Samekto Wibowo

Cytidine 5'-diphosphocholine administration prevents peripheral neuropathic pain after sciatic nerve crush injury in rats.

Background Cytidine 5′-diphosphocholine (citicoline) has been shown to have beneficial effects in central nervous system injury as well as in motoric functional recovery after peripheral nerve injury. This study aimed to examine the effect of citicoline on prevention of neuropathic pain in a rat model of sciatic nerve crush injury. Methods Forty experimental rats were divided into four groups. In three groups, the right sciatic nerves were crushed in the mid-thigh region, and a gelatin sponge moistened with 0.4 or 0.8 mL of 100 µmol/L citicoline, or saline 0.4 mL in the control group, was applied. The fourth group of rats was sham-operated, ie the sciatic nerve was exposed with no crush. Functional assessments were performed 4 weeks after crush injury. von Frey filaments (100 g threshold) were used to assess neuropathic pain. In addition, the sciatic functional index and extensor postural thrust (EPT) tests were used to assess motoric function. Results The crush/citicoline 0.4 mL group had a lower percentage of pain (23.53%, n=17) compared with the crush/saline group (53.33%, n=15, P<0.005). The crush/citicoline 0.4 mL group also showed better motoric recovery, as seen in stronger EPT results (P<0.001). However, the sciatic functional index analysis did not show significant differences between groups (P=0.35). The crush/citicoline 0.8 mL group showed a higher percentage of pain (66.67%, n=18) and less EPT recovery. These results may be explained by more severe nerve injury due to compression with a larger administered volume. Conclusion In situ administration of 0.4 mL of 100 µmol/L citicoline prevents the occurrence of neuropathic pain and induces motoric recovery, evaluated by EPT test, 4 weeks after sciatic nerve injury.

Vascular Endothelial Growth Factor 936 C/T Gene Polymorphism in Indonesian Subjects with Diabetic Polyneuropathy

AIM: This study aimed to confirm the role of f VEGF gene 936 C/T polymorphism and Diabetic Polyneuropathy (DPN) in the Indonesian population as well as to investigate its relationship with VEGF-A level and the role of vascular risk factors. MATERIAL AND METHODS: This was a cross-sectional study involving 152 subjects. Clinical symptoms and signs of DPN were examined using DNE and DNS scoring followed by nerve conduction study. All subjects underwent anthropometric, clinical examination and laboratory procedures to obtain body mass index, HbA1C level, lipid profile, Polymorphism of +936 C/T VEGF gene (PCR-RFLP technique), and VEGF-A plasma level (ELISA). Statistical analysis using a t-test or Mann-Whitney was performed to assess continuous data and Chi-square for categorical data. Multivariate logistic regressions were also performed to determine the relationship between independent variables and DPN. RESULTS: Sixty-nine (45.4%) fulfilled the diagnostic criteria of DPN. There was a significant association between CT + TT genotype and DPN (OR 0.35 95%CI 0.16-0.79 p = 0.01). Multivariate logistic regression showed that plasma VEGF-A level (OR = 1.003; 95% CI = 1.000-1.007; p = 0.03), diabetes duration (OR = 1.108; 95% CI = 1.045-1.175; p = 0.001), and CT+TT genotype (OR = 0.347; 95%CI = 0.148-0.817; p = 0.013) were associated with DPN. Sub-group analysis on subjects with HbA1C level ≥7% showed that VEGF-A (OR = 1.011; 95%CI = (1.004-1.017; p = 0.03), diabetes duration (OR = 1.245; 95% CI = 1.117-1.388; p < 0.001), CT + TT genotype (OR = 0.259; 95%CI = 0.074-0.911p = 0.035), with an adition of HDL (OR = 0.916; 95% CI = 0.857-0.978; p = 0.009) were significant predictors of DPN while LDL (OR = 1.017; 95% CI = 1.000-1.035; p = 0.053) acted as modifying factor. CONCLUSION: It appeared that CT + TT genotype of VEGF +936 gene might act as a protecting factor for DPN while VEGF-A, diabetes duration, HDL, and LDL acted as risk factors especially on subjects with HbA1C level ≥ 7.

Hypertension Drug Suitability Evaluation Based On Patient Condition with Improved Profile Matching

Received Mar 3, 2018 Revised Apr 11, 2018 Accepted Apr 21, 2018 Paintball has gained a huge popularity in Malaysia with growing number of tournaments organized nationwide. Currently, Ideal Pro Event, one of the paintball organizer found difficulties to pair a suitable opponent to against one another in a tournament. This is largely due to the manual matchmaking method that only randomly matches one team with another. Consequently, it is crucial to ensure a balanced tournament bracket where eventual winners and losers not facing one another in the very first round. This study proposes an intelligent matchmaking using Particle Swarm Optimization (PSO) and tournament management system for paintball organizers. PSO is a swarm intelligence algorithm that optimizes problems by gradually improving its current solutions, therefore countenancing the tournament bracket to be continually improved until the best is produced. Indirectly, through the development of the system, it is consider as an intelligence business idea since it able to save time and enhance the company productivity. This algorithm has been tested using 3 size of population; 100, 1000 and 10,000. As a result, the speed of convergence is consistent and has not been affected through big population.N. N. S. Abdul Rahman, N.M. Saad, A. R. Abdullah, M. R. M. Hassan, M. S. S. M. Basir, N. S. M. Noor 1,2,4,6Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia 2,3Center for Robotics and Industrial Automation, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia 3,5Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, MalaysiaLight rail transit (LRT), or fast tram is urban public transport using rolling stock similar to a tramway, but operating at a higher capacity, and often on an exclusive right-of-way. Indonesia as one of developing countries has been developed the LRT in two cities of Indonesia, Palembang and Jakarta. There are opinions toward the development of LRT, negative and positive opinions. To reveal the level of LRT development acceptance, this research uses machine learning approach to analyze the data which is gathered through social media. By conducting this paper, the data is modeled and classified in order to analyze the social sentiment towards the LRT development.Mohamad, S., Nasir, F.M., Sunar, M.S., Isa, K., Hanifa, R.M., Shah, S.M., Ribuan, M.N., Ahmad, A. 1,4,6,7,8Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia 1,2,3UTM-IRDA Digital Media Centre, Media and Game Innovation Centre of Excellence, Universiti Teknologi Malaysia, Johor, Malaysia 1,2,3Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia 5Centre for Diploma Studies, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia 6Research Centre for Applied Electromagnetics, Universiti Tun Hussein Onn Malaysia, Johor, MalaysiaReceived Jan 31, 2018 Revised Apr 21, 2018 Accepted Apr 30, 2018 Bluetooth is an emerging mobile ad-hoc network that accredits wireless communication to connect various short range devices. A single hop network called piconet is the basic communication topology of bluetooth which allows only eight active devices for communication among them seven are active slaves controlled by one master. Multiple piconets are interconnected through a common node, known as Relay, to form a massive network called as Scatternet. It is obvious that the performance of Scatternet scheduling is highly dependent and directly proportionate with the performance of the Relay node. In contrary, by reducing the number of Relays, it may lead to poor performance, since every Relay has to perform and support several piconet connections. The primary focus of this study is to observe the performance metrics that affects the inter-piconet scheduling since the Relay node’s role is like switch between multiple piconets. In this paper, we address and analyze the performance issues to be taken into consideration for efficient data flow in Scatternet based on Relay node.

The role of insulin receptor substrate 1 gene polymorphism Gly972Arg as a risk factor for ischemic stroke among Indonesian subjects

ObjectiveThe identification of new genetic-associated risk factor of ischemic stroke could improves strategies for stroke prevention. This study aims to identify insulin receptor substrate 1 (IRS-1) gene polymorphism Gly972Arg as the risk factor for ischemic stroke among Indonesian subjects. The case–control study was conducted by matching the gender and race on 85 cases of patients with ischemic stroke and 86 healthy non-stroke control subjects. Ischemic stroke was established by the complete neurology examination and brain computed tomography scan or magnetic resonance imaging. Polymerase chain reaction–Restriction Fragment Length Polymorphism was performed to analyze IRS-1 gene Gly972Arg genotype.ResultsThere were 85 ischemic stroke cases and 86 control subjects. The distribution of nucleotide IRS-1 gene polymorphism Gly972Arg in the ischemic stroke vs health controls for GG were 32.2% vs 41.5%, for GR were 16% vs 7.6%, and for RR were 0.5% vs 1.9%. IRS-1 gene polymorphism Gly972Arg was found as significant risk factor for ischemic stroke [odds ratio of 2.6 (1.27–5.27); CI 95%, p = 0.008]. Conclusively, the IRS-1 gene polymorphism Gly972Arg should be considered as an important factor in the prevention and treatment of ischemic stroke.

Classification of epileptic and non-epileptic EEG events by feature selection f-score

Epilepsy is defined as a collection of symptoms and clinical signs are emerging due to intermittent brain dysfunction, which occur due to loose or excessive abnormal electrical discharges of neurons in paroxysmal with various etiologies. In this article the implemented software detection of disease epilepsy, characteristics which will represent in the detection of epilepsy and not epilepsy are from 19 electrodes, FP1, FP2, F7, F3, Fz, F4, F8, C3, Cz, T3, T4, T5, T6, P3, P4, Pz, O1, O2. The signals extracted based on the statistical characteristics of the mean, variance, standard deviation, skewness, kurtosis, minimum, maximal, correlation, energy, each electrode will produce 9 features, feature ability to detect epilepsy and non-epilepsy were analyzed using feature selection methods f-score best feature selection results will be tested using a classification algorithm Backpropagation Neural Network (BPNN). The results of 5-fold cross validation) shows that the characteristic feature vector originated from standard deviations from the electrode P4, Cz, FP1, Pz, T3, O2, C4, C3, P3, T5, O1, F2, FP2, F4, F3, T6, F8 the Maximal feature vector is Pz, Cz, FP1, P4, minimum feature vector is P4, FP2, FP1, Cz, C3, Pz, P3, F3 can detect epilepsy compared to the other electrode with a mean level of 96.2% accuracy.

An epileptic signal preictal ictal using PCA, KMEANS and K nearest neighbors

This paper presents the result of research epilepsy signals preictal and ictal. Some express type of marks are to probably to know as early possible some special symptoms that a ictal is in improve. Nineteen Electrodes were applied, namely the FP1, FP2, F7, F3, Fz, F4, F8, C3, Cz, C4, T3, T4, T5, T6, P3, P4, Pz, O1 and O2. The acquisition of the values of the statistical quantity datas, namely, the mean, variances, kurtosises, entropies, standard deviation, minimal, skewnesses, maximal do the standard Principle Component Analysis followed by and their extreme values from preictal and ictal epilepsy patients, kind groupings are noted accordingly. We used KNN and KMeans as classifier, This research result show can be achievement the sensitivity variance, standard deviation, minimal of 100%, specificity variance, standard deviation, maximal of 100% and accuracy standard deviation of 100%.

An epileptic attack detection based on the princple components analysis(PCA)

An Epilepsy signals classification system is expected to reveal the specific characteristics of the patients EEG signals. Some representative models of the signals are to open the possibility to detect as early as possible some specific symptoms that a seizure is in progress. The standard Principle Component Analysis followed by the acquisition of the values of the statistical quantities, namely, the mean, variances, skewnesses, kurtosises, entropies, standard deviation, minimal, maximal and their extreme values from the ictal epilepsy patients and normal persons, specific groupings are noted accordingly. The results this algorithm can achieve the sensitivity of 98.70% and specificity of 98.25% total accuracy of 99.78%.