Universiti Teknologi Petronas

About: Universiti Teknologi Petronas is a education organization based out in Ipoh, Malaysia. It is known for research contribution in the topics: Adsorption & Ionic liquid. The organization has 6127 authors who have published 11284 publications receiving 119400 citations.

Ionic liquid-mediated transcutaneous protein delivery with solid-in-oil nanodispersions

Abstract: As a potentially safe and non-invasive vaccination method, transcutaneous immunization represents an attractive alternative to conventional vaccine delivery by injection. However, the development of transcutaneous immunization has remained a challenge for a large number of hydrophilic macromolecules including protein and peptide antigens. We report a novel ionic liquid (IL)-mediated transcutaneous vaccine formulation consisting of a solid-in-oil (S/O) nanodispersion of antigen coated with pharmaceutically accepted surfactants dispersed in IL-containing oil. The introduction of the IL [C12mim][Tf2N] (1-dodecyl-3-methyl imidazolium bis(trifluoromethyl sulfonyl) amide) as a penetration enhancer in the formulation significantly enhanced the skin permeability of ovalbumin (OVA), a model antigen. It was also found that the IL-mediated S/O nanodispersion obtained high levels of OVA-specific serum IgG compared with both S/O nanodispersions without IL and PBS control. These findings clearly indicate that ILs – which are potentially attractive “green” and “designer” solvents – could serve as potential skin penetration enhancers in transcutaneous vaccination for hydrophilic macromolecules.

Facial geometry and speech analysis for depression detection

Abstract: Depression is one of the most prevalent mental disorders, burdening many people world-wide. A system with the potential of serving as a decision support system is proposed, based on novel features extracted from facial expression geometry and speech, by interpreting non-verbal manifestations of depression. The proposed system has been tested both in gender independent and gender based modes, and with different fusion methods. The algorithms were evaluated for several combinations of parameters and classification schemes, on the dataset provided by the Audio/Visual Emotion Challenge of 2013 and 2014. The proposed framework achieved a precision of 94.8% for detecting persons achieving high scores on a self-report scale of depressive symptomatology. Optimal system performance was obtained using a nearest neighbour classifier on the decision fusion of geometrical features in the gender independent mode, and audio based features in the gender based mode; single visual and audio decisions were combined with the OR binary operation.