IoT and Biosensors: A Smart PortablePotentiostat With AdvancedCloud-Enabled Features

Recent advances in Internet-of-Things technology have opened the doors to new scenariosfor biosensor applications. Flexibility, portability, and remote control and access are of utmost importanceto move these devices to people’s homes or in a Point-of-Care context and rapidly share the results withusers and their physicians. In this paper, an innovative portable device for both quantitative and semi-quantitative electrochemical analysis is presented. This device can operate autonomously without the needof relying on other devices (e.g., PC, tablets, or smartphones) thanks to built-in Wi-Fi connectivity. Thedeveloped hardware is integrated into a cloud-based platform, exploiting the cloud computational powerto perform innovative algorithms for calibration (e.g., Machine Learning tools). Results and configurationscan be accessed through a web page without the installation of dedicated APPs or software. The electricalinput/output characteristic was measured with a dummy cell as a load, achieving excellent linearity.Furthermore, the device response to five different concentrations of potassium ferri/ferrocyanide redox probewas compared with a bench-top laboratory instrument. No difference in analytical sensitivity was found.Also, some examples of application-specific tests were set up to demonstrate the use in real-case scenarios.In addition, Support Vector Machine algorithm was applied to semi-quantitative analyses to classify theinput samples into four classes, achieving an average accuracy of 98.23%. Finally, COVID-19 related testsare presented and discussed (PDF) IoT and Biosensors: A Smart Portable Potentiostat With Advanced Cloud-Enabled Features. Available from: https://www.researchgate.net/publication/355214115_IoT_and_Biosensors_A_Smart_Portable_Potentiostat_With_Advanced_Cloud-Enabled_Features [accessed Oct 25 2021]

Single-Walled Carbon Nanotubes as Enhancing Substrates for PNA-Based Amperometric Genosensors

A new amperometric sandwich-format genosensor has been implemented on single-walled carbon nanotubes screen printed electrodes (SWCNT-SPEs) and compared in terms of performance with analogous genoassays developed using the same methodology on non-nanostructured glassy carbon platforms (GC-SPE). The working principle of the genosensors is based on the covalent immobilization of Peptide Nucleic Acid (PNA) capture probes (CP) on the electrode surface, carried out through the carboxylic functions present on SWCNT-SPEs (carboxylated SWCNT) or electrochemically induced on GC-SPEs. The sequence of the CP was complementary to a 20-mer portion of the target DNA; a second biotin-tagged PNA signalling probe (SP), with sequence complementary to a different contiguous portion of the target DNA, was used to obtain a sandwich hybrid with an Alkaline Phosphatase-streptavidin conjugate (ALP-Strp). Comparison of the responses obtained from the SWCNT-SPEs with those produced from the non-nanostructured substrates evidenced the remarkable enhancement effect given by the nanostructured electrode platforms, achieved both in terms of loading capability of PNA probes and amplification of the electron transfer phenomena exploited for the signal transduction, giving rise to more than four-fold higher sensitivity when using SWCNT-SPEs. The nanostructured substrate allowed to reach limit of detection (LOD) of 71 pM and limit of quantitation (LOQ) of 256 pM, while the corresponding values obtained with GC-SPEs were 430 pM and 1.43 nM, respectively

A Wi-Fi cloud-based portable potentiostat for electrochemical biosensors

The measurement of the analyte concentration in electrochemical biosensors traditionally requires costly laboratory equipment to obtain accurate results. Innovative portable solutions have recently been proposed, but usually, they lean on personal computers (PCs) or smartphones for data elaboration and they exhibit poor resolution or portability and proprietary software. This paper presents a low-cost portable system, assembling an ad hoc -designed analog front end (AFE) and a development board equipped with a system on chip integrating a microcontroller and a Wi-Fi network processor. The wireless module enables the transmission of measurements directly to a cloud service for sharing device outcome with users (physicians, caregivers, and so on). In doing so, the system does not require neither the customized software nor other devices involved in data acquisition. Furthermore, when any Internet connection is lost, the data are stored on board for subsequent transmission when a Wi-Fi connection is available. The noise output voltage spectrum has been characterized. Since the designed device is intended to be battery-powered to enhance portability, investigations about battery lifetime were carried out. Finally, data acquired with a conventional benchtop Autolab PGSTAT-204 electrochemical workstation are compared with the outcome of our developed device to validate the effectiveness of our proposal. To this end, we selected ferri/ferrocyanide as redox probe, obtaining the calibration curves for both the platforms. The final outcomes are shown to be feasible, accurate, and repeatable

A self-calibrating IoT portable electrochemical immunosensor for serum human epididymis protein 4 as a tumor biomarker for ovarian cancer

Nowadays analytical techniques are moving towards the development of smart biosensing strategies for point-of-care accurate screening of disease biomarkers, such as human epididymis protein 4 (HE4), a recently discovered serum markers for early ovarian cancer diagnosis. In this context, the present work represents the first implementation of a competitive enzyme-labelled magneto-immunoassay exploiting a homemade IoT Wi-Fi cloud-based portable potentiostat for differential pulse voltammetry readout. The electrochemical device was specifically designed capable of autonomous calibration and data processing, switching between calibration and measurement modes: in particular, firstly a baseline estimation algorithm is applied for correct peak computation, then calibration function is built by interpolating data with a four-parameter logistic function. The calibration function parameters are stored on the cloud for inverse prediction to determine the concentration of unknown samples. Interpolation function calibration and concentration evaluation are performed directly on-board, reducing the power consumption. The analytical device was validated in human serum, demonstrating good sensing performance for analysis of HE4 with detection and quantitation limits in human serum of 3.5 and 29.2 pM, respectively, reaching the sensitivity required for diagnostic purposes, with high potential for applications as portable and smart diagnostic tool for point-of-care testing

A Folding-Based Electrochemical Aptasensor for the Single-Step Detection of the SARS-CoV-2 Spike Protein

Efficient and timely testing has taken center stage in the management, control, and monitoring of the current COVID-19 pandemic. Simple, rapid, cost-effective diagnostics are needed that can complement current polymerase chain reaction-based methods and lateral flow immunoassays. Here, we report the development of an electrochemical sensing platform based on single-walled carbon nanotube screen-printed electrodes (SWCNT-SPEs) functionalized with a redox-tagged DNA aptamer that specifically binds to the receptor binding domain of the SARS-CoV-2 spike protein S1 subunit. Single-step, reagentless detection of the S1 protein is achieved through a binding-induced, concentration-dependent folding of the DNA aptamer that reduces the efficiency of the electron transfer process between the redox tag and the electrode surface and causes a suppression of the resulting amperometric signal. This aptasensor is specific for the target S1 protein with a dissociation constant (K-D) value of 43 +/- 4 nM and a limit of detection of 7 nM. We demonstrate that the target S1 protein can be detected both in a buffer solution and in an artificial viral transport medium widely used for the collection of nasopharyngeal swabs, and that no cross-reactivity is observed in the presence of different, non-target viral proteins. We expect that this SWCNT-SPE-based format of electrochemical aptasensor will prove useful for the detection of other protein targets for which nucleic acid aptamer ligands are made available

Controlling Dynamic DNA Reactions at the Surface of Single-Walled Carbon Nanotube Electrodes to Design Hybridization Platforms with a Specific Amperometric Readout

: Carbon nanotube (CNT)-based electrodes are cheap, highly performing, and robust platforms for the fabrication of electrochemical sensors. Engineering programmable DNA nanotechnologies on the CNT surface can support the construction of new electrochemical DNA sensors providing an amperometric output in response to biomolecular recognition. This is a significant challenge, since it requires gaining control of specific hybridization processes and functional DNA systems at the interface, while limiting DNA physisorption on the electrode surface, which contributes to nonspecific signal. In this study, we provide design rules to program dynamic DNA structures at the surface of single-walled carbon nanotubes electrodes, showing that specific DNA interactions can be monitored through measurement of the current signal provided by redox-tagged DNA strands. We propose the use of pyrene as a backfilling agent to reduce nonspecific adsorption of reporter DNA strands and demonstrate the controlled formation of DNA duplexes on the electrode surface, which we then apply in the design and conduction of programmable DNA strand displacement reactions. Expanding on this aspect, we report the development of novel amperometric hybridization platforms based on artificial DNA structures templated by the small molecule melamine. These platforms enable dynamic strand exchange reactions orthogonal to conventional toehold-mediated strand displacement and may support new strategies in electrochemical sensing of biomolecular targets, combining the physicochemical properties of nanostructured carbon-based materials with programmable nucleic acid hybridization

Time-dependent metabolic disorders induced by short-term exposure to polystyrene microplastics in the Mediterranean mussel Mytilus galloprovincialis.

Abstract In the modern society, plastic has achieved a crucial status in a myriad of applications because of its favourable properties. Despite the societal benefits, plastic has become a growing global concern due to it is persistence and bioavailability as microplastics (MPs) to aquatic biota. In order to provide mechanistic insights into the early toxicity effects of MPs on aquatic invertebrates, a short-term (up to 72 h) exposure to 3 µm red polystyrene MPs (50 particles/mL) was conducted on marine mussels Mytilus galloprovincialis, selected as model organism for their ability to ingest MPs and their commercial relevance. The use of protonic Nuclear Magnetic Resonance (1H NMR)-based metabolomics, combined with chemometrics, enabled a comprehensive exploration at fixed exposure time-points (T24, T48, T72) of the impact of MPs accumulated in mussel digestive glands, chosen as the major site for pollutants storage and detoxification processes. In detail, 1H NMR metabolic fingerprints of MP-treated mussels were clearly separated from control and grouped for experimental time-points by a Principal Component Analysis (PCA). Numerous metabolites, including amino acids, osmolytes, metabolites involved in energy metabolism, and antioxidants, participating in various metabolic pathways significantly changed over time in MP-exposed mussel digestive glands related to control, reflecting also the fluctuations in MPs accumulation and pointing out the occurrence of disorders in amino acid metabolism, osmotic equilibrium, antioxidant defense system and energy metabolism. Overall, the present work provides the first insights into the early mechanisms of toxicity of polystyrene MPs in marine invertebrates

Effects of pesticides on Chelon labrosus (Risso, 1827) evaluated by enzymatic activities along the north eastern Sicilian coastlines (Italy)

Pesticides are frequently applied to agricultural activities to improve harvest, in terms of yield and product quality. Useful tools for ecotoxicological studies of marine ecosystems are based on biomarker application on bioindicator key fish species. The main aim of the present study was to detect the potential presence of pesticides in a polluted coastal marine environment, namely Milazzo Gulf, situated in the north eastern coast of Sicily (Italy), by measuring the enzymatic activities of the ecotoxicological biomarkers acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) in brain and blood samples of Chelon labrosus. Also, Marinello Reserve was selected as a reference site. The data showed a significant inhibition in AChE (81%) and BChE (71%) activities in fish from Milazzo Gulf in respect to those from the reference site. The esterase inhibition is primarily due to the presence of organophosphorus insecticides and carbamates that resulted, in Milazzo Gulf, higher in concentration in respect to the reference quality standard decree (D.M. 260, 2010). The results obtained in this study confirm the suspected presence of insecticides in waters and fish from Milazzo Gulf, which may lead to a considerable hazard to humans. This study confirms the relevant advantages of the biomarker approach on fish species in the ecotoxicological evaluation of marine environments

Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable Immunosensor

An IoT-WiFi smart and portable electrochemical immunosensor for the quantification of SARS-CoV-2 spike protein integrated with machine learning features was developed. The immunoenzymatic sensor is based on the immobilization of monoclonal antibodies directed to SARS-CoV-2 S1 subunit on Screen-Printed Electrodes functionalized with gold nanoparticles, the analytical protocol involving a single-step sample incubation. Immunosensor performance was assessed by validation carried out in viral transfer medium, which is commonly used for de-sorption of nasopharyngeal swabs. Remarkable specificity of the response was demonstrated by testing H1N1 Hemagglutinin from swine-origin influenza A virus and Spike Protein S1 from Middle East respiratory syndrome coronavirus. Machine learning was successfully used for data processing and analysis: different support vector machine classifiers were evaluated proving that algorithms affect the classifier accuracy. The test accuracy of the best classification model in terms of true positive/true negative sample classification was 97.3%. In addition, ML algorithm can be easily integrated into the developed cloud-based portable Wi-Fi device. Finally, the immunosensor was successfully tested using a third generation replicating incompetent lentiviral vector pseudotyped with SARS-CoV-2 spike glycoprotein, thus proving the applicability of the immunosensor to whole virus detection

MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study

Background: Pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer (LARC) is achieved in 15–30% of cases. Our aim was to implement and externally validate a magnetic resonance imaging (MRI)-based radiomics pipeline to predict response to treatment and to investigate the impact of manual and automatic segmentations on the radiomics models. Methods: Ninety-five patients with stage II/III LARC who underwent multiparametric MRI before chemoradiotherapy and surgical treatment were enrolled from three institutions. Patients were classified as responders if tumour regression grade was 1 or 2 and nonresponders otherwise. Sixty-seven patients composed the construction dataset, while 28 the external validation. Tumour volumes were manually and automatically segmented using a U-net algorithm. Three approaches for feature selection were tested and combined with four machine learning classifiers. Results: Using manual segmentation, the best result reached an accuracy of 68% on the validation set, with sensitivity 60%, specificity 77%, negative predictive value (NPV) 63%, and positive predictive value (PPV) 75%. The automatic segmentation achieved an accuracy of 75% on the validation set, with sensitivity 80%, specificity 69%, and both NPV and PPV 75%. Sensitivity and NPV on the validation set were significantly higher (p = 0.047) for the automatic versus manual segmentation. Conclusion: Our study showed that radiomics models can pave the way to help clinicians in the prediction of tumour response to chemoradiotherapy of LARC and to personalise per-patient treatment. The results from the external validation dataset are promising for further research into radiomics approaches using both manual and automatic segmentations