Background Platelet‐to‐lymphocyte ratio (PLR) is a haematological index which reflects increased level of inflammation and thrombosis. We aimed to summarize the potential prognostic role of PLR for the in‐hospital and long‐term outcomes in ST‐segment elevation myocardial infarction (STEMI) patients treated with primary percutaneous coronary intervention (pPCI) in a meta‐analysis. Materials and methods Relevant cohort studies were identified by search the PubMed, Cochrane's Library and Embase databases. A random‐effect model was applied to pool the results. In‐hospital and long‐term outcomes were compared between patients with higher and lower preprocedural PLR. Results Eleven cohorts with 12 619 patients were included. Pooled results showed that higher preprocedural PLR was independently associated with increased risk of in‐hospital major adverse cardiovascular events (MACE, risk ratio [RR]: 1.76, 95% confidence interval [CI]: 1.39 to 2.22, P < .001; I2 = 49%), cardiac mortality (RR: 1.91, 95% CI: 1.18 to 3.09, P = .009; I2 = 0), all‐cause mortality (RR: 2.14, 95% CI: 1.52 to 3.01, P < .001, I2 = 24%) and no reflow after pPCI (RR: 2.22, 95% CI: 1.70 to 2.90, P < .001, I2 = 59%). Moreover, higher preprocedural PLR was associated with increased risk of MACE (RR: 1.60, 95% CI: 1.25 to 2.03, I2 = 57%, P < .001) and all‐cause mortality (RR: 2.36, 95% CI: 1.53 to 3.66, I2 = 78%, P < .001) during long‐term follow‐up of up to 82 months after discharge. Conclusions Higher PLR predicts poor in‐hospital and long‐term prognosis in STEMI patients after pPCI.

Background:Aloe vera is a medically valuable plant with anti-epileptic activity; however, its mechanism of action remains unknown. In this study, network pharmacological, in vitro, and in vivo experiments were carried out to explore the potential anti-epileptic components and targets of Aloe vera.Methods: The main active components of Aloe vera were identified by searching the Traditional Chinese Medicine System Pharmacology database. Targets of Aloe vera were predicted using SwissTargetPrediction, whereas information about the epilepsy disease targets was obtained from Gene Cards. The protein–protein interaction network and core targets were screened according to the topological structure and CytoNCA plugin. The glutamate-induced HT22 cell line and pentylenetetrazol-induced seizure rats were used to confirm the effect of aloesone by detecting reactive oxygen species (ROS) and apoptosis, and predicting the targets.Results: A total of 14 core active components were selected based on the screening criteria of oral bioavailability ≥30% and drug-likeness ≥ 0.10. Four compounds, namely linoleic acid, aloesone, isoeleutherol glucosiden qt, and anthranol, demonstrated the potential ability of crossing the blood-brain barrier. A total of 153 targets associated with epilepsy were predicted for the four compounds. Moreover, after network analysis with CytoNCA, 10 targets, namely, MAPK1, SRC, MARK3, EGFR, ESR1, PTGS2, PTPN11, JAK2, PPKCA, and FYN, were selected as the core genes, and SRC, which has been predicted to be the target of aloesone and anthranol, exhibited the highest subgraph centrality value. In vitro experiments confirmed that aloesone treatment significantly inhibited the glutamate-induced neuronal injury by reducing the intracellular ROS content and the early phase of apoptosis. Additionally, treatment with 50 mg/kg aloesone resulted in anti-seizure effects by reducing the seizure score and prolonging the latent period in acute and chronic rats. Furthermore, aloesone treatment increased the phosphorylation of c-SRC at Y418 and reduced the phosphorylation at Y529, simultaneously activating c-SRC.Conclusion: Integrating network pharmacology with in vitro and in vivo experiments demonstrated that aloesone, which inhibited seizure by activating c-SRC, is a potential anti-seizure compound present in Aloe vera.

The imperialist competitive algorithm (ICA) is a new heuristic algorithm proposed for continuous optimization problems. The research about its application on solving the traveling salesman problem (TSP) is still very limited. Aiming to explore its ability on solving TSP, we present a discrete imperialist competitive algorithm in this paper. The proposed algorithm modifies the original rules of the assimilation and introduces the 2-opt algorithm into the revolution process. To examine its performance, we tested the proposed algorithm on 10 small-scale and 2 large-scale standard benchmark instances from the TSPLIB and compared the experimental results with that obtained by two other ICA-based algorithms and six other existing algorithms. The proposed algorithm shows excellent performance in the experiments and comparisons.

Control valve is a kind of essential terminal control component which is hard to model by traditional methodologies because of its complexity and nonlinearity. This paper proposes a new modeling method for the upstream pressure of control valve using the least squares support vector machine (LS-SVM), which has been successfully used to identify nonlinear system. In order to improve the modeling performance, the fruit fly optimization algorithm (FOA) is used to optimize two critical parameters of LS-SVM. As an example, a set of actual production data from a controlling system of chlorine in a salt chemistry industry is applied. The validity of LS-SVM modeling method using FOA is verified by comparing the predicted results with the actual data with a value of MSE 2.474 × 10 −3. Moreover, it is demonstrated that the initial position of FOA does not affect its optimal ability. By comparison, simulation experiments based on PSO algorithm and the grid search method are also carried out. The results show that LS-SVM based on FOA has equal performance in prediction accuracy. However, from the respect of calculation time, FOA has a significant advantage and is more suitable for the online prediction.