Mitochondrial proteins of Plasmodium falciparum (MPPF) are an important target for anti-malarial drugs, but their identification through manual experimentation is costly, and in turn, their related drugs production by pharmaceutical institutions involves a prolonged time duration. Therefore, it is highly desirable for pharmaceutical companies to develop computationally automated and reliable approach to identify proteins precisely, resulting in appropriate drug production in a timely manner. In this direction, several computationally intelligent techniques are developed to extract local features from biological sequences using machine learning methods followed by various classifiers to discriminate the nature of proteins. Unfortunately, these techniques demonstrate poor performance while capturing contextual features from sequence patterns, yielding non-representative classifiers. In this paper, we proposed a sequence-based framework to extract deep and representative features that are trust-worthy for Plasmodium mitochondrial proteins identification. The backbone of the proposed framework is MPPF identification-net (MPPFI-Net), that is based on a convolutional neural network (CNN) with multilayer bi-directional long short-term memory (MBD-LSTM). MPPIF-Net inputs protein sequences, passes through various convolution and pooling layers to optimally extract learned features. We pass these features into our sequence learning mechanism, MBD-LSTM, that is particularly trained to classify them into their relevant classes. Our proposed model is experimentally evaluated on newly prepared dataset PF2095 and two existing benchmark datasets i.e., PF175 and MPD using the holdout method. The proposed method achieved 97.6%, 97.1%, and 99.5% testing accuracy on PF2095, PF175, and MPD datasets, respectively, which outperformed the state-of-the-art approaches.

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Internet of Medical Things is a smart provision of medical services to patients interacting with the doctors in harmony to uplift healthcare facilities. It enables the automated diagnosis of diseases for patients in remote areas. Alzheimer's disease is one of the most chronic diseases and the main cause of dementia in human beings. Dementia affects the patient by a process of gradual degeneration of the human brain and results in an inability to perform daily routine tasks and actions. An automated system needs to be developed, to classify the subject with dementia and to determine the prodromal stage of dementia. Considering such requirement, a fully automated classification system is proposed. The proposed algorithm works on the hybrid feature vector combining the textural, statistical, and shape features extracted from three-dimensional views. The feature length is reduced using principal component analysis and relevant features are extracted for classification. The proposed algorithm is tested for both binary and multi-class problems. The method achieves the average precision of 99.2% and 99.02% for binary and multi-class classifications, respectively. The results outperform the existing methods. The algorithm showed accurate results with the average computational time of 0.05 s per magnetic resonance imaging scan.

= Abstract = Anomalous origins of coronary arteries are a rare type of disease among children. These anomalies can be categorized into 3 types according to the anatomical relationship of the aorta and pulmonary trunks. Among these types, the interarterial type, as observed in our case, needs early diagnosis and treatment, because it can increase the risk for the patient, causing sudden cardiac death in young individuals. Although there are controversies concerning the management of anomalous origins of the left coronary artery (LCA) in children, the result can be very beneficial, if treated accurately. Three well-known methods for correction of anomalous origins of LCA are re-implantation, coronary arterial bypass grafting (CABG), and unroofing. We report on the case of a 12-year-old girl who had chest discomfort and syncope with physical exercise and was later diagnosed with an anomalous origin of LCA by transthoracic echocardiography (TTE) and heart computed tomography (CT). She underwent a corrective operation by re-implantation, CABG, and unroofing. (Korean J Pediatr 2010;53:248-252)