Correction: An impedimetric sensor based on molecularly imprinted nanoparticles for the determination of trypsin in artificial matrices - towards point-of-care diagnostics.

Correction for 'An impedimetric sensor based on molecularly imprinted nanoparticles for the determination of trypsin in artificial matrices - towards point-of-care diagnostics' by Sabrina Di Masi et al., Anal. Methods, 2024, 16, 742-750, https://doi.org/10.1039/D3AY01762A.

Rational Design of a Portable Chemometric-Assisted Voltammetric Sensor Based on Ion-Imprinted Polymeric Film for Co(II) Determination in Water.

Herein, chemometric-assisted synthesis of electrochemical sensors based on electropolymerised ion-imprinted polymeric (e-IIP) films was explored. Co(II)-IIPs sensors were prepared by performing electropolymerisation procedures of polymerisation mixtures comprising varying concentrations of an electroactive o-aminophenol (o-AP) monomer and Co(II) ions, respectively, according to the Taguchi L9 experimental design, exploiting the simultaneous evaluation of other controlled parameters during electrosynthesis. Each e-IIP developed from Taguchi runs was compared with the respective non-imprinted polymer (NIP) films and fitted according to Langmuir-Freudlich isotherms. Distinctive patterns of low and high-affinity films were screened based on the qualities and properties of the developed IIPs in terms of binding kinetics (KD), imprinting factor, and the heterogeneity index of produced cavities. These results can provide a generic protocol for chemometric-assisted synthesis of e-IIPs based on poly-o-AP, providing highly stable, reproducible, and high-affinity imprinted polymeric films for monitoring purposes.

An impedimetric sensor based on molecularly imprinted nanoparticles for the determination of trypsin in artificial matrices - towards point-of-care diagnostics.

A high-performance impedimetric sensing platform was designed to detect proteins by employing molecularly imprinted polymeric nanoparticles (nanoMIPs) as selective receptors. This was achieved via the combination of the nanoMIPs with a self-assembled thioctic acid (SAM-TA) monolayer onto screen-printed gold electrodes, providing stable covalent attachment of the selective binder to the transducer. Taguchi design has been modelled to achieve the optimal level of sensor fabrication parameters and to maximise the immobilisation of nanoMIPs and their response (e.g. the response of imprinted polymers compared with the non-imprinted control). The developed sensor was tested towards a range of concentrations of trypsin dissolved in ammonium acetate (pH = 6) and showed promising applicability in artificial saliva, with a recovery percentage between 103 and 107%.

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.

HPLC-MS/MS method applied to an untargeted metabolomics approach for the diagnosis of "olive quick decline syndrome".

Olive quick decline syndrome (OQDS) is a disorder associated with bacterial infections caused by Xylella fastidiosa subsp. pauca ST53 in olive trees. Metabolic profile changes occurring in infected olive trees are still poorly investigated, but have the potential to unravel reliable biomarkers to be exploited for early diagnosis of infections. In this study, an untargeted metabolomic method using high-performance liquid chromatography coupled to quadrupole-time-of-flight high-resolution mass spectrometry (HPLC-ESI-Q-TOF-MS) was used to detect differences in samples (leaves) from healthy (Ctrl) and infected (Xf) olive trees. Both unsupervised and supervised data analysis clearly differentiated the groups. Different metabolites have been identified as potential specific biomarkers, and their characterization strongly suggests that metabolism of flavonoids and long-chain fatty acids is perturbed in Xf samples. In particular, a decrease in the defence capabilities of the host after Xf infection is proposed because of a significant dysregulation of some metabolites belonging to flavonoid family. Moreover, oleic acid is confirmed as a putative diffusible signal factor (DSF). This study provides new insights into the host-pathogen interactions and confirms LC-HRMS-based metabolomics as a powerful approach for disease-associated biomarkers discovery in plants.

Determination of sitagliptin in human plasma using a smart electrochemical sensor based on electroactive molecularly imprinted nanoparticles.

Sitagliptin is a hypoglycaemic agent used to reduce blood sugar levels in patients with type 2 diabetes mellitus (T2DM). Real time monitoring of sitagliptin levels is crucial to prevent overdose, which might cause liver, kidney and pancreatic diseases. As an alternative solution, a sitagliptin voltammetric sensor was fabricated using artificial receptors called electroactive molecularly imprinted polymer nanoparticles (nanoMIPs). The nanoMIP tagged with a redox probe (ferrocene) combines both the recognition and reporting functions. Traditional electrochemical sensors determine the redox activity of an analyte. Thus, they are influenced by interfering molecules and the nature of the sample. These innovative nanoMIPs allow us to easily design and customise sensors, increase their sensitivity and minimise the cross reactivity in biological samples. The present technology replaces the traditional enzyme-mediator pairs used in traditional biosensors. The polymer composition was optimized "in silico" using docking and screening methods. Nanoparticles were synthesized via free radical polymerization and a solid phase method and then characterized by infrared spectroscopy (FTIR), transmission electron microscopy (TEM) and dynamic light scattering (DLS). The specific sitagliptin nanoparticles were covalently immobilized on platinum electrodes via silane and carbodiimide chemistry. The determination of sitagliptin in human plasma by a nanoMIP sensor was assessed by differential pulse voltammetry (DPV). The sensor current response was directly related to the change in nanoMIP conformation triggered by the analyte. The optimisation of the sensor response was made by adjusting (i) the silane concentration, (ii) nanoMIP concentration, and (iii) immobilization time. The sensor measurements in plasma revealed high selectivity and a sensitivity of 32.5 ± 0.6 nA pM-1 towards sitagliptin, and the limit of detection of the fabricated sensor was found to be 0.06 pM. The sensor displayed a satisfactory performance for the determination of sitagliptin in spiked human plasma, demonstrating the potential of this technology for drug monitoring and clinical diagnosis.

Design and fabrication of a smart sensor using in silico epitope mapping and electro-responsive imprinted polymer nanoparticles for determination of insulin levels in human plasma.

A robust and highly specific sensor based on electroactive molecularly imprinted polymer nanoparticles (nanoMIP) was developed. The nanoMIP tagged with a redox probe, combines both recognition and reporting capabilities. The developed nanoMIP replaces enzyme-mediator pairs used in traditional biosensors thus, offering enhanced molecular recognition for insulin, improving performance in complex biological samples, and yielding high stability. Also, most of existing sensors show poor performance after storage. To improve costs of the logistics and avoid the need of cold storage in the chain supply, we developed an alternative to biorecognition system that relies on nanoMIP. NanoMIP were computationally designed using "in-silico" insulin epitope mapping and synthesized by solid phase polymerisation. The characterisation of the polymer nanoparticles was performed by transmission electron microscopy (TEM), dynamic light scattering (DLS), Fourier-transform Infrared (FT-IR) and surface plasmon resonance (SPR). The electrochemical sensor was developed by chemical immobilisation of the nanoMIP on screen printed platinum electrodes. The insulin sensor displayed satisfactory performances and reproducible results (RSD = 4.2%; n = 30) using differential pulse voltammetry (DPV) in the clinically relevant concentration range from 50 to 2000 pM. The developed nanoMIP offers the advantage of large number of specific recognition sites with tailored geometry, as the resultant, the sensor showed high sensitivity and selectivity to insulin with a limit of detection (LOD) of 26 and 81 fM in buffer and human plasma, respectively, confirming the practical application for point of care monitoring. Moreover, the nanoMIP showed adequate storage stability of 168 days, demonstrating the robustness of sensor for several rounds of insulin analysis.

Response Surface Methodology for the Optimisation of Electrochemical Biosensors for Heavy Metals Detection.

Herein, we report the application of a chemometric tool for the optimisation of electrochemical biosensor performances. The experimental design was performed based on the responses of an amperometric biosensor developed for metal ions detection using the flow injection analysis. The electrode preparation and the working conditions were selected as experimental parameters, and thus, were modelled by a response surface methodology (RSM). In particular, enzyme concentration, flow rates, and number of cycles were reported as continuous factors, while the sensitivities of the biosensor (S, µA·mM-1) towards metals, such as Bi3+ and Al3+ were collected as responses and optimised by a central composite design (CCD). Bi3+ and Al3+ inhibition on the Pt/PPD/GOx biosensor response is for the first time reported. The optimal enzyme concentration, scan cycles and flow rate were found to be 50 U·mL-1, 30 and, 0.3 mL·min-1, respectively. Descriptive/predictive performances are discussed: the sensitivities of the optimised biosensor agreed with the experimental design prediction. The responses under the optimised conditions were also tested towards Ni2+ and Ag⁺ ions. The multivariate approach used in this work allowed us to obtain a wide working range for the biosensor, coupled with a high reproducibility of the response (RSD = 0.72%).

From Electrochemical Biosensors to Biomimetic Sensors Based on Molecularly Imprinted Polymers in Environmental Determination of Heavy Metals.

Recent work relevant to heavy metal determination by inhibition-enzyme electrochemical biosensors and by selected biomimetic sensors based on molecularly imprinted polymers has been reviewed. General features and peculiar aspects have been evidenced. The replace of biological component by artificial receptors promises higher selectivity and stability, while biosensors keep their capability of producing an integrated response directly related to biological toxicity of the samples.