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Due to the recent pandemic caused by coronavirus disease 2019 (COVID-19), the lateral flow immunoassay used for its rapid antigen test is more popular than ever before. However, the history of the lateral flow immunoassay is about 60 years old, and its original purpose of use, such as a COVID-19 rapid antigen test or a pregnancy test, was the qualitative detection of a target analyte. Recently, the demand for quantitative analysis of lateral flow immunoassays is increasing in various fields. Lateral flow immunoassays for quantitative detection using various materials and sensor technologies are being introduced, and readers for analyzing them are being developed. Quantitative analysis readers are highly anticipated for their future development in line with technological advancements such as optical, magnetic field, photothermal, and electrochemical sensors and trends such as weight reduction, miniaturization, and cost reduction of systems. In addition, the sensing, processing, and communication functions of portable personal devices such as smartphones can be used as tools for the quantitative analysis of lateral flow immunoassays. As a result, lateral flow immunoassays can efficiently achieve the goal of rapid diagnosis by point-of-care testing. Readers used for the quantification of lateral flow immunoassays were classified according to the adopted sensor technology, and the research trends in each were reviewed in this paper. The development of a quantitative analysis system was often carried out in the assay aspect, so not only the readers but also the assay development cases were reviewed if necessary. In addition, systems for quantitative analysis of COVID-19, which have recently been gaining importance, were introduced as a separate section.

Pinellia ternata Breitenbach (PTB) is a widely used herbal medicine in China, Japan, and South Korea. It has antiemetic, anti-inflammatory, antitussive, and sedative properties. The raw material is toxic, but can be made safer using alum solution or by boiling it for a long time. In addition, PTB seems to be effective for gastrointestinal motility disorders (GMDs), but this is yet to be conclusively proven. Herein, PTB compounds, targets, and related diseases were investigated using the traditional Chinese medical systems pharmacology database and an analysis platform. Information on target genes was confirmed using the UniProt database. Using Cytoscape 3.8.2, a network was established and GMD-related genes were searched using the Cytoscape stringApp. The effects of the PTB extract on the pacemaker potential of interstitial cells of Cajal and GMD mouse models were investigated. In total, 12 compounds were found to target 13 GMD-related genes. In animal experiments, PTB was found to better regulate pacemaker potential in vitro and inhibit GMD signs compared to control groups in vivo. Animal studies showed that the mechanism underlying the effects of PTB is closely related to gastrointestinal motility. The results obtained demonstrated that PTB offers a potential means to treat GMDs, and we suggested that the medicinal mechanism of GMDs can be explained by the relationship between 12 major components of PTB, including oleic acid, and 13 GMD-related genes.

A glucose sensor is used as an essential tool for diagnosing and treating diabetic patients and controlling processes during cell culture. Since the development of an electrochemical-based glucose sensor, an optical glucose sensor has been devised to overcome its shortcomings, but this also poses a problem because it requires a complicated manufacturing process. This study aimed to develop an optical glucose sensor film that could be fabricated with a simple process using commercial pressure sensitive paints. The sensor manufacturing technology developed in this work could simplify the complex production process of the existing electrochemical or optical glucose sensors. In addition, a photometric method for glucose concentration analysis was developed using the color image of the sensor. By developing this sensor and analysis technology, the basis for glucose measurement was established that enables two-dimensional, online, and continuous measurement. The proposed sensor showed good linearity at 0–4 mM glucose in an aqueous sample solution, its limit of detection was 0.37 mM, and the response time was 2 min.

A series of poly(pyridinium salt)s-fluorene main-chain ionic polymers with various organic counterions were synthesized by using ring-transmutation polymerization and metathesis reactions. Their chemical structures were characterized by Fourier Transform Infrared (FTIR), proton (1H), and fluorine 19 (19F) nuclear magnetic resonance (NMR) spectrometers. These polymers showed a number-average molecular weight (Mns) between 96.5 and 107.8 kg/mol and polydispersity index (PDI) in the range of 1.12–1.88. They exhibited fully-grown lyotropic phases in polar protic and aprotic solvents at different critical concentrations. Small-angle X-ray scattering for one polymer example indicates lyotropic structure formation for 60–80% solvent fraction. A lyotropic smectic phase contains 10 nm polymer platelets connected by tie molecules. The structure also incorporates a square packing motif within platelets. Thermal properties of polymers were affected by the size of counterions as determined by differential scanning calorimetry and thermogravimetric analysis measurements. Their ultraviolet-visible (UV-Vis) absorption spectra in different organic solvents were essentially identical, indicating that the closely spaced π-π* transitions occurred in their conjugated polymer structures. In contrast, the emission spectra of polymers exhibited a positive solvatochromism on changing the polarity of solvents. They emitted green lights in both polar and nonpolar organic solvents and showed blue light in the film-states, but their λem peaks were dependent on the size of the counterions. They formed aggregates in polar aprotic and protic solvents with the addition of water (v/v, 0–90%), and their λem peaks were blue shifted.

Utilizing a simple fluidic structure, we demonstrate the improved performance of oxidase-based enzymatic biosensors. Electrolysis of water is utilized to generate bubbles to manipulate the oxygen microenvironment close to the biosensor in a fluidic channel. For the proper enzyme reactions to occur, a simple mechanical procedure of manipulating bubbles was developed to maximize the oxygen level while minimizing the pH change after electrolysis. The sensors show improved sensitivities based on the oxygen dependency of enzyme reaction. In addition, this oxygen-rich operation minimizes the ratio of electrochemical interference signal by ascorbic acid during sensor operation (i.e., amperometric detection of hydrogen peroxide). Although creatinine sensors have been used as the model system in this study, this method is applicable to many other biosensors that can use oxidase enzymes (e.g., glucose, alcohol, phenol, etc.) to implement a viable component for in-line fluidic sensor systems.

Deep learning is a fast-growing field of research, in particular, for autonomous application. In this study, a deep learning network based on various sensor data is proposed for identifying the roads where the vehicle is driving. Long-Short Term Memory (LSTM) unit and ensemble learning are utilized for network design and a feature selection technique is applied such that unnecessary sensor data could be excluded without a loss of performance. Real vehicle experiments were carried out for the learning and verification of the proposed deep learning structure. The classification performance was verified through four different test roads. The proposed network shows the classification accuracy of 94.6% in the test data.

Despite its broad penetration of various markets, the quantitative lateral flow immunoassay (LFIA) suffers from sensitivity issues in some cases. To solve this problem, an optimized colorimetric readout method for LFIA quantification is proposed in this study. An assay reader device utilizing a color camera and an analysis method using a Bayer filtered image were developed. Spectrometric measurements of the assay test line were performed to determine the color channel that contains the test line information and effectively minimizes noise. The change in the intensity ratio with increasing concentration of the target substance in the sample was largest in the green channel. The linear range of the output curve ranged from 0 to 10 ng/mL, and the detection limit was 2 ng/mL. The suggested instrumentation and analysis methods are expected to effectively resolve the low-sensitivity problems of the former LFIA systems and to offer other prospective functionalities for LFIA quantification.