Recent Advances of Biosensors for Detection of Multiple Antibiotics

Optimisation of electrochemical sensors based on molecularly imprinted polymers: from OFAT to machine learning

Molecularly imprinted polymers (MIPs) rely on synthetic engineered materials able to selectively bind and intimately recognise a target molecule through its size and functionalities. The way in which MIPs interact with their targets, and the magnitude of this interaction, is closely linked to the chemical properties derived during the polymerisation stages, which tailor them to their specific target. Hence, MIPs are in-deep studied in terms of their sensitivity and cross-reactivity, further being used for monitoring purposes of analytes in complex analytical samples. As MIPs are involved in sensor development within different approaches, a systematic optimisation and rational data-driven sensing is fundamental to obtaining a best-performant MIP sensor. In addition, the closer integration of MIPs in sensor development requires that the inner properties of the materials in terms of sensitivity and selectivity are maintained in the presence of competitive molecules, which focus is currently opened. Identifying computational models capable of predicting and reporting the best-performant configuration of electrochemical sensors based on MIPs is of immense importance. The application of chemometrics using design of experiments (DoE) is nowadays increasingly adopted during optimisation problems, which largely reduce the number of experimental trials. These approaches, together with the emergent machine learning (ML) tool in sensor data processing, represent the future trend in design and management of point-of-care configurations based on MIP sensing. This review provides an overview on the recent application of chemometrics tools in optimisation problems during development and analytical assessment of electrochemical sensors based on MIP receptors. A comprehensive discussion is first presented to cover the recent advancements on response surface methodologies (RSM) in optimisation studies of MIPs design. Therefore, the recent advent of machine learning in sensor data processing will be focused on MIPs development and analytical detection in sensors.

Sensing Levofloxacin with an RNA Aptamer as a Bioreceptor

To combat the growing threat of antibiotic resistance, environmental testing for antibiotic contamination is gaining an increasing role. This study aims to develop an easy-to-use assay for the detection of the fluoroquinolone antibiotic levofloxacin. Levofloxacin is used in human and veterinary medicine and has been detected in wastewater and river water. An RNA aptamer against levofloxacin was selected using RNA Capture-SELEX. The 73 nt long aptamer folds into three stems with a central three-way junction. It binds levofloxacin with a Kd of 6 µM and discriminates the closely related compound ciprofloxacin. Furthermore, the selection process was analyzed using a next-generation sequencing approach to better understand the sequence evolution throughout the selection. The aptamer was used as a bioreceptor for the development of a lateral flow assay. The biosensor exploited the innate characteristic of RNA Capture-SELEX to select aptamers that displace a complementary DNA oligonucleotide upon ligand binding. The lateral flow assay achieved a limit of visual detection of 100 µM. While the sensitivity of this assay constrains its immediate use in environmental testing, the present study can serve as a template for the selection of RNA aptamer-based biosensors.

Emerging Contaminants: An Emerging Risk Factor for Diabetes Mellitus

Emerging contaminants have been increasingly recognized as critical determinants in global public health outcomes. However, the intricate relationship between these contaminants and glucose metabolism remains to be fully elucidated. The paucity of comprehensive clinical data, coupled with the need for in-depth mechanistic investigations, underscores the urgency to decipher the precise molecular and cellular pathways through which these contaminants potentially mediate the initiation and progression of diabetes mellitus. A profound understanding of the epidemiological impact of these emerging contaminants, as well as the elucidation of the underlying mechanistic pathways, is indispensable for the formulation of evidence-based policy and preventive interventions. This review systematically aggregates contemporary findings from epidemiological investigations and delves into the mechanistic correlates that tether exposure to emerging contaminants, including endocrine disruptors, perfluorinated compounds, microplastics, and antibiotics, to glycemic dysregulation. A nuanced exploration is undertaken focusing on potential dietary sources and the consequential role of the gut microbiome in their toxic effects. This review endeavors to provide a foundational reference for future investigations into the complex interplay between emerging contaminants and diabetes mellitus.

Microbial Fuel Cells as Effective Tools for Energy Recovery and Antibiotic Detection in Water and Food

This work demonstrates that microbial fuel cells (MFCs), optimized for energy recovery, can be used as an effective tool to detect antibiotics in water-based environments. In MFCs, electroactive biofilms function as biocatalysts by converting the chemical energy of organic matter, which serves as the fuel, into electrical energy. The efficiency of the conversion process can be significantly affected by the presence of contaminants that act as toxicants to the biofilm. The present work demonstrates that MFCs can successfully detect antibiotic residues in water and water-based electrolytes containing complex carbon sources that may be associated with the food industry. Specifically, honey was selected as a model fuel to test the effectiveness of MFCs in detecting antibiotic contamination, and tetracycline was used as a reference antibiotic within this study. The results show that MFCs not only efficiently detect the presence of tetracycline in both acetate and honey-based electrolytes but also recover the same performance after each exposure cycle, proving to be a very robust and reliable technology for both biosensing and energy recovery.