Cell Phenotype Classification Based on Joint of Texture Information and Multilayer Feature Extraction in DenseNet.

Cell phenotype classification is a critical task in many medical applications, such as protein localization, gene effect identification, and cancer diagnosis in some types. Fluorescence imaging is the most efficient tool to analyze the biological characteristics of cells. So cell phenotype classification in fluorescence microscopy images has received increased attention from scientists in the last decade. The visible structures of cells are usually different in terms of shape, texture, relationship between intensities, etc. In this scope, most of the presented approaches use one type or joint of low-level and high-level features. In this paper, a new approach is proposed based on a combination of low-level and high-level features. An improved version of local quinary patterns is used to extract low-level texture features. Also, an innovative multilayer deep feature extraction method is performed to extract high-level features from DenseNet. In this respect, an output feature map of dense blocks is entered in a separate way to pooling and flatten layers, and finally, feature vectors are concatenated. The performance of the proposed approach is evaluated on the benchmark dataset 2D-HeLa in terms of accuracy. Also, the proposed approach is compared with state-of-the-art methods in terms of classification accuracy. Comparison of results demonstrates higher performance of the proposed approach in comparison with some efficient methods.

Study of Small Non-Coding RNA (miRNA) Expression Pattern of Fertile/Infertile Male Semen.

Background: Infertility is a serious health issue that affects people all around the world. One of the most common reasons for male infertility is sperm abnormalities. Researchers and scientists have been searching for a novel genetic marker to detect or recognize the genetic malfunction that causes sperm abnormalities. Micro-RNA (miRNAs) are small non-coded RNA molecules that present intra and extra-cellular and regulate gene expression. Objective: This studies began to search for a relation between miRNA expression levels and other diseases that may be related to them, considering that the main role of miRNAs was the down-regulation of genes. Methods: The main technique used in this study was to synthesize a complementary DNA (cDNA) (revers transcription method) of extracted total RNA by TRIzol then amplification of candidates' miRNAs genes by Reverse Transcriptase Quantitative Polymerase Chain Reaction RT-qPCR. Results: Studies found that miRNAs have a role in defining sperm qualities such as sperm count, motility, and shape. In this study, we chose the most miRNAs referred to in the previous study as a potential seminal fluid marker (miR-10a, miR-10b, miR-135a and miR-135b) to test them as potential infertility-related miRNAs markers (Asthenospermia AS, Oligospermia OS, Astheno-Oligospermia ASOS) in addition to health normal sperm NS. Conclusion: the main aim of this study was to find the miRNAs expression pattern to find a way to help scientists track the genetic causes of male infertility issues and a novel method to distinguish infertility genetically diseases. Conclusion: The findings may serve as a potential genetic marker for male infertility and provide a background for future research that targeted miRNAs as a molecular marker for medical and forensic fields, also as an infertility disease potential treatment.

MEF: Multidimensional Examination Framework for Prioritization of COVID-19 Severe Patients and Promote Precision Medicine Based on Hybrid Multi-Criteria Decision-Making Approaches.

Effective prioritization plays critical roles in precision medicine. Healthcare decisions are complex, involving trade-offs among numerous frequently contradictory priorities. Considering the numerous difficulties associated with COVID-19, approaches that could triage COVID-19 patients may help in prioritizing treatment and provide precise medicine for those who are at risk of serious disease. Prioritizing a patient with COVID-19 depends on a variety of examination criteria, but due to the large number of these biomarkers, it may be hard for medical practitioners and emergency systems to decide which cases should be given priority for treatment. The aim of this paper is to propose a Multidimensional Examination Framework (MEF) for the prioritization of COVID-19 severe patients on the basis of combined multi-criteria decision-making (MCDM) methods. In contrast to the existing literature, the MEF has not considered only a single dimension of the examination factors; instead, the proposed framework included different multidimensional examination criteria such as demographic, laboratory findings, vital signs, symptoms, and chronic conditions. A real dataset that consists of data from 78 patients with different examination criteria was used as a base in the construction of Multidimensional Evaluation Matrix (MEM). The proposed framework employs the CRITIC (CRiteria Importance Through Intercriteria Correlation) method to identify objective weights and importance for multidimensional examination criteria. Furthermore, the VIKOR (VIekriterijumsko KOmpromisno Rangiranje) method is utilized to prioritize COVID-19 severe patients. The results based on the CRITIC method showed that the most important examination criterion for prioritization is COVID-19 patients with heart disease, followed by cough and nasal congestion symptoms. Moreover, the VIKOR method showed that Patients 8, 3, 9, 59, and 1 are the most urgent cases that required the highest priority among the other 78 patients. Finally, the proposed framework can be used by medical organizations to prioritize the most critical COVID-19 patient that has multidimensional examination criteria and to promptly give appropriate care for more precise medicine.