The aim of this study was to compare the skeletal and soft tissue patterns between obstructive sleep apnoea (OSA) patients and control group of non-OSA patients. Fifty Malays (32 males and 18 females) aged 18-65 years divided into two equal groups 25 (17 males and 8 females) with OSA and a control group 25 subjects (15 males and 10 females). Both groups were diagnosed using polysomnography. Nineteen variables related to craniofacial skeletal and soft tissue morphology were measured on lateral cephalometric films. Analysis of covariance was used to compare the means between the two groups. The results showed that OSA subjects had a significant increase in body mass index (BMI) and neck circumference than the control group. The soft palate and tongue were longer and thicker in OSA patients. In addition, upper, middle, and lower posterior airway spaces were narrower, the hyoid bone was more inferior and posterior, and the cranial base flexure angle was significantly acute when compared with the control group. The findings indicate that craniofacial abnormalities play significant roles in the pathogenesis of OSA in Malay patients.

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Ovarian cancer is the third type of gynecologic cancer in terms of prevalence after cervical and uterine cancer. The disease is known as the silent killer as it is slowly spread without a diagnosis, which leads to the worst prognosis and high mortality rate. Libya shows low disease incidence while the high mortality rate is seen in developed countries due to a lack of proper diagnostic and treatment options. This research aims to understand the various risk factors of ovarian cancer among the women in Libya. A questionnaire is used in a quantitative research methodology using stratified random sample over the population is divided in two classes namely healthy and diseased, based on earlier diagnosis and ovarian cancer care. The diseased and healthy women were targeted in a written survey in different hospitals and clinics in Libya. The data analysis is done in four stages. The first stage is cleaning and coding of the data. The second step is the demographic profile of the respondents. The third stage. Frequency distribution of the data in order to compare the healthy and diseased women. The fourth stage is the linear logistic regression in order to determine the risk factors for ovarian cancer and test the research hypotheses. The logistic regression analysis indicated good fit of the model. The model was able to correctly predicted 81.4 % of the cases which is 19.6 % increase over the null model. The logistic regression analysis was also used to indicate the predictors namely family history, level of obesity, ovulation information, use of birth control and breast feeding unlike awareness, nature of diet which was not predictor in the model.

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