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A novel yet simple electrochemical immunosensor for specific and reliable detection of endometriosis is developed. Alpha-1-B glycoprotein (A1BG) exhibits higher specificity and sensitivity compared to earlier reported endometriosis serum markers. It holds promise towards clinically significant diagnosis of endometriosis. The sensing device, fabrica...
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... This illustration offers the advantages of intuitive comprehension and potential integration with machine learning techniques for intelligent VEGF detection, in which the real and imaginary impedance values of the Nyquist plot can be arranged to be the feature vector of the machine-learning classifier. Such a machine-learning-based approach was adopted in article [28] for the development of an impedimetric immunosensor on the detection of endometriosis. Thus, from Figures 5-7, the proposed functionalized sensor was experimentally confirmed via impedance spectroscopy to be effective in its function, sensitivity, and specificity for VEGF detection. ...
... This illustration offers the advantages of intuitive comprehension and potential integration with machine learning techniques for intelligent VEGF detection, in which the real and imaginary impedance values of the Nyquist plot can be arranged to be the feature vector of the machine-learning classifier. Such a machine-learning-based approach was adopted in article [28] for the development of an impedimetric immunosensor on the detection of endometriosis. ...
Vascular endothelial growth factor (VEGF), a clinically important biomarker, often plays a key role in angiogenesis, would healing, tumor growth, lung development, and in retinal diseases. Hence, detecting and quantifying VEGF is deemed medically important in clinical diagnosis for many diseases. In this report, a simple yet highly cost-effective platform was proposed for VEGF protein detection using commercially available interdigitated sensors that are surface modified to present DNA optimally for VEGF capture. The dielectric characteristics between the fingers of the sensor were modulated by the negatively charged aptamer-VEGF capture, and the impedance was estimated using an impedance analyzer. Impedance-spectra tests were compared among pristine unmodified surfaces, functionalized monolayer surfaces, and aptamer-grafted surfaces in order to evaluate the efficacy of VEGF detection. From our results, the sensitivity experiments as conducted showed the ability of the interdigitated sensor to detect VEGF at a low concentration of 5 pM (200 pg/mL). The specificity of the functionalized sensor in detecting VEGF was further examined by comparing the impedance to platelet-derived growth factor, and the results confirm the specificity of the sensor. Finally, the Nyquist plot of impedance spectra was also presented to help data visualization and the overall performance of the device was found to be a highly suitable template for a smart biosensor for the detection of VEGF.
... Remembering that the ultimate goal of most electrical impedance measurement systems is improving the speed, cost, and overall accessibility of diagnosis, one of the most important challenges to address in a measurement system is the sensitivity to distinguish populations. The applications of this can include identifying healthy from diseased cell states [44][45][46][47], determining the proliferation of patient cells for clinical study [48,49], or quantifying the response of cells to a potential treatment [50][51][52]. In each case, there exist multiple populations representing different changes that can be difficult to determine, especially in cases where cells each have individual responses to treatment or levels of disease. ...
Individual cells have many unique properties that can be quantified to develop a holistic understanding of a population. This can include understanding population characteristics, identifying subpopulations, or elucidating outlier characteristics that may be indicators of disease. Electrical impedance measurements are rapid and label-free for the monitoring of single cells and generate large datasets of many cells at single or multiple frequencies. To increase the accuracy and sensitivity of measurements and define the relationships between impedance and biological features, many electrical measurement systems have incorporated machine learning (ML) paradigms for control and analysis. Considering the difficulty capturing complex relationships using traditional modelling and statistical methods due to population heterogeneity, ML offers an exciting approach to the systemic collection and analysis of electrical properties in a data-driven way. In this work, we discuss incorporation of ML to improve the field of electrical single cell analysis by addressing the design challenges to manipulate single cells and sophisticated analysis of electrical properties that distinguish cellular changes. Looking forward, we emphasize the opportunity to build on integrated systems to address common challenges in data quality and generalizability to save time and resources at every step in electrical measurement of single cells.
... Its classification capabilities range from simple binary to multi-class or multi-label tasks. Pal et al. [150] reported an example of binary classification that proposed a faradaic impedimetric immunosensor for the detection of alpha-1-B glycoprotein (A1BG), a specific biomarker for the diagnosis of endometriosis. Screenprinted gold electrodes were first modified with a self-assembly monolayer of 3-mercaptopropionic acid (3MPA), and the carboxylic acid groups were activated with a reaction of 1-ethyl-3-(3dimethyl aminopropyl) carbodiimide and n-hydroxysuccinimide to allow covalent functionalization of the anti-A1BG monoclonal antibody, and bovine serum albumin was used to block the nonfunctionalized areas. ...
... Confusion matrix and receiver operating characteristics curve of A-B) QDA, C-D) QSVM, E-F) Cubic SVM, and G-H) ensemble model. Reproduced with permission fromRef.[150]. ...
... Please note that the starting value of C μ(BSA) is different for each electrode because we used independent electrodes for each antibody adsorption time. The 1/ΔC μ reached a maximum value when antibody molecules saturated the electrode surface [57,58]. We observed the best S/N = 1.3 and most significant 1/ΔC μ by antibody adsorption at 120 min. ...
β-1,4-GalT-V is an enzyme with glycosyltransferase activity that glycosylates proteins and synthesizes the lactosylceramide sphingolipid in the Golgi apparatus. Colorectal cancer (CRC) tumor cells produce these biomolecules in high concentrations concerning normal cells, releasing them into the blood serum. Hence, β-1,4-GalT-V glycoprotein has emerged as a promising CRC biomarker, and its detection opens opportunities for the diagnosis/prognosis of CRC. We report the first capacitive nanobiosensor for the detection of β-1,4-GalT-V based on disposable screen-printed carbon electrodes (SPCEs) nanostructured with gold nanorods (AuNRs) and the Prussian blue (PrB) redox-active compound. AuNRs increased the SPCE surface area for antibody immobilization by physical adsorption and promoted the deposition of PrB onto the SPCE surface. Antibody-glycoprotein molecular biorecognition event onto the SPCE/AuNRs/PrB surface perturbed the PrB redox density-of-states and hence redox capacitance (Cμ̅). We found that changes in the inverse of Cμ̅ correlated well with the increasing concentrations of β-1,4-GalT-V in the linear range from 50 to 400 fM with a sensitivity of 1.5 µF fM⁻¹ cm⁻² and a limit of detection of 20 fM. The nanobiosensor detected the β-1,4-GalT-V glycoprotein from raw human serum samples with high specificity, ultrasensitivity, and reagentless label-free electrochemical transduction. The nanobiosensor could be applied as a CRC diagnosis/prognosis tool in a decentralized setting, with minimal patient sample manipulation and rapid response.
... The algorithm was trained using EIS data fitted with the Randles model to extrapolate associated parameters. Pal et al. 365 used machine learning approaches to relate charge-transfer resistances from Nyquist plots to changes in antigen concentration. Le et al. 366 and Shao et al. 367 applied a machine learning approach to solid oxide fuel cell (SOFC) system diagnostics. ...
Interpretation of impedance spectroscopy data requires both a description of the chemistry and physics that govern the system and an assessment of the error structure of the measurement. The approach presented here includes use of graphical methods to guide model development, use of a measurement model analysis to assess the presence of stochastic and bias errors, and a systematic development of interpretation models in terms of the proposed reaction mechanism and physical description. Application to corrosion, batteries, and biological systems is discussed, and emerging trends in interpretation and implementation of impedance spectroscopy are presented.
... Recently, the literature reported the application of ML algorithms to obtain a classification model for a precise decision in the diagnosis of diseases such as endometriosis with a simple electrochemical immunosensor, allowing non-technical personnel to evaluate the result [25]. Also, ML algorithms were applied as a model to identify patterns of the analyte recognition (heavy metal salts, pesticides, and petrochemicals) in the response output, allowing to identify of these pollutants as well as their concentrations [1,2]. ...
The outstanding properties of organic semiconductors arouse interest in their application as electrochemical biosensors and their performance strongly depends on functionalization and detection strategies used. Therefore, this chapter presents some strategies for electrochemical biosensing, including different approaches for biomolecules immobilization and biorecognition by using several methodologies for detection and reading out. Finally, some strategies for designing miniaturized high-performance devices are described, such as the use of machine learning and screen-printed electrodes.
... Because we used independent electrodes for each antibody concentration, the starting value of R ct0 is different for each electrode. The R ct0 reached a maximum value when antibodies molecules saturated the electrode surface [50,51]. The best S/N = 1.54 and most significant ΔR ct was observed by antibody covalent attachment at 40 μg mL − 1 . ...
β-1,4-Galactosyltransferase-V (β-1,4-GalT-V) is a membrane-bound glycoprotein with glycosyltransferase enzyme activity that synthesizes lactosylceramide and glycosylates high-branched N-glycans in the Golgi apparatus. Colorectal cancer (CRC) tumor cells have shown to overexpress these biomolecules concerning normal cells, releasing them into the body fluids. Thus, their detection has been suggested as a diagnosis/prognosis CRC biomarker. We report the first electrochemical immunosensor for the detection of such a novel β-1,4-GalT-V CRC biomarker. The label-free electrochemical immunosensor covalently coupled an anti-β-1,4-GalT-V antibody at a mixed self-assembled monolayer-coated screen-printed gold electrode (SPAuE) surface. This functionalized platform captured the β-1,4-GalT-V glycoprotein from human serum samples with high specificity, which response monitored by electrochemical impedance spectroscopy (EIS) was protein concentration-dependent. The resultant electrochemical immunosensor showed a linear dynamic range from 5 to 150 pM, with a sensitivity of 14 Ω pM⁻¹ and a limit of detection of 7 pM, of clinical relevance. This outstanding performance makes it great potential for including it in a biomarker signature for the early diagnosis/prognosis of CRC.
... This feature is particularly useful for POC sensing because ML can output a simple response that is understandable to the public. In recent years, ML has been combined with label-free electrochemical sensing to screen cell viability (122), detect virus particles (82) and antibodies (121) for SARS-CoV-2, and detect biomarkers for endometriosis (123) using FET (122), ion conduction through nanopores (82), and electrode blocking via EIS (121,123). The field combining label-free electrochemical sensing with ML for disease detection will continue to grow in the future as the advantages of each are synergistic and highly favorable for POC testing. ...
... This feature is particularly useful for POC sensing because ML can output a simple response that is understandable to the public. In recent years, ML has been combined with label-free electrochemical sensing to screen cell viability (122), detect virus particles (82) and antibodies (121) for SARS-CoV-2, and detect biomarkers for endometriosis (123) using FET (122), ion conduction through nanopores (82), and electrode blocking via EIS (121,123). The field combining label-free electrochemical sensing with ML for disease detection will continue to grow in the future as the advantages of each are synergistic and highly favorable for POC testing. ...
Label-free electrochemical biosensing leverages the advantages of label-free techniques, low cost, and fewer user steps, with the sensitivity and portability of electrochemical analysis. In this review, we identify four label-free electrochemical biosensing mechanisms: ( a) blocking the electrode surface, ( b) allowing greater access to the electrode surface, ( c) changing the intercalation or electrostatic affinity of a redox probe to a biorecognition unit, and ( d) modulating ion or electron transport properties due to conformational and surface charge changes. Each mechanism is described, recent advancements are summarized, and relative advantages and disadvantages of the techniques are discussed. Furthermore, two avenues for gaining further diagnostic information from label-free electrochemical biosensors, through multiplex analysis and incorporating machine learning, are examined.
Expected final online publication date for the Annual Review of Analytical Chemistry, Volume 16 is June 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
