A Sensor Array Based on Molecularly Imprinted Polymers and Machine Learning for the Analysis of Fluoroquinolone Antibiotics

Wearable, Biocompatible, and Dual-Emission Ocular Multisensor Patch for Continuous Profiling of Fluoroquinolone Antibiotics in Tears

The abuse or misuse of antibiotics in clinical and agricultural settings severely endangers human health and ecosystems, which has raised profound concerns for public health worldwide. Trace detection and reliable discrimination of commonly used fluoroquinolone (FQ) antibiotics and their analogues have consequently become urgent to guide the rational use of antibiotic medicines and deliver efficient treatments for associated diseases. Herein, we report a wearable eye patch integrated with a quadruplex nanosensor chip for noninvasive detection and discrimination of primary FQ antibiotics in tears during routine eyedrop treatment. A set of dual-mode fluorescent nanoprobes of red- or green-emitting CdTe quantum dots integrated with lanthanide ions and a sensitizer, adenosine monophosphate, were constructed to provide an enhanced fluorescence up to 45-fold and nanomolar sensitivity toward major FQs owing to the aggregation-regulated antenna effect. The aggregation-driven, CdTe-Ln(III)-based microfluidic sensor chip is highly specific to FQ antibiotics against other non-FQ counterparts or biomolecular interfering species and is able to accurately discriminate nine types of FQ or non-FQ eyedrop suspensions using linear discriminant analysis. The prototyped wearable sensing detector has proven to be biocompatible and nontoxic to human tissues, which integrates the entire optical imaging modules into a miniaturized, smartphone-based platform for field use and reduces the overall assay time to ∼5 min. The practicability of the wearable eye patch was demonstrated through accurate quantification of antibiotics in a bactericidal event and the continuous profiling of FQ residues in tears after using a typical prescription antibiotic eyedrop. This technology provides a useful supplement to the toolbox for on-site and real-time examination and regulation of inappropriate daily drug use that might potentially lead to long-term antibiotic abuse and has great implications in advancing personal healthcare techniques for the regulation of daily medication therapy.

Integrating target-responsive microfluidic-based biosensing chip with smartphone for simultaneous quantification of multiple fluoroquinolones

The presence of fluoroquinolone (FQs) antibiotic residues in the food and environment has become a significant concern for human health and ecosystems. In this study, the background-free properties of upconversion nanoparticles (UCNPs), the high specificity of the target aptamer (Apt), and the high quenching properties of graphene oxide (GO) were integrated into a microfluidic-based fluorescence biosensing chip (MFBC). Interestingly, the microfluidic channels of the MFBC were prepared by laser-printing technology without the need for complex preparation processes and additional specialized equipment. The target-responsive fluorescence biosensing probes loaded on the MFBC were prepared by self-assembly of the UCNPs-Apt complex with GO based on π-π stacking interactions, which can be used for the detection of the two FQs on a large scale without the need for multi-step manipulations and reactions, resulting in excellent multiplexed, automated and simultaneous sensing capabilities with detection limits as low as 1.84 ng/mL (enrofloxacin) and 2.22 ng/mL (ciprofloxacin). In addition, the MFBC was integrated with a smartphone into a portable device to enable the analysis of a wide range of FQs in the field. This research provides a simple-to-prepare biosensing chip with great potential for field applications and large-scale screening of FQs residues in the food and environment.

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.

The Effect of Polymerization Techniques on the Creation of Molecularly Imprinted Polymer Sensors and Their Application on Pharmaceutical Compounds

Molecularly imprinted polymers (MIPs) have become more prevalent in fabricating sensor applications, particularly in medicine, pharmaceuticals, food quality monitoring, and the environment. The ease of their preparation, adaptability of templates, superior affinity and specificity, improved stability, and the possibility for downsizing are only a few benefits of these sensors. Moreover, from a medical perspective, monitoring therapeutic medications and determining pharmaceutical compounds in their pharmaceutical forms and biological systems is very important. Additionally, because medications are hazardous to the environment, effective, quick, and affordable determination in the surrounding environment is of major importance. Concerning a variety of performance criteria, including sensitivity, specificity, low detection limits, and affordability, MIP sensors outperform other published technologies for analyzing pharmaceutical drugs. MIP sensors have, therefore, been widely used as one of the most crucial techniques for analyzing pharmaceuticals. The first part of this review provides a detailed explanation of the many polymerization techniques that were employed to create high-performing MIP sensors. In the subsequent section of the review, the utilization of MIP-based sensors for quantifying the drugs in their pharmaceutical preparation, biological specimens, and environmental samples are covered in depth. Finally, a critical evaluation of the potential future research paths for MIP-based sensors clarifies the use of MIP in pharmaceutical fields.

Exploring the potential of molecularly imprinted polymers and metal/metal oxide nanoparticles in sensors: recent advancements and prospects

Metal/metal oxide nanoparticles have gained increasing attention in recent years due to their outstanding features, including optical and catalytic properties, as well as their excellent conductivity. The implementation of metal/metal oxide nanoparticles, combined with molecularly imprinted polymers (MIPs) has paved the way for a new generation of building blocks to engineer and enhance the fascinating features of advanced sensors. This review critically evaluates the impact of combining metal/metal oxide nanoparticles with MIPs in sensors. It covers synthesis strategies, advantages of coupling these materials with MIPs, and addresses questions about the selectivity of these hybrid materials. In the end, the current challenges and future perspectives of this field are discussed, with a particular focus on the potential applications of these hybrid composites in the sensor field. This review highlights the exciting opportunities of using metal/metal oxide nanoparticles along with MIPs for the development of next-generation sensors.

Molecularly Imprinted Polymer Interface on Screen-Printed ZnO Nanorod Field Effect Transistors for Serotonin Detection in Clinical Samples

Ultrasensitive detection of serotonin is crucial for the early diagnosis of several diseases like Parkinson's and Alzheimer's. Most of the existing detection strategies are still not suitable for sensitive point-of-care applications. This study presents direct molecular imprinting of serotonin on the surface of three-dimensional zinc oxide (ZnO) nanorod devices connected in a field effect transistor (FET) configuration to achieve ultrasensitive, real-time, and rapid detection with a convenient and affordable approach, which has significant potential for translation to clinical settings. This strategy has enabled pushing the detection limit to 0.1 fM in a physiological analyte in real time with screen-printed electrodes, thereby resulting in the convenient batch fabrication of sensors for clinical validation. The response of the sensor with the clinical sample has been correlated with that of the gold standard and has been observed to be statistically similar.

Surface imprinted-covalent organic frameworks for efficient solid-phase extraction of fluoroquinolones in food samples

Molecularly imprinting on covalent organic frameworks (MI-COF) is a promising way to prepare selective adsorbents for effective extraction of fluoroquinolones (FQs). However, the unstable framework structure and complex imprinting process are challenging for the construction of MI-COF. Here, we report a facile surface imprinting approach with dopamine to generate imprinted cavities on the surface of irreversible COF for highly efficient extraction of FQs in food samples. The irreversible-linked COF was fabricated from hexahydroxytriphenylene and tetrafluorophthalonitrile to ensure COF stability. Moreover, the introduction of dopamine surface imprinted polymer into COF provides abundant imprinted sites and endows excellent selectivity for FQs recognition against other antibiotics. Taking enrofloxacin as a template molecule, the prepared MI-COF gave an exceptional adsorption capacity of 581 mg g-1, a 2.2-fold enhancement of adsorption capacity compared with nonimprinted COF. The MI-COF was further explored as adsorbent to develop a novel solid-phase extraction method coupled with high-performance liquid chromatography for the simultaneous determination of enrofloxacin, norfloxacin and ciprofloxacin. The developed method gave the low limits of detection at 0.003-0.05 ng mL-1, high precision with relative standard deviations less than 3.5%. The recoveries of spiked FQs in food samples ranged from 80.4% to 110.7%.

One Stone Two Birds: An Upconversion Nanosensor for Sensitive Detection of Fluoroquinolones in Aquatic Products Based on Chelation Recognition

Excessive residues of fluoroquinolones (FQs) in aquatic products have become a growing issue in recent years. Herein, we demonstrate an upconversion fluorescence nanosensor constructed by a one-stone-two-birds strategy, where Fe3+ not only quenches upconversion fluorescence with high efficiency but also specifically recognizes the bidentate ligand structure of FQs. Compared to existing methods, the proposed sensor is simpler to synthesize and cheap and has more storage stability due to the unification of the quencher and recognition molecule. Enrofloxacin (ENR) was chosen as a representative veterinary drug for FQs to verify the effectiveness of the nanosensor. Under optimal conditions, the range of detection for ENR was 2.0 × 10-2 to 2.0 × 102 μg/mL, with a limit of detection of 1.08 × 10-3 μg/mL. The developed nanosensor was further validated by high-performance liquid chromatography-ultraviolet (HPLC-UV) without significant differences in practical detection. Hence, this study offers a potential strategy for the detection of FQs.

Machine-learning-based predictions of imprinting quality using ensemble and non-linear regression algorithms

The molecularly imprinted polymers are artificial polymers that, during the synthesis, create specific sites for a definite purpose. These polymers due to their characteristics such as stability, easy of synthesis, reproducibility, reusability, high accuracy, and selectivity have many applications. However, the variety of the functional monomers, templates, solvents, and synthesis conditions like pH, temperature, the rate of stirring, and time, limit the selectivity of imprinting. The Practical optimization of the synthetic conditions has many drawbacks, including chemical compound usage, equipment requirements, and time costs. The use of machine learning (ML) for the prediction of the imprinting factor (IF), which indicates the quality of imprinting is a very interesting idea to overcome these problems. The ML has many advantages, for example a lack of human error, high accuracy, high repeatability, and prediction of a large amount of data in the minimum time. In this research, ML was used to predict the IF using non-linear regression algorithms, including classification and regression tree, support vector regression, and k-nearest neighbors, and ensemble algorithms, like gradient boosting (GB), random forest, and extra trees. The data sets were obtained practically in the laboratory, and inputs, included pH, the type of the template, the type of the monomer, solvent, the distribution coefficient of the MIP (K_MIP), and the distribution coefficient of the non-imprinted polymer (K_NIP). The mutual information feature selection method was used to select the important features affecting the IF. The results showed that the GB algorithm had the best performance in predicting the IF, and using this algorithm, the maximum R² value (R² = 0.871), and the minimum mean absolute error (MAE = - 0.982), and mean square error were obtained (MSE = - 2.303).