A NOVEL APPROACH TO INCREASE THE BIOAVAILABILITY OF CANDESARTAN CILEXETIL BY PRONIOSOMAL GEL FORMULATION: IN-VITRO AND IN-VIVO EVALUATION

Objective: The oral bioavailability of Candesartan cilexetil is less (<15%), so in this study an approach has been made to increase its bioavailability by proniosomal gel formulation.Methods: The proniosomal formulation of Candesartan cilexetil was prepared by slurry method, using span 60 and Tween 60 as non-ionic surfactants, maltodextrin as carrier and cholesterol and soya lecithin as stabilizers. Prepared gel formulations were evaluated for compatibility study, entrapment efficiency, vesicle size, surface morphology, in-vitro diffusion studies, in-vitro skin permeation studies, in-vivo pharmacokinetics studies, various release kinetic studies and stability studies.Results: FT-IR study showed no interaction between drugs and other excipients, drugs and excipients are compatible. Mean vesicles size of proniosome derived niosome was found in the range of 16.34 µm-32.48 µm and 7.25-16.45 µm before and after shaking. An optimized formulation A3 containing a 2:1 ratio of span 60 and cholesterol showed maximum entrapment (86.17%) and in-vitro drug release (93.8%) compared to other formulations. In-vitro skin permeation studies were carried out using Albino rat skin and results showed that formulation A3 exhibited 88.65% drug permeation in a steady-state manner over a period of 24 h with a flux value of 1.94 µg/cm2/h and enhancement ratio of 3.73. In-vivo pharmacokinetics studies of proniosomal gel formulation A3 showed a significant increase in bioavailability (1.425 folds) compared with an oral formulation of Candesartan cilexetil. Stability studies showed that proniosomal gel formulation was stable throughout its study period.Conclusion: Physiochemically stable Candesartan cilexetil proniosomal gel was formulated, which could deliver significant amount of the drug across the skin in a steady-state manner for the prolong period of time in the treatment of hypertension.Â

Biofuel production potential from wastewater in India by integrating anaerobic membrane reactor with algal photobioreactor

The authors would like to express sincere gratitude towards the Director, Birla Institute of Technology and Science, Pilani K. K. Birla Goa Campus for the support in using the institutional infrastructure for the development of this paper.Peer reviewedPostprin

A Posteriori Multiobjective Self-Adaptive Multipopulation Jaya Algorithm for Optimization of Thermal Devices and Cycles

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Development of a cloud-assisted classification technique for the preservation of secure data storage in smart cities

Cloud computing is the most recent smart city advancement, made possible by the increasing volume of heterogeneous data produced by apps. More storage capacity and processing power are required to process this volume of data. Data analytics is used to examine various datasets, both structured and unstructured. Nonetheless, as the complexity of data in the healthcare and biomedical communities grows, obtaining more precise results from analyses of medical datasets presents a number of challenges. In the cloud environment, big data is abundant, necessitating proper classification that can be effectively divided using machine language. Machine learning is used to investigate algorithms for learning and data prediction. The Cleveland database is frequently used by machine learning researchers. Among the performance metrics used to compare the proposed and existing methodologies are execution time, defect detection rate, and accuracy. In this study, two supervised learning-based classifiers, SVM and Novel KNN, were proposed and used to analyses data from a benchmark database obtained from the UCI repository. Initially, intrusions were detected using the SVM classification method. The proposed study demonstrated how the novel KNN used for distance capacity outperformed previous studies. The accuracy of the results of both approaches is evaluated. The results show that the intrusion detection system (IDS) with a 98.98% accuracy rate produces the best results when using the suggested system

Classification of Electrocardiogram Signals Based on Hybrid Deep Learning Models

According to the analysis of the World Health Organization (WHO), the diagnosis and treatment of heart diseases is the most difficult task. Several algorithms for the classification of arrhythmic heartbeats from electrocardiogram (ECG) signals have been developed over the past few decades, using computer-aided diagnosis systems. Deep learning architecture adaption is a recent effective advancement of deep learning techniques in the field of artificial intelligence. In this study, we developed a new deep convolutional neural network (CNN) and bidirectional long-term short-term memory network (BLSTM) model to automatically classify ECG heartbeats into five different groups based on the ANSI-AAMI standard. End-to-end learning (feature extraction and classification work together) is done in this hybrid model without extracting manual features. The experiment is performed on the publicly accessible PhysioNet MIT-BIH arrhythmia database, and the findings are compared with results from the other two hybrid deep learning models, which are a combination of CNN and LSTM and CNN and Gated Recurrent Unit (GRU). The performance of the model is also compared with existing works cited in the literature. Using the SMOTE approach, this database was artificially oversampled to address the class imbalance problem. This new hybrid model was trained on the oversampled ECG database and validated using tenfold cross-validation on the actual test dataset. According to experimental observations, the developed hybrid model outperforms in terms of recall, precision, accuracy and F-score performance of the hybrid model are 94.36%, 89.4%, 98.36% and 91.67%, respectively, which is better than the existing methods

Real-Time Survivor Detection System in SaR Missions Using Robots

This paper considers the issue of the search and rescue operation of humans after natural or man-made disasters. This problem arises after several calamities, such as earthquakes, hurricanes, and explosions. It usually takes hours to locate the survivors in the debris. In most cases, it is dangerous for the rescue workers to visit and explore the whole area by themselves. Hence, there is a need for speeding up the whole process of locating survivors accurately and with less damage to human life. To tackle this challenge, we present a scalable solution. We plan to introduce the usage of robots for the initial exploration of the calamity site. The robots will explore the site and identify the location of human survivors by examining the video feed (with audio) captured by them. They will then stream the detected location of the survivor to a centralized cloud server. It will also monitor the associated air quality of the selected area to determine whether it is safe for rescue workers to enter the region or not. The human detection model for images that we have used has a mAP (mean average precision) of 70.2%. The proposed approach uses a speech detection technique which has an F1 score of 0.9186 and the overall accuracy of the architecture is 95.83%. To improve the detection accuracy, we have combined audio detection and image detection techniques

Brain tumor detection using CNN, AlexNet & GoogLeNet ensembling learning approaches

The detection of neurological disorders and diseases is aided by automatically identifying brain tumors from brain magnetic resonance imaging (MRI) images. A brain tumor is a potentially fatal disease that affects humans. Convolutional neural networks (CNNs) are the most common and widely used deep learning techniques for brain tumor analysis and classification. In this study, we proposed a deep CNN model for automatically detecting brain tumor cells in MRI brain images. First, we preprocess the 2D brain image MRI image to generate convolutional features. The CNN network is trained on the training dataset using the GoogleNet and AlexNet architecture, and the data model's performance is evaluated on the test data set. The model's performance is measured in terms of accuracy, sensitivity, specificity, and AUC. The algorithm performance matrices of both AlexNet and GoogLeNet are compared, the accuracy of AlexNet is 98.95, GoogLeNet is 99.45 sensitivity of AlexNet is 98.4, and GoogLeNet is 99.75, so from these values, we can infer that the GooGleNet is highly accurate and parameters that GoogLeNet consumes is significantly less; that is, the depth of AlexNet is 8, and it takes 60 million parameters, and the image input size is 227 × 227. Because of its high specificity and speed, the proposed CNN model can be a competent alternative support tool for radiologists in clinical diagnosis

Real-Time Survivor Detection System in SaR Missions Using Robots

This paper considers the issue of the search and rescue operation of humans after natural or man-made disasters. This problem arises after several calamities, such as earthquakes, hurricanes, and explosions. It usually takes hours to locate the survivors in the debris. In most cases, it is dangerous for the rescue workers to visit and explore the whole area by themselves. Hence, there is a need for speeding up the whole process of locating survivors accurately and with less damage to human life. To tackle this challenge, we present a scalable solution. We plan to introduce the usage of robots for the initial exploration of the calamity site. The robots will explore the site and identify the location of human survivors by examining the video feed (with audio) captured by them. They will then stream the detected location of the survivor to a centralized cloud server. It will also monitor the associated air quality of the selected area to determine whether it is safe for rescue workers to enter the region or not. The human detection model for images that we have used has a mAP (mean average precision) of 70.2%. The proposed approach uses a speech detection technique which has an F1 score of 0.9186 and the overall accuracy of the architecture is 95.83%. To improve the detection accuracy, we have combined audio detection and image detection techniques