Abstract. Gynaecological malignancies contribute significantly to cancer burden and have a higher rate of mortality and morbidity. The aim of this retrospective study was to determine the pattern of gynaecological malignancies identified between January, 2000 and December, 2011, at the Centre for Nuclear Medicine and Radiotherapy (CENAR). At CENAR 5,072 female patients were registered with different malignancies, of which 632 cases were gynaecological malignancies. Ovarian cancer (47%) was the most common gynaecological malignancy, followed by cervical cancer (29%), uterine cancer (14%), vulvar and vaginal cancer (6%), and gestational trophoblastic neoplasm (4%). Of the ovarian cancer cases, 72.5% had epithelial while 26.5% had non-epithelial cancer. Squamous cell carcinoma was 75.9% in cervix and 87.8% in vulva and vagina while endometrial carcinoma (75.9%) was more frequent in uterus. For gestational trophoblastic neoplasm, 69.2% of patients had choriocarcinoma. Ovarian cancer was the most common type for the age range of 50-59 years. In the case of cervical and gestational trophoblastic neoplasm the majority of patients presented at the ages of 40-49 and 30-39 years while uterus, vulvar and vaginal tumor presented in the elderly (>60 years). Thus, ovarian cancer is the leading gynecological malignancy in Pakistan.
Flooding from the Indus river and its tributaries has regularly influenced the region of Pakistan. Therefore, in order to limit the misfortune brought about by these inevitable happenings, it requires taking measures to estimate the occurrence and effects of these events. The current study uses flood frequency analysis for the forecast of floods along the Indus river of Pakistan (Tarbela). The peak and volume are the characteristics of a flood that commonly depend on one another. For progressively proficient hazard investigation, a bivariate copula method is used to measure the peak and volume. A univariate analysis of flood data fails to capture the multivariate nature of these data. Copula is the most common technique
Background: Chronic myeloid leukemia (CML) is a myeloproliferative disorder of pluripotent stem cells, caused by reciprocal translocation between the long arms of chromosomes 9 and 22, t(9;22)(q34;q11), known as the Philadelphia chromosome. Materials and Methods: A total of 51 CML patients were recruited in this study. Complete blood counts of all CML patients were performed to find out their total leukocytes, hemoglobin and platelets. FISH was performed for the detection of BCR-ABL fusion and cryptogenic tests using bone marrow samples were performed for the conformation of Ph (9;22)(q34;q11) and variant translocation mechanisms. Results: In cytogenetic analysis we observed that out of 51 CML patients 40 (88.9%) were Ph positive and 4 (8.88%) had Ph negative chromosomes. Mean values of WBC 134.5 10 3 /µl, hemoglobin 10.44 mg/dl, and platelets 288.6 10 3 /µl were observed in this study. Conclusions: In this study, Ph positive translocation between chromosome (9:22)(q34;q11) were observed in 40 (88.9%) CML patients.
This paper proposes a streamlined form of simplex method which provides some great benefits over traditional simplex method. For instance, it does not need any kind of artificial variables or artificial constraints; it could start with any feasible or infeasible basis of an LP. This method follows the same pivoting sequence as of simplex phase 1 without showing any explicit description of artificial variables which also makes it space efficient. Later in this paper, a dual version of the new method has also been presented which provides a way to easily implement the phase 1 of traditional dual simplex method. For a problem having an initial basis which is both primal and dual infeasible, our methods provide full freedom to the user, that whether to start with primal artificial free version or dual artificial free version without making any reformulation to the LP structure. Last but not the least, it provides a teaching aid for the teachers who want to teach feasibility achievement as a separate topic before teaching optimality achievement.
In recent years immense growth of data i.e. big data is observed resulting in a brighter and more optimized future. Big Data demands large computational infrastructure with high-performance processing capabilities. Preparing big data for mining and analysis is a challenging task and requires data to be preprocessed to improve the quality of raw data. The data instance representation and quality are foremost. Data preprocessing is preliminary data mining practice in which raw data is transformed into a format suitable for another processing procedure. Data preprocessing improves the data quality by cleaning, normalizing, transforming and extracting relevant feature from raw data. Data preprocessing significantly improve the performance of machine learning algorithms which in turn leads to accurate data mining. Knowledge discovery from noisy, irrelevant and redundant data is a difficult task therefore precise identification of extreme values and outlier, filling up missing values poses challenges. This paper discusses various big data pre-processing techniques in order to prepare it for mining and analysis tasks.
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