanism of action.Methods: The ALI model was established through intranasal instillation of lipopolysaccharide (LPS). Lung tissue damage was determined using hematoxylin and eosin (H&E) staining and lung wet-to-dry–weight ratio. Bronchoalveolar lavage fluid (BALF) protein concentration, cell count, and cytokine level were evaluated. Western blotting was used to determine protein expression level.Results: ECH attenuated lung tissue injury and lung wet-to-dry–weight ratio in the ALI model (p < 0.01). The total protein content and number of total cells, neutrophils, and macrophages increased in BALF of mice treated with LPS, but these increases were reversed by ECH treatment (p < 0.01). The levels of TNF-α and IL-1β increased in BALF and lung tissue of LPS-treated mice; however, ECH treatment decreased these changes (p < 0.01). In addition, ECH inhibited the activation of the nuclear factor-κB (NF-κB)/NLR family pyrin domain containing 3 (NLRP3) pathway in LPS-treated mice (p < 0.01).Conclusion: Echinacoside attenuates LPS-induced ALI via inactivation of the NF-κB/NLRP3 pathway, making echinacoside a potential drug for the treatment of ALI. Keywords: Echinacoside, Acute lung injury, Lipopolysaccharide, Nuclear factor-κB, NLR family pyrin domain containing

Improving Topic-Based Data Exchanges among IoT Devices

Data exchange is one of the huge challenges in Internet of Things (IoT) with billions of heterogeneous devices already connected and many more to come in the future. Improving data transfer efficiency, scalability, and survivability in the fragile network environment and constrained resources in IoT systems is always a fundamental issues. In this paper, we present a novel message routing algorithm that optimizes IoT data transfers in a resource constrained and fragile network environment in publish-subscribe model. The proposed algorithm can adapt the dynamical network topology of continuously changing IoT devices with the rerouting method. We also present a rerouting algorithm in Message Queuing Telemetry Transport (MQTT) to take over the topic-based session flows with a controller when a broker crashed down. Data can still be communicated by another broker with rerouting mechanism. Higher availability in IoT can be achieved with our proposed model. Through demonstrated efficiency of our algorithms about message routing and dynamically adapting the continually changing device and network topology, IoT systems can gain scalability and survivability. We have evaluated our algorithms with open source Eclipse Mosquitto. With the extensive experiments and simulations performed in Mosquitto, the results show that our algorithms perform optimally. The proposed algorithms can be widely used in IoT systems with publish-subscribe model. Furthermore, the algorithms can also be adopted in other protocols such as Constrained Application Protocol (CoAP)

Multinomial machine learning identifies independent biomarkers by integrated metabolic analysis of acute coronary syndrome

Abstract A multi-class classification model for acute coronary syndrome (ACS) remains to be constructed based on multi-fluid metabolomics. Major confounders may exert spurious effects on the relationship between metabolism and ACS. The study aims to identify an independent biomarker panel for the multiclassification of HC, UA, and AMI by integrating serum and urinary metabolomics. We performed a liquid chromatography-tandem mass spectrometry (LC–MS/MS)-based metabolomics study on 300 serum and urine samples from 44 patients with unstable angina (UA), 77 with acute myocardial infarction (AMI), and 29 healthy controls (HC). Multinomial machine learning approaches, including multinomial adaptive least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), and assessment of the confounders were applied to integrate a multi-class classification biomarker panel for HC, UA and AMI. Different metabolic landscapes were portrayed during the transition from HC to UA and then to AMI. Glycerophospholipid metabolism and arginine biosynthesis were predominant during the progression from HC to UA and then to AMI. The multiclass metabolic diagnostic model (MDM) dependent on ACS, including 2-ketobutyric acid, LysoPC(18:2(9Z,12Z)), argininosuccinic acid, and cyclic GMP, demarcated HC, UA, and AMI, providing a C-index of 0.84 (HC vs. UA), 0.98 (HC vs. AMI), and 0.89 (UA vs. AMI). The diagnostic value of MDM largely derives from the contribution of 2-ketobutyric acid, and LysoPC(18:2(9Z,12Z)) in serum. Higher 2-ketobutyric acid and cyclic GMP levels were positively correlated with ACS risk and atherosclerosis plaque burden, while LysoPC(18:2(9Z,12Z)) and argininosuccinic acid showed the reverse relationship. An independent multiclass biomarker panel for HC, UA, and AMI was constructed using the multinomial machine learning methods based on serum and urinary metabolite signatures