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At present, manual observation of the electroencephalogram (EEG) signals is the prime method for diagnosis of epileptic seizure disorders. The method is a time consuming and error prone as it involves errors due to fatigue in continuous monitoring of nonlinear and nonstationary EEG signals. Out of approximate 1% of the world's epilepsy patients more than 25% cannot be treated correctly due to erroneous diagnosis. The automated seizure detection system can prove efficient by making the process reliable and faster. This paper reviews multi-domain feature extraction and machine learning classification techniques used in automated seizure detection systems. To analyse subtle variations in EEG, signal decomposition algorithms have been used in time, frequency, joint time-frequency, and nonlinear domain. The statistical and entropy parameters are the key features to discern normal from the seizure EEG signals. Machine learning plays a critical role in extracting meaningful information out of the extracted features. The paper also evaluates the performance of Multilayer Perceptron Neural Network, naïve Bayes, Least Square Support Vector Machine, k nearest neighbour, and random forest classifiers using sensitivity, specificity and accuracy metrics. A seizure detection technique is developed by decomposing the EEG signals by means of Tunable-Q Wavelet Transform (TQWT). To quantify the complexity of the individual multivariate sub-bands of the biomedical signals TQWT proves effective with varied values of Q factor suitable for analyzing signals with oscillatory and non-oscillatory nature. The highest accuracy of 97.3% is obtained using random forest classifier for the combination of spectral, Shannon and Kraskov entropy features. The paper compares the performance of feature extraction and classification techniques for the implemented system. The comparison explores possibility of hardware implementation of real time seizure detection scheme. [ABSTRACT FROM AUTHOR]

Introduction: Root canal treatment is incomplete without usage of intra canal medicaments. They help in the reduction of bacterial count and its by-products, making canals clean and decreasing postoperative pains. Aim: The aim of this study was to evaluate and compare the antimicrobial activity of triple antibiotic paste, Moxifloxacin, calcium hydroxide and 2% Chlorhexidine (CHX) gel in elimination of Enterococcous faecalis (E. faecalis). Materials and Methods: seventy-five root blocks were obtained from extracted single rooted human teeth. The canal diameter was increased using Gates-Glidden drill up to no 3 and then in coagulated with E. faecalis for 21 days. The contaminated samples were then divided into following 5 groups. Group 1: Saline (negative group), Group 2: Calcium hydroxide Ca(OH)2, Group 3: 2% CHX gel, Group 4: Triple Antibiotic Paste (TAP) (50 μg - metronidazole of 400 mg, 50 μg - minocycline of 100 mg, 50 μg - ciprofloxacin of 100 mg) and Group 5: Moxifloxacin (50 μg - moxifloxacin of 400 mg). Dentin debris was obtained at the end of first, 7th, and 10th day using Gates Glidden drill sizes 4 and 5. The bacterial load was assessed by counting the number of Colony Forming Units (CFUs). The data were analyzed with the ANOVA and Post-Hoc tests to assess the differences in antibacterial efficacy between groups (p=<0.001). Results: A 2% CHX gel alone completely inhibited the growth of E. faecalis after one, seven and 10 days. The 2% CHX gel was the most effective medicament against E. faecalis, as it showed significant differences with normal saline, calcium hydroxide, Moxifloxacin or triple antibiotic paste at all time intervals. The triple antibiotic paste group showed a moderate antibacterial effect as its difference with all group was significantly better at all days. Moxifloxacin was more effective than calcium hydroxide on 7th and 10th day. Conclusion: Best antimicrobial efficacy was shown by 2% CHX gel. Moxifloxacin was equally efficient compared to triple antibiotic paste against E. faecalis at longer intervals of time. [ABSTRACT FROM AUTHOR]

The Wiener index of a graph G denoted by W(G) is the sum of distances between all (unordered) pairs of vertices of G. In practice G corresponds to what is known as the molecular graph of an organic compound. In this paper, we obtain the Wiener index of quasi-total graph and its complement for some standard class of graphs, we give bounds for Wiener index of quasi-total graph and its complement also establish Nordhaus-Gaddum type of inequality for it. [ABSTRACT FROM AUTHOR]

Several plant products are known to exhibit immense medicinal value against human diseases. Our earlier studies showed that dry stem crude extract (DSCE) of Tinospora cordifolia contained a polyclonal B cell mitogen, G1-4A. DSCE as well as G1-4A also enhanced immune response in mice. In order to explore the possibility of using G1-4A/PPI (partially purified immunomodulator) to modulate radiation induced immunosuppression, the antioxidant effect of PPI from this plant was examined against reactive oxygen and nitrogen species (ROS/RNS), generated by photosensitization/peroxynitrite. Levels of lipid peroxidation products, superoxide dismutase (SOD) and catalase in liver/spleen homogenate from mouse were monitored. Photosensitization induced significant increase in thiobarbituric acid reactive substances (TBARS) in liver. The activities of SOD and catalase were reduced considerably. PPI, present during photosensitisation, prevented lipid peroxidation and restored the activities of both the enzymes. Likewise, oxidative damage induced by peroxynitrite was inhibited by PPI. The degradation of proteins due to photosensitization as assessed by SDS-PAGE was effectively reduced by simultaneous treatment with PPI during photosensitization. Selective inhibitors of ROS like mannitol, SOD, sodium azide and antioxidants, GSH and vitamin C brought about significant inhibition of formation of TBARS suggesting possible involvement of O ,OH andO. Photosensitization in deuterated buffer enhanced formation of TBARS thus indicating generation ofO. Thus, the action of PPI may be against oxidative damage through Type I and II photosensitization mechanisms. Therefore, the immunomodulator from Tinospora cordifolia may also be beneficial as an antioxidant. [ABSTRACT FROM AUTHOR]

Background: Oral submucous fibrosis (OSMF) is a chronic progressive disease affecting many people in our country. Aim: The aim of the study is to assess the severity of the disease and to analyze collagen distribution in different stages of OSMF using picrosirius red stain under polarizing microscope. Materials and Methods: Vernier calliper, biopsy specimen, tissue processing kit, picrosirius red stain, hematoxylin and eosin (H and E) stain and polarizing microscopy. The study included total 100 cases of which 80 were OSMF and 20 were control group. Clinical staging was done depending upon definite criterias. Histopathological study was conducted using H and E stain and picrosirius red stain. Collagen was analyzed for thickness and polarizing colors. Statistical Analysis Used: Chi-square test, Student'st-test. Results: As the severity of disease increased, there was gradual decrease in green-greenish yellow color fibers and shift to orange red to red color. Thereby it appeared that tight packing of collagen fibers in OSMF progressively increased as disease progressed from early to advanced cases. Conclusion: In this study, histopathological studies revealed that as the severity of the disease increased, the collagen fibers were more closely packed and there was also a transition of greenish yellow fibers to orange red to red under polarizing microscopy. These observations are important to assess the actual stage of the disease and to plan further management. [ABSTRACT FROM AUTHOR]

The detection of seizure onset and events using electroencephalogram (EEG) signals are important tasks in epilepsy research. The literature available on seizure detection has discussed the implementation of advanced signal processing algorithms using tools accessed over the cloud. However, seizure monitoring application needs near sensor processing due to privacy and latency issues. In this paper, a real time seizure detection system has been implemented using an embedded system. The proposed system is based on ensemble empirical mode decomposition (EEMD) and tunable-Q wavelet transform (TQWT) algorithms. The analysis and classification of non-stationary EEG signals require the wavelet transform with high Q -factor. However, direct use of TQWT increases the computational complexity of feature extraction from multivariate EEG signals. In this paper, the first step is to process the signal by using EEMD to obtain 8 intrinsic mode functions (IMFs). The Kraskov (KraEn), sample (SampEn), and permutation (PermEn) entropy features of IMFs are extracted and based on optimum values, and 4 IMFs are decomposed using TQWT. Secondly, centered correntropy (CenCorrEn) features of the 1 st and 16 th sub-band of TQWT have been used as classifier inputs. The performance of multilayer perceptron neural networks (MLPNN), least squares support vector machine (LSSVM), and random forest (RF) classifiers has been tested on the multichannel EEG data recorded from a local hospital. The RF classifier has produced the highest accuracy of 96.2% in classifying the signals. The proposed scheme has been employed in developing an embedded seizure detection system to assist neurologists in making seizure diagnostic decisions.

Objectives: The aim of present study is to analyze assess the awareness about forensic odontology among dental practitioners in center part of India., Subjects and Methods: A cross-sectional study was conducted in a sample of 100 dental practitioners in Bhilai-Durg and data was collected by means of a questionnaire., Results: About 30% of dental practitioners not maintain dental records in their clinic, 70% maintained dental records. Nearly, 60% dental practitioners use the appropriate method for diagnosis, while rest are not. Sixty-five percent dental practitioners know the accurate and sensitive way of identify individuals. Thirty percent dental practitioner did not know the significance of bite-mark patterns of the teeth, about 75% dental practitioners did not aware that they could testify as an expert witness in the court of law. Only 15% dental practitioners have formal training in collecting, evaluating, and presenting dental evidence. Seventy-five percent dental practitioners not confident to deal with forensic cases., Conclusions: Our study revealed inadequate knowledge, lack of awareness about forensic odontology, among dental practitioners in Chhattisgarh., Competing Interests: There are no conflicts of interest.