Post-operative seizure control in patients with glioblastoma

While the median survival in patients with glioblastoma has not improved significantly over the past decade, aggressive attempts have been made on palliation and improving quality of life in these patients. A confluence of two debilitating pathologies which massively distorts the normal day-to-day functioning of the patients who experience it, seizures in glioblastoma patients portends a poor prognosis. There exists a paucity of reported seizure outcomes after glioblastoma treatment in neurosurgical literature. Herein we present a review examining post-operative seizure control in patients with glioblastoma

SURVIVAL OUTCOMES IN EARLY GLOTTIC CARCINOMA; A SINGLE INSTITUTION EXPERIENCE

Purpose: Laryngeal cancers are amongst the most common cancers affecting head and neck region. In this study, we analyse the overall survival (OS) following hypofractionated radiotherapy (RT) in early stage glottic carcinoma treated at Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore. Methods: Between October 2003 and June 2009, 87 patients with early stage glottic carcinoma were treated with hypofractionated RT. All patients were included in the study. The ratio of male: female is 94%:6%. Mean age was 62 years (range 31–83 years). 66% of the patients were smokers. AJCC stage was T1a in 76%, T1b 20% and T2 in 4% of the patients. Histological distribution was; squamous cell carcinoma 97%, verrucous carcinoma 2% and squamous cell spindle variant 1%. Median follow-up time was 59 months (range 4–122 months). RT dose was 55 Gy in 20 fractions over a period of 4 weeks. Median RT treatment time was 28 days (range 23–35 days). Patients that lost to follow-up were contacted through telephone. Results: The 10-year OS was 83%. Patterns of failure was 7 local and 1 distant while 1 patient had persistent disease. 15 patients were dead at the time of study. Cause of death; 13 patients died due to Ischemic heart disease and 2 due to primary disease. Conclusion: Hypofractionated RT 55 Gy in 20 fractions seems to achieve good OS while offering potential for optimizing resources usage. Key words: Glottic carcinoma, hypofractionated, overall survival, radiotherapy

UAV-assisted Cluster-head Selection Mechanism for Wireless Sensor Network Applications

The use of unmanned aerial vehicles (UAVs) is gaining popularity in many applications, i.e. data collection, surveillance, wireless sensor networks (WSNs) etc. In the WSN domain, the UAVs are used to create a more flexible data-gathering platform. This integration maximizes the lifetime of a WSN by optimizing the energy budget. In this paper, we have utilized these benefits of UAVs and have proposed an optimum cluster head (CH) selection strategy to maximize the lifetime of WSNs. The proposed method uses the average residual energy, the channel condition and the Euclidean distance of each sensor node (SN) with a UAV to nominate a group of CHs. Based on the initial analytical analysis, the proposed scheme maximizes the lifetime of a WSN by a fair amount in comparison to the state-of-the-art methods

Management tactics for handling Parthenium hysterophorus in non-native environment through phytotoxic compounds of local species

Parthenium hysterophorus L. is an invasive, ubiquitous and infamous herbaceous weed causing suppression of natural vegetation and crop plants. The identification of phytotoxins in local weed species (Datura stramonium, Achyranthes aspera, Chenopodium album, Calotropis procera, C. murale and Melilotus indica) was done through high performance liquid chromatography (HPLC) to check their herbicidal potential against P. hysterophorus. Additionally, filter paper petri plate based and soil filled pot experiments were conducted in laboratory and wire house to evaluate the pre- and post-emergence herbicidal potential of plant water extract of D. stramonium alone and in combination with A. aspera, C. album C. procera, C. murale and M. indica at 2.5, 5 and 10% (w/v) concentrations against germination and seedling growth of P. hysterophorus. The phytotoxins detected in extracts of these plant species were quercetin, gallic, chlorogenic, p-coumaric, m-coumaric, sinapinic, caffeic, benzoic and syringic acids with variable concentrations. Total highest concentration of phytotoxins (61.37 mg L-1) was found in A. aspera while the lowest concentration (7.69 mg L-1) was found in C. album aqueous extract. Significant reduction in germination and seedling growth of P. hysterophorus was shown by all extract combinations that increased in direct proportion to their concentrations. The 10% water extract of D. stramonium in combination with C. procera and A. aspera proved to be the best as they resulted maximum reductions in germination percentage (100 and 95%), shoot length (67 and 62%), and shoot dry weight (67 and 78%) of P. hysterophorus, respectively. (C) 2019 Friends Science Publisher

Performance Enhancement in P300 ERP Single Trial by Machine Learning Adaptive Denoising Mechanism

The P300-based lie detection scheme is yet another and advantageous tactic for unadventurous Polygraphy. In the proposed scheme, the raw electroencephalogram (EEG) signals are assimilated from 15 subjects during deception detection. After the assimilation, EEG signals are separated using an independent component analysis (ICA). The proposed adaptive denoising approach, extracts three kinds of features from denoised wave to reproduce P300 waveform and identify the P300 components at the Pz electrode. Finally, in order to enhance the performance, four classifiers are used, i.e., support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbor (KNN), and back propagation neural network (BPNN), achieving the accuracy of 74.5%, 79.4%, 97.9% and 89%, respectively

Performance Enhancement in P300 ERP Single Trial by Machine Learning Adaptive Denoising Mechanism

The P300-based lie detection scheme is yet another and advantageous tactic for unadventurous Polygraphy. In the proposed scheme, the raw electroencephalogram (EEG) signals are assimilated from 15 subjects during deception detection. After the assimilation, EEG signals are separated using an independent component analysis (ICA). The proposed adaptive denoising approach, extracts three kinds of features from denoised wave to reproduce P300 waveform and identify the P300 components at the Pz electrode. Finally, in order to enhance the performance, four classifiers are used, i.e., support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbor (KNN), and back propagation neural network (BPNN), achieving the accuracy of 74.5%, 79.4%, 97.9% and 89%, respectively

Generalized Regression Neural Network and Fitness Dependent Optimization: Application to energy harvesting of centralized TEG systems

The thermoelectric generator (TEG) system has attracted extensive attention because of its applications in centralized solar heat utilization and recoverable heat energy. The operating efficiency of the TEG system is highly affected by operating conditions. In a series-parallel structure, due to diverse temperature differences, the TEG modules show non-linear performance. Due to the non-uniform temperature distribution (NUTD) condition, several maximum power points (MPPs) appear on the P/V curve. In multiple MPPs, the true global maximum power points (GMPP) are very important for optimum action. The existing conventional technologies have slow tracking speed, low productivity, and unwanted fluctuations in voltage curves. To overcome the TEG system behavior and shortcomings, A novel control technology for the TEG system is proposed, which utilizes the improved generalized regression neural network and fitness dependent optimization (GRNNFDO) to track the GMPP under dynamic operating conditions. Conventional TEG system control techniques are not likely to trace true GMPP. Our novel GRNNFDO can trace the true GMPP for NUTD and under varying temperature conditions In this article, some major contributions in the area of the TEG systems are investigated by solving the issues such as NUTD global maxima tracking, low efficiency of TEG module due to mismatch, and oscillations around optimum point. The results of GRNNFDO are compared with the Cuckoo-search algorithm (CSA), and grasshopper optimization (GHO) algorithm and particle swarm optimization (PSO) algorithm. Results of GRNNFDO are verified with experiments and authenticated with MATLAB/SIMULINK. The proposed GRNNFDO control technique generates up to 7% more energy than PSO and 60% fast-tracking than meta-heuristic algorithms

National guidelines for the diagnosis and treatment of hilar cholangiocarcinoma

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