METHODS: A total of 288 university students aged 18 to 29 years participated in this comparative cross-sectional study. We assessed dietary intake, level of physical activity, knowledge of diabetes and T2DM risk.
RESULTS: Respondents with a family history of diabetes had significantly higher weight (P = 0.003), body mass index (P < 0.001), waist circumference (P < 0.001), diabetes knowledge level (P < 0.005) and T2DM risk (P < 0.001). Ethnicity, fibre intake, T2DM risk score and knowledge about diabetes were significant contributors toward family history of diabetes (P = 0.025, 0.034, < 0.001 and 0.004, respectively).
CONCLUSION: Young adults with a family history of diabetes had suboptimal nutritional status. Despite being more knowledgeable about diabetes, they did not practice a healthy lifestyle. Family history status can be used to screen young adults at the risk of developing T2DM for primary disease prevention.
MATERIALS AND METHODS: The development process of the new 2D CB SLE includes, (i) the identification of common errors made by students in the audiology clinic, (ii) the development of five case simulations that include four routine audiology tests incorporating learning assistance derived from the errors commonly made by audiology students and, (iii) the development of 2D CB SLE from a technical perspective. A preliminary evaluation of the use of the 2D CB SLE software was conducted among twenty-six second-year undergraduate audiology students.
RESULTS: The pre-analysis evaluation of the new 2D CB SLE showed that the majority of the students perceived the new 2D CB SLE software as realistic and helpful for them in achieving the course learning outcomes and in improving their clinical skills. The mean overall scores among the twenty-six students using the self-reported questionnaire were significantly higher when using the 2D CB SLE software than with the existing software typically used in their SLE training.
CONCLUSIONS: This new 2D CB SLE software has the potential for use by audiology students for enhancing their learning.
MATERIALS AND METHODS: This was a retrospective study that included all RA patients receiving biologics therapy in 13 tertiary hospitals in Malaysia from January 2008 to December 2018.
RESULTS: We had 735 RA patients who received biologics therapy. Twenty-one of the 735 patients were diagnosed with TB infection after treatment with biologics. The calculated prevalence of TB infection in RA patients treated with biologics was 2.9% (29 per 1000 patients). Four groups of biologics were used in our patient cohort: monoclonal TNF inhibitors, etanercept, tocilizumab, and rituximab, with monoclonal TNF inhibitors being the most commonly used biologic. The median duration of biologics therapy before the diagnosis of TB was 8 months. 75% of patients had at least one co-morbidity and all patients had at least one ongoing cDMARD therapy at the time of TB diagnosis. More than half of the patients were on steroid therapy with an average prednisolone dose of 5 mg daily.
CONCLUSION: Although the study population and data were limited, this study illustrates the spectrum of TB infections in RA patients receiving biologics and potential risk factors associated with biologics therapy in Malaysia.
METHODS: This study aimed to explore the acceptance of medical delivery drones among medical practitioners as well as the public community in Malaysia using a knowledge, attitude, and perception (KAP) model and statistical analysis to decrease uncertainty. Bivariate and multivariate analyses of the results were performed in SPSS.
RESULTS: A total of 639 respondents took part in the survey, of which 557 complete responses were finally analyzed. The results showed that the overall acceptance rate for medical delivery drones was positive. The acceptance rate was significantly correlated with knowledge, attitude, and perception scores but not with sociodemographic factors.
DISCUSSION: Raising awareness and educating the medical as well as public communities regarding the potential role and benefits of drones are therefore important in garnering support for drone usage for medical purposes.
AIMS AND OBJECTIVES: In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life.
CONCLUSION: The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.