Nonstructural protein 1 characteristic peak from NS1-saliva mixture with Surface-Enhanced Raman spectroscopy

Salivary Detection of Dengue Virus NS1 Protein with a Label-Free Immunosensor for Early Dengue Diagnosis

Dengue virus (DENV) is a highly pathogenic, arthropod-borne virus transmitted between people by Aedes mosquitoes. Despite efforts to prevent global spread, the potential for DENV epidemics is increasing world-wide. Annually, 3.6 billion people are at risk of infection. With no licensed vaccine, early diagnosis of dengue infection is critical for clinical management and patient survival. Detection of DENV non-structural protein 1 (NS1) is a clinically accepted biomarker for the early detection of DENV infection. Unfortunately, virtually all of the laboratory and commercial DENV NS1 diagnostic methods require a blood draw for sample analysis, limiting point-of-care diagnostics and decreases patient willingness. Alternatively, NS1 in human saliva has been identified for the potential early diagnosis of DENV infection. The collection of saliva is simple, non-invasive, painless, and inexpensive, even by minimally trained personnel. In this study, we present a label-free chemiresistive immunosensor for the detection of the DENV NS1 protein utilizing a network of single-walled carbon nanotubes functionalized with anti-dengue NS1 monoclonal antibodies. NS1 was successfully detected in adulterated artificial human saliva over the range of clinically relevant concentrations with high sensitivity and selectivity. It has potential application in clinical diagnosis and the ease of collection allows for self-testing, even within the home.

Detection of inflammatory biomarkers in saliva and urine: Potential in diagnosis, prevention, and treatment for chronic diseases

Inflammation is a part of the complex biological response of inflammatory cells to harmful stimuli, such as pathogens, irritants, or damaged cells. This inflammation has been linked to several chronic diseases including cancer, atherosclerosis, rheumatoid arthritis, and multiple sclerosis. Major biomarkers of inflammation include tumor necrosis factor, interleukins (IL)-1, IL-6, IL-8, chemokines, cyclooxygenase, 5-lipooxygenase, and C-reactive protein, all of which are regulated by the transcription factor nuclear factor-kappaB. Although examining inflammatory biomarkers in blood is a standard practice, its identification in saliva and/or urine is more convenient and non-invasive. In this review, we aim to (1) discuss the detection of these inflammatory biomarkers in urine and saliva; (2) advantages of using salivary and urinary inflammatory biomarkers over blood, while also weighing on the challenges and/or limitations of their use; (3) examine their role(s) in connection with diagnosis, prevention, treatment, and drug development for several chronic diseases with inflammatory consequences, including cancer; and (4) explore the use of innovative salivary and urine based biosensor strategies that may permit the testing of biomarkers quickly, reliably, and cost-effectively, in a decentralized setting.

Model Selection for PCA-Linear SVM for automated detection of NS1 molecule from Raman spectra of salivary mixture

Of recent, detection of Non-structural Protein 1 (NS1) in saliva has become appealing, as it may lead to a noninvasive detection method for NS1-related diseases at the febrile phase, before complication developed. NS1 is found to have a molecular fingerprint with the use of SERS technique. Our work here intends to determine an optimum PCA-Linear SVM model for automated detection of NS1 molecules from Raman spectra of NS1 adulterated saliva. Raman spectra of normal saliva (n=64) and saliva adulterated with low concentration NS1 (n=64) are used. Since Raman features extracted for each spectrum numbered at 1801, ranking and selection of features in order of their contribution is important prior to classification, for efficient computation. Hence, PCA for feature selection and SVM with linear kernel for classification are integrated. It is found that the Cattel's Scree test is the best stopping criteria for PCA with a selection of 5 PCs and a box constraint of 20 is optimum for Linear SVM. Together they achieve a classification performance, [accuracy sensitivity, specificity], of [98.71% 98.97% 98.44%].