FORMULATION AND IN VITRO IN VIVO EVALUATION OF BOSENTAN PELLETS FOR PROLONGED DRUG RELEASE

Objective: The objective of this study was to develop extended release (ER) pellets of Bosentan, an endothelin receptor antagonist using fluid bed processor (coating).Method: The ER drug pellets of Bosentan were prepared using fluid bed coating. These drug-loaded pellets were further coated with ethyl cellulose of two viscosity grades and Eudragit as rate controlling polymers individual and in combination, hypromellose as pore former and binder, acetyl tributyl citrate as plasticizer, and magnesium stearate as anti-adhering agent.Results: The drug release was extended up to 24 h, and the drug release was mainly depends on the polymer type and polymer proportion. In vivo study of Bosentan, ER pellets were performed in healthy rabbits (New Zealand, White) of either sex weighing (3.0–3.3 kg) and were divided into two separate groups, each group consisting of 6 animals. Maximum plasma concentration (Cmax), maximum time (Tmax), area under the curve (AUC0-t), elimination rate constant (Kel), and half-life (T1/2) were studied for optimized formulation. Formulation was releasing the drug for a prolonged period of time.Conclusion: By the above results, it was observed that the prepared pellets could release the drug for an extended period when compared with the conventional dosage form of Bosentan, optimized formulation was shown longer half-life and Cmax indicates its acceptability. Finally, ER pellets of Bosentan were prepared for the treatment of pulmonary artery hypertension by fluid bed processor

Facial expression (mood) recognition from facial images using committee neural networks

<p>Abstract</p> <p>Background</p> <p>Facial expressions are important in facilitating human communication and interactions. Also, they are used as an important tool in behavioural studies and in medical rehabilitation. Facial image based mood detection techniques may provide a fast and practical approach for non-invasive mood detection. The purpose of the present study was to develop an intelligent system for facial image based expression classification using committee neural networks.</p> <p>Methods</p> <p>Several facial parameters were extracted from a facial image and were used to train several generalized and specialized neural networks. Based on initial testing, the best performing generalized and specialized neural networks were recruited into decision making committees which formed an integrated committee neural network system. The integrated committee neural network system was then evaluated using data obtained from subjects not used in training or in initial testing.</p> <p>Results and conclusion</p> <p>The system correctly identified the correct facial expression in 255 of the 282 images (90.43% of the cases), from 62 subjects not used in training or in initial testing. Committee neural networks offer a potential tool for image based mood detection.</p

Gene Expression Based Leukemia Sub-Classification Using Committee Neural Networks

Analysis of gene expression data provides an objective and efficient technique for sub-classification of leukemia. The purpose of the present study was to design a committee neural networks based classification systems to subcategorize leukemia gene expression data. In the study, a binary classification system was considered to differentiate acute lymphoblastic leukemia from acute myeloid leukemia. A ternary classification system which classifies leukemia expression data into three subclasses including B-cell acute lymphoblastic leukemia, T-cell acute lymphoblastic leukemia and acute myeloid leukemia was also developed. In each classification system gene expression profiles of leukemia patients were first subjected to a sequence of simple preprocessing steps. This resulted in filtering out approximately 95 percent of the non-informative genes. The remaining 5 percent of the informative genes were used to train a set of artificial neural networks with different parameters and architectures. The networks that gave the best results during initial testing were recruited into a committee. The committee decision was by majority voting. The committee neural network system was later evaluated using data not used in training. The binary classification system classified microarray gene expression profiles into two categories with 100 percent accuracy and the ternary system correctly predicted the three subclasses of leukemia in over 97 percent of the cases

Neural network committees for finger joint angle estimation from surface EMG signals

<p>Abstract</p> <p>Background</p> <p>In virtual reality (VR) systems, the user's finger and hand positions are sensed and used to control the virtual environments. Direct biocontrol of VR environments using surface electromyography (SEMG) signals may be more synergistic and unconstraining to the user. The purpose of the present investigation was to develop a technique to predict the finger joint angle from the surface EMG measurements of the extensor muscle using neural network models.</p> <p>Methodology</p> <p>SEMG together with the actual joint angle measurements were obtained while the subject was performing flexion-extension rotation of the index finger at three speeds. Several neural networks were trained to predict the joint angle from the parameters extracted from the SEMG signals. The best networks were selected to form six committees. The neural network committees were evaluated using data from new subjects.</p> <p>Results</p> <p>There was hysteresis in the measured SMEG signals during the flexion-extension cycle. However, neural network committees were able to predict the joint angle with reasonable accuracy. RMS errors ranged from 0.085 ± 0.036 for fast speed finger-extension to 0.147 ± 0.026 for slow speed finger extension, and from 0.098 ± 0.023 for the fast speed finger flexion to 0.163 ± 0.054 for slow speed finger flexion.</p> <p>Conclusion</p> <p>Although hysteresis was observed in the measured SEMG signals, the committees of neural networks were able to predict the finger joint angle from SEMG signals.</p

Spectrum of morbid anatomy of liver in autopsy cases

Background: Purpose of autopsy is to learn the truth about the person’s health during and how the person died. Thus, autopsy study provides valuable information about the disease. The main aim of the study was to know the spectrum of morbid anatomical changes in liver autopsy.Methods: A prospective study was carried out on 100 liver specimens of autopsy cases comprising of 37 cases of roadside accidents, 17 of poisoning, 13 of burns, 10 of chronic illness, 7 each of hanging and head injury, 4 of myocardial infarction and 5 of death due to miscellaneous causes. Representative microsections of liver were evaluated for histopathological parameters like congestion, ballooning degeneration, hepatocellular necrosis, sinusoidal dilation, fatty change, bile stasis, fibrosis, lobular inflammation and portal inflammation.Results: Out of total 100 cases, 77 were males and 23 females. Male to female ratio was 3.34:1. Hepatomegaly was seen in 15% of cases. Chronic venous congestion was the main histopathological diagnosis seen in 61% of the cases followed by chronic hepatitis in 12%, normal liver histology in 9%, hepatic steatosis and cirrhosis in 6%, granulomatous hepatitis in 2%, and sinusoidal congestion, portal triaditis and secondary neoplasm in 1% each.Conclusions: Chronic venous congestion, chronic hepatitis, cirrhosis and hepatic steatosis were the common liver diseases identified. Autopsy study is useful to monitor the cause of death and to plan medical strategy. Histopathological examination of the liver is specialized learning tool to study the various diseases of liver which is a great value in improving the diagnosis

Detecting the Anti-Social Activity on Twitter using EGBDT with BCM

The rise of social media and its consequences is a hot topic on research platforms. Twitter has drawn the attention of the research community in recent years due to various qualities it possesses. They include Twitter's open nature, which, unlike other platforms, allows visitors to see posts posted by Twitter users without having to register. In twitter the sentiment analysis of tweets are used for detecting the anti-social activity event which is one of the challenging tasks in existing works. There are many classification algorithms are used to detect the anti-social activities but they obtains less accuracy. The EGBDT (Enhanced Gradient-Boosted Decision Tree) is used to optimize the best features from the NSD dataset and it is given as input to BCM (Bayesian Certainty Method) for detecting the anti-social activities. In this work, tweets from NSD dataset are used for analyzing the sentiment polarity i.e. positive or negative. The efficiency of the proposed work is compared with SVM, KNN and C4.5. From this analysis the proposed EGBDT and BCM obtained better results than other techniques

Postprandial Hypotension in Clinical Geriatric Patients and Healthy Elderly: Prevalence Related to Patient Selection and Diagnostic Criteria

The aims of this study were to find out whether Postprandial hypotension (PPH) occurs more frequently in patients admitted to a geriatric ward than in healthy elderly individuals, what the optimal interval between blood pressure measurements is in order to diagnose PPH and how often it is associated with symptoms.The result of this study indicates that PPH is present in a high number of frail elderly, but also in a few healthy older persons. Measuring blood pressure at least every 10 minutes for 60 minutes after breakfast will adequately diagnose PPH, defined as >20 mmHg systolic fall, in most patients. However with definition of PPH as >30 mmHg systolic fall, measuring blood pressure every 10 minutes will miss PPH in one of three patients. With the latter definition of PPH the presence of postprandial complaints is not associated with the existence of PPH

Critical limit of copper in soil and plant for predicting response of oat (Avena sativa) in soils of Haryana

In order to evaluate critical level in oat (Avena sativa L.), laboratory and a screen house experiment was conducted at CCS HAU, Hisar. Bulk surface soil samples (0-15 cm) were collected from eighteen (18) different locations, in the state representing major soil groups of Haryana. The results of the study revealed that the relationship between Bray's per cent yield against DTPA-Cu in soil and Cu concentration in plants indicated critical deficiency level of Cu in soil as 0.30 mg/kgand for oat plant it was 11.7 mg/kg which was statistically also at par