Abdominal obesity is an important contributor for health risk factors such as hypertension, diabetes mellitus and hypercholesterolemia. Therefore, the application of a proper method is important prerequisite in performing abdominal obesity assessment. In this study, we applied 3D body scanning technology to measure waist circumference (WC), hip circumference (HC) and waist to hip ratio (WHR) precisely in an effort to improve the current health assessment for abdominal obesity. A total of 200 Malaysian women with sedentary lifestyle, aged between 18 and 60 years participated in this study. Paired t-test was used to determine the differences between the automated (3D body scanner) and manual measurements of WC, HC and WHR. 3D body scanner measurements show that 27% of subjects had mild abdominal obesity (80 - 90 cm) and 34.5% of subjects had severe abdominal obesity (≥90 cm) based on WC cutoff points. Based on WHR cutoff points, 57% of subjects had abdominal obesity (≥0.85) while the remaining were without abdominal obesity (<0.85). Lower percentages of abdominal obesity prevalence were reported for both WC and WHR categories using manual measurements. We also found that in normal BMI category, 8.5% of subjects have mild abdominal obesity based on automated measurements while only 5.5% of subjects were identified on manual measurements. The result of this study indicated that 3D body scanner provided better assessment method as it enables detection of abdominal obesity in more subjects based on WC and WHR categories. Public agencies are encouraged to consider the application of 3D body scanning in health assessment of abdominal obesity.
Lichen samples were collected from Gunung Machincang, Langkawi Islands based on an alternation of altitudes, which are 0, 300 and above 600 m. Morphological identification resulted in 15 genera of microlichens (crustose) and five genera of macrolichens (foliose) and they fall under 14 families. As the altitude increases, the number of foliose type of lichen also increased. The common microlichens obtained were from the Family of Graphidaceae and can be found from the sea level right up to the peak of Gunung Machincang. The most common crustose lichens found were Heterodermia sp., while Eugenia sp. is the most common tree habitat for lichens in Gunung Machincang, Langkawi Islands. This study represents the first record of lichens in Gunung Machincang, Langkawi Islands, Malaysia.
There are very few prognostic studies that combine both clinicopathologic and genomic data. Most of the studies use only clinicopathologic factors without taking into consideration the tumour biology and molecular information, while some studies use genomic markers or microarray information only without the clinicopathologic parameters. Thus, these studies may not be able to prognoses a patient effectively. Previous studies have shown that prognosis results are more accurate when using both clinicopathologic and genomic data. The objectives of this research were to apply hybrid artificial intelligent techniques in the prognosis of oral cancer based on the correlation of clinicopathologic and genomic markers and to prove that the prognosis is better with both markers. The proposed hybrid model consisting of two stages, where stage one with ReliefF-GA feature selection method to find an optimal feature of subset and stage two with ANFIS classification to classify either the patients alive or dead after certain years of diagnosis. The proposed prognostic model was experimented on two groups of oral cancer dataset collected locally here in Malaysia, Group 1 with clinicopathologic markers only and Group 2 with both clinicopathologic and genomic markers. The results proved that the proposed model with optimum features selected is more accurate with the use of both clinicopathologic and genomic markers and outperformed the other methods of artificial neural network, support vector machine and logistic regression. This prognostic model is feasible to aid the clinicians in the decision support stage and to identify the high risk markers to better predict the survival rate for each oral cancer patient.
Foot arch determines the shape of the foot, whether it is normal, flat or high. Excessive body weight is known to be the
main factor in altering the foot arches. The foot arches of adult women were determined based on five different footprint
parameters (Clarke index, Chippaux-Smirak index, Staheli index, Arch index and the Harris-imprint index) and the
relationship between Body Mass Index (BMI) and foot arches were studied. A total of 309 adult women from the age of
20 to 60 years were recruited in this study. The shape of participants’ feet were obtained and their left and right foot
arches were determined using five different footprint parameters. Body weight and height were measured and BMI was
calculated. Paired t-test and one-way ANOVA were applied to perform the statistical analysis. Our analysis showed that
two third of the participants have different foot arches between the left and right feet. The Harris-imprint index exhibited
the most significant (p=0.004) differences between the left (mean=0.168) and right (mean=1.011) foot arches. Most of
the overweight (53%) and obese (15%) participants have normal arches; however the prevalence of flat and high arches
is still higher in overweight (flat arch= 51%; high arch= 52% ) and obese (flat arch= 18%; high arch= 12%) compared
to other BMI categories. Harris-imprint index was successfully studied as a suitable parameter in determining the left
and right foot arches.
The incidence of oral cancer is high for those of Indian ethnic origin in Malaysia. Various clinical and pathological data are usually used in oral cancer prognosis. However, due to time, cost and tissue limitations, the number of prognosis variables need to be reduced. In this research, we demonstrated the use of feature selection methods to select a subset of variables that is highly predictive of oral cancer prognosis. The objective is to reduce the number of input variables, thus to identify the key clinicopathologic (input) variables of oral cancer prognosis based on the data collected in the Malaysian scenario. Two feature selection methods, genetic algorithm (wrapper approach) and Pearson's correlation coefficient (filter approach) were implemented and compared with single-input models and a full-input model. The results showed that the reduced models with feature selection method are able to produce more accurate prognosis results than the full-input model and single-input model, with the Pearson's correlation coefficient achieving the most promising results.