Background: Complement (C) & CRP in patients with Rheumatoid arthritis (RA)'could be trigger disease activity.
Aim: To study the correlation between C, CRP, IgM,, IgG, IgA & some clinical presentation in RA patients.
Methods: Latex agglutination test(AG) & single radial immunoassay(SRIA) were used to asses CRP, IgMJgGJgA,, C, in 74 patients with RA.
Results: IgA & IgG were significantly increased, while the mean ofC3, C4 were slightly elevated in RA patients .
Conclusion: There is correlation between IgG and IgM with joint deformity and joint swelling respectively, while C3 was showed statistically significant P<0.01 with joint
stiffness, joint swelling and Rheumatoid nodule, where as CRP was statistically significant P<0.01withjoint swelling.. Key words : RA.C.CRP, IgM, IgG, IgA, SRI A,
AG.
In this paper, we generalized the principle of Banach contractive to the relative formula and then used this formula to prove that the set valued mapping has a fixed point in a complete partial metric space. We also showed that the set-valued mapping can have a fixed point in a complete partial metric space without satisfying the contraction condition. Additionally, we justified an example for our proof.
Automated medical diagnosis is an important topic, especially in detection and classification of diseases. Malaria is one of the most widespread diseases, with more than 200 million cases, according to the 2016 WHO report. Malaria is usually diagnosed using thin and thick blood smears under a microscope. However, proper diagnosis is difficult, especially in poor countries where the disease is most widespread. Therefore, automatic diagnostics helps in identifying the disease through images of red blood cells, with the use of machine learning techniques and digital image processing. This paper presents an accurate model using a Deep Convolutional Neural Network build from scratch. The paper also proposed three CNN
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Agriculture improvement is a national economic issue that extremely depends on productivity. The explanation of disease detection in plants plays a significant role in the agriculture field. Accurate prediction of the plant disease can help treat the leaf as early as possible, which controls the economic loss. This paper aims to use the Image processing techniques with Convolutional Neural Network (CNN). It is one of the deep learning techniques to classify and detect plant leaf diseases. A publicly available Plant village dataset was used, which consists of 15 classes, including 12 diseases classes and 3 healthy classes. The data augmentation techniques have been used. In addition to dropout and weight reg
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