Online ISSN: 2515-8260

Keywords : Lymphocytes


Rohatoy Takhirova; Shakhnoza Abzalova; Gulchehra Pirnazarova; Kamola Yakubova

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 2307-2313

Abstract. Rheumatoid arthritis is a systemic inflammatory progressive disease of the
connective tissue with a predominant lesion of small joints of the type of erosive-destructive
polyarthritis of unknown etiology with complex autoimmune pathogenesis, which often
leads to combined pathology of the body and disability of children and adults. The disease
is characterized by a rather early manifestation of high disability (70%). The main causes
of death from the disease are infectious complications and renal failure. Treatment focuses
mainly on pain relief, slowing the progression of the disease and repairing injuries
through surgery. Early detection of the disease using modern means can significantly
reduce the damage that can be done to joints and other tissues. The first cases of
manifestation can be recorded after severe physical exertion, emotional shock, fatigue,
during hormonal adjustment, exposure to adverse factors or infection. With all this, it is
necessary to study the intersystemic relationships of this disease. In this regard, the goal of
our research is to study the relationship of immunological parameters and indicators of the
endocrine system in rheumatoid arthritis in children. We examined 98 children, 58 of
which were girls (59%) and 40 boys (41%) aged 7 to 17 years with the JRA. The duration
of the JRA ranged from 6 months to 7 years. Among the patients examined by us, about
half were children with a disease period of 1-3 years. The articular form of the disease was
observed in 66 children (the maximum activity of the disease was in 19, moderate in 27,
minimal in 20), articular-visceral in 32. The control group consisted of 20 healthy children
of the same age. The results of our studies showed that with rheumatoid arthritis,
depending on the form of the disease in children, there was a high level of TSH and ACTH
and a low content of T3, T4 and cortisol. With prolonged exposure to stress, the excretion
of hormones by the effector glands decreases and the level of pituitary hormones increases
according to the principle of negative feedback. A similar type of endocrine system
functioning has been identified in children with rheumatoid arthritis. There are also
significant changes in the immune status: the content of T-lymphocytes in the blood
decreases and the activity of the B-system of immunity increases. Depending on the severity
and form of the disease, the closeness of correlations was also revealed. Based on this, our
observations of children with articular rheumatoid arthritis over three years showed that
changes in the parameters of the immune and endocrine systems persist. The inclusion of
glucocorticoid hormones in therapy in patients with articular-visceral form of rheumatoid
arthritis showed a positive dynamics of indicators, the state of the immune, as well as the

Classification of Leukemia Using Convolution Neural Network

Dr. T. C. Kalaiselvi; D.Santhosh Kumar; K.S. Subhashri; S.M. Siddharth

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1286-1293

The death caused by Leukemia has been ranked in the top ten most dangerous mortality cause for the human being. There are numerous reasons and causes, in spite of the causes and reasons the profound problem is the slow decision-making process which delays the time required to proceed with medical treatment for the patients. That’s why the enhanced medical support process has become necessary for the classification of leukemia. The four different types of Leukemia are as follows Myeloid Leukemia where we have acute and chronic subcategories and in the same way, it goes for the myeloid type as well, these affect various cells and systems such as the blood cells, bone marrow, lymphatic system and which causes the death of patients. The proposed method improves the CML, CLL, AML and ALL characteristic accuracy by scanning color and textural features from the blood image using image processing and to aid in the grouping of CML, CLL, AML and ALL. The following technique proposes a quantitative microscopic approach toward the grouping of blood sample images. A model using Modified Convolution Neural Network (CNN) architecture is used to optimize the classification process. Based on optimized feature space, a CNN model with various kernel functions (filters) used to abstract the features from the pixel values. The proposed method is tested using nearly 10000 microscopic blood images. The outcome confirmed that the accuracy of the classification using blood sampled images which was up to 98%.