Abstract Introduction: MMP3 plays a crucial role in the process of bone erosion in the pathomechanism of rheumatoid arthritis (RA). It acts by removing the outer osteoid layer, which allows the osteoclasts to tightly connect and carry out the subsequent damage to the underlying bone. MMP3 can trigger the production of other MMPs like MMP-1, MMP-7, and MMP-9, it plays a pivotal role in the remodeling of connective tissues. Aim of the study: to assess the influence of MMP-3 serum levels and single-nucleotide polymorphisms of rs679620 in the rheumatoid arthritis patients' group in comparison to the control group. Subjects: eighty eight samples, 45 rheumatoid arthritis patients after being referred by their treating physician for regular RA test. The remaining 43 samples all represent apparently healthy people. The present study investigated the serum concentration of MMP-3 and rs679620 SNPs in the group of patients with RA, in comparison to the control group. Results: The results indicated a significant elevation in MMP-3 levels in RA patients in comparison to healthy individuals (12.75 ± 0.38 vs. 9.69 ± 0.37) and the findings of rs679620 SNPs appeared that the patient group has a non-significant increase in both allele frequency A and genotype frequency AA when compared to the control group (66.2 vs. 52.2 %; p = 0.172; OR = 1.79 and 35.3 vs. 17.4 %; p = 0.229; OR = 2.59) , but a non-significant decrease in both allele frequency C and genotype frequency CC when compared to the control group (2.94 vs. 4.4 %; p = 1.0; OR = 0.67 and 2.9 vs. 4.3 %; p = 1.0; OR = 0.67), as well as a non-significant decrease in allele frequency G and both genotypes frequency GG and AG when compared to the control group (30.9 vs. 43.5 %; p = 0.233; OR = 0.58, 0.0 vs. 8.7 %; p = 0.159; OR = 0.12 and 61.8 vs. 69.6 %; p = 0.585; OR = 0.71 ). Patients carrying the AA and AG genotype, had significantly higher serum levels of MMP-3 compared to control (P= 0.005 and 0.004) respectively. Conclusion: Rs679620 may influence joint destruction via increase MMP-3 production.
Abstract: The utility of DNA sequencing in diagnosing and prognosis of diseases is vital for assessing the risk of genetic disorders, particularly for asymptomatic individuals with a genetic predisposition. Such diagnostic approaches are integral in guiding health and lifestyle decisions and preparing families with the necessary foreknowledge to anticipate potential genetic abnormalities. The present study explores implementing a define-by-run deep learning (DL) model optimized using the Tree-structured Parzen estimator algorithm to enhance the precision of genetic diagnostic tools. Unlike conventional models, the define-by-run model bolsters accuracy through dynamic adaptation to data during the learning process and iterative optimization
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Most recognition system of human facial emotions are assessed solely on accuracy, even if other performance criteria are also thought to be important in the evaluation process such as sensitivity, precision, F-measure, and G-mean. Moreover, the most common problem that must be resolved in face emotion recognition systems is the feature extraction methods, which is comparable to traditional manual feature extraction methods. This traditional method is not able to extract features efficiently. In other words, there are redundant amount of features which are considered not significant, which affect the classification performance. In this work, a new system to recognize human facial emotions from images is proposed. The HOG (Histograms of Or
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An efficient modification and a novel technique combining the homotopy concept with Adomian decomposition method (ADM) to obtain an accurate analytical solution for Riccati matrix delay differential equation (RMDDE) is introduced in this paper . Both methods are very efficient and effective. The whole integral part of ADM is used instead of the integral part of homotopy technique. The major feature in current technique gives us a large convergence region of iterative approximate solutions .The results acquired by this technique give better approximations for a larger region as well as previously. Finally, the results conducted via suggesting an efficient and easy technique, and may be addressed to other non-linear problems.
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