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- 저자 : Katheri, Abdull Malika (검색결과 2 건)
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Ahmad, Dilshad,Bakairy, Abdul Karieem,Katheri, Abdull Malika,Tamimi, Waleed Asian Pacific Journal of Cancer Prevention 2015 Asian Pacific journal of cancer prevention Vol.16 No.9
Cigarette smoke contains oxidants and free radicals which are carcinogens that can induce mutations in humans. Single nucleotide polymorphisms (SNPs) are the most frequent genetic alterations found in the human genome. In the present study, we have examined the ability of the murine double minute 2 (Mdm2) (rs769412) A>G polymorphism in cigarette smokers to predict risk of cancers. Our results showed that of smokers, 87% were found with AA genotype, 10% with heterozygous AG genotype, and 3% with GG genotype. The heterozygous AG genotype was observed in a lower percentage of smokers (10%) as compared to non-smokers (18%), whereas, homozygous AA genotype was observed in lower percentage of non-smokers (81%) as compared to the smokers (87%). The results from present study support the association with an allele and AG genotype in non-smokers. However, further studies are required to establish the role of Mdm2 (rs769412) C>T in cigarettes smokers and diseases.
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Ahmed M. Youssef,Mohamed Al-Kathery,Biswajeet Pradhan 한국지질과학협의회 2015 Geosciences Journal Vol.19 No.1
Mountain areas in the southern western corner ofthe Kingdom of Saudi Arabia frequently suffer from various typesof landslides due to rain storms and anthropogenic activities. Toresolve the problem related to landslides, landslide susceptibilitymap is important as a quick and safe mitigation measure and tohelp making strategic planning by identifying the most vulnerableareas. This paper summarizes findings of landslide susceptibilityanalysis at Al-Hasher area, Jizan, KSA, using two statistical models:frequency ratio and index of entropy models with the aid ofGIS tools and remote sensing data. The landslide locations (inventorymap) were identified in the study area using historical records,interpretation of high-resolution satellite images that include Geo-Eye in 2.5 m and Quickbird in 0.6m resolution, topographic mapsof 1:10,000 scale, and multiple field investigations. A total of 207landslides (80% out of 257 detected landslides) were randomlyselected for model training, and the remaining 50 landslides (19%)were used for the model validation. Ten landslide conditioning factorsincluding slope angle, slope-aspect, altitude, curvature, lithology,distance to lineaments, normalized difference vegetation index (NDVI),distance to roads, precipitation, and distance to streams, were extractedfrom spatial database. Using these conditioning factors and landslidelocations, landslide susceptibility and weights of each factorwere analyzed by using frequency ratio and index of entropy models. Our findings showed that the existing landslides of high and very highsusceptibility classes cover nearly 80.4% and 79.1% of the susceptibilitymaps produced by frequency ratio and index of entropy modelsrespectively. For verification, receiver operating characteristic (ROC)curves were drawn and the areas under the curve (AUC) were calculatedfor success and prediction rates. For success rate the resultsrevealed that for the index of entropy model (AUC = 77.9%) is slightlylower than frequency ratio model (AUC = 78.8%). For the predictionrate, it was found that the index of entropy model (AUC = 74.9%)is slightly lower than the frequency ratio model (AUC = 76.7%). The landslide susceptibility maps produced from this study couldhelp decision makers, planners, engineers, and urban areas developersto make suitable decisions.
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