Development of DNA-Based Lateral Flow Assay for Detection of LDLR Gene Mutation for Familial Hypercholesterolemia.

Background: The techniques for detecting single nucleotide polymorphisms (SNP) require lengthy and complex experimental procedures and expensive instruments that may only be available in some laboratories. Thus, a deoxyribonucleic acid (DNA)-based lateral flow assay (LFA) was developed as a point-of-care test (POCT) diagnostic tool for genotyping. In this study, single nucleotide variation (E101K) in the low-density lipoprotein receptor (LDLR) gene leading to familial hypercholesterolemia (FH) was chosen as a model. Methods: Hypercholesterolemic individuals (n = 103) were selected from the Malaysian Cohort project (UKM Medical Molecular Biology Institute) while the control samples were selected from the Biobank (UKM Medical Molecular Biology Institute). The DNA samples were isolated from whole blood. Polymerase chain reaction (PCR) amplification process was performed using bifunctional labelled primers specifically designed to correspond to the variant that differentiates wild-type and mutant DNA for visual detection on LFA. The variant was confirmed using Sanger sequencing, and the sensitivity and specificity of the LFA detection method were validated using the Agena MassARRAY® technique. Results: Out of 103 hypercholesterolemic individuals, 5 individuals (4.8%) tested positive for E101K, LDLR mutation and the rest, including healthy control individuals, tested negative. This result was concordant with Sanger sequencing and Agena MassARRAY®. These five individuals could be classified as Definite FH, as the DNA diagnosis was confirmed. The sensitivity and specificity of the variant detection by LFA is 100% compared to results using the genotyping method using Agena MassARRAY®. Conclusion: The developed LFA can potentially be used in the POC setting for detecting the E101K variant in the LDLR gene. This LFA can also be used to screen family members with E101K variant in the LDLR gene and is applicable for other SNP's detection.

Geometrical Characterisation of TiO2-rGO Field-Effect Transistor as a Platform for Biosensing Applications.

The performance of the graphene-based field-effect transistor (FET) as a biosensor is based on the output drain current (Id). In this work, the signal-to-noise ratio (SNR) was investigated to obtain a high-performance device that produces a higher Id value. Using the finite element method, a novel top-gate FET was developed in a three-dimensional (3D) simulation model with the titanium dioxide-reduced graphene oxide (TiO2-rGO) nanocomposite as the transducer material, which acts as a platform for biosensing application. Using the Taguchi mixed-level method in Minitab software (Version 16.1.1), eighteen 3D models were designed based on an orthogonal array L18 (6134), with five factors, and three and six levels. The parameters considered were the channel length, electrode length, electrode width, electrode thickness and electrode type. The device was fabricated using the conventional photolithography patterning technique and the metal lift-off method. The material was synthesised using the modified sol-gel method and spin-coated on top of the device. According to the results of the ANOVA, the channel length contributed the most, with 63.11%, indicating that it was the most significant factor in producing a higher Id value. The optimum condition for the highest Id value was at a channel length of 3 µm and an electrode size of 3 µm × 20 µm, with a thickness of 50 nm for the Ag electrode. The electrical measurement in both the simulation and experiment under optimal conditions showed a similar trend, and the difference between the curves was calculated to be 28.7%. Raman analyses were performed to validate the quality of TiO2-rGO.

Detection of SARS-CoV-2 in Environment: Current Surveillance and Effective Data Management of COVID-19.

Since diagnostic laboratories handle large COVID-19 samples, researchers have established laboratory-based assays and developed biosensor prototypes. Both share the same purpose; to ascertain the occurrence of air and surface contaminations by the SARS-CoV-2 virus. However, the biosensors further utilize internet-of-things (IoT) technology to monitor COVID-19 virus contamination, specifically in the diagnostic laboratory setting. The IoT-capable biosensors have great potential to monitor for possible virus contamination. Numerous studies have been done on COVID-19 virus air and surface contamination in the hospital setting. Through reviews, there are abundant reports on the viral transmission of SARS-CoV-2 through droplet infections, person-to-person close contact and fecal-oral transmission. However, studies on environmental conditions need to be better reported. Therefore, this review covers the detection of SARS-CoV-2 in airborne and wastewater samples using biosensors with comprehensive studies in methods and techniques of sampling and sensing (2020 until 2023). Furthermore, the review exposes sensing cases in public health settings. Then, the integration of data management together with biosensors is well explained. Last, the review ended with challenges to having a practical COVID-19 biosensor applied for environmental surveillance samples.