Information Visualization and Feature Selection Methods Applied to Detect Gliadin in Gluten-Containing Foodstuff with a Microfluidic Electronic Tongue

American College of Gastroenterology Guidelines Update: Diagnosis and Management of Celiac Disease

This guideline presents an update to the 2013 American College of Gastroenterology Guideline on the Diagnosis and Management of Celiac Disease with updated recommendations for the evaluation and management of patients with celiac disease (CD). CD is defined as a permanent immune-mediated response to gluten present in wheat, barley, and rye. CD has a wide spectrum of clinical manifestations that resemble a multisystemic disorder rather than an isolated intestinal disease, and is characterized by small bowel injury and the presence of specific antibodies. Detection of CD-specific antibodies (e.g., tissue transglutaminase) in the serum is very helpful for the initial screening of patients with suspicion of CD. Intestinal biopsy is required in most patients to confirm the diagnosis. A nonbiopsy strategy for the diagnosis of CD in selected children is suggested and discussed in detail. Current treatment for CD requires strict adherence to a gluten-free diet (GFD) and lifelong medical follow-up. Most patients have excellent clinical response to a GFD. Nonresponsive CD is defined by persistent or recurrent symptoms despite being on a GFD. These patients require a systematic workup to rule out specific conditions that may cause persistent or recurrent symptoms, especially unintentional gluten contamination. Refractory CD is a rare cause of nonresponsive CD often associated with poor prognosis.

Three-Dimensional Printing and Its Potential to Develop Sensors for Cancer with Improved Performance

Cancer is the second leading cause of death globally and early diagnosis is the best strategy to reduce mortality risk. Biosensors to detect cancer biomarkers are based on various principles of detection, including electrochemical, optical, electrical, and mechanical measurements. Despite the advances in the identification of biomarkers and the conventional 2D manufacturing processes, detection methods for cancers still require improvements in terms of selectivity and sensitivity, especially for point-of-care diagnosis. Three-dimensional printing may offer the features to produce complex geometries in the design of high-precision, low-cost sensors. Three-dimensional printing, also known as additive manufacturing, allows for the production of sensitive, user-friendly, and semi-automated sensors, whose composition, geometry, and functionality can be controlled. This paper reviews the recent use of 3D printing in biosensors for cancer diagnosis, highlighting the main advantages and advances achieved with this technology. Additionally, the challenges in 3D printing technology for the mass production of high-performance biosensors for cancer diagnosis are addressed.

Gold-based nanoplatform for a rapid lateral flow immunochromatographic test assay for gluten detection

Background

Gluten, a food allergen, is available in foods derived from wheat, rye and barley. It damages the small intestine and causes celiac disease. Herein, we designed a rapid immunochromatographic lateral flow test assay for detecting the gluten contents of raw materials. In this rapid test, the presence of gluten was screened through the capturing of gliadin (a toxic component of gluten) by two identical gliadin monoclonal antibodies. One of the antibodies was immobilized on the membrane in the test zone as a capture reagent. The other antibody was labeled with gold nanoparticles (AuNPs) as a detector reagent.

Results

Gold nanoparticles with a size of about 20 nm were synthesized and conjugated to the gliadin monoclonal antibodies. The detection limit of the experimental assay was 20 ppm and positive results were visualized after 15 min using only 40 μL of the extracted sample for each test. Analysis of different flour samples identified the best sensitivity and specificity of the lateral flow test strip (LFTS).

Conclusion

The experimental LFTS is an easy-to-use and rapid method for the screening of gluten level in raw materials. The LFTS may be employed to ensure the safety of foods.

How does a celiac iceberg really float? The relationship between celiac disease and gluten

Celiac disease (CD) is an autoimmune intestinal disease caused by intolerance of genetically susceptible individuals after intake of gluten-containing grains (including wheat, barley, etc.) and their products. Currently, CD, with "iceberg" characteristics, affects a large population and is distributed over a wide range of individuals. This present review summarizes the latest research progress on the relationship between CD and gluten. Furthermore, the structure and function of gluten peptides related to CD, gluten detection methods, the effects of processing on gluten and gluten-free diets are emphatically reviewed. In addition, the current limitations in CD research are also discussed. The present work facilitates a comprehensive understanding of CD as well as gluten, which can provide a theoretical reference for future research.

Big data and machine learning for materials science

Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. However, the potential benefits of ML come at the cost of big data production; that is, the algorithms demand large volumes of data of various natures and from different sources, from material properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discovery of new materials and analysis of chemical sensing compounds, both prominent research fields for ML in the context of materials science. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to materials science, outlining processes, discussing pitfalls, and reviewing cases of success and failure.

Influence of the Flow Rate in an Automated Microfluidic Electronic Tongue Tested for Sucralose Differentiation

Incorporating electronic tongues into microfluidic devices brings benefits as dealing with small amounts of sample/discharge. Nonetheless, such measurements may be time-consuming in some applications once they require several operational steps. Here, we designed four collinear electrodes on a single printed circuit board, further comprised inside a straight microchannel, culminating in a robust e-tongue device for faster data acquisition. An analog multiplexing circuit automated the signal's routing from each of the four sensing units to an impedance analyzer. Both instruments and a syringe pump are controlled by dedicated software. The automated e-tongue was tested with four Brazilian brands of liquid sucralose-based sweeteners under 20 different flow rates, aiming to systematically evaluate the influence of the flow rate in the discrimination among sweet tastes sold as the same food product. All four brands were successfully distinguished using principal component analysis of the raw data, and despite the nearly identical sucralose-based taste in all samples, all brands' significant distinction is attributed to small differences in the ingredients and manufacturing processes to deliver the final food product. The increasing flow rate improves the analyte's discrimination, as the silhouette coefficient reaches a plateau at ~3 mL/h. We used an equivalent circuit model to evaluate the raw data, finding a decrease in the double-layer capacitance proportional to improvements in the samples' discrimination. In other words, the flow rate increase mitigates the formation of the double-layer, resulting in faster stabilization and better repeatability in the sensor response.

Polysaccharide Multilayer Films in Sensors for Detecting Prostate Tumor Cells Based on Hyaluronan-CD44 Interactions

The increasing need for point-of-care diagnosis has sparked the development of label-free sensing platforms, some of which are based on impedance measurements with biological cells. Here, interdigitated electrodes were functionalized with layer-by-layer (LbL) films of hyaluronan (HA) and chitosan (CHI) to detect prostatic tumor cells (PC3 line). The deposition of LbL films was confirmed with atomic force microscopy and polarization-modulated infrared reflection absorption spectroscopy (PM-IRRAS), which featured the vibrational modes of the HA top layer capable of interacting specifically with glycoprotein CD44 receptors overexpressed in tumor cells. Though the CHI/HA LbL films cannot be considered as a traditional biosensor due to their limited selectivity, it was possible to distinguish prostate tumor cells in the range from 50 to 600 cells/µL in in vitro experiments with impedance spectroscopy. This was achieved by treating the impedance data with information visualization methods, which confirmed the distinguishing ability of the films by observing the absence of false positives in a series of control experiments. The CD44–HA interactions may, therefore, be exploited in clinical analyses and point-of-care diagnostics for cancer, particularly if computational methods are used to process the data.

Microfluidic Point-of-Care Devices: New Trends and Future Prospects for eHealth Diagnostics

Point-of-care (PoC) diagnostics is promising for early detection of a number of diseases, including cancer, diabetes, and cardiovascular diseases, in addition to serving for monitoring health conditions. To be efficient and cost-effective, portable PoC devices are made with microfluidic technologies, with which laboratory analysis can be made with small-volume samples. Recent years have witnessed considerable progress in this area with “epidermal electronics”, including miniaturized wearable diagnosis devices. These wearable devices allow for continuous real-time transmission of biological data to the Internet for further processing and transformation into clinical knowledge. Other approaches include bluetooth and WiFi technology for data transmission from portable (non-wearable) diagnosis devices to cellphones or computers, and then to the Internet for communication with centralized healthcare structures. There are, however, considerable challenges to be faced before PoC devices become routine in the clinical practice. For instance, the implementation of this technology requires integration of detection components with other fluid regulatory elements at the microscale, where fluid-flow properties become increasingly controlled by viscous forces rather than inertial forces. Another challenge is to develop new materials for environmentally friendly, cheap, and portable microfluidic devices. In this review paper, we first revisit the progress made in the last few years and discuss trends and strategies for the fabrication of microfluidic devices. Then, we discuss the challenges in lab-on-a-chip biosensing devices, including colorimetric sensors coupled to smartphones, plasmonic sensors, and electronic tongues. The latter ones use statistical and big data analysis for proper classification. The increasing use of big data and artificial intelligence methods is then commented upon in the context of wearable and handled biosensing platforms for the Internet of things and futuristic healthcare systems.

A Microfluidic E-Tongue System Using Layer-by-Layer Films Deposited onto Interdigitated Electrodes Inside a Polydimethylsiloxane Microchannel

An electronic tongue (e-tongue) is a multisensory system employed in the analysis of liquid samples, transforming the raw data into specific recognition patterns through computational and statistical analysis. Distinct types of e-tongues have been reported in the literature, with a plethora of applications in several areas of research. Recently, e-tongues have been integrated into microfluidic devices, which offer advantages such as the use of continuous flow for faster and more accurate analysis, and reduction in size of the devices and volumes for sampling and discharge, which in turn reduces waste and cost. Here we describe the procedures and methodologies recently used in our research group in the development of a microfluidic e-tongue sensing system.

Microfluidic Mixer with Automated Electrode Switching for Sensing Applications

An electronic tongue (e-tongue) is a multisensory system usually applied to complex liquid media that uses computational/statistical tools to group information generated by sensing units into recognition patterns, which allow the identification/distinction of samples. Different types of e-tongues have been previously reported, including microfluidic devices. In this context, the integration of passive mixers inside microchannels is of great interest for the study of suppression/enhancement of sensorial/chemical effects in the pharmaceutical, food, and beverage industries. In this study, we present developments using a stereolithography technique to fabricate microfluidic devices using 3D-printed molds for elastomers exploring the staggered herringbone passive mixer geometry. The fabricated devices (microchannels plus mixer) are then integrated into an e-tongue system composed of four sensing units assembled on a single printed circuit board (PCB). Gold-plated electrodes are designed as an integral part of the PCB electronic circuitry for a highly automated platform by enabling faster analysis and increasing the potential for future use in commercial applications. Following previous work, the e-tongue sensing units are built functionalizing gold electrodes with layer-by-layer (LbL) films. Our results show that the system is capable of (i) covering basic tastes below the human gustative perception and (ii) distinguishing different suppression effects coming from the mixture of both strong and weak electrolytes. This setup allows for triplicate measurements in 12 electrodes, which represents four complete sensing units, by automatically switching all electrodes without any physical interaction with the sensor. The result is a fast and reliable data acquisition system, which comprises a suitable solution for monitoring, sequential measurements, and database formation, being less susceptible to human errors.

Pathogenesis of Celiac Disease and Other Gluten Related Disorders in Wheat and Strategies for Mitigating Them

Wheat is a major cereal crop providing energy and nutrients to the billions of people around the world. Gluten is a structural protein in wheat, that is necessary for its dough making properties, but it is responsible for imparting certain intolerances among some individuals, which are part of this review. Most important among these intolerances is celiac disease, that is gluten triggered T-cell mediated autoimmune enteropathy and results in villous atrophy, inflammation and damage to intestinal lining in genetically liable individuals containing human leukocyte antigen DQ2/DQ8 molecules on antigen presenting cells. Celiac disease occurs due to presence of celiac disease eliciting epitopes in gluten, particularly highly immunogenic alpha-gliadins. Another gluten related disorder is non-celiac gluten-sensitivity in which innate immune-response occurs in patients along with gastrointestinal and non-gastrointestinal symptoms, that disappear upon removal of gluten from the diet. In wheat allergy, either IgE or non-IgE mediated immune response occurs in individuals after inhalation or ingestion of wheat. Following a life-long gluten-free diet by celiac disease and non-celiac gluten-sensitivity patients is very challenging as none of wheat cultivar or related species stands safe for consumption. Hence, different molecular biology, genetic engineering, breeding, microbial, enzymatic, and chemical strategies have been worked upon to reduce the celiac disease epitopes and the gluten content in wheat. Currently, only 8.4% of total population is affected by wheat-related issues, while rest of population remains safe and should not remove wheat from the diet, based on false media coverage.

A highly specific and sensitive nanoimmunosensor for the diagnosis of neuromyelitis optica spectrum disorders

A precise diagnosis for neuromyelitis optica spectrum disorders (NMOSD) is crucial to improve patients' prognostic, which requires highly specific and sensitive tests. The cell-based assay with a sensitivity of 76% and specificity of 100% is the most recommended test to detect anti-aquaporin-4 antibodies (AQP4-Ab). Here, we tested four AQP4 external loop peptides (AQP4_61-70, AQP4_131-140, AQP4_141-150, and AQP4_201-210) with an atomic force microscopy nanoimmunosensor to develop a diagnostic assay. We obtained the highest reactivity with AQP4_61-70-nanoimunosensor. This assay was effective in detecting AQP4-Ab in sera of NMOSD patients with 100% specificity (95% CI 63.06-100), determined by the cut-off adhesion force value of 241.3 pN. NMOSD patients were successfully discriminated from a set of healthy volunteers, patients with multiple sclerosis, and AQP4-Ab-negative patients. AQP4_61-70 sensitivity was 81.25% (95% CI 56.50-99.43), slightly higher than with the CBA method. The results with the AQP4_61-70-nanoimmunosensor indicate that the differences between NMOSD seropositive and seronegative phenotypes are related to disease-specific epitopes. The absence of AQP4-Ab in sera of NMOSD AQP4-Ab-negative patients may be interpreted by assuming the existence of another potential AQP4 peptide sequence or non-AQP4 antigens as the antibody target.

3D-Printed Graphene Electrodes Applied in an Impedimetric Electronic Tongue for Soil Analysis

The increasing world population leads to the growing demand for food production without expanding cultivation areas. In this sense, precision agriculture optimizes the production and input usage by employing sensors to locally monitor plant nutrient within agricultural fields. Here, we have used an electronic tongue sensing device based on impedance spectroscopy to recognize distinct soil samples (sandy and clayey) enriched with macronutrients. The e-tongue setup consisted of an array of four sensing units formed by layer-by-layer (LbL) films deposited onto 3D-printed graphene-based interdigitated electrodes (IDEs). The IDEs were fabricated in 20 min using the fused deposition modeling process and commercial polylactic acid-based graphene filaments. The e-tongue comprised one bare and three IDEs functionalized with poly(diallyldimethylammonium chloride) solution/copper phthalocyanine-3,4′,4′′,4′′′-tetrasulfonic acid tetrasodium salt (PDDA/CuTsPc), PDDA/montmorillonite clay (MMt-K), and PDDA/poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) LbL films. Control samples of sandy and clayey soils were enriched with different concentrations of nitrogen (N), phosphorus (P), and potassium (K) macronutrients. Sixteen soil samples were simply diluted in water and measured using electrical impedance spectroscopy, with data analyzed by principal component analysis. All soil samples were easily distinguished without pre-treatment, indicating the suitability of 3D-printed electrodes in e-tongue analysis to distinguish the chemical fertility of soil samples. Our results encourage further investigations into the development of new tools to support precision agriculture.

Heavy Metal/Toxins Detection Using Electronic Tongues

The growing concern for sustainability and environmental preservation has increased the demand for reliable, fast response, and low-cost devices to monitor the existence of heavy metals and toxins in water resources. An electronic tongue (e-tongue) is a multisensory array mostly based on electroanalytical methods and multivariate statistical techniques to facilitate information visualization in a qualitative and/or quantitative way. E-tongues are promising analytical devices having simple operation, fast response, low cost, easy integration with other systems (microfluidic, optical, etc) to enable miniaturization and provide a high sensitivity for measurements in complex liquid media, providing an interesting alternative to address many of the existing environmental monitoring challenges, specifically relevant emerging pollutants such as heavy metals and toxins.

Use of data processing for rapid detection of the prostate-specific antigen biomarker using immunomagnetic sandwich-type sensors

Diagnosis of cancer using electroanalytical methods can be achieved at low cost and in rapid assays, but this may require the combination with data treatment for determining biomarkers in real samples. In this paper, we report an immunomagnetic nanoparticle-based microfluidic sensor (INμ-SPCE) for the amperometric detection of the prostate-specific antigen (PSA) biomarker, the data of which were treated with information visualization methods. The INμ-SPCE consists of eight working electrodes, reference and counter electrodes. On the working electrodes, magnetic nanoparticles with secondary antibodies with the enzyme horseradish peroxidase were immobilized for the indirect detection of PSA in a sandwich-type procedure. Under optimal conditions, the immunosensor could operate within a wide range from 12.5 to 1111 fg·L^-1, with a low detection limit of 0.062 fg·L^-1. Multidimensional projections combined with feature selection allowed for the distinction of cell lysates with different levels of PSA, in agreement with results from the traditional enzyme-linked immunosorbent assay. The approaches for immunoassays and data processing are generic, and therefore the strategies described here may provide a simple platform for clinical diagnosis of cancers and other types of diseases.

Electrical detection of pathogenic bacteria in food samples using information visualization methods with a sensor based on magnetic nanoparticles functionalized with antimicrobial peptides

Outbreaks of foodborne diseases demand simple, rapid techniques for detecting pathogenic bacteria beyond the standard methods that are not applicable to routine analysis in the food industry and in the points of food consumption. In this work, we developed a sensitive, rapid and low-cost assay for detecting Escherichia coli (E. coli), Staphylococcus aureus (S. aureus) and Salmonella typhimurium (S. typhi) in potable water and apple juice. The assay is based on electrical impedance spectroscopy measurements with screen-printed interdigitated electrodes coupled with magnetite nanoparticles functionalized with the antimicrobial peptide melittin (MLT). The data were analyzed with the information visualization methods Sammon's Mapping and Interactive Document Map to distinguish samples at two levels of contamination from food suitable for consumption. With this approach it has been possible to detect E. coli concentration down to 1 CFU mL^-1 in potable water and 3.5 CFU mL^-1 in apple juice without sample preparation, within only 25 min. This approach may serve as a low-cost, quick screening procedure to detect bacteria-related food poisoning, especially if the impedance data of several sensing units are combined.

Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples

Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA) and Partial Least Squares (PLS). Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) were applied to three different feature data sets for classification and level assessment of paraffin. For classification, the model based on SVM, with an accuracy rate of 100%, was superior to that based on RF, with an accuracy rate of 98.33-100%, and ELM, with an accuracy rate of 98.01-100%. For level assessment, the R² related to the training set was above 0.97 and the R² related to the test set was above 0.87. Through comprehensive comparison, the generalization of the model based on ELM was superior to those based on SVM and RF. The scoring errors for the three models were 0.0016-0.3494, lower than the error of 0.5-1.0 measured by industry standard experts, meaning these methods have a higher prediction accuracy for scoring paraffin level.

Electronic Tongue-A Tool for All Tastes?

Electronic tongue systems are traditionally used to analyse: food products, water samples and taste masking technologies for pharmaceuticals. In principle, their applications are almost limitless, as they are able to almost completely reduce the impact of interferents and can be applied to distinguish samples of extreme complexity as for example broths from different stages of fermentation. Nevertheless, their applications outside the three principal sample types are, in comparison, rather scarce. In this review, we would like to take a closer look on what are real capabilities of electronic tongue systems, what can be achieved using mixed sensor arrays and by introduction of biosensors or molecularly imprinted polymers in the matrix. We will discuss future directions both in the sense of applications as well as system development in the ever-growing trend of low cost analysis.

Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose

Multi-sensor data fusion can provide more comprehensive and more accurate analysis results. However, it also brings some redundant information, which is an important issue with respect to finding a feature-mining method for intuitive and efficient analysis. This paper demonstrates a feature-mining method based on variable accumulation to find the best expression form and variables’ behavior affecting beer flavor. First, e-tongue and e-nose were used to gather the taste and olfactory information of beer, respectively. Second, principal component analysis (PCA), genetic algorithm-partial least squares (GA-PLS), and variable importance of projection (VIP) scores were applied to select feature variables of the original fusion set. Finally, the classification models based on support vector machine (SVM), random forests (RF), and extreme learning machine (ELM) were established to evaluate the efficiency of the feature-mining method. The result shows that the feature-mining method based on variable accumulation obtains the main feature affecting beer flavor information, and the best classification performance for the SVM, RF, and ELM models with 96.67%, 94.44%, and 98.33% prediction accuracy, respectively.