Biological and Clinical Relevance of microRNAs in Mitochondrial Diseases

Mitochondrial dysfunction arises from an inadequate number of mitochondria, an inability to provide necessary substrates to mitochondria, or a dysfunction in their electron transport and a denosine triphosphate synthesis machinery. Occurrences of mitochondrial dysfunction are due to genetic or environmental changes in the mitochondria or in the nuclear DNA that codes mitochondrial components. Currently, drug options are available, yet no treatment exists in sight of this disease and needs a new insight into molecular and signaling pathways for this disease. microRNAs (miRNAs) are small, endogenous, and noncoding RNAs function as a master regulator of gene expression. The evolution of miRNAs in the past two decades emerged as a key regulator of gene expression that controls physiological pathological cellular differentiation processes, and metabolic homeostasis such as development and cancer. It has been known that miRNAs are a potential biomarker in both communicable and noncommunicable diseases. But, in the case of mitochondrial dysfunction in miRNAs, the number of studies and investigations are comparatively less than those on other diseases and dysfunctions. In this review, we have elaborated the roles of miRNAs in the mitochondrial diseases and dysfunctions

Comprehensive Review of Deep learning Techniques in Electronic Medical Records

A digital collection of patient’s health care data like diagnosis history of patient, treatment details, medical prescriptions are stored electronically. This electronic patient health records (EPHR) model provides huge volume of real time data and used for clinical research. Natural Language processing (NLP) automatically retrieve the patient’s information based on decision support system. NLP performs traditional techniques of machine learning, deep learning algorithms and focussing on word embeddings, classification and prediction, extraction, knowledge graphs, phenotyping, etc. By using NLP technique, extract the information from clinical data and analysis it provides valuable patient medical information. NLP based on clinical systems are evaluated on document level annotations which contains document of patient report, health status of patient, document section types contain past medical history of patient, summary of discharge statement, etc. similarly the semantic properties contain severity of disease in the aspects of positivity, negativity. These documents are developed and implemented on word level or sentence level. In this survey article, we summarize the recent NLP techniques which are used in EPHR applications. This survey paper focuses on prediction, classification, extraction, embedding, phenotyping, multilingually etc techniques

A Smart Energy Management System for Residential Buildings Using IoT and Machine Learning

The Smart Energy Management System (SEMS) for Residential Buildings using IOT-based back propagation with ANN is a novel approach to optimize energy consumption in buildings by leveraging data from internet of things (IOT) devices. This system collects data on energy consumption, weather conditions, occupancy patterns, and sensor data from IOT devices such as motion sensors, temperature sensors, and smart appliances. The collected data is then preprocessed and used to train an artificial neural network (ANN) using back propagation algorithm. The trained model can then predict future energy demands, leading to cost savings and reduced environmental impact by optimizing energy consumption in a residential building. The proposed algorithm can be used as a foundation for building an effective SEMS using IOT-based back propagation with ANN

The Proneural Proteins Atonal and Scute Regulate Neural Target Genes through Different E-Box Binding Sites

For a particular functional family of basic helix-loop-helix (bHLH) transcription factors, there is ample evidence that different factors regulate different target genes but little idea of how these different target genes are distinguished. We investigated the contribution of DNA binding site differences to the specificities of two functionally related proneural bHLH transcription factors required for the genesis of Drosophila sense organ precursors (Atonal and Scute). We show that the proneural target gene, Bearded, is regulated by both Scute and Atonal via distinct E-box consensus binding sites. By comparing with other Ato-dependent enhancer sequences, we define an Ato-specific binding consensus that differs from the previously defined Scute-specific E-box consensus, thereby defining distinct E(Ato) and E(Sc) sites. These E-box variants are crucial for function. First, tandem repeats of 20-bp sequences containing E(Ato) and E(Sc) sites are sufficient to confer Atonal- and Scute-specific expression patterns, respectively, on a reporter gene in vivo. Second, interchanging E(Ato) and E(Sc) sites within enhancers almost abolishes enhancer activity. While the latter finding shows that enhancer context is also important in defining how proneural proteins interact with these sites, it is clear that differential utilization of DNA binding sites underlies proneural protein specificity