Remote ischaemic preconditioning (RIPC) is well known to protect the myocardium against ischaemia/reperfusion injury (IRI). Exosomes are small extracellular vesicles that have become the key mediators of intercellular communication. Various studies have confirmed that circulating exosomes mediate RIPC. However, the underlying mechanisms for RIPC-induced exosome-mediated cardioprotection remain elusive. In our study, we found that the expression level of miR-24 was higher in exosomes derived from the plasma of rats subjected to RIPC than in exosomes derived from the plasma of control rats in vivo. The rat plasma exosomes could be taken up by H9c2 cells. In addition, miR-24 was present in RIPC-induced exosomes and played a role in reducing oxidative stress-mediated injury and decreasing apoptosis by downregulating Bim expression in H2O2-treated H9c2 cells in vitro. In vivo, miR-24 in RIPC-induced exosomes reduced cardiomyocyte apoptosis, attenuated the infarct size and improved heart function. Furthermore, the apoptosis-reducing effect of miR-24 was counteracted by miR-24 antagomirs or inhibitors both in vitro and in vivo. Therefore, we provided evidence that RIPC-induced exosomes could reduce apoptosis by transferring miR-24 in a paracrine manner and that miR-24 in the exosomes plays a central role in mediating the protective effects of RIPC.

The present study aimed to examine the anti-inflammatory actions of oleoylethanolamide (OEA) in lipopolysaccharide (LPS)-induced THP-1 cells. The cells were stimulated with LPS (1 μg/ml) in the presence or absence of OEA (10, 20 and 40 μM). The pro-inflammatory cytokines were evaluated by qRT-PCR and ELISA. The THP-1 cells were transiently transfected with PPARα small-interfering RNA, and TLR4 activity was determined with a blocking test using anti-TLR4 antibody. Additionally, a special inhibitor was used to analyse the intracellular signaling pathway. OEA exerted a potent anti-inflammatory effect by reducing the production of pro-inflammatory cytokines and TLR4 expression, and by enhancing PPARα expression. The modulatory effects of OEA on LPS-induced inflammation depended on PPARα and TLR4. Importantly, OEA inhibited LPS-induced NF-κB activation, IκBα degradation, expression of AP-1, and the phosphorylation of ERK1/2 and STAT3. In summary, our results demonstrated that OEA exerts anti-inflammatory effects by enhancing PPARα signaling, inhibiting the TLR4-mediated NF-κB signaling pathway, and interfering with the ERK1/2-dependent signaling cascade (TLR4/ERK1/2/AP-1/STAT3), which suggests that OEA may be a therapeutic agent for inflammatory diseases.

BackgroundAcute pulmonary embolism (APE) remains a diagnostic challenge due to a variable clinical presentation and the lack of a reliable screening tool. MicroRNAs (miRNAs) regulate gene expression in a wide range of pathophysiologic processes. Circulating miRNAs are emerging biomarkers in heart failure, type 2 diabetes and other disease states; however, using plasma miRNAs as biomarkers for the diagnosis of APE is still unknown.MethodsThirty-two APE patients, 32 healthy controls, and 22 non-APE patients (reported dyspnea, chest pain, or cough) were enrolled in this study. The TaqMan miRNA microarray was used to identify dysregulated miRNAs in the plasma of APE patients. The TaqMan-based miRNA quantitative real-time reverse transcription polymerase chain reactions were used to validate the dysregulated miRNAs. The receiver-operator characteristic (ROC) curve analysis was conducted to evaluate the diagnostic accuracy of the miRNA identified as the candidate biomarker.ResultsPlasma miRNA-134 (miR-134) level was significantly higher in the APE patients than in the healthy controls or non-APE patients. The ROC curve showed that plasma miR-134 was a specific diagnostic predictor of APE with an area under the curve of 0.833 (95% confidence interval, 0.737 to 0.929; P < 0.001).ConclusionsOur findings indicated that plasma miR-134 could be an important biomarker for the diagnosis of APE. Because of this finding, large-scale investigations are urgently needed to pave the way from basic research to clinical utilization.

To reduce the high mortality rate from cardiovascular disease (CVD), the electrocardiogram (ECG) beat plays a significant role in computer-aided arrhythmia diagnosis systems. However, the complex variations and imbalance of ECG beats make this a challenging issue. Since ECG beat data exist in heavily imbalanced category, an effective long short-term memory (LSTM) recurrence network model with focal loss (FL) is proposed. For this purpose, the LSTM network can disentangle the timing features in complex ECG signals, while the FL is used to resolve the category imbalance by downweighting easily identified normal ECG examples. The advantages of the proposed network have been verified in the MIT-BIH arrhythmia database. Experimental results show that the LSTM network with FL achieved a reliable solution to the problem of imbalanced datasets in ECG beat classification and was not sensitive to quality of ECG signals. The proposed method can be deployed in telemedicine scenarios to assist cardiologists into more accurately and objectively diagnosing ECG signals.

Numerical simulation results show that the upper bound order of random packing densities of basic 3D objects is cube (0.78) > ellipsoid (0.74) > cylinder (0.72) > spherocylinder (0.69) > tetrahedron (0.68) > cone (0.67) > sphere (0.64), while the upper bound order of ordered packing densities of basic 3D objects is cube (1.0) > cylinder and spherocylinder (0.9069) > cone (0.7854) > tetrahedron (0.7820) > ellipsoid (0.7707) > sphere (0.7405); these two orders are significantly different. The random packing densities of ellipsoid, cylinder, spherocylinder, tetrahedron and cone are closely related to their shapes. The optimal aspect ratios of these objects which give the highest packing densities are ellipsoid (axes ratio = 0.8:1:1.25), cylinder (height/diameter = 0.9), spherocylinder (height of cylinder part/diameter = 0.35), tetrahedron (regular tetrahedron) and cone (height/bottom diameter = 0.8).packing, particle, nonspherical particle, cylinder, cone, spherocylinder, tetrahedron Citation:Li S X, Zhao J, Lu P, et al. Maximum packing densities of basic 3D objects.To pursue the densest packing has never lost its attraction to human beings. The earliest history of studies on packing problem can be traced back to the famous Kepler Conjecture (the problem of maximum packing density of identical spheres, 1661) and the debate between Newton and Gregory (the problem of maximum coordinate number of identical spheres, 1694). In 1900, Hilbert further presented the packing problem, especially the densest packing of spheres and regular tetrahedra, as the 18th problem in his celebrated list of 23 mathematical problems [1]. For centuries, packing problem has always been attractive since it is not only a basic problem of mathematics and physics, but also extensively applied to many branches of science, engineering and even in daily life. These applications range from the macroscale of celestial body motions to the microscale of molecular arrangements. According to the packing structures, packing problems can be classified into ordered packing and disordered packing. For ordered packing, Hales proposed a proof of the Kepler Conjecture in 1998 [2]. However, it still leaves a long way to the solution of the Hilbert's 18th problem. For disordered packing, random packing which is closely related to matter structure has been investigated extensively. The first systematic study on random packing was undertaken by Bernal in 1950s on the random packing of spheres [3]. Nowadays, numerical simulation has become the main means of random packing researches. Zhao et al.[4] gave a summarization and classification of numerical simulation approaches available on random packing. In respect of particle shapes, sphere is the most comprehensively studied particle shape on random packing, and the packing results are accepted widely within the academic community. Nonspherical particles are often simplified to equivalent spheres in engineering applications. However, recent investigations indicated that the packing properties of nonspherical particles ...

Organic photovoltaic (OPV) cells have shown effectiveness as off-grid power entities to drive the low power consumption electronics among the Internet of Things. The trap states and the induced recombination in OPV cells are critically relevant to the photovoltaic performance but remain ambiguous in OPV cells for indoor application. Here, we investigate the trap effects on the indoor photovoltaic performance by employing PBBD-T series donors and wide bandgap acceptor BTA3. It is revealed that the discrete density of state in OPV cells introduces low-lying trap states and further aggravates the trap-induced recombination. Instead of the domination of bimolecular recombination under solar radiation, trap-induced recombination prevails under indoor scenarios because of the low level of carrier densities under indoor weak illuminations. This work illustrates the details of charge carrier recombination behavior in OPV cells for indoor application and points out the importance of trap controlling in achieving high-performance indoor OPV cells.

Drug combinations have been widely applied to treat complex diseases, like cancer, HIV and cardiovascular diseases. One of the most important characteristics for drug combinations is the synergistic effects among different drugs, that is to say, the combination effects are larger than the sum of individual effects. Although quantitative methods can be utilized to evaluate the synergistic effects based on experimental dose-response data, it is both time and resource consuming to screen all possible combinations by experimental trials. This problem makes it a formidable challenge to recognize synergistic combinations. Various attempts have been made to predict drug synergy by network biology, however, most of them are limited to estimating target associations on the PPI network. Here, we proposed a novel "pathway-pathway interaction" network-based synergy evaluation method to predict the potential synergistic drug combinations. Comparison with previous target-based methods shows that inclusion of systematic pathway-pathway interactions makes this novel method outperform others in predicting drug synergy. Moreover, it can also help to interpret how different drugs in a combination cooperate with each other to implement synergistic therapeutic effects. In general, drugs acting on the same pathway through different targets or drugs regulating a relatively small number of highly-connected pathways are more likely to produce synergistic effects.

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