Information visualization to enhance sensitivity and selectivity in biosensing

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.

Designing Silver Nanoparticles for Detecting Levodopa (3,4-Dihydroxyphenylalanine, L-Dopa) Using Surface-Enhanced Raman Scattering (SERS)

Detection of the drug Levodopa (3,4-dihydroxyphenylalanine, L-Dopa) is essential for the medical treatment of several neural disorders, including Parkinson's disease. In this paper, we employed surface-enhanced Raman scattering (SERS) with three shapes of silver nanoparticles (nanostars, AgNS; nanospheres, AgNP; and nanoplates, AgNPL) to detect L-Dopa in the nanoparticle dispersions. The sensitivity of the L-Dopa SERS signal depended on both nanoparticle shape and L-Dopa concentration. The adsorption mechanisms of L-Dopa on the nanoparticles inferred from a detailed analysis of the Raman spectra allowed us to determine the chemical groups involved. For instance, at concentrations below/equivalent to the limit found in human plasma (between 10-7-10-8 mol/L), L-Dopa adsorbs on AgNP through its ring, while at 10-5-10-6 mol/L adsorption is driven by the amino group. At even higher concentrations, above 10-4 mol/L, L-Dopa polymerization predominates. Therefore, our results show that adsorption depends on both the type of Ag nanoparticles (shape and chemical groups surrounding the Ag surface) and the L-Dopa concentration. The overall strategy based on SERS is a step forward to the design of nanostructures to detect analytes of clinical interest with high specificity and at varied concentration ranges.

Paper based electronic tongue - a low-cost solution for the distinction of sugar type and apple juice brand

This work reports on a low cost microfluidic electronic tongue (e-tongue) made with carbon interdigitated electrodes, printed on paper, and coated with boronic acid-containing hydrogels. Using capacitance measurements, the e-tongue was capable of distinguishing between different types of sugars (e.g. glucose, fructose and sucrose), in addition to distinguishing between commercial brands of apple juice using a small volume of sample (6 μL). The channels of the microfluidic e-tongue were made using a wax printer, and were modified with hydrogels containing acrylamide copolymerized with 5 or 20 mol% 3-(acrylamido) phenyl boronic acid (Am-PBA), or a crosslinked homopolymeric hydrogel based on N-(2-boronobenzyl)-2-hydroxy-N,N-dimethylethan-1-aminium-3-sulfopropyl acrylate (DMA-PBA). Such hydrogels, containing a phenyl boronic acid (PBA) moiety, can bind saccharides. Combining various hydrogels of this nature in an e-tongue device enabled discrimination between apple juices, which are known to contain higher amounts of fructose compared to glucose or sucrose. Changes in capacitance were captured with impedance spectroscopy in the frequency range from 0.1 to 10 MHz for solutions with varying concentrations of glucose, fructose and sucrose (from 0 to 0.056 g mL-1). The capacitance data were treated with Principal Component Analysis (PCA) and Interactive Document Map (IDMAP), which then correlated overall sugar content from different brands of apple juice. This low-cost, easy-to-use, disposable e-tongue offers great potential in the routine analysis of food and beverages, while offering comparative performance to alternatives in the literature.

Carbon Nanotube Matrix for Highly Sensitive Biosensors To Detect Pancreatic Cancer Biomarker CA19-9

Biosensors fabricated with nanomaterials promise faster, cheaper, and more efficient alternatives to traditional, often bulky devices for early cancer diagnosis. In this study, we fabricated a thin film sensing unit on interdigitated gold electrodes combining polyethyleneimine and carbon nanotubes in a layer by layer fashion, onto which antibodies anti-CA19-9 were adsorbed with a supporting layer of N-hydroxysuccinimide and 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide solution. By use of impedance spectroscopy, the pancreatic cancer biomarker CA19-9 was detected in a buffer with limit of detection of 0.35 U/mL. This high sensitivity allowed for distinction between samples of blood serum from patients with distinct probabilities to develop pancreatic cancer. The selectivity of the biosensor was confirmed in subsidiary experiments with HT-29 and SW-620 cell lines and possible interferents, e.g., p53 protein, ascorbic acid, and glucose, where significant changes in capacitance could only be measured with HT-29 that contained the CA19-9 biomarker. Chemisorption of CA19-9 molecules onto the layer of anti-CA19-9 antibodies was the mechanism responsible for sensing while electrostatic interactions drove the adsorption of carbon nanotubes, according to polarization-modulated infrared reflection absorption spectroscopy (PM-IRRAS). The adsorption behavior was successfully described by the Langmuir-Freundlich isotherm.

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

The fast growth of celiac disease diagnosis has sparked the production of gluten-free food and the search for reliable methods to detect gluten in foodstuff. In this paper, we report on a microfluidic electronic tongue (e-tongue) capable of detecting trace amounts of gliadin, a protein of gluten, down to 0.005 mg kg^-1 in ethanol solutions, and distinguishing between gluten-free and gluten-containing foodstuff. In some cases, it is even possible to determine whether gluten-free foodstuff has been contaminated with gliadin. That was made possible with an e-tongue comprising four sensing units, three of which made of layer-by-layer (LbL) films of semiconducting polymers deposited onto gold interdigitated electrodes placed inside microchannels. Impedance spectroscopy was employed as the principle of detection, and the electrical capacitance data collected with the e-tongue were treated with information visualization techniques with feature selection for optimizing performance. The sensing units are disposable to avoid cross-contamination as gliadin adsorbs irreversibly onto the LbL films according to polarization-modulated infrared reflection absorption spectroscopy (PM-IRRAS) analysis. Small amounts of material are required to produce the nanostructured films, however, and the e-tongue methodology is promising for low-cost, reliable detection of gliadin and other gluten constituents in foodstuff.

On the convergence of nanotechnology and Big Data analysis for computer-aided diagnosis

An overview is provided of the challenges involved in building computer-aided diagnosis systems capable of precise medical diagnostics based on integration and interpretation of data from different sources and formats. The availability of massive amounts of data and computational methods associated with the Big Data paradigm has brought hope that such systems may soon be available in routine clinical practices, which is not the case today. We focus on visual and machine learning analysis of medical data acquired with varied nanotech-based techniques and on methods for Big Data infrastructure. Because diagnosis is essentially a classification task, we address the machine learning techniques with supervised and unsupervised classification, making a critical assessment of the progress already made in the medical field and the prospects for the near future. We also advocate that successful computer-aided diagnosis requires a merge of methods and concepts from nanotechnology and Big Data analysis.

Supramolecular Control in Nanostructured Film Architectures for Detecting Breast Cancer

The need for early detection of various diseases, including breast cancer, has motivated research into nanomaterials that can be assembled in organized films which serve as biosensors. Owing to the variety of possible materials and film architectures, procedures are required to design optimized biosensors. In this study, we combine surface-specific methods to monitor the assembly of antibodies on nanostructured films with two distinct architectures. In the first, a layer of the antibody type mouse anti-HER2 (clone tab250) was immobilized on a self-assembled monolayer (SAM) of 11-mercaptoundecanoic acid modified with N-hydroxysuccinimide (NHS) and 1-ethyl-3-(3-(dimethylamino)propyl)carbodiimide (EDC). In the second approach, a SAM of cysteamine was coated with a biotin/spreptavidin bilayer on which a layer of biotinylated antibody type MSx2HUp185/her biotin was adsorbed. The rougher, less passivating coating with cysteamine determined from cyclic voltammetry and scanning electron microscopy led to biosensors that are more sensitive to detect the breast cancer ERBB2 (HER2) biomarker in impedance spectroscopy measurements. This higher distinguishing ability of the cysteamine-containing film architecture was proven with information visualization methods to treat the impedance data. Polarization-modulated infrared reflection absorption spectroscopy (PM-IRRAS) confirmed that biosensing resulted from the antibody-ERBB2 antigen affinity.

Nanomaterials for diagnosis: challenges and applications in smart devices based on molecular recognition

Clinical diagnosis has always been dependent on the efficient immobilization of biomolecules in solid matrices with preserved activity, but significant developments have taken place in recent years with the increasing control of molecular architecture in organized films. Of particular importance is the synergy achieved with distinct materials such as nanoparticles, antibodies, enzymes, and other nanostructures, forming structures organized on the nanoscale. In this review, emphasis will be placed on nanomaterials for biosensing based on molecular recognition, where the recognition element may be an enzyme, DNA, RNA, catalytic antibody, aptamer, and labeled biomolecule. All of these elements may be assembled in nanostructured films, whose layer-by-layer nature is essential for combining different properties in the same device. Sensing can be done with a number of optical, electrical, and electrochemical methods, which may also rely on nanostructures for enhanced performance, as is the case of reporting nanoparticles in bioelectronics devices. The successful design of such devices requires investigation of interface properties of functionalized surfaces, for which a variety of experimental and theoretical methods have been used. Because diagnosis involves the acquisition of large amounts of data, statistical and computational methods are now in widespread use, and one may envisage an integrated expert system where information from different sources may be mined to generate the diagnostics.

Molecularly designed layer-by-layer (LbL) films to detect catechol using information visualization methods

The control of molecular architectures has been exploited in layer-by-layer (LbL) films deposited on Au interdigitated electrodes, thus forming an electronic tongue (e-tongue) system that reached an unprecedented high sensitivity (down to 10(-12) M) in detecting catechol. Such high sensitivity was made possible upon using units containing the enzyme tyrosinase, which interacted specifically with catechol, and by processing impedance spectroscopy data with information visualization methods. These latter methods, including the parallel coordinates technique, were also useful for identifying the major contributors to the high distinguishing ability toward catechol. Among several film architectures tested, the most efficient had a tyrosinase layer deposited atop LbL films of alternating layers of dioctadecyldimethylammonium bromide (DODAB) and 1,2-dipalmitoyl-sn-3-glycero-fosfo-rac-(1-glycerol) (DPPG), viz., (DODAB/DPPG)5/DODAB/Tyr. The latter represents a more suitable medium for immobilizing tyrosinase when compared to conventional polyelectrolytes. Furthermore, the distinction was more effective at low frequencies where double-layer effects on the film/liquid sample dominate the electrical response. Because the optimization of film architectures based on information visualization is completely generic, the approach presented here may be extended to designing architectures for other types of applications in addition to sensing and biosensing.