Aptamer-Based Lateral Flow Assays: Current Trends in Clinical Diagnostic Rapid Tests

The lateral flow assay (LFA) is an extensively used paper-based platform for the rapid and on-site detection of different analytes. The method is user-friendly with no need for sophisticated operation and only includes adding sample. Generally, antibodies are employed as the biorecognition elements in the LFA. However, antibodies possess several disadvantages including poor stability, high batch-to-batch variation, long development time, high price and need for ethical approval and cold chain. Because of these limitations, aptamers screened by an in vitro process can be a good alternative to antibodies as biorecognition molecules in the LFA. In recent years, aptamer-based LFAs have been investigated for the detection of different analytes in point-of-care diagnostics. In this review, we summarize the applications of aptamer technology in LFAs in clinical diagnostic rapid tests for the detection of biomarkers, microbial analytes, hormones and antibiotics. Performance, advantages and drawbacks of the developed assays are also discussed.

Review of the algorithms used in exhaled breath analysis for the detection of diabetes

Currently, intensive work is underway on the development of truly noninvasive medical diagnostic systems, including respiratory analysers based on the detection of biomarkers of several diseases including diabetes. In terms of diabetes, acetone is considered as a one of the potential biomarker, although is not the single one. Therefore, the selective detection is crucial. Most often, the analysers of exhaled breath are based on the utilization of several commercially available gas sensors or on specially designed and manufactured gas sensors to obtain the highest selectivity and sensitivity to diabetes biomarkers present in the exhaled air. An important part of each system are the algorithms that are trained to detect diabetes based on data obtained from sensor matrices. The prepared review of the literature showed that there are many limitations in the development of the versatile breath analyser, such as high metabolic variability between patients, but the results obtained by researchers using the algorithms described in this paper are very promising and most of them achieve over 90% accuracy in the detection of diabetes in exhaled air. This paper summarizes the results using various measurement systems, feature extraction and feature selection methods as well as algorithms such as Support Vector Machines, k-Nearest Neighbours and various variations of Neural Networks for the detection of diabetes in patient samples and simulated artificial breath samples.

Application progress of magnetic molecularly imprinted polymer chemical sensors in the detection of biomarkers

Specific recognition and highly sensitive detection of biomarkers play an essential role in identifying, early diagnosis and prevention of many diseases. Magnetic molecularly imprinted polymers (MMIP) have been widely used...

A sample-to-answer electrochemical biosensor system for biomarker detection

We interfaced with a painless blood collection device and integrated on-chip blood-to-plasma separation with an electronic bead-based biomarker detection assay to enable true sample-to-answer detection of biomarkers.

A Systematic Study and Potential Limitations of Proton-ELISA Platform for α-Synuclein Antigen Detection

To evaluate point-of-care testing (POCT) for the potential early detection of biomarkers of Parkinson’s disease, a systematic investigation of portable and low-cost platforms is performed based on the Proton-enzyme-linked immunosorbent assay (Proton-ELISA) methodology. The detection of the α-synuclein antigen was first presented by biotin-relative linkers, and glucose substrate solution was first performed with a systematic experimental design to optimize the sensing results. All materials in this study are commercially available. Three different experiments with the partitional check were performed to investigate the Proton-ELISA platform, including proton catalyzed efficiency, blocking efficiency, and full Proton-ELISA procedure. The response time was selected as 15 min by the time-dependent curves of a full reaction. The limit of detection of conventional ELISA kits is 0.169 ng/mL, which is much lower than the Proton-ELISA results. The final response of the full Proton-ELISA procedure to pH changes was approximately 0.60 and 0.12 for α-synuclein antigen concentrations of 100 ng/mL and 4 ng/mL, respectively. With the partitional check, pH changes of pure glucose substrate and conjugated oxidase and interference of the nonspecific binding are 1.7 and 0.04, respectively. The lower pH changes far from the partitional check results can be concluded for the properties of glucose oxidase conjugation, including the isoelectric point and binding affinity modification by the testing environment. This preliminary guideline can be used as a lesson learnt to speed up following studies of the evaluation and optimization of other antigen detection. Therefore, Proton-ELISA can be suggested for some special applications with the help of custom-designed conjugation in the environment with less degradation or interference and a proper detection concentration range.

Wearable Sensors for the Detection of Biomarkers for Wound Infection

Infection represents a major complication that can affect wound healing in any type of wound, especially in chronic ones. There are currently certain limitations to the methods that are used for establishing a clinical diagnosis of wound infection. Thus, new, rapid and easy-to-use strategies for wound infection diagnosis need to be developed. To this aim, wearable sensors for infection diagnosis have been recently developed. These sensors are incorporated into the wound dressings that are used to treat and protect the wound, and are able to detect certain biomarkers that can be correlated with the presence of wound infection. Among these biomarkers, the most commonly used ones are pH and uric acid, but a plethora of others (lactic acid, oxygenation, inflammatory mediators, bacteria metabolites or bacteria) have also been detected using wearable sensors. In this work, an overview of the main types of wearable sensors for wound infection detection will be provided. These sensors will be divided into electrochemical, colorimetric and fluorimetric sensors and the examples will be presented and discussed comparatively.