Xuefeng Xing,Wanyao Liu,Tao Li,Shu Xing,Xueqi Fu,Dongyang Wu,Dianjun Liu,Zhenxin Wang
In the present work, a portable and low-cost planar waveguide based resonance light scattering (RLS) scanner (termed as: PW-RLS scanner) has been developed for microarray detection. The PW-RLS scanner employs a 2 × 4 white light emitting diode array (WLEDA) as the excitation light source, a folded optical path with a complementary metal oxide semiconductor (CMOS) as the signal/image acquisition device and stepper motors with gear drives as the mechanical drive system. The biological binding/recognizing events on the microarray can be detected with an evanescent waveguide-directed illumination and light-scattering label (e.g., nanoparticles) while the microarray slide acts as an evanescent waveguide substrate. The performance of the as-developed PW-RLS scanner has been evaluated by analyzing type 2 diabetes mellitus (T2DM) risk genes. Highly selective and sensitive (less than 1% allele frequency at the attomole-level) T2DM risk gene detection is achieved using single-stranded DNA functionalized gold nanoparticles (ssDNA-GNPs) as detection probes. Additionally, the successful simultaneous analysis of 15 T2DM patient genotypes suggests that the device has great potential for the realization of a personalized diagnostic test for a given disease or patient follow-up.
Lei Wang,Ai Xu,Jing Yuan,Fan Jiang,Meixuan Li,Wuzhen Qi,Yanbin Li,Jianhan Lin
An hourglass-mimicking biosensor was developed to detect target bacteria in 15 mL centrifugal tube using immune magnetic nanobeads to isolate the target from large-volume sample, [email protected] nanozymes to label the target for amplification of biological signals, and a microplate reader/colorimetric card for determination of the target. First, a centrifugal tube with an iron ball framework was first coaxially assembled in the center of a Halbach ring magnet. After immune magnetic nanobeads, [email protected] nanozymes and bacterial sample were mixed by repeated bottom-up of the tube using a stepper motor , nanobead-bacteria-nanozyme complexes were formed. Then, colorless H 2 O 2 -TMB was catalyzed by the nanozymes to produce blue TMBox. The color change was finally analyzed using the microplate reader or colorimetric card to determine bacterial concentration . This hourglass-mimicking biosensor could separate ∼95% targets from 10 mL bacteria sample and detect targets from 1.6 × 10 1 to 1.6 × 10 6 CFU/mL in 1.0 h with low detection limit of 16 CFU/mL.
The spread of the FluA virus poses significant public health concerns worldwide. Fluorescent lateral flow immunoassay (LFIA) test strips have emerged as vital tools for the early detection and monitoring of influenza infections. However, existing quantitative virus-detection methods, particularly those utilizing smartphone-based sensing platforms, encounter accessibility challenges in resource-limited areas and among the elderly population. Despite their advantages in speed and portability, these platforms often lack user-friendliness for these demographics, impeding their widespread utilization. To address these challenges, this study proposes leveraging the optical pick-up unit (OPU) sourced from commercial optical drives as a readily available fluorescence excitation module for the quantitative detection of antibodies labeled with quantum-dot fluorescent microspheres. Additionally, we utilize miniaturized and high-performance optical components and 3D-printed parts, along with a customized control system, to develop an affordable point-of-care testing (POCT) device. Within the system, a stepping motor scans the test strip from the T-line to the C-line, enabling the calculation of the fluorescence-intensity ratio between the two lines. This simple yet effective design facilitates rapid and straightforward field or at-home testing for FluA. The proposed prototype platform demonstrates promising performance, achieving a limit of detection (LOD) of 2.91 ng/mL, a total detection time of no more than 15 min, and dimensions of 151 mm × 11.2 mm × 10.8 mm3. We believe that the proposed approach holds great potential for improving access to an accurate influenza diagnosis.
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