• Journal of Genetic Medicine
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• v.16 no.1
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• pp.31-38
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• 2019

Mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome is a maternally inherited mitochondrial disorder of which m.3243A>G is the most commonly associated mutation, resulting in an inability to meet the energy requirements of various organs. MELAS poses a diagnostic challenge owing to its multiple organ involvement and great clinical variability due to its heteroplasmic nature. We report three cases from a family who were initially misdiagnosed with myasthenia gravis or undiagnosed. Although there is no optimal consensus treatment approach for patients with MELAS because of the disease's heterogeneity, our 21-year-long therapy regimen of ${\text\tiny{L}}-arginine$, ${\text\tiny{L}}-carnitine$, and coenzyme Q10 supplementation combined with dietary management appeared to provide noticeable protection from the symptoms and complications. Prompt early diagnosis is important, as optimal multidisciplinary management and early intervention may improve outcomes.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.40-48
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• 2021

Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.

• Journal of Pharmacopuncture
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• v.25 no.2
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• pp.88-100
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• 2022

Gastric cancer (GC) is a significant cause of cancer mortality which has led to focused exploration of the pathology of GC. The advent of genome-wide analysis methods has made it possible to uncover genetic and epigenetic fluctuation such as abnormal DNA methylation in gene promoter regions that is expected to play a key role in GC. The study of gastric malignancies requires an etiological perspective, and Helicobacter pylori (H. pylori) was identified to play a role in GC. H. pylori infection causes chronic inflammation of the gastric epithelium causing abnormal polyclonal methylation, which might raise the risk of GC. In the last two decades, various pathogenic factors by which H. pylori infection causes GC have been discovered. Abnormal DNA methylation is triggered in several genes, rendering them inactive. In GC, methylation patterns are linked to certain subtypes including microsatellite instability. Multiple cancer-related processes are more usually changed by abnormal DNA methylation than through mutations, according to current general and combined investigations. Furthermore, the amount of acquired abnormal DNA methylation is heavily linked to the chances of developing GC. Therefore, we investigated abnormal DNA methylation in GC and the link between methylation and H. pylori infection.

• Molecules and Cells
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• v.45 no.5
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• pp.343-352
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• 2022

The advent of the assay for transposase-accessible chromatin using sequencing (ATAC-seq) has shown great potential as a leading method for analyzing the genome-wide profiling of chromatin accessibility. A comprehensive reference to the ATAC-seq dataset for disease progression is important for understanding the regulatory specificity caused by genetic or epigenetic changes. In this study, we present a genome-wide chromatin accessibility profile of 44 liver samples spanning the full histological spectrum of nonalcoholic fatty liver disease (NAFLD). We analyzed the ATAC-seq signal enrichment, fragment size distribution, and correlation coefficients according to the histological severity of NAFLD (healthy control vs steatosis vs fibrotic nonalcoholic steatohepatitis), demonstrating the high quality of the dataset. Consequently, 112,303 merged regions (genomic regions containing one or multiple overlapping peak regions) were identified. Additionally, we found differentially accessible regions (DARs) and performed transcription factor binding motif enrichment analysis and de novo motif analysis to determine new biomarker candidates. These data revealed the gene-regulatory interactions and noncoding factors that can affect NAFLD progression. In summary, our study provides a valuable resource for the human epigenome by applying an advanced approach to facilitate diagnosis and treatment by understanding the non-coding genome of NAFLD.

• Journal of Nutrition and Health
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• v.55 no.1
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• pp.1-9
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• 2022

In the past few decades, great progress has been made on understanding the interaction between nutrition and health status. But despite this wealth of knowledge, health problems related to nutrition continue to increase. This leads us to postulate that the continuing trend may result from a lack of consideration for intra-individual biological variation on dietary responses. Precision nutrition utilizes personal information such as age, gender, lifestyle, diet intake, environmental exposure, genetic variants, microbiome, and epigenetics to provide better dietary advices and interventions. Recent technological advances in the artificial intelligence, big data analytics, cloud computing, and machine learning, have made it possible to process data on a scale and in ways that were previously impossible. A big data platform is built by collecting numerous parameters such as meal features, medical metadata, lifestyle variation, genome diversity and microbiome composition. Sophisticated techniques based on machine learning algorithm can be used to integrate and interpret multiple factors and provide dietary guidance at a personalized or stratified level. The development of a suitable machine learning algorithm would make it possible to suggest a personalized diet or functional food based on analysis of intra-individual metabolic variation. This novel precision nutrition might become one of the most exciting and promising approaches of improving health conditions, especially in the context of non-communicable disease prevention.

• Advances in Computational Design
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• v.7 no.2
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• pp.153-171
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• 2022

The present study aimed to find a way to create forms that can simultaneously meet several architectural requirements by applying generative design methods specifically focused on cellular automata. In other words, it is tried to find various forms of architecture that all have common features. Because of the useful features of cellular automata, we decided to use it to generate various forms, but make a relation between the discrete nature of cellular automata and the continuous nature of architecture, was the major problem of our project. To achieve this goal, three consecutive stages were designed. In the first stage, independent variables including the location of the building, the height of the building, and the building area were considered as the inputs of the model. In the second stage, after locating the building, the building's main shell was designed as a hidden geometry for the cellular automata and then the cellular automata were determined based on this shell. The main result of this research is establishing a logical relationship between the discrete geometry of the cellular automata and the continuous search space such that it creates various optimized forms. Although we specify the site plan of this project at Iran-Tehran, this research can be generalized to various design sites as well as different projects, allowing the architectsto alter the cell dimensions, cell density, etc., based on their opinion and project needs.

• Mycobiology
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• v.49 no.6
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• pp.559-581
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• 2021

The coastal marine ecosystems of Vietnam are one of the global biodiversity hotspots, but the biodiversity of marine fungi is not well known. To fill this major gap of knowledge, we assessed the genetic diversity (ITS sequence) of 75 fungal strains isolated from 11 surface coastal marine and deeper waters in Nha Trang Bay and Van Phong Bay using a culture-dependent approach and 5 OTUs (Operational Taxonomic Units) of fungi in three representative sampling sites using next-generation sequencing. The results from both approaches shared similar fungal taxonomy to the most abundant phylum (Ascomycota), genera (Candida and Aspergillus) and species (Candida blankii) but were different at less common taxa. Culturable fungal strains in this study belong to 3 phyla, 5 subdivisions, 7 classes, 12 orders, 17 families, 22 genera and at least 40 species, of which 29 species have been identified and several species are likely novel. Among identified species, 12 and 28 are new records in global and Vietnamese marine areas, respectively. The analysis of enzyme activity and the checklist of trophic mode and guild assignment provided valuable additional biological information and suggested the ecological function of planktonic fungi in the marine food web. This is the largest dataset of marine fungal biodiversity on morphology, phylogeny and enzyme activity in the tropical coastal ecosystems of Vietnam and Southeast Asia. Biogeographic aspects, ecological factors and human impact may structure mycoplankton communities in such aquatic habitats.

• Journal of Microbiology and Biotechnology
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• v.32 no.12
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• pp.1497-1505
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• 2022

Recently, the concept of personalized nutrition has been developed, which states that food components do not always lead to the same metabolic responses, but vary from person to person. Although this concept has been studied based on individual genetic backgrounds, researchers have recently explored its potential role in the gut microbiome. The gut microbiota physiologically communicates with humans by forming a bidirectional relationship with the micronutrients, macronutrients, and phytochemicals consumed by the host. Furthermore, the gut microbiota can vary from person to person and can be easily shifted by diet. Therefore, several recent studies have reported the application of personalized nutrition to intestinal microflora. This review provides an overview of the interaction of diet with the gut microbiome and the latest evidence in understanding the inter-individual differences in dietary responsiveness according to individual baseline gut microbiota and microbiome-associated dietary intervention in diseases. The diversity of the gut microbiota and the presence of specific microorganisms can be attributed to physiological differences following dietary intervention. The difference in individual responsiveness based on the gut microbiota has the potential to become an important research approach for personalized nutrition and health management, although further well-designed large-scale studies are warranted.

• BMB Reports
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• v.55 no.12
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• pp.621-626
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• 2022

Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disease characterized by the degeneration of motor neurons in the spinal cord. Main symptoms are manifested as weakness, muscle loss, and muscle atrophy. Some studies have reported that alterations in sphingolipid metabolism may be intimately related to neurodegenerative diseases, including ALS. Acid sphingomyelinase (ASM), a sphingolipid-metabolizing enzyme, is considered an important mediator of neurodegenerative diseases. Herein, we show that ASM activity increases in samples from patients with ALS and in a mouse model. Moreover, genetic inhibition of ASM improves motor function impairment and spinal neuronal loss in an ALS mouse model. Therefore, these results suggest the role of ASM as a potentially effective target and ASM inhibition may be a possible therapeutic approach for ALS.

• ALGAE
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• v.37 no.4
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• pp.281-291
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• 2022

Quantifying the abundance of Chattonella species is necessary to effectively manage the threats from ichthyotoxic raphidophytes, which can cause large-scale mortality of aquacultured fish in temperate waters. The identification and cell counting of Chattonella species have been conducted primarily on living cells without fixation by light microscopy because routine fixatives do not retain their morphological features. Species belonging to the Chattonella marina complex, including C. marina and C. marina var. ovata, had high genetic similarities and the lack of clear morphological delimitations between the species. To estimate the abundance of C. marina complex in marine plankton samples, we developed a protocol based on the droplet digital polymerase chain reaction (ddPCR) assay, with C. marina complex-specific primers targeting the internal transcribed spacer (ITS) region of the rDNA. Cell abundance of the C. marina complex can be determined using the ITS copy number per cell, ranging from 25 ± 1 for C. marina to 112 ± 7 for C. marina var. ovata. There were no significant differences in ITS copies estimated by the ddPCR assay between environmental DNA samples from various localities spiked with the same number of cells of culture strains. This approach can be employed to improve the monitoring efficiency of various marine protists and to support the implementation of management for harmful algal blooms, which are difficult to analyze using microscopy alone.