The Performance Analysis for Embedded Systems using Statistics Methods

Performance comparison for the computer system under different hardware platform & system structure is of vital importance in the study of the performance evaluation. The Performance Analysis for Embedded Systems by using statistics methods based on the randomized complete block designs was proposed. Using the randomized block design, the differences between conditions can be separated from the difference in the processing, and be separated from the experimental bias. A case study of automatic gate machines used in the automatic fare collection system of Shanghai Metro is presented. The obtained assessment results show that our approach is helpful and effective. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.2864

Metabolic reprogramming induced by DCA enhances cisplatin sensitivity through increasing mitochondrial oxidative stress in cholangiocarcinoma

Background: Cholangiocarcinoma has obvious primary multidrug resistance and is generally resistant to cisplatin and other chemotherapy drugs and high glycolytic levels may be associated with chemotherapy resistance of cholangiocarcinoma cells. Dichloroacetate (DCA) is a specific inhibitor of PDK, which can promote mitochondrial aerobic oxidation process by activating PDH. In the past few years, there have been an increasing number of studies supporting the action of DCA against cancer, which also provided evidence for targeting metabolism to enhance the efficacy of cholangiocarcinoma chemotherapy.Methods: Glucose uptake and lactic acid secretion were used to detect cell metabolism level. Cell apoptosis and cell cycle were detected to confirm cell fate induced by cisplatin combined with DCA. Mito-TEMPO was used to inhibit mtROS to explore the relationship between oxidative stress and cell cycle arrest induced by DCA under cisplatin stress. Finally, PCR array and autophagy inhibitor CQ were used to explore the potential protective mechanism under cell stress.Results: DCA changed the metabolic model from glycolysis to aerobic oxidation in cholangiocarcinoma cells under cisplatin stress. This metabolic reprogramming increased mitochondrial reactive oxygen species (mtROS) levels, which promoted cell cycle arrest, increased the expression of antioxidant genes and activated autophagy. Inhibition of autophagy further increased the synergistic effect of DCA and cisplatin.Conclusion: DCA increased cisplatin sensitivity in cholangiocarcinoma cells via increasing the mitochondria oxidative stress and cell growth inhibition. Synergistic effects of DCA and CQ were observed in cholangiocarcinoma cells, which further increased the cisplatin sensitivity via both metabolic reprogramming and inhibition of the stress response autophagy

Exploring diagnosis and imaging biomarkers of Parkinson's disease via iterative canonical correlation analysis based feature selection

Parkinson's disease (PD) is a neurodegenerative disorder that progressively hampers the brain functions and leads to various movement and non-motor symptoms. However, it is difficult to attain early-stage PD diagnosis based on the subjective judgment of physicians in clinical routines. Therefore, automatic and accurate diagnosis of PD is highly demanded, so that the corresponding treatment can be implemented more appropriately. In this paper, we focus on finding the most discriminative features from different brain regions in PD through T1-weighted MR images, which can help the subsequent PD diagnosis. Specifically, we proposed a novel iterative canonical correlation analysis (ICCA) feature selection method, aiming at exploiting MR images in a more comprehensive manner and fusing features of different types into a common space. To state succinctly, we first extract the feature vectors from the gray matter and the white matter tissues separately, represented as insights of two different anatomical feature spaces for the subject's brain. The ICCA feature selection method aims at iteratively finding the optimal feature subset from two sets of features that have inherent high correlation with each other. In experiments we have conducted thorough investigations on the optimal feature set extracted by our ICCA method. We also demonstrate that using the proposed feature selection method, the PD diagnosis performance is further improved, and also outperforms many state-of-the-art methods

Overall Survival Time Prediction for High-grade Glioma Patients based on Large-scale Brain Functional Networks

High-grade glioma (HGG) is a lethal cancer with poor outcome. Accurate preoperative overall survival (OS) time prediction for HGG patients is crucial for treatment planning. Traditional presurgical and noninvasive OS prediction studies have used radiomics features at the local lesion area based on the magnetic resonance images (MRI). However, the highly complex lesion MRI appearance may have large individual variability, which could impede accurate individualized OS prediction. In this paper, we propose a novel concept, namely brain connectomics-based OS prediction. It is based on presurgical resting-state functional MRI (rs-fMRI) and the non-local, large-scale brain functional networks where the global and systemic prognostic features rather than the local lesion appearance are used to predict OS. We propose that the connectomics features could capture tumor-induced network-level alterations that are associated with prognosis. We construct both low-order (by means of sparse representation with regional rs-fMRI signals) and high-order functional connectivity (FC) networks (characterizing more complex multi-regional relationship by synchronized dynamics FC time courses). Then, we conduct a graph-theoretic analysis on both networks for a jointly, machine-learning-based individualized OS prediction. Based on a preliminary dataset (N = 34 with bad OS, mean OS, ~400 days; N = 34 with good OS, mean OS, ~1030 days), we achieve a promising OS prediction accuracy (86.8%) on separating the individuals with bad OS from those with good OS. However, if using only conventionally derived descriptive features (e.g., age and tumor characteristics), the accuracy is low (63.2%). Our study highlights the importance of the rs-fMRI and brain functional connectomics for treatment planning

Challenges from Variation across Regions in Cost Effectiveness Analysis in Multi-Regional Clinical Trials

Economic evaluation in the form of cost-effectiveness analysis has become a popular means to inform decisions in healthcare. With multi-regional clinical trials in a global development program becoming a new venue for drug efficacy testing in recent decades, questions in methods for cost-effectiveness analysis in the multi-regional clinical trials setting also emerge. This paper addresses some challenges from variation across regions in cost effectiveness analysis in multi-regional clinical trials. Several discussion points are raised for further attention and a multi-regional clinical trial example is presented to illustrate the implications in industrial application. A general message is delivered to call for a depth discussion by all stakeholders to reach an agreement on a good practice in cost-effectiveness analysis in the multi-regional clinical trials. Meanwhile, we recommend an additional consideration of cost-effectiveness analysis results based on the clinical evidence from a certain homogeneous population as sensitivity or scenario analysis upon data availability

The protective effect of hydrogen sulfide on systemic sclerosis associated skin and lung fibrosis in mice model

BACKGROUD: Systemic sclerosis (SSc) caused fibrosis can be fatal and it still lack of effective treatment. Hydrogen sulfide (H(2)S) appears to be an attractive therapeutic candidates. This study aimed to investigate the protective effect of H(2)S on SSc-associated skin and lung fibrosis. METHODS: We developed a model of SSc by subcutaneous injecting BLM to female C3H mice. The mice received daily subcutaneous injections of NaHS (56 and 112 μg/kg), an H(2)S donor. On days 7, 28, and 42, the mice were killed and blood samples were collected to measure the plasma H(2)S concentration, the skin and lung tissues was harvested for microscopic examination, immunohistochemistry and quantify biological parameters (hydroxyproline content, RT-qPCR and Western blot). RESULTS: In model group, the dermis of skin tissues at different time points gradually thickened, collagen deposition increased. The lung tissues presented pathological changes such as obvious inflammatory cell infiltration, increased collagen deposition and the plasma H(2)S concentrations points significantly decreased. Administration of NaHS markedly decreased the biomarkers of fibrosis such as α-smooth muscle actin, collagen-I, collagen-III, fibronectin, transforming growth factor-β1, Smad2/3 phosphorylation and inflammation including the marker protein of monocyte/macrophage and monocyte chemoattractant protein-1 in the lung. Compared to the low dose group, the expression in the high dose group have decreased trend, but the difference was not significant. CONCLUSION: We demonstrate the beneficial effects of H(2)S on SSc-associated skin and lung fibrosis. H(2)S may be a potential therapy against this intractable disease

Mitochondrial stress response and myogenic differentiation

Regeneration and repair are prerequisites for maintaining effective function of skeletal muscle under high energy demands, and myogenic differentiation is one of the key steps in the regeneration and repair process. A striking feature of the process of myogenic differentiation is the alteration of mitochondria in number and function. Mitochondrial dysfunction can activate a number of transcriptional, translational and post-translational programmes and pathways to maintain cellular homeostasis under different types and degrees of stress, either through its own signaling or through constant signaling interactions with the nucleus and cytoplasm, a process known as the mitochondrial stress responses (MSRs). It is now believed that mitochondrial dysfunction is closely associated with a variety of muscle diseases caused by reduced levels of myogenic differentiation, suggesting the possibility that MSRs are involved in messaging during myogenic differentiation. Also, MSRs may be involved in myogenesis by promoting bioenergetic remodeling and assisting myoblast survival during myogenic differentiation. In this review, we will take MSRs as an entry point to explore its concrete regulatory mechanisms during myogenic differentiation, with a perspective to provide a theoretical basis for the treatment and repair of related muscle diseases

The Performance Analysis for Embedded Systems using Statistics Methods

Performance comparison for the computer system under different hardware platform & system structure is of vital importance in the study of the performance evaluation. The Performance Analysis for Embedded Systems by using statistics methods based on the randomized complete block designs was proposed. Using the randomized block design, the differences between conditions can be separated from the difference in the processing, and be separated from the experimental bias. A case study of automatic gate machines used in the automatic fare collection system of Shanghai Metro is presented. The obtained assessment results show that our approach is helpful and effective. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.2864