Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience.

Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.This research is supported by the Center forDynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant no. MOST103-2911-I-008-001). Also, it is supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302)

Oct-based air-jet indentation system and applications : detection of change of stiffness in articular cartilage

2010-2011 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Influence of silencing the MC4R gene by lentivirusmediated RNA interference in bovine fibroblast cells

Melanocortin receptor 4 (MC4R) is a key element in the mechanisms used to regulate both aspects of keeping the balance between energy uptake and energy expenditure. MC4R was knocked down by lentivirus-mediated shRNA expressing plasmids, which were controlled by the U6 promoter in bovine fibroblast cells, and the expression of MC4R was examined by the real time-PCR and Western blot analysis. Real time-PCR analysis was used to characterize the expression of Leptin, POMC, AGRP, MC3R and NPY gene. The relative genes [leptin, proopiomelanocortin (POMC), agouti-related peptide (AGRP), MC3R and neuropeptide Y (NPY)] expression level seemed to be closely associated with the MC4R gene in bovine fibroblast cell lines (BFCs). The levels of both MC4R mRNA and protein were significantly reduced by RNA interference (RNAi) mediated knockdown of MC4R in BFCs cells transfected with plasmid-based MC4R-specific shRNAs. The finding of this study demonstrated that vector based siRNA expression systems were an efficient approach to the knockdown of the MC4R gene expression in bovine fibroblast cells and they provided a new molecular basis for understanding the relationship of MC4R and other genes, which were responsible for the regulation of energy homeostasis by the melanocortin system.Key words: Melanocortin receptor 4 (MC4R), RNAi, bovine fibroblast cells, energy homeostasis

The TOC-Based Algorithm for Solving Multiple Constraint Resources: a Re-examination

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Unique walnut-shaped porous MnO<inf>2</inf>/C nanospheres with enhanced reaction kinetics for lithium storage with high capacity and superior rate capability

Unique walnut-shaped porous MnO2/carbon nanospheres via in situ carbonization of amorphous MnO2 nanospheres demonstrate enhanced reaction kinetics for lithium storage.This work is realized in the frame of a program for Changjiang Scholars and Innovative Research Team (IRT_15R52) of Chinese Ministry of Education. B. L. Su acknowledges the Chinese Central Government for an “Expert of the State” position in the Program of the “Thousand Talents” and a Life Membership at the Clare Hall, Cambridge and the financial support of the Department of Chemistry, University of Cambridge. Y. Li acknowledges Hubei Provincial Department of Education for the “Chutian Scholar” program. T. Hasan acknowledges funding from the Royal Academy of Engineering (Graphlex) and an Impact Acceleration Award (GRASS). This work is also financially supported by the National Science Foundation for Young Scholars of China (No. 21301133 and 51302204), International Science & Technology Cooperation Program of China (2015DFE52870) and and Self-determined and Innovative Research Funds of the SKLWUT (2015‐ZD‐7). The authors also would like to thank Dr. Bin-Jie Wang from Shanghai Nanoport (FEI, Shanghai) for TEM analysis, and thank Hang Ping from Wuhan University of Technology for the TGA/DSC tests.This is the author accepted manuscript. The final version is available from the Royal Society of Chemistry via http://dx.doi.org/10.1039/C6TA00594

Survey on Vision-based Path Prediction

Path prediction is a fundamental task for estimating how pedestrians or vehicles are going to move in a scene. Because path prediction as a task of computer vision uses video as input, various information used for prediction, such as the environment surrounding the target and the internal state of the target, need to be estimated from the video in addition to predicting paths. Many prediction approaches that include understanding the environment and the internal state have been proposed. In this survey, we systematically summarize methods of path prediction that take video as input and and extract features from the video. Moreover, we introduce datasets used to evaluate path prediction methods quantitatively.Comment: DAPI 201

Cell separation using tilted-angle standing surface acoustic waves

Separation of cells is a critical process for studying cell properties, disease diagnostics, and therapeutics. Cell sorting by acoustic waves offers a means to separate cells on the basis of their size and physical properties in a label-free, contactless, and biocompatible manner. The separation sensitivity and efficiency of currently available acoustic-based approaches, however, are limited, thereby restricting their widespread application in research and health diagnostics. In this work, we introduce a unique configuration of tilted-angle standing surface acoustic waves (taSSAW), which are oriented at an optimally designed inclination to the flow direction in the microfluidic channel. We demonstrate that this design significantly improves the efficiency and sensitivity of acoustic separation techniques. To optimize our device design, we carried out systematic simulations of cell trajectories, matching closely with experimental results. Using numerically optimized design of taSSAW, we successfully separated 2- and 10-µm-diameter polystyrene beads with a separation efficiency of ~99%, and separated 7.3- and 9.9-µm-polystyrene beads with an efficiency of ~97%. We illustrate that taSSAW is capable of effectively separating particles–cells of approximately the same size and density but different compressibility. Finally, we demonstrate the effectiveness of the present technique for biological–biomedical applications by sorting MCF-7 human breast cancer cells from nonmalignant leukocytes, while preserving the integrity of the separated cells. The method introduced here thus offers a unique route for separating circulating tumor cells, and for label-free cell separation with potential applications in biological research, disease diagnostics, and clinical practice.National Institutes of Health (U.S.) (Grant U01HL114476)National Institutes of Health (U.S.) (New Innovator Award 1DP2OD007209-01)National Science Foundation (U.S.). Materials Research Science and Engineering Centers (Program) (Grant DMR-0820404

Evodiamine Induces Transient Receptor Potential Vanilloid-1-Mediated Protective Autophagy in U87-MG Astrocytes

Cerebral ischemia is a leading cause of mortality and morbidity worldwide, which results in cognitive and motor dysfunction, neurodegenerative diseases, and death. Evodiamine (Evo) is extracted from Evodia rutaecarpa Bentham, a plant widely used in Chinese herbal medicine, which possesses variable biological abilities, such as anticancer, anti-inflammation, antiobesity, anti-Alzheimer’s disease, antimetastatic, antianoxic, and antinociceptive functions. But the effect of Evo on ischemic stroke is unclear. Increasing data suggest that activation of autophagy, an adaptive response to environmental stresses, could protect neurons from ischemia-induced cell death. In this study, we found that Evo induced autophagy in U87-MG astrocytes. A scavenger of extracellular calcium and an antagonist of transient receptor potential vanilloid-1 (TRPV-1) decreased the percentage of autophagy accompanied by an increase in apoptosis, suggesting that Evo may induce calcium-mediated protective autophagy resulting from an influx of extracellular calcium. The same phenomena were also confirmed by a small interfering RNA technique to knock down the expression of TRPV1. Finally, Evo-induced c-Jun N-terminal kinases (JNK) activation was reduced by a TRPV1 antagonist, indicating that Evo-induced autophagy may occur through a calcium/c-Jun N-terminal kinase (JNK) pathway. Collectively, Evo induced an influx of extracellular calcium, which led to JNK-mediated protective autophagy, and this provides a new option for ischemic stroke treatment

Competing risks analysis for neutrophil to lymphocyte ratio as a predictor of diabetic retinopathy incidence in the Scottish population

Background Diabetic retinopathy (DR) is a major sight-threatening microvascular complication in individuals with diabetes. Systemic inflammation combined with oxidative stress is thought to capture most of the complexities involved in the pathology of diabetic retinopathy. A high level of neutrophil–lymphocyte ratio (NLR) is an indicator of abnormal immune system activity. Current estimates of the association of NLR with diabetes and its complications are almost entirely derived from cross-sectional studies, suggesting that the nature of the reported association may be more diagnostic than prognostic. Therefore, in the present study, we examined the utility of NLR as a biomarker to predict the incidence of DR in the Scottish population. Methods The incidence of DR was defined as the time to the first diagnosis of R1 or above grade in the Scottish retinopathy grading scheme from type 2 diabetes diagnosis. The effect of NLR and its interactions were explored using a competing risks survival model adjusting for other risk factors and accounting for deaths. The Fine and Gray subdistribution hazard model (FGR) was used to predict the effect of NLR on the incidence of DR. Results We analysed data from 23,531 individuals with complete covariate information. At 10 years, 8416 (35.8%) had developed DR and 2989 (12.7%) were lost to competing events (death) without developing DR and 12,126 individuals did not have DR. The median (interquartile range) level of NLR was 2.04 (1.5 to 2.7). The optimal NLR cut-off value to predict retinopathy incidence was 3.04. After accounting for competing risks at 10 years, the cumulative incidence of DR and deaths without DR were 50.7% and 21.9%, respectively. NLR was associated with incident DR in both Cause-specific hazard (CSH = 1.63; 95% CI: 1.28–2.07) and FGR models the subdistribution hazard (sHR = 2.24; 95% CI: 1.70–2.94). Both age and HbA1c were found to modulate the association between NLR and the risk of DR. Conclusions The current study suggests that NLR has a promising potential to predict DR incidence in the Scottish population, especially in individuals less than 65 years and in those with well-controlled glycaemic status

Neuroimaging alterations related to status epilepticus in an adult population: Definition of MRI findings and clinical-EEG correlation

Magnetic resonance imaging (MRI) provides an opportunity for identifying peri-ictal MRI abnormalities (PMAs) related to status epilepticus (SE). Extremely variable MRI alterations have been reported previously during or after SE, mainly in small selected populations. In a retrospective monocentric study, we analyzed brain MRI changes observed in the ictal/postictal periods of SE in an adult population. We included all consecutive patients observed in a 5-year period with an electroclinical diagnosis of SE and an MRI performed within 30 days from the beginning of SE. We identified 277 patients. Among them, 32 (12%) showed PMAs related to SE. The duration of SE was strongly associated with MRI alterations, showing a mean duration of 6 days vs 2 days (P =.011) in the group with and without MRI alterations, respectively. Focal electroencephalography (EEG) abnormalities (P =.00003) and in particular, lateralized periodic discharges (LPDs) (P <.0001) were strongly associated with PMAs. MRI alterations were unilateral (23 patients, 72%), located in multiple brain structures (19 patients, 59%), and involving mesiotemporal structures (17 patients, 53%). Sixteen patients (50%) had good spatial correspondence between cortical PMAs and the focal EEG pattern; 12 patients (38%) with focal EEG pattern showed cortical PMAs plus MRI signal changes also involving subcortical structures. A follow-up MRI was available for 14 of 32 patients (44%): 10 patients presented a disappearance of PMAs, whereas in 4, PMAs were still present. This study demonstrates that a long duration SE and the presence of certain EEG patterns (LPDs) are associated with the occurrence of PMAs. A good spatial concordance was observed between cortical PMA location and the EEG focus