Biosensor Analysis Research Articles

An Electrochemical Biosensor Analysis of the Interaction of a Two-Vector Phospholipid Composition of Doxorubicin with dsDNA and Breast Cancer Cell Models In Vitro

Objectives: The main aim of our experiments was to demonstrate the suitability of cell-based biosensors for searching for new anticancer medicinal preparations. Methods: The effect of the substance doxorubicin, doxorubicin embedded in phospholipid nanoparticles, and doxorubicin with phospholipid nanoparticles modified by targeting vectors (cRGD and folic acid) on dsDNA and breast cancer cell lines (MCF-7, MDA-MB-231) was studied. Results: In the obtained doxorubicin nanoforms, the particle size was less than 60 nm. Our study of the percentage of doxorubicin inclusion showed the almost complete embeddability of the substance into nanoparticles for all samples, with an average of 95.4 ± 4.6%. The calculation of the toxicity index of the studied doxorubicin samples showed that all substances were moderately toxic drugs in terms of adenine and guanine. The biosensor analysis using electrodes modified with carbon nanotubes showed an intercalation interaction between doxorubicin and its derivatives and dsDNA, except for the composition of doxorubicin with folic acid with a linker length of 2000 (NPh-Dox-Fol(2.0)). The results of the electroanalysis were normalized to the total cell protein (mg) and cell concentration. The highest intensity of the electrochemical signals was observed in intact control cells of the MCF-7 and MDA-MB-231 cell lines. Conclusions: The proposed electrochemical approach is useful for the analysis of cell line responses to the medicinal preparations.

Exploring the Performance Impact of Neural Network Optimization on Energy Analysis of Biosensor

With the popularization of new energy vehicles, lithium battery systems, as the main components of new energy vehicles, have the characteristics of short life cycles and harmful substances inside. The green treatment of lithium battery systems has become a research hotspot. Disassembly and recycling are essential means of reusing waste in lithium battery systems. Due to the wide variety of lithium battery systems, the lack of unified design standards, and the high flexibility requirements for disassembly, manual disassembly is currently the primary method used. However, this method can cause health hazards to oneself when dismantling some harmful components. The optimization of the dismantling process route for lithium batteries is a crucial step before dismantling, which directly determines the economic benefits of dismantling. However, unlike general electromechanical products, lithium batteries have prominent safety issues during the dismantling process, so the safety requirements for their dismantling process route are relatively high. Given the substantial absence of parametric evaluation and modification in prior research, this work investigates the influence of the most significant factors on the power density of biosensors. A conduction-based framework was employed to ascertain these variables, and the calculations were performed utilizing a neural network. The neural network was developed with Particle Swarm Optimization (PSO). Based on this, this article considers studying the optimization method of the lithium battery safety disassembly process to maximize safety and economic benefits comprehensively. Based on the essential characteristics of lithium-ion battery systems, an analysis is conducted on the allocation method of difficulty level for human-machine cooperation tasks and the impact indicators of task allocation. Then, a product disassembly hybrid diagram is established, and on this basis, multiple sets of human-machine cooperation disassembly sequences are generated. Finally, a multi-objective optimization model for disassembly cost, difficulty, and time is established. Finally, taking the Tesla Model 1sPBS waste lithium battery as an example, the safety prediction model for dismantling the waste lithium battery and the optimization model for the safety dismantling process route were solved to verify the effectiveness of the above optimization method.

Unraveling the Bivalent and Rapid Interactions Between a Multivalent RNA Recognition Motif and RNA: A Kinetic Approach.

The kinetics of the interaction between Musashi-1 (MSI1) and RNA have been characterized using surface plasmon resonance biosensor analysis. Truncated variants of human MSI1 encompassing the two homologous RNA recognition motifs (RRM1 and RRM2) in tandem (aa 1-200), and the two RRMs in isolation (aa 1-103 and aa 104-200, respectively) were produced. The proteins were injected over sensor surfaces with immobilized RNA, varying in sequence and length, and with one or two RRM binding motifs. The interactions of the individual RRMs with all RNA variants were well described by a 1:1 interaction model. The interaction between the MSI1 variant encompassing both RRM motifs was bivalent and rapid for all RNA variants. Due to difficulties in fitting this complex data using standard procedures, we devised a new method to quantify the interactions. It revealed that two RRMs in tandem resulted in a significantly longer residence time than a single RRM. It also showed that RNA with double UAG binding motifs and potential hairpin structures forms less stable bivalent complexes with MSI1 than the single UAG motif containing linear RNA. Substituting the UAG binding motif with a CAG sequence resulted in a reduction of the affinity of the individual RRMs, but for MSI1, this reduction was strongly enhanced, demonstrating the importance of bivalency for specificity. This study has provided new insights into the interaction between MSI1 and RNA and an understanding of how individual domains contribute to the overall interaction. It provides an explanation for why many RNA-binding proteins contain dual RRMs.

CRISPR/Cas12a dual-mode biosensor for Staphylococcus aureus detection via enzyme-free isothermal amplification

Accurate and reliable detection of Staphylococcus aureus (S. aureus) is essential for preventing infections, particularly in healthcare and food safety contexts. This work presents a novel dual-mode biosensor that integrates the CRISPR/Cas12a system with an enzyme-free isothermal amplification method for detecting S. aureus. Hybridization chain reaction (HCR) and catalytic hairpin assembly (CHA) amplify the aptamer-triggered response, significantly enhancing sensitivity. CRISPR/Cas12a′s nuclease activity is utilized in two modes: cis cleavage generates a fluorescence signal, while trans cleavage produces an electrochemical signal, enabling dual-mode detection. The biosensor demonstrates outstanding performance, with a limit of detection (LOD) as low as 5.7 CFU mL−1 in electrochemical mode and 133.7 CFU mL−1 in fluorescence mode, showcasing excellent accuracy, stability, and sensitivity. It has been successfully applied to detecting actual samples, confirming its practical applicability. This innovative approach offers a powerful tool for the swift and precise identification of S. aureus and paves the way for developing next-generation dual-mode biosensors for various analytes. Future research will aim to simplify the detection process further, making it more accessible for use in resource-limited settings.

Novel electrochemical sensor based on poly([Cu(H2O)2P2]I2)/GCE for the determination of trimethoprim in clinical samples and cow's milk samples

A Novel bioanalytical detector based on Poly(Diaquabis(1,10-phenanthroline copper (II) Iodide) (poly([Cu(H2O)2P2]I2)/GCE) has been fabricated by potentiodynamic polymerization of [Cu(H2O)2P2]I2. The synthesized complex and the fabricated electrodes showed a promising electrocatalytic behavior towards the electrooxidation of Trimethoprim (TPM). Based on the regression coefficient value of scan rate with peak current and square root of peak current, the oxidation of TMP at poly([Cu(H2O)2P2]I2)/GCE was dominated by diffusion. Under the optimized experimental conditions and square wave voltammetric parameters, the poly([Cu(H2O)2P2]I2)/GCE revealed very wide responses in the range of concentration of 500 nM–160 μM with a limit of detection of 3.91 nM and a Limit of quantification of 13.02 nM with the relative standard deviation below 1.75 %. The valuable applicability of the fabricated sensor for the determination of TMP in real samples, including cow's milk, blood serum, and human urine, was successfully investigated. The electroanalysis results from the spike recovery and interference study provided an excellent range of recovery with an error percent of below 3.0 % in the combination effect with the potential interferents. The sensor exhibited excellent reproducibility, sensitivity, long-term stability, high accuracy, and fast response. The developed bioanalytical sensor revealed the best application prospect for biomedicine and environmental monitoring activities.

Enhanced detection of HER2 using a TiVC MXenes/gold nanocomposite amplified analytical biosensor for precise cancer biomarker monitoring

Enhanced detection of HER2 using a TiVC MXenes/gold nanocomposite amplified analytical biosensor for precise cancer biomarker monitoring

Is the brightness- contrast level of virtual reality videos significant for visually induced motion sickness? Experimental real-time biosensor and self-report analysis

Is the brightness- contrast level of virtual reality videos significant for visually induced motion sickness? Experimental real-time biosensor and self-report analysis

Design and analysis of a multi-modal refractive index plasmonic biosensor based on split ring resonator for detection of the various cancer cells

Design and analysis of a multi-modal refractive index plasmonic biosensor based on split ring resonator for detection of the various cancer cells

(Invited) Developing a Graphene Field-Effect Biosensor for Genetic Biomarkers: Simulations Unveil How the Device Sensitivity Is Related to DNA-Graphene Interactions at the Nanoscale

Graphene field-effect transistor biosensors (GFETs) offer a promising platform to detect cancer DNA biomarkers at low concentration [1]. At their core, they have an electronic circuit containing a graphene sheet – a two-dimensional nanomaterial made of a single layer of carbon atoms – whose electronic properties are very sensitive to nearby electric charges. Leveraging that feature, the electrical current generated by a GFET can thus be made very sensitive to the concentration of a specific DNA sequence in a biological sample. To achieve this, the complementary DNA sequence is linked to the graphene to bind specifically the target DNA. In most GFETs, the link is done using 1-pyrenebutanoic acid succinimidyl ester (PBASE) because its pyrene group stacks on the graphene through pi-pi interactions, without disrupting the electronic properties of the graphene. However, recent experiments in the Bouilly group have shown variations in GFET sensitivity depending on the incubation time of graphene with PBASE.Here, we use a computational approach developed in our previous work [2] to investigate at the nanoscale the role of PBASE linker molecules on the sensitivity of the GFET. First, we use molecular dynamics simulations to characterize the conformational ensemble of a 10-nt single-stranded DNA sequence linked to a graphene sheet using PBASE. Second, we estimate the electrical response of the GFET from the electrostatic potential generated by these DNA conformations on the graphene, as determined using electrostatic Poisson-Boltzmann simulations. Our results show that the conformational ensemble of DNA is strongly affected by the presence of other PBASE adsorbed on the graphene, thus changing the electrostatic potential generated on the graphene. The predicted change of the electrostatic potential should impact measurably the detection signal of the GFET.We are now using these simulations to explain the experimental characterizations of a GFET designed to detect a genetic biomarker important in breast cancer profiling. Our simulations have the potential to support the development of GFETs with better sensitivity for DNA detection.[1] Béraud, A., Sauvage, M., Bazán, C. M., Tie, M., Bencherif, A., and Bouilly, D. (2021) Graphene field-effect transistors as bioanalytical sensors: design, operation and performance. Analyst. 146:403-428.[2] Côté, S., Mousseau, N., and Bouilly, D. (2022) The molecular origin of the electrostatic gating of single-molecule field-effect biosensors investigated by molecular dynamics simulations. Phys. Chem. Chem. Phys. 24:4174-4186.

Electrochemical Aptamer-Based Biosensors for Real-Time Monitoring of Biomarkers In Vivo

There is a need for bioanalytical sensors that operate reliably for real-time monitoring of biomarkers in vivo. To address this need, we have created a hybrid sensor integrating microneedle patches with aptamer-based electrochemical sensors for monitoring glucose and lactate in vivo. We have developed a series of quality control and programmable calibration curves for measuring concentration changes in vivo. Using these methods, we have been able to monitor both lactate and glucose simultaneously in the interstitial fluid of two live animal models. Our measurements are in excellent correlation with analysis performed on blood using gold standard laboratory techniques.

(Nanocarbons Division SES Young Investigator Award) Surface Chemistry of Nanocarbon Field-Effect Transistors: From Fundamental Physics to Biosensing

Conductive carbon nanomaterials are particularly well-suited to form field-effect biosensors (bioFETs) because they are stable in aqueous media and can be chemically coupled to biomolecules. Their reduced dimensionality, either 2D (graphene) or 1D (carbon nanotubes), is key to their sensitivity: because they are entirely surfaces, these materials present remarkable electrical transport properties that are easily altered by microscopic surface interactions. Biochemical processes occurring in close proximity to their surface can therefore be transduced as changes in their electrical conductance. Selectivity for specific biochemical events, however, must be engineered via functionalization of the nanomaterial, i.e. by modifying its surface with bioactive or biorecognition elements (e.g., antibodies, aptamers, enzymes, polymer layer). Our group recently analyzed a corpus of studies presenting graphene field-effect transistors (GFETs) as bioanalytical sensors for the quantitative detection of biomarkers in the form of nucleic acids, proteins, ions or small molecules [1]. We found a striking variance in the reported sensing performance between these studies, that could not be explained by controlling for differences in graphene material, analyte type and for evolution over the years. Our hypothesis is that the lack of microscopic control on the biointerface is likely the most important source of variation in such bioFETs, highlighting the critical need to improve our knowledge and control of the functionalization of nanocarbon materials.In this talk, I will discuss our work investigating the interplay between surface chemistry and electrical properties in nanocarbon field-effect transistors. In the first part, I will focus on covalent additions using aryldiazonium salts. I will review what we have learned on the fundamental physics of this chemistry from experiments and simulations on carbon nanotube devices [2-5], in particular on its disruptive character because of the formation of sp 3 defects in the carbon lattice. More recently, we investigated this same chemistry on GFET devices and observed a heterogeneous response in their electrical conductance. Aiming to solve this, we developed a novel approach to precisely trigger and stop the reaction in situ using the applied gate voltage [6], enabling unprecedented control on the functionalization rate and yield on GFETs. In the second part, I will describe our ongoing efforts to characterize and control the biofunctionalization of GFETs through non-covalent pyrene derivatives, using a combination of electrical measurements, microscopy and spectroscopy techniques, and theoretical simulations. Finally, I will discuss our ongoing translational projects in oncology, on adapting nanocarbon-based bioFETs in cancer diagnostics applications.[1] Béraud et al, Analyst 146 (2), 403-428 (2021)[2] Bouilly et al, Applied Physics Letters 101 (5), 053116 (2012)[3] Bouilly et al, ACS nano 9 (3), 2626-2634 (2015)[4] Bouilly et al, Nano Letters 16 (7), 4679-4685 (2016)[5] Côté et al, Physical Chemistry Chemical Physics 24 (7), 4174-4186 (2021)[6] Bazan et al, Nano Letters 22 (7), 2635-2642 (2022)

The Tunable Parameters of Graphene-Based Biosensors.

Graphene-based surface plasmon resonance (SPR) biosensors have emerged as a promising technology for the highly sensitive and accurate detection of biomolecules. This study presents a comprehensive theoretical analysis of graphene-based SPR biosensors, focusing on configurations with single and bimetallic metallic layers. In this study, we investigated the impact of various metallic substrates, including gold and silver, and the number of graphene layers on key performance metrics: sensitivity of detection, detection accuracy, and quality factor. Our findings reveal that configurations with graphene first supported on gold exhibit superior performance, with sensitivity of detection enhancements up to 30% for ten graphene layers. In contrast, silver-supported configurations, while demonstrating high sensitivity, face challenges in maintaining detection accuracy. Additionally, reducing the thickness of metallic layers by 30% optimizes light coupling and enhances sensor performance. These insights highlight the significant potential of graphene-based SPR biosensors in achieving high sensitivity of detection and reliability, paving the way for their application in diverse biosensing technologies. Our findings pretend to motivate future research focusing on optimizing metallic layer thickness, improving the stability of silver-supported configurations, and experimentally validating the theoretical findings to further advance the development of high-performance SPR biosensors.

Research Progress of Biosensor Based on Organic Photoelectrochemical Transistor.

In order to solve the food safety problem better, it is very important to develop a rapid and sensitive technology for detecting food contamination residues. Organic photoelectrochemical transistor (OPECT) biosensor rely on the photovoltage generated by a semiconductor upon excitation by light to regulate the conductivity of the polymer channels and realize biosensor analysis under zero gate bias. This technology integrates the excellent characteristics of photoelectrochemical (PEC) bioanalysis and the high sensitivity and inherent amplification ability of organic electrochemical transistor (OECT). Based on this, OPECT biosensor detection has been proven to be superior to traditional biosensor detection methods. In this review, we summarize the research status of OPECT biosensor in disease markers and food residue analysis, the basic principle, classification, and biosensing mechanism of OPECT biosensor analysis are briefly introduced, and the recent applications of biosensor analysis are discussed according to the signal strategy. We mainly introduced the OPECT biosensor analysis methods applied in different fields, including the detection of disease markers and food hazard residues such as prostate-specific antigen, heart-type fatty acid binding protein, T-2 toxin detection in milk samples, fat mass and objectivity related protein, ciprofloxacin in milk. The OPECT biosensor provides considerable development potential for the construction of safety analysis and detection platforms in many fields, such as agriculture and food, and hopes to provide some reference for the future development of biosensing analysis methods with higher selectivity, faster analysis speed and higher sensitivity.

Design of a Multiparametric Biosensing Platform and Its Validation in a Study on Spontaneous Cell Detachment from Temperature Gradients.

This article reports on a bioanalytical sensor device that hosts three different transducer principles: impedance spectroscopy, quartz-crystal microbalance with dissipation monitoring, and the thermal-current-based heat-transfer method. These principles utilize a single chip, allowing one to perform either microbalance and heat transfer measurements in parallel or heat transfer and impedance measurements. When taking specific precautions, the three measurement modalities can even be used truly simultaneously. The probed parameters are distinctly different, so that one may speak about multiparametric or "orthogonal" sensing without crosstalk between the sensing circuits. Hence, this sensor allows one to identify which of these label-free sensing principles performs best for a given bioanalytical application in terms of a high signal amplitude and signal-to-noise ratio. As a proof-of-concept, the three-parameter sensor was validated by studying the spontaneous, collective detachment of eukaryotic cells in the presence of a temperature gradient between the QCM chip and the supernatant liquid. In addition to heat transfer, detachment can also be monitored by the impedance- and QCM-related signals. These features allow for the distinguishing between different yeast strains that differ in their flocculation genes, and the sensor device enables proliferation monitoring of yeast colonies over time.

Identification and Quantification of Extracellular Vesicles: Comparison of SDS-PAGE Analysis and Biosensor Analysis with QCM and IDT Chips.

This study presents and compares two methods for identifying the types of extracellular vesicles (EVs) from different cell lines. Through SDS-PAGE analysis, we discovered that the ratio of CD63 to CD81 in different EVs is consistent and distinct, making it a reliable characteristic for recognizing EVs secreted by cancer cells. However, the electrophoresis and imaging processes may introduce errors in the concentration values, especially at lower concentrations, rendering this method potentially less effective. An alternative approach involves the use of quartz crystal microbalance (QCM) and electroanalytical interdigitated electrode (IDT) biosensors for EV type identification and quantification. The QCM frequency shift caused by EVs is directly proportional to their concentration, while electroanalysis relies on measuring the curvature of the I-V curve as a distinguishing feature, which is also proportional to EV concentration. Linear regression lines for the QCM frequency shift and the electroanalysis curvature of various EV types are plotted separately, enabling the estimation of the corresponding concentration for an unknown EV type on the graphs. By intersecting the results from both biosensors, the unknown EV type can be identified. The biosensor analysis method proves to be an effective means of analyzing both the type and concentration of EVs from different cell lines.

Advances in electrochemical biosensors employing carbon-based electrodes for detection of biomarkers in diabetes mellitus.

The increase in diabetes cases has become a major concern in the healthcare sector, necessitating the development of efficient and minimal diagnostic methods. This study aims to provide a comprehensive examination of electrochemical biosensors for detecting diabetes mellitus biomarkers, with a special focus on the utilization of carbon-based electrodes. A detailed analysis of electrochemical biosensors incorporating various carbon electrodes, including screen-printed carbon electrodes, glassy carbon electrodes, and carbon paste electrodes, is presented. The advantages of carbon-based electrodes in biosensor design are highlighted. The review covers the detection of several key diabetes biomarkers, such as glucose, glycated hemoglobin (HbA1c), glycated human serum albumin (GHSA), insulin, and novel biomarkers. Recent developments in electrochemical biosensor technology over the last decade are summarized, emphasizing their potential in clinical applications, particularly in point-of-care settings. The utilization of carbon-based electrodes in biosensors is shown to offer significant advantages, including enhanced sensitivity, selectivity, and cost-effectiveness. This review underscores the importance of carbon-based electrodes in the design of electrochemical biosensors and raises awareness for the detection of novel biomarkers for more specific and personalized diabetes mellitus cases. The advancements in this field highlight the potential of these biosensors in future clinical applications, especially in point-of-care diagnostics.

Design and analysis of Si-Ge heterostructure tunnel FET biosensors for detection of a wide range of biomolecules in both wet and dry environments

Design and analysis of Si-Ge heterostructure tunnel FET biosensors for detection of a wide range of biomolecules in both wet and dry environments

Adaptation of Conductometric Monoenzyme Biosensor for Rapid Quantitative Analysis of L-arginine in Dietary Supplements.

The present study reports on the development, adaptation, and optimization of a novel monoenzyme conductometric biosensor based on a recombinant arginine deiminase (ADI) for the determination of arginine in dietary supplements with a high accuracy of results. Aiming for the highly sensitive determination of arginine in real samples, we studied the effect of parameters of the working buffer solution (its pH, buffer capacity, ionic strength, temperature, and protein concentration) on the sensitivity of the biosensor to arginine. Thus, it was determined that the optimal buffer is a 5 mM phosphate buffer solution with pH 6.2, and the optimal temperature is 39.5 °C. The linear functioning range is 2.5-750 µM of L-arginine with a minimal limit of detection of 2 µM. The concentration of arginine in food additive samples was determined using the developed ADI-based biosensor. Based on the obtained results, the most effective method of biosensor analysis using the method of standard additions was chosen. It was also checked how the reproducibility of the biosensor changes during the analysis of pharmaceutical samples. The results of the determination of arginine in real samples using a conductometric biosensor based on ADI clearly correlated with the data obtained using the method of ion-exchange chromatography and enzymatic spectrophotometric analysis. We concluded that the developed biosensor would be effective for the accurate and selective determination of arginine in dietary supplements intended for the prevention and/or elimination of arginine deficiency.

Biosensors for Cancer Biomarkers Based on Mesoporous Silica Nanoparticles.

Mesoporous silica nanoparticles (MSNs) exhibit highly beneficial characteristics for devising efficient biosensors for different analytes. Their unique properties, such as capabilities for stable covalent binding to recognition groups (e.g., antibodies or aptamers) and sensing surfaces, open a plethora of opportunities for biosensor construction. In addition, their structured porosity offers capabilities for entrapping signaling molecules (dyes or electroactive species), which could be released efficiently in response to a desired analyte for effective optical or electrochemical detection. This work offers an overview of recent research studies (in the last five years) that contain MSNs in their optical and electrochemical sensing platforms for the detection of cancer biomarkers, classified by cancer type. In addition, this study provides an overview of cancer biomarkers, as well as electrochemical and optical detection methods in general.

Design and analysis of efficient refractive index Biosensor for detection of Mycobacterium tuberculosis

Design and analysis of efficient refractive index Biosensor for detection of Mycobacterium tuberculosis