Beyond hemostasis, thrombosis and wound healing, it is becoming increasingly clear that platelets play an integral role in inflammatory response and immune regulation. Platelets recognize pathogenic microorganisms and secrete various immunoregulatory cytokines and chemokines, thus facilitating a variety of immune effects and regulatory functions. In this review, we discuss recent advances in signaling of platelet activation-related biomarkers in inflammatory settings and application prospects to apply for disease diagnosis and treatment.

Key Points• MEKK3 regulates platelet activation through ERK1/2 and JNK2.• MEKK3 2/2 mice are protected from microthrombosis and myocardial infarct expansion post-MI.MAPKs play important roles in platelet activation. However, the molecular mechanisms by which MAPKs are regulated in platelets remain largely unknown. Real-time polymerase chain reaction and western blot data showed that MEKK3, a key MAP3K family member, was expressed in human and mouse platelets. Then, megakaryocyte/platelet-specific MEKK3-deletion (MEKK3 2/2 ) mice were developed to elucidate the platelet-related function(s) of MEKK3. We found that agonist-induced aggregation and degranulation were reduced in MEKK3 2/2 platelets in vitro. MEKK3 deficiency significantly impaired integrin aIIbb3-mediated inside-out signaling but did not affect the outside-in signaling.At the molecular level, MEKK3 deficiency led to severely impaired activation of extracellular signal-regulated kinases 1/2 (ERK1/2) and c-Jun NH 2 -terminal kinase 2 but not p38 or ERK5. In vivo, MEKK3 2/2 mice showed delayed thrombus formation following FeCl 3 -induced carotid artery injury. Interestingly, the tail bleeding time was normal in MEKK3 2/2 mice. Moreover, MEKK3 2/2 mice had fewer microthrombi, reduced myocardial infarction (MI) size, and improved post-MI heart function in a mouse model of MI.These results suggest that MEKK3 plays important roles in platelet MAPK activation and may be used as a new effective target for antithrombosis and prevention of MI expansion.

Graph based multi-view clustering has been paid great attention by exploring the neighborhood relationship among data points from multiple views. Though achieving great success in various applications, we observe that most of previous methods learn a consensus graph by building certain data representation models, which at least bears the following drawbacks. First, their clustering performance highly depends on the data representation capability of the model. Second, solving these resultant optimization models usually results in high computational complexity. Third, there are often some hyper-parameters in these models need to tune for obtaining the optimal results. In this work, we propose a general, effective and parameter-free method with convergence guarantee to learn a unified graph for multi-view data clustering via cross-view graph diffusion (CGD), which is the first attempt to employ diffusion process for multi-view clustering. The proposed CGD takes the traditional predefined graph matrices of different views as input, and learns an improved graph for each single view via an iterative cross diffusion process by 1) capturing the underlying manifold geometry structure of original data points, and 2) leveraging the complementary information among multiple graphs. The final unified graph used for clustering is obtained by averaging the improved view associated graphs. Extensive experiments on several benchmark datasets are conducted to demonstrate the effectiveness of the proposed method in terms of seven clustering evaluation metrics.