Applied Intelligence for Medical Diagnosing

Medical imaging is associated with different techniques and processes that are used to create visual representations of internal parts of the human body for diagnostic and treatment purposes within digital health. Machine learning plays a crucial role in the medical imaging field including analysis of various medical images, computer-aided diagnosis or detection, image retrieval, gene data analysis, image reconstruction, and organ segmentation. The machine learning algorithm framework recognizes the best combination of the medical image features for categorizing the medical images or processing some metric for the given image area. The images obtained are then processed using algorithms such as K-means, support vector machines, decision trees, neural networks, and deep learning techniques.

Molecular Analysis Reveals a High Diversity of Anopheline Mosquitoes in Yanomami Lands and the Pantanal Region of Brazil

Identifying the species of the subfamily Anophelinae that are Plasmodium vectors is important to vector and malaria control. Despite the increase in cases, vector mosquitoes remain poorly known in Brazilian indigenous communities. This study explores Anophelinae mosquito diversity in the following areas: (1) a Yanomami reserve in the northwestern Amazon Brazil biome and (2) the Pantanal biome in southwestern Brazil. This is carried out by analyzing cytochrome c oxidase (COI) gene data using Refined Single Linkage (RESL), Assemble Species by Automatic Partitioning (ASAP), and tree-based multi-rate Poisson tree processes (mPTP) as species delimitation approaches. A total of 216 specimens collected from the Yanomami and Pantanal regions were sequenced and combined with 547 reference sequences for species delimitation analyses. The mPTP analysis for all sequences resulted in the delimitation of 45 species groups, while the ASAP analysis provided the partition of 48 groups. RESL analysis resulted in 63 operational taxonomic units (OTUs). This study expands our scant knowledge of anopheline species in the Yanomami and Pantanal regions. At least 18 species of Anophelinae mosquitoes were found in these study areas. Additional studies are now required to determine the species that transmit Plasmodium spp. in these regions.

Blockchain-Based Privacy-Preserving System for Genomic Data Management Using Local Differential Privacy

The advances made in genome technology have resulted in significant amounts of genomic data being generated at an increasing speed. As genomic data contain various privacy-sensitive information, security schemes that protect confidentiality and control access are essential. Many security techniques have been proposed to safeguard healthcare data. However, these techniques are inadequate for genomic data management because of their large size. Additionally, privacy problems due to the sharing of gene data are yet to be addressed. In this study, we propose a secure genomic data management system using blockchain and local differential privacy (LDP). The proposed system employs two types of storage: private storage for internal staff and semi-private storage for external users. In private storage, because encrypted gene data are stored, only internal employees can access the data. Meanwhile, in semi-private storage, gene data are irreversibly modified by LDP. Through LDP, different noises are added to each section of the genomic data. Therefore, even though the third party uses or exposes the shared data, the owner’s privacy is guaranteed. Furthermore, the access control for each storage is ensured by the blockchain, and the gene owner can trace the usage and sharing status using a decentralized application in a mobile device.

An integrative bioinformatics analysis for identifying hub genes associated with infection of lung samples in patients infected with SARS-CoV-2

Background At the end of 2019, the world witnessed the emergence and ravages of a viral infection induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Also known as the coronavirus disease 2019 (COVID-19), it has been identified as a public health emergency of international concern (PHEIC) by the World Health Organization (WHO) because of its severity. Methods The gene data of 51 samples were extracted from the GSE150316 and GSE147507 data set and then processed by means of the programming language R, through which the differentially expressed genes (DEGs) that meet the standards were screened. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on the selected DEGs to understand the functions and approaches of DEGs. The online tool STRING was employed to construct a protein–protein interaction (PPI) network of DEGs and, in turn, to identify hub genes. Results A total of 52 intersection genes were obtained through DEG identification. Through the GO analysis, we realized that the biological processes (BPs) that have the deepest impact on the human body after SARS-CoV-2 infection are various immune responses. By using STRING to construct a PPI network, 10 hub genes were identified, including IFIH1, DDX58, ISG15, EGR1, OASL, SAMD9, SAMD9L, XAF1, IFITM1, and TNFSF10. Conclusion The results of this study will hopefully provide guidance for future studies on the pathophysiological mechanism of SARS-CoV-2 infection.

HashSeq: a Simple, Scalable, and Conservative De Novo Variant Caller for 16S rRNA Gene Data Sets

Recent bioinformatics development has enabled the detection of sequence variants with a high resolution of only one single-nucleotide difference in 16S rRNA gene sequence data. Despite this progress, there are several limitations that can be associated with variant calling pipelines, such as producing a large number of low-abundance sequence variants which need to be filtered out with arbitrary thresholds in downstream analyses or having a slow runtime.

Machine-Learning-Based m5C Score for the Prognosis Diagnosis of Osteosarcoma

Background. Osteosarcoma is a common and highly metastatic malignant tumor, and m5C RNA methylation regulates various biological processes. The purpose of this study was to explore the prognostic role of m5C in osteosarcoma using machine learning. Methods. Osteosarcoma gene data and the corresponding clinical information were downloaded from the GEO database. Machine learning methods were used to screen m5C-related genes and construct m5C scores. In addition, the clusterProfiler package was used to predict the m5C-related functional pathways. xCell and CIBERSORT were used to calculate the immune microenvironment cells. GSVA was applied to analyze different categories of m5C genes, and the correlation between the GSVA and m5C scores was evaluated. Results. Twenty m5C genes were identified, and 54 related genes were screened. The m5C score was constructed based on the PCA score. With an increase in the m5C score, the expression of m5C genes and their related genes changed. Functional analysis indicated that the focal adhesion, cell-substrate adherens junction, cell adhesion molecule binding, and E2F targets might change with the m5C score. The naive B cells and CD4+ memory T cell also changed with the m5C score. The results of the correlation analysis showed that the m5C score was significantly correlated with the reader and eraser genes. Conclusion. The m5C score might be a prognostic index for osteosarcoma.

Complete Plastome of Three Korean Asarum (Aristolochiaceae): Confirmation Tripartite Structure within Korean Asarum and Comparative Analyses

The genus Asarum (Aristolochiaceae) is a well-known resource of medicinal and ornamental plants. However, the taxonomy of Korean Asarum is ambiguous due to their considerable morphological variations. Previously, a unique plastome structure has been reported from this genus. Therefore, we investigated the structural change in the plastomes within three Korean Asarum species and inferred their phylogenetic relationships. The plastome sizes of Asarum species assembled here range from 190,168 to 193,356 bp, which are longer than a typical plastome size (160 kb). This is due to the incorporation and duplication of the small single copy into the inverted repeat, which resulted in a unique tripartite structure. We first verified this unique structure using the Illumina Miseq and Oxford Nanopore MinION platforms. We also investigated the phylogeny of 26 Aristolochiaceae species based on 79 plastid protein-coding genes, which supports the monophyly of Korean Asarum species. Although the 79 plastid protein-coding gene data set showed some limitations in supporting the previous classification, it exhibits its effectiveness in delineating some sections and species. Thus, it can serve as an effective tool for resolving species-level phylogeny in Aristolochiaceae. Last, we evaluated variable sites and simple sequence repeats in the plastome as potential molecular markers for species delimitation.