Background: Breast cancer is the most common malignancy affecting the Iraqi population and the leading cause of cancer related mortality among Iraqi women. It has been well documented that prognosis of patients depends largely upon the hormone receptor contents and HER-2 over expression of their neoplasm. Recent studies suggest that Triple Positive (TP) tumors, bearing the three markers, tend to exhibit a relatively favorable clinical behavior in which overtreatment is not recommended. Aim: To document the different frequencies of ER/PR/HER2 breast cancer molecular subtypes focusing on the Triple Positive pattern; correlating those with the corresponding clinico-pathological characteristics among a sample of Iraqi patients diagnosed with the disease. Patients and Methods: This retrospective study involved 570 female patients diagnosed with breast cancer who visited the Main Referral Center for Early Detection of Breast Cancer and the National Cancer Research Center. The requested data was mainly extracted from an established information system database, developed by the principal author over a 4-years period from 2012 to 2016. The registered information comprised data related to clinical presentation, diagnostic and pathologic findings. ER, PR and HER2 status were analyzed immunohistochemically and the detected profiles were correlated with the corresponding clinico-pathological characteristics. Results: The rates of ER (+), PR (+) and HER-2 (+) tumors were 66.8%, 64% and 29.3% respectively. The tumor marker expression status was documented in eight subtypes; 83 cases (14.6%) of the examined breast carcinomas were categorized as ER+/PR+/HER2+ (Triple Positive/Luminal B), 89 cases (15.6%) were ER-/PR-/HER2- (Triple Negative), 241 (42.2%) were ER+/PR-/HER2- (Luminal A) while 67 (11.8%) showed ER-/PR-/HER2+ expression (HER-2 variant). No significant variations were noted when comparing age, menopausal status and tumor grade of Luminal B pattern to Luminal A and the rest of the studied subtypes. Although our data revealed a statistical difference with respect to the distribution of tumor types; where infiltrative ductal carcinoma constituted the major histology among patients with Triple Positive/Luminal B subtype (86.7% as compared to 67.6% in Luminal A), nevertheless, more than two third of those patients (67.4%) were diagnosed at earlier stages (I and II) compared to 55.2% of those in the remaining groups. Conclusions and Recommendations: Our findings support the hypothesis that the Triple Positive subtype might be driven primarily by the hormone receptor status. Further comprehensive surveys are recommended to focus on the intrinsic interactions and tumor cell heterogeneity of that pattern to avoid overtreatment and predict accurate response to therapy.
Serum adenosine deaminase (ADA) activity was determined in 30 blood sample of type 1 diabetic individuals 30 blood sample for the type 2 and 15 normal children as a control for type 1 15 normal adults as control for type 2. The mean ADA activity and specific activity in type 1 was (8.85± 5.55 U/mg of protein) which is compared with control (32.11± 1.54 U/mg of protein) while in type 2 was (48.46±11.91 U/mg of protein) is compared with control (5.18± 2.27 U/mg of protein ). We conclude that the altered blood level of ADA activity may help in predicting immunological dysfunction in diabetic individuals and also has a prognostic value.
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Arum maculatum is traditionally used for the control of many diseases and illnesses such as kidney pain, liver injury, hemorrhoids. However, the detailed biomedical knowledge about this species is still lacking. This study reports on the bioactive components and the possible mechanisms underlying the antioxidant, anti-inflammatory and cytotoxic activity of A. maculatum leaf extract. Gas chromatography-mass spectrometry (GC-MS) was used for phytochemical analysis. Assay of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide ) (MTT) was used to determine the cytotoxicity in the murine cell line L20B upon exposure to different extract concentrations for 24 h. Enzyme-linked immunosorbent assay (ELISA) was used to detect pro-in
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Big data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such a
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Big data analysis is essential for modern applications in areas such as healthcare, assistive technology, intelligent transportation, environment and climate monitoring. Traditional algorithms in data mining and machine learning do not scale well with data size. Mining and learning from big data need time and memory efficient techniques, albeit the cost of possible loss in accuracy. We have developed a data aggregation structure to summarize data with large number of instances and data generated from multiple data sources. Data are aggregated at multiple resolutions and resolution provides a trade-off between efficiency and accuracy. The structure is built once, updated incrementally, and serves as a common data input for multiple mining an
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