Future perspectives on aptamer for application in food authentication.

Food fraudulence and food contamination are major concerns, particularly among consumers with specific dietary, cultural, lifestyle, and religious requirements. Current food authentication methods have several drawbacks and limitations, necessitating the development of a simpler, more sensitive, and rapid detection approach for food screening analysis, such as an aptamer-based biosensor system. Although the use of aptamer is growing in various fields, aptamer applications for food authentication are still lacking. In this review, we discuss the limitations of existing food authentication technologies and describe the applications of aptamer in food analyses. We also project several potential targets or marker molecules to be targeted in the SELEX process. Finally, this review highlights the drawbacks of current aptamer technologies and outlines the potential route of aptamer selection and applications for successful food authentication. This review provides an overview of the use of aptamer in food research and its potential application as a molecular reporter for rapid detection in food authentication process. Developing databases to store all biochemical profiles of food and applying machine learning algorithms against the biochemical profiles are urged to accelerate the identification of more reliable biomarker molecules as aptamer targets for food authentication.

Increased risks of cardiovascular diseases and insulin resistance among the Orang Asli in Peninsular Malaysia.

BACKGROUND: Despite the strategic development plan by the authorities for the Orang Asli, there are six subtribes of which their population numbers are small (less than 700). These minorities were not included in most of the health related studies published thus far. A comprehensive physiological and biomedical updates on these small subtribes in comparison to the larger subtribes and the urban Malay population is timely and important to help provide appropriate measures to prevent further reduction in the numbers of the Orang Asli. METHODS: A total of 191 Orang Asli from different villages in Peninsular Malaysia and 115 healthy urban Malays were recruited. Medical examinations and biochemical analyses were conducted. Framingham risk scores were determined. Data was analyzed using IBM SPSS Statistics, Version 20.0. RESULTS: A higher percentage of the Orang Asli showed high insulin levels and hsCRP compared to the healthy Malays denoting possible risk of insulin resistance. High incidences of low HDL-c levels were observed in all the Orang Asli from the six subtribes but none was detected among the urban Malays. A higher percentage of inlanders (21.1% of the males and 4.2% of the females) were categorized to have high Framingham Risk Score. CONCLUSIONS: Orang Asli staying both in the inlands and peripheries are predisposed to cardiovascular diseases and insulin resistance diabetes mellitus. The perception of Orang Asli being healthier than the urban people no longer holds. We believed that this information is important to the relevant parties in strategizing a healthier community of the Orang Asli to avoid the vanishing of the vulnerable group(s).

Metabolomics and partial least square discriminant analysis to predict history of myocardial infarction of self-claimed healthy subjects: validity and feasibility for clinical practice.

BACKGROUND: The dynamics of metabolomics in establishing a prediction model using partial least square discriminant analysis have enabled better disease diagnosis; with emphasis on early detection of diseases. We attempted to translate the metabolomics model to predict the health status of the Orang Asli community whom we have little information. The metabolite expressions of the healthy vs. diseased patients (cardiovascular) were compared. A metabotype model was developed and validated using partial least square discriminant analysis (PLSDA). Cardiovascular risks of the Orang Asli were predicted and confirmed by biochemistry profiles conducted concurrently. RESULTS: Fourteen (14) metabolites were determined as potential biomarkers for cardiovascular risks with receiver operating characteristic of more than 0.7. They include 15S-HETE (AUC = 0.997) and phosphorylcholine (AUC = 0.995). Seven Orang Asli were clustered with the patients' group and may have ongoing cardiovascular risks and problems. This is supported by biochemistry tests results that showed abnormalities in cholesterol, triglyceride, HDL and LDL levels. CONCLUSIONS: The disease prediction model based on metabolites is a useful diagnostic alternative as compared to the current single biomarker assays. The former is believed to be more cost effective since a single sample run is able to provide a more comprehensive disease profile, whilst the latter require different types of sampling tubes and blood volumes.