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Goud, Tadakal Mallana,Al Salmani, Kamla Khalfan,Al Harasi, Salma Mohammed,Al Musalhi, Muhanna,Wasifuddin, Shah Mohammed,Rajab, Anna Asian Pacific Journal of Cancer Prevention 2015 Asian Pacific journal of cancer prevention Vol.16 No.16
Genetic changes associated with acute lymphoblastic leukemia (ALL) provide very important diagnostic and prognostic information with a direct impact on patient management. Detection of chromosome abnormalities by conventional cytogenetics combined with fluorescence in situ hybridization (FISH) play a very significant role in assessing risk stratification. Identification of specific chromosome abnormalities has led to the recognition of genetic subgroups based on reciprocal translocations, deletions and modal number in B or T-cell ALL. In the last twelve years 102 newly diagnosed childhood/adult ALL bone marrow samples were analysed for chromosomal abnormalities with conventional G-banding, and FISH (selected cases) using specific probes in our hospital. G-banded karyotype analysis found clonal numerical and/or structural chromosomal aberrations in 74.2% of cases. Patients with pseudodiploidy represented the most frequent group (38.7%) followed by high hyperdiploidy group (12.9%), low hyperdiploidy group (9.7%), hypodiploidy (<46) group (9.7%) and high hypertriploidy group (3.2%). The highest observed numerical chromosomal alteration was high hyperdiploidy (12.9%) with abnormal karyotypes while abnormal 12p (7.5%) was the highest observed structural abnormality followed by t(12;21)(p13.3;q22) resulting in ETV6/RUNX1 fusion (5.4%) and t(9;22)(q34.1;q11.2) resulting in BCR/ABL1 fusion (4.3%). Interestingly, we identified 16 cases with rare and complex structural aberrations. Application of the FISH technique produced major improvements in the sensitivity and accuracy of cytogenetic analysis with ALL patients. In conclusion it confirmed heterogeneity of ALL by identifying various recurrent chromosomal aberrations along with non-specific rearrangements and their association with specific immunophenotypes. This study pool is representative of paediatric/adult ALL patients in Oman.
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Obesity Level Prediction Based on Data Mining Techniques
Alqahtani, Asma,Albuainin, Fatima,Alrayes, Rana,Al muhanna, Noura,Alyahyan, Eyman,Aldahasi, Ezaz International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.3
Obesity affects individuals of all gender and ages worldwide; consequently, several studies have performed great works to define factors causing it. This study develops an effective method to trace obesity levels based on supervised data mining techniques such as Random Forest and Multi-Layer Perception (MLP), so as to tackle this universal epidemic. Notably, the dataset was from countries like Mexico, Peru, and Colombia in the 14- 61year age group, with varying eating habits and physical conditions. The data includes 2111 instances and 17 attributes labelled using NObesity, which facilitates categorization of data using Overweight Levels l I and II, Insufficient Weight, Normal Weight, as well as Obesity Type I to III. This study found that the highest accuracy was achieved by Random Forest algorithm in comparison to the MLP algorithm, with an overall classification rate of 96.7%.
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