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Evaluation of the level of some Interleukins in serum of Iraqi patients with Endometrial Carcinoma
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Endometrial Cancer (EC) is one of the most common malignancy of the female reproductive system. With an increasing incidence, it is important to improve the new prognosis ways for its pre-diagnosis that must be early, accurate and effective. This study aimed to search for biological (like some new interleukins) which could help in early diagnosis of EC before the hysterectomy. Currently not enough research is being done exploiting linking between the interleukins submitted in this study and EC. Epically IL-36 and IL-38, which have been recently described and are still under study in the world. This study is the first of its kind in Iraq. Fifty-five patients with EC (mainly in their first or second stage, due to early diagnosis and who newly have the symptoms and pain as a result of cancer) and 57 healthy controls (with ages up to 45) were involved in this study. To measure the concentration of the following interleukins: IL-27, 31, 33, 35, 36 and 38 by ELISA, blood samples were collected from women via vein puncture. The results of this study showed a highly significant (P≤0.01) increase in Interleukin 27 (IL-27), Interleukin 31 (IL-31) and Interleukin 33 (IL-33) among all studied interleukins levels in EC patient as compared with healthy controls. Interleukin 35 (IL-35), Interleukin 36 (IL-36) and Interleukin 38 (IL-38) also showed highly significant (P≤0.01) increase in EC patients as compared with healthy women. The significant increase (P≤0.01) of these interleukins can be used to help in the early diagnosis and treatment of EC, without need to hysterectomy or histological diagnosis.

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Publication Date
Fri Apr 30 2021
Journal Name
Iraqi Journal Of Science
Enhanced Supervised Principal Component Analysis for Cancer Classification
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In this paper, a new hybridization of supervised principal component analysis (SPCA) and stochastic gradient descent techniques is proposed, and called as SGD-SPCA, for real large datasets that have a small number of samples in high dimensional space. SGD-SPCA is proposed to become an important tool that can be used to diagnose and treat cancer accurately. When we have large datasets that require many parameters, SGD-SPCA is an excellent method, and it can easily update the parameters when a new observation shows up. Two cancer datasets are used, the first is for Leukemia and the second is for small round blue cell tumors. Also, simulation datasets are used to compare principal component analysis (PCA), SPCA, and SGD-SPCA. The results sh

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