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.
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.
Diabetes mellitus type 2 (DMT2) is one of the modern societies’ highest public health threats. For a while, it was believed that DM had no impact on male reproductive function; however, new research has cast doubt on that assumption. To find out whether repaglinide and metformin may improve sperm motility and testosterone levels in diabetic and non-diabetic albino rats, researchers in this study used these two drugs to treat diabetes. Methods: Alloxan injections at three dosages of 120 mg/kg intraperitoneal produced type 2 diabetes in male rats. Experimental rats were classified into two main groups. The first group included four subgroups of male rats treated with alloxan (DM inducer). Each subgroup contained seven rats “1. Control without any treatment (positive control), 2. treated by 500 mg/kg metformin, 3. treated by 4 mg/kg repaglinide, and 4. treated by 500 mg/kg metformin and 4 mg/kg repaglinide”. The second group also includes four subgroups but without alloxan treated. Each subgroup has seven rats; all are categorized in the same initial group and receive identical treatment dosages.
A modest quantity of fluoride can increase the mineralization of teeth and reduce their cavities. But the presomerence of fluoride in excess in water can lead to severe disease infertility. In the past few decades, scientists have thus been preoccupied with developing ways to reduce sewage fluoride concentrations and reduce their effects on human health. The present study is aimed at using the technology of electrocoagulation to remove fluoride from polluted water. Tests have been done to examine the elimination of fluoride with a rectangular electrocoagulation cell and examine the impact of the experimental aspects on fluoride extraction, specifically electrical current, electrode spacing, and pH. The authors found that 93% of the fluoride has been extracted using 5mm spaced electrodes with a current density of 2 mA/cm2 and a level of pH of 7 from the polluted water after 20 min of processing. Experimental factors considerably impact the efficacy of fluoride removal. In the acidic environment, greater effectiveness of fluoride removal is being attained. The elimination effectiveness depends directly on the electric current, whereas the distance between poles is adversely linked to fluoride elimination.
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