Surface plasmon resonance analysis for detecting non-structural protein 1 of dengue virus in Indonesia

Surface plasmon resonance based-optical biosensor: Emerging diagnostic tool for early detection of diseases

The development of diagnostic tools remains at the center of the health care system. In recent times optical biosensors have been widely applied in the scientific community, especially for monitoring protein-protein or nucleic acid hybridization interactions. Optical biosensors-derived surface plasmon resonance (SPR) technology has appeared as a revolutionary technology at the current times. This review focuses on the research work in molecular biomarker evaluation using the technique based on SPR for translational clinical diagnosis. The review has covered both communicable and noncommunicable diseases by using different bio-fluids of the patient's sample for diagnosis of the diseases. An increasing number of SPR approaches have been developed in healthcare research and fundamental biological studies. The utility of SPR in the area of biosensing basically lies in its noninvasive diagnostic and prognostic feature due to its label-free high sensitivity and specificity properties. This makes SPR an invaluable tool with precise application in the recognition of different stages of the disease.

Differential Sensing with Replicated Plasmonic Gratings Interrogated in the Optical Switch Configuration

A new plasmonic configuration is proposed for application in a sensor and demonstrated for the detection of variations in the bulk refractive index of solutions. The configuration consists of monitoring two diffracted orders resulting from the interaction of a TM-polarized optical beam incident on a grating coupler, operating based on an effect termed the "optical switch". The two monitored diffracted orders enable differential measurements which cancel the drift and perturbations common to both, leading to an improved detection limit, as demonstrated experimentally. The measured switch pattern associated with the grating coupler is in good agreement with theory. Bulk sensing is demonstrated under intensity interrogation via the sequential injection of solutions comprised of glycerol in water into a fluidic cell. A limit of detection of about 10-6 RIU was achieved. The optical switch configuration is easy to implement and is cost-effective, yielding a highly promising approach for the sensing and the real-time detection of biological species.

An Optical Sensor for Dengue Envelope Proteins Using Polyamidoamine Dendrimer Biopolymer-Based Nanocomposite Thin Film: Enhanced Sensitivity, Selectivity, and Recovery Studies

This paper proposes a novel idea to enhance the sensitivity and selectivity of surface plasmon resonance (SPR) optical sensor for detection of dengue virus type-2 envelope proteins (DENV-2 E-proteins) using polyamidoamine (PAMAM) dendrimer biopolymer-based nanocomposite thin film. For this purpose, two ranges of DENV-2 E-protein concentrations, i.e., 0.000008-0.0001 nM and 0.00008-0.005 nM were evaluated, and the lowest detectable concentration was achieved at 0.00008 nM. The incorporation of PAMAM dendrimer-based nanocomposite thin film with an SPR sensor exhibited a significant increase in sensitivity and binding affinity to a lower range DENV-2 E-protein concentrations. Moreover, the proposed sensor displayed good selectivity towards DENV-2 E-proteins and have an average recovery of 80-120%. The findings of this study demonstrated that PAMAM dendrimer-based nanocomposite thin film combined with SPR sensor is a promising diagnostic tool for sensitive and selective detection of DENV-2 E-proteins.

Comparison of Dengue Predictive Models Developed Using Artificial Neural Network and Discriminant Analysis with Small Dataset

In Indonesia, dengue has become one of the hyperendemic diseases. Dengue consists of three clinical phases—febrile phase, critical phase, and recovery phase. Many patients have died in the critical phase due to the lack of proper and timely treatment. Therefore, we developed models that can predict the severity level of dengue based on the laboratory test results of the corresponding patients using Artificial Neural Network (ANN) and Discriminant Analysis (DA). In developing the models, we used a very small dataset. It is shown that ANN models developed using logistic and hyperbolic tangent activation function with 70% training data yielded the highest accuracy (90.91%), sensitivity (91.11%), and specificity (95.51%). This is the proposed model in this research. The proposed model will be able to help physicians in predicting the severity level of dengue patients before entering the critical phase. Furthermore, it will ease physicians in treating dengue patients early, so fatal cases or deaths can be avoided.