A novel G-quadruplex DNA-based biosensor for sensitive electrochemical determination of thallium(I) ions

Electrochemical aptasensor based on carboxylated graphene oxide modified carbon paste electrode for strontium ultrasensitive detection

Determination of strontium ions (Sr²⁺) is crucial with regard to human health and environmental protection. In this work, an electrochemical aptasensor was designed using carboxylated graphene oxide (CGO)-modified carbon paste electrode (CGO/CPE) for ultrasensitive determination of Sr²⁺ ions. The electrochemical determination was accomplished with employing the constructed G-quadruplex (G4) aptamer at the surface of aptasensor in presence of carmoisine (CA) as an electrochemical label. Moreover, NH₂-functionalized aptamer was immobilized onto CGO/CPE via carboxylic group. Hence, differential pulse voltammetry was applied for detection of any possible signal changes of CA on the aptasensor surface. The reduction peak currents of CA in the absence and presence of Sr²⁺ in solution were different and this difference was linearly dependent to the concentration of Sr²⁺ in solution. The analytical results revealed that our novel aptasensor showed two appropriate linear ranges (0.1-8.0 pM and 3.0-20.0 nM) versus to Sr²⁺ ion concentrations with the limit of detection of 0.06 pM (S/N = 3). Excellent stability, selectivity and reproducibility were achieved with this new electrochemical aptasensor. Additionally, the aptasensor showed good achievements in analysis of Sr²⁺ in aqueous and urine real samples, which making this proposed method a promising candidate for electrochemical detection of Sr²⁺ in real samples.

Machine Learning-Reinforced Noninvasive Biosensors for Healthcare

The emergence and development of noninvasive biosensors largely facilitate the collection of physiological signals and the processing of health-related data. The utilization of appropriate machine learning algorithms improves the accuracy and efficiency of biosensors. Machine learning-reinforced biosensors are started to use in clinical practice, health monitoring, and food safety, bringing a digital revolution in healthcare. Herein, the recent advances in machine learning-reinforced noninvasive biosensors applied in healthcare are summarized. First, different types of noninvasive biosensors and physiological signals collected are categorized and summarized. Then machine learning algorithms adopted in subsequent data processing are introduced and their practical applications in biosensors are reviewed. Finally, the challenges faced by machine learning-reinforced biosensors are raised, including data privacy and adaptive learning capability, and their prospects in real-time monitoring, out-of-clinic diagnosis, and onsite food safety detection are proposed.