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Abstract Epidemiological surveys have shown that the incidence of type 2 diabetes mellitus (T2DM) and malignancies is rapidly increasing worldwide and has become a major disease that threatens human life. In this study, we quantitatively analyzed the proteome of tumor tissues and adjacent normal tissues from six patients withT2DM combined with colorectal cancer (CRC) and eight non-diabetic CRC, focusing on the effect of T2DM on tumor tissues. We analyzed the functional enrichment of differentially expressed proteins (DEPs) using clusterProfiler in R and the expression level of protein tyrosine phosphatase non-receptor type 11 (PTPN11) and other key proteins in the TIMER and GEPIA2 databases. The HPA database was used to validate PTPN11 protein expression. The correlation between PTPN11 expression and clinicopathological features was analyzed by UALCAN database. The impact of PTPN11 on clinical prognosis was evaluated utilizing Kaplan-Meier Plotter. The correlation between PTPN11 expression and tumor-infiltrated immune cells was investigated via TIMER and TISIDB databases. Gene set enrichment analysis (GSEA) was performed to examined the pathway of PTPN11 enrichment in CRC using data from The Cancer Genome Atlas (TCGA) database. Furthermore, small interfering (si) RNA was used to knock down PTPN11 in CRC cell line SW480. Western blot analysis was used to detect PTPN11 expression in tissue samples or cells and the effect of PTPN11 knockdown on key proteins related to PI3K/AKT and cell cycle pathway in SW480 cells. Cell proliferation and wound healing assays were used to detect the effects of cell proliferation and migration after knockdown of PTPN11 or treatment with high glucose. We found that metabolic pathways such as oxidative phosphorylation, glycolysis/gluconeogenesis, and insulin secretion were significantly enriched in tumor tissues from diabetic patients compared to non-diabetic patients. In addition, PTPN11, a marker gene associated with T2DM and CRC, were mined in diabetic tumor tissues. PTPN11 showed high expression in diabetic tumor tissues compared to normal tissues. High PTPN11 expression predicted poor prognosis in CRC. PTPN11 expression was strongly associated with immune infiltrating cells in CRC. GSEA analysis revealed that PTPN11 was enriched in cancer-related pathways. Western blotting analysis indicated that PTPN11 knockdown reduced the protein levels of p-PI3K, p-AKT, CDK1 and CYCLIN D, without altering PI3K and AKT protein levels. Cell proliferation and wound healing data showed that PTPN11 and high glucose could increase the proliferation and migration ability. These findings showed that PTPN11 may be a potential key biomarker for CRC in patients with diabetes, which will provide new potential targets for future intervention of T2DM complicated with CRC.

Antiplatelet therapy is the foundational treatment for the prevention and treatment of coronary and cerebrovascular ischemic events in patients with coronary heart disease, ischemic stroke, and transient ischemic attack (TIA). However, with more and more studies reporting an increased risk of thrombosis in some patients due to poor response to therapeutic agents, the selection of appropriate P2Y12 inhibitors has become a major challenge that needs to be addressed urgently. Currently, commonly used oral P2Y12 inhibitors include clopidogrel, ticagrelor, and prasugrel. Assessing patients' risk factors before the development of treatment regimens by effectively predicting the risk of high platelet reactivity with specific P2Y12 inhibitors in advance to avert the occurrence of major adverse cardiovascular and cerebrovascular events (MACCE) is the key point to the problem. Up to now, methods available for predicting platelet reactivity include genetic testing, platelet function testing, and risk scores. This review provides a summarization of the existent available identification methods and analyzes the advantages and drawbacks of different methods in specific clinical settings, intending to guide the rational clinical application of P2Y12 receptor inhibitors.

Abstract Background Myocardial infarction (MI) represents a severe cardiovascular disease with limited therapeutic agents. This study was aimed to elucidate the role of the exosomes derived from human placental mesenchymal stem cells (PMSCs-Exos) in MI. Methods PMSCs were isolated and cultured in vitro, with identification by both transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA). To further investigate the effects of PMSC-Exos on MI, C57BL/6 mice were randomly divided into Sham group, MI group, and PMSC-Exos group. After 4 weeks of the intervention, cardiac function was assessed by cardiac echocardiography, electrocardiogram and masson trichrome staining; lipid indicators were determined by automatic biochemical instrument; inflammatory cytokines were measured by cytometric bead array (CBA); gut microbiota, microbial metabolites short chain fatty acids (SCFAs) as well as lipopolysaccharide (LPS) were separately investigated by 16S rRNA high throughput sequencing, gas chromatography mass spectrometry (GC–MS) and tachypleus amebocyte lysate kit; transcriptome analysis was used to test the transcriptional components (mRNA\miRNA\cirRNA\lncRNA) of PMSC-Exos. Results We found that human PMSC-Exos were obtained and identified with high purity and uniformity. MI model was successfully established. Compared to MI group, PMSC-Exos treatment ameliorated myocardial fibrosis and left ventricular (LV) remodeling (P

Abstract This paper presents a multivariable finite‐time observer‐based adaptive‐gain sliding mode control scheme for a fixed‐wing unmanned aerial vehicle (UAV) subject to the unmeasurable angular rates and unknown matched/unmatched disturbance. The control‐oriented model is obtained through the dynamics of fixed‐wing UAV and is composed of attitude subsystem and airspeed subsystem. For the attitude subsystem, a multi‐variable finite‐time observer (MFO) is constructed to achieve the estimation values of unknown states. With the estimation values provided by MFO, a novel adaptive dual‐layer continuous terminal sliding mode (ADL‐CTSM) controller is proposed to track the reference attitude command in finite time. The advantage of the proposed ADL‐CTSM controller is that the bounds of the disturbances are not required and the adaptive gains are obtained as small as possible to attenuate chattering efficiently. For the airspeed subsystem, the tracking differentiator (TD), which enhances the tracking performance, is employed to obtain the smooth reference command and its derivative. The Lipschitz continuous control signal is generated by the designed airspeed controller which is based on the linear proportional‐integral (PI) and the integral of the adaptive‐dual layer twisting algorithm (PIATCI). The rigorous proof of the finite‐time stability of the close‐loop system is provided through Lyapunov criteria and homogeneous technique. Finally, three cases of simulations are carried out to verify the effectiveness and superiority of the proposed control scheme.

Abstract This paper considers the problem of adaptive fault estimation and fault‐tolerant tracking control for a class of uncertain nonlinear systems with model uncertainty and process fault. By utilising the approximation abilities of fuzzy logic systems, a novel fault estimation fuzzy observer is designed to estimate the unmeasured states, nonlinear functions and process fault. Based on backstepping technologies, a adaptive fuzzy fault‐tolerant controller with the adaptive updated laws is established, which can not only ensure the prescribed tracking performances, but also effectively compensate the process fault. It is proved that all the closed‐loop signals of systems are uniformly ultimately bounded and the tracking errors can constrain to the prescribed bounds. Finally, simulation results can verify the efficiency of the proposed approach.

Abstract Background Acupuncture and moxibustion has been widely applied to hyperlipidemia treatment in clinical practice in China, serving as an alternative treatment to statins. Warm-needling acupuncture and medicinal cake-separated moxibustion have been separately reported with potential therapeutic effects on hyperlipidemia treatment in several studies but with limitations in study methodology. Combining these two modalities may provide a more advantageous strategy in treating hyperlipidemia. Therefore, a strict evaluation through well-designed randomized controlled trials (RCT) is necessary to determine their efficacy and safety on hyperlipidemia. Methods The study a multicenter, open-label, randomized, stratified, active-controlled, noninferiority trial with two parallel groups. Subjects with hyperlipidemia will be stratified into different groups by risk levels of heart diseases. They then will be instructed to the Therapeutic Lifestyle Change (TLC) diet. Those who have not reached the target lipid level will be randomly assigned to the treatments of either acupuncture and moxibustion or simvastatin with a 1:1 allocation. One hundred and thirty subjects are aimed to be recruited. The duration of intervention for this study will be 12 weeks, followed by another 4 weeks for post-treatment assessment. The primary outcome is percentage change from baseline to the end of the study in low-density lipoprotein cholesterol (LDL-C). Other indicators in lipid change, safety and adherence will also be assessed secondarily. The repeated measures, linear mixed-effects model will be applied to the analysis. Discussion Acupuncture and moxibustion could be a potentially effective treatment alternative for hyperlipidemia. A study with careful design is developed to evaluate the efficacy and safety of combined acupuncture and moxibustion, by integrating the traditional Chinese Medicine (TCM) regimens with the standardized Western medicine appraisal approach. Trial registration ClinicalTrials.gov, NCT02269046 . Registered on 26 September 2014.

This paper is devoted to the perfect tracking problem of consensus and the monotonic convergence problem of input errors for the uncertain nonlinear fractional-order multiagent systems (FOMASs), where there exist the linear coupling relations between the fractional order, the perturbations of the system matrix, and input matrix. For the FOMASs including one leader agent and multiple follower agents, an observer-type fractional-order iterative learning consensus protocol is proposed. Based on the two-dimensional analysis for the FOMASs, a novel performance index, which can exhibit the monotonic convergence of the input errors, is constructed by using the definition of fractional integral. A Lyapunov-like method is applied to derive the sufficient conditions in terms of linear matrix inequalities, which can guarantee the perfect tracking of consensus and the monotonic convergence of input errors. Finally, the numerical simulation results including comparisons to traditional two-dimensional analysis are presented to demonstrate the effectiveness of the proposed methods.

This study proposes a new method for content‐based image retrieval by finding an optimal classifier. The optimal classifier is achieved by a new active learning support vector machine (SVM) which combines the model selection with the active learning. The unlabelled samples close to the boundary of the SVM classifier are selected based on the feature similarity for the active learning, and the adaptive regularisation is used to select the optimal model. The combination of model selection with active learning accelerates the convergence of the classifier. The new method can improve the image retrieval accuracy and reduce the time consumption. The experimental results show that the proposed method has a better performance with fewer samples and less time consumption for image retrieval.

We sought to assess light characteristics and user acceptability of a prototype Bright Classroom (BC), designed to prevent children's myopia by exposing them to light conditions resembling the outdoors. Conditions were measured throughout the school year in the glass-constructed BC, a traditional classroom (TC) and outdoors. Teachers and children completed user questionnaires, and children rated reading comfort at different light intensities. A total of 230 children (mean age 10.2 years, 57.4% boys) and 13 teachers (36.8 years, 15.4% men) completed questionnaires. The median (Inter Quartile Range) light intensity in the BC (2,540 [1,330-4,060] lux) was greater than the TC (477 [245-738] lux, P < 0.001), though less than outdoors (19,500 [8,960-36,000] lux, P < 0.001). A prominent spectral peak at 490-560 nm was present in the BC and outdoors, but less so in the TC. Teachers and children gave higher overall ratings to the BC than TC, and light intensity in the BC in summer and on sunny days (>5,000 lux) was at the upper limit of children's comfort for reading. In summary, light intensity in the BC exceeds TC, and is at the practical upper limit for routine use. Children and teachers prefer the BC.

Multi-objective programming with uncertain information has been widely applied in modeling of industrial produce and logistic distribution problems. Usually the expectation value model and chance-constrained model as solution models are used to deal with such uncertain programming. In this paper, we consider the uncertain programming problem which contains random information and rough information and is hard to be solved. A new solution model, called stochastic rough multi-objective synthesis effect (MOSE) model, is developed to deal with a class of multi-objective programming problems with random rough coefficients. The MOSE model contains expectation value model and chance-constrained model by choosing different synthesis effect functions and can be considered as an extension of crisp multi-objective programming model. Combined with genetic algorithm, the optimal solution of the MOSE model can be obtained. It shows that the solutions of the MOSE model are better than that of other solution models. Finally, an illustrative example is provided to show the effectiveness of the proposed method.

This paper proposes a scheduling strategy based on real-time pricing in smart grids. A hierarchical game is employed to analyze the decision-making process of generators and consumers. We prove the existence and uniqueness of Nash equilibrium and utilize a backward induction method to obtain the generation and consumption strategies. Then, we propose two dynamic algorithms for the generators and consumers to search for the equilibrium in a distributed fashion. Simulation results demonstrate that the proposed scheduling strategy can match supply with demand and shift load away from peak time.