Rapid Ultrasensitive and Specific BNP Biosensor with LED Readout

Rapid Ultrasensitive and Specific BNP Biosensor with LED Readout | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Rapid Ultrasensitive and Specific BNP Biosensor with LED Readout Seth So, Jorge Torres Quiñones, Soonkon Kim, Byoungdeog Choi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3855022/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Biosensing for diagnostics has risen rapidly in popularity over the past decades. With the discovery of new nanomaterials and morphologies, sensitivity is being constantly improved enough for reliable detection of trace biomarkers in human samples, like serum or sweat. This precision has enabled detailed research on the efficacy of biosensors. However, current biosensors suffer from reduced speed of operation. To make better use of this sensitivity, the development of a conductometric biosensor with in-situ use of an LED display can provide rapid determination of sample results, steadily pushing biosensors toward more clinical, point-of-care (POC) applications. In this research, a simple LED (laser emitting diode) was used for facile optical determination and visual output of an ultrasensitive bio-signal amplification circuit was made to interface with a b-type natriuretic peptide (BNP) biosensor. Tuning circuit gain enables an elegant method for adjustable separation of concentrations into 3 discrete categories: sub-threshold, analog, and saturation regions. These regions corresponded to 0 < [C] < 500 pg/mL (LED off), 500 < [C] < 1000 pg/mL (LED varying intensity), and 1000 pg/mL < [C] (LED full intensity). System efficacy was tested using human blood serum samples from University of Pittsburgh Medical Center patients, which were able to be accurately detected and sorted for rapid lo-fi. determination without need for complex digital elements. Additional specificity testing suggests insignificant impact of non-target biomarkers. biosensor circuit amplification optical BNP readout Figures Figure 1 Figure 2 Figure 3 1. Introduction Modern day biosensing technology is approaching an inflection point from research to clinical use. As a technology, biosensors are no longer in their infancy and are beginning to make large strides almost daily in both performance and understanding of mechanism. Sensing methodologies and morphologies are becoming more complex and varied without sacrificing precision, presenting exciting opportunities for new applications and inquiries. Over the past decade, conductometric sensors have been significantly developed [ 1 – 3 ]. This sensing methodology has well established its broad range of capabilities and greatly expanded the repertoire of biosensors as a whole. Due to mechanistic advantages in sensitivity and specificity especially, conductometric biosensors have become of particular use in a plethora of biosensing applications. A major area of study currently is the detection of pathogens through use of functionalized electrodes [ 4 ]. The method is to modulate the conductivity of an electrode bridge using either primary or secondary binding sites that accumulate or disperse charge in the presence of target molecules. Such a method can be easily accomplished with conjugate molecules, but utilizing coupling complexes to indirectly entrap targets inherently introduces a significant specificity to the sensing. This has been accomplished with bacterial entrapment for the detection of phenol [ 5 ]. Researchers were able to immobilize a strain of bacteria onto gold electrodes in order to detect down to 0.2 mg/L of phenol, an environmentally corrosive by pollutant. This is in contention for the first conductometric sensor to sense the compound. The method of indirect sensing creates potential to detect nearly any biological target with precision and accuracy, so long as there exists a target-coupling complex. One of the most studied topics is the use of antibodies since they provide great affinities with their associated antigens. Researchers from the University of Lyon demonstrated detection of the protein creatinine through entrapment of its metabolic enzyme, creatine deaminase. in a biosensor [ 6 ]. They were able to achieve down to 2 𝝁M sensitivity and an ultimate range of 10–600 𝝁M range with strong repeatability. There is still a long path left improving conductometric sensors; however, their inherent advantages over current biosensing methodologies in essentials like sensitivity, size, and response time display great promise in achieving the end goal of clinical use. Current biosensing methodologies can be very powerful, but they lack in several key practical characteristics that reduce usability. First there is often a slow response time – methods like mass or flow cytometry operate on a per cell basis. This enables great analysis depth, but the process is bottlenecked by sample size. The machines required to do this are also often expensive and large, unable to be housed in the hospitals themselves. As such, the machines, as powerful as they are, are often housed in specialized 3rd party laboratories – the logistics of which introduce multiple potential failure points as well as weeks of lead time. There is also a trend of increasing complexity for other biosensors and established methods. In order to compensate for perceived shortcomings, auxiliary systems are being introduced. A great deal of work has been done on augmenting biosensors with these systems. An international collaboration between Harvard, the Massachusetts Institute of Technology, Technical University Munich, and Institute for Basic Science (IBS) in Seoul garnered attention after creating a new architecture to greatly improve their heteronuclear magnetic resonance (NMR) biosensor[ 7 ]. NMR systems have become a standard and are widely used for their powerful resolutions, but they suffer from long assay times and single channel detection. This issue of limited throughput often bottlenecks different procedures, affecting the technologies usefulness. The team aimed to by fix this problem by digitizing much of the hardware and adding multi-channel detection capabilities. Doing so greatly increases throughput and versatility, while also decreasing complexity and cost. The system, HERMES (heteronuclear resonance multichannel electronic system), was then used to tested for application in dengue fever detection and cancer cell profiling to prove successful realization. Aside from technical advancements, improvements to form factor are also a popular topic. Other works include creating much more integrated solutions into portable, handheld packages. Researchers from Greece created a capacitive sensor readout circuit for non-destructive analysis [ 8 ]. The circuit fully digitizes capacitance readings to fempto regime resolution, and sends the information through USB and R232 connections for extremely high-speed sensing. Similarly, researchers from Germany and Vietnam made a miniaturized readout circuit for variable ISFET arrays, along with LabVIEW software for a comprehensive analysis of results, all housed in a case just inches in length [ 9 ]. While these are all fantastic works in increasing the power or portability and usability of biosensor systems, there can be many improvements made to reduce to the complexity of the system even further, such as removal of processing units. For biosensors to be more useful in point-of-care settings, they need to be smaller, maintain high standards of sensitivity and specificity, and above all be simpler. These are all areas where conductometric biosensors offer advantages. The simplistic sensing mechanism only requires 2 electrodes and a bridge. This enables a 2-dimensional topology suitable for both miniaturization and mass production. Consequently, operating procedure is also extremely simple, and output signals are single parameters that are easily interpreted. Bridge topology can be modified at the nano-level, through limitless surface textures (gratings, digitization, and others…) or functionalization (nanoparticles, carbon nanotubes, graphene, antibodies, and so forth…), in order to improve sensitivity and specificity measurements [ 10 – 15 ]. At certain stages of the clinical process, it is desirable to have as low complexity systems as possible. For instance, large scale batch testing requires high throughput, so any non-scalable design can dramatically slow down the process. Aside from time, in diagnostic scenarios, cost is also an important consideration. Biological reagents can often be extremely costly, so mismanagement of resources could lead to shortages. While high power machines have their uses, conductometric sensors offer to fill the void in triage-capacity devices for time-sensitive and high-risk applications, such as cardiovascular diagnosis. Cardiovascular disease (CVD) has the extremely high prevalence and is the leading cause of global mortality [ 16 – 18 ]. In pathologies like myocardial infarction where the ventricles undergo strain, the biomarker B-type natriuretic peptide (BNP) is released, and as such has proven to be an excellent indicator for CVDs [ 20 – 21 ]. However due to expensive testing and long result wait times, regular cardiovascular health testing has yet to be adopted as a standard practice, even though common indicators like BNP and cholesterol are known causes. Biosensors are becoming more viable options in order to change this and enable easier cardiovascular health monitoring. Recently a group from China pushed the bounds of cholesterol detection with a novel gold nanoparticle sensing system [ 22 ]. They achieved a low limit of 2nM with an SNR of 3 and displayed linear characterization from 0.0004 to 15.36 mM (R 2 = 0.9986), a range well-suited for early diagnosis of heart disease. In order to encourage regular testing and thereby mitigating overall risk of chronic CVDs, biosensor systems need usable, focusing on simplicity, speed, and diagnosis. Doing so will be a formative step in making biosensors a mainstay of modern medical clinical technology, and small sensor applications are being developed towards better point of care options with advantages in price, efficacy, form factor, and usability [ 23 – 28 ]. In this research we have investigated a low-complexity system with high sensitivity, specificity, and fast response time for simple determination of BNP concentration in a blood sample as either low or high. Onboard processing is removed in favor of a facile near-binary optical output LED. Signals were generated by testing University of Pittsburgh Medical Center (UPMC) patient blood serum on in-house polyaniline-based (PANI) ion selective field effect transistor (ISFET) biosensors, and amplifying the output to logic voltage levels. Noise was minimized through circuit design to provide accurate and stable determinations. 2. Materials and Methods 2.1 Biosensors Biomarkers and monoclonal antibodies were purchased form Novus Biologicals. Chemicals (PBS, EDC, NHS, Ammonium Persulfate, BSA) were purchased from Sigma Aldrich. Procedure for biosensor fabrication followed the steps outlined in So et al. (2021) [ 29 ]. Sensors were fabricated in a cleanroom on a flexible polyethylene-terephthalate substrate, and PANI growth and surface modification were completed in a biological hood. Each wafer contained 150 devices in a 5 x 5 array of 6 device cells (Fig. 1 a). A CAD closeup of a single sensor shows the sum structure of the device: a flexible PET substrate (gray) with Au/Cr electrodes (yellow) deposited on via e-beam evaporation. A thin film of PANI (green) is then chemically synthesized across the electrodes. The surface is functionalized overnight with antibodies, and then during testing a solution with target or nontarget molecules is deposited directly on top. The PANI bridge itself is 50 𝝁m x 70 𝝁m (Fig. 1 b). 2.2 PANI Growth PANI was chemically synthesized directly onto the wafer in a cold bath process to carefully control the film thickness. Prior, Au/Cr electrodes were deposited onto a PET wafer, followed by lithography templating and exposure to determine the growth region. The wafer was then submerged into a beaker filled with 200 mL of deionized water (> 13 g𝝮) set in an ice bath. The water was protonated with 6 mL of perchloric to stabilize synthesis and avoid unwanted products. After the wafer was submerged, 911 µg of aniline was mixed in, followed by 700 mg of ammonium per sulfate (aqueous) to serve as the oxidant. The solution was left to stir at 300 rpm via stir bar in the dark in order to promote polymerization of aniline monomer nucleation sites. After 90 minutes, the wafer was removed and dried with nitrogen to leave the aniline in a half-oxidized state and prevent over polymerization. 2.3 Surface Modification BNP antibodies cannot directly bind to the PANI. Binding was achieved by utilizing a 2-step cross linking process with 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) as the main bridge molecule and N-Hydroxysuccinimide (NHS) as a reaction accelerant. A uniform mixture of EDC, NHS, and the antibody were prepared in a 2:49:49 ratio at room temperature, before depositing 2.5 𝝁L directly onto the PANI bridge and left to adhere over 6 hours, with care taken to prevent evaporation of the solution. Next, 2.5 𝝁L of 20 ng/mL reconstituted lyophilized bovine serum albumin was pipetted onto each sensor and left to settle for 30 minutes to reduce non-specific binding. Devices were tested immediately after in order to prevent antibody variation. 2.4 Noise Reduction Chipsets include AD8428 and TLE207X. All components including resistors and capacitors were acquired from Mouser Electronics. Testing was carried out utilizing a Rigol power supply and CH Instruments potentiostat. The circuit has 3 main stages: 1. Signal-voltage transimpedance amplification, 2. Low-noise logic amplification, and 3. optical readout with LED. Sequentially, the biosensor output is connected to stage 1, where the nanoampere signal is converted to a microvolt range. Then, the signal is simplified via low noise, ground-tie operational amplifiers up to small logic level. One of the largest challenges with the circuit was optimizing noise reduction, which was especially important due to regular single digit nanoampere measurements. For this, a PCB with a common ground plane was used, and any active components were ultra-low noise. The AD8428 chip is a low noise instrumentation amplifier with a gain of 2000 and stable temperature sensitivity and wide bandwidth. It has a 1.3 nV/√(Hz) at 1 kHz and an outstanding 40 nV pp noise from 0.1–10 Hz, making the chipset ideal for low range, high sensitivity applications. Another major feature of the chipset is custom filtering available on each amplifier, which allows for a number of system level modifications. One creative way is by daisy-chaining multiple AD8428 filters together. The method works by making use of the fact that the 1.3 nV/√(Hz) inherent noise is uncorrelated between devices, while input noise is positively correlated, thus causing a √2 factor reduction with each additional instrumentation amplifier chained. 3. Results and Discussion 3.1 Circuit Development and Testing As stated previously, the circuit has 3 main stages: 1. ISFET input, 2. Transimpedance amplification, and 3. Low noise amplification and read out (Fig. 2 a). The gain of the system is tuned precisely so that samples of 500 pg/mL BNP concentration will exceed the LED threshold voltage V t , thereby creating a trinary determination system, LED off for typical levels of BNP and LED dim for potentially concerning levels of BNP, and LED bright for predetermined high levels of BNP. This gain can be adjusted in order to alter the threshold concentration at which the LED begins to shine. Evaluation of circuit performance involved 3 stages: simulation, secondary measuring, and final optical output. Simulation was used for tuning the final gain of the system, and an external potentiostat was used to monitor the input signal to verify proper function of the biosensor. During testing, the biosensors were biased at -0.4V, as optimized by prior testing, and a constant voltage of 0.4V was applied across electrodes to induce a current. Characterization involved measuring the current response of a sequence of solutions. 3.2 BNP Sensitivity Six blood serum aliquots from different UPMC patients with varying. BNP concentration samples were applied successively in 30 second intervals, cumulatively increasing the current response of the sensor. This resulted in a step response with increasing magnitude produced by the sensor over a 2.5-minute period (Fig. 2 b). The antibody-antigen reaction time can be observed as taking several seconds after the application of a higher graded sample. As sampling rate was low in order to accommodate for longer trials, the transitions appear to be abrupt. However, closer inspection shows a more gradual levelling, indicating that the binding occurs at relatively high speed. This confirms expectation from existing literature on antigen-antibody interaction speeds. As theorized, samples with higher BNP concentration produced a higher current response due to an increased immobilization of charged BNP molecules via antibody-antigen interactions. This accumulation of negative charge induces a positive charge over the PANI surface, increasing the conductivity of the p-type semiconducting material, and allowing increased carrier flow. Notably, the effect of adding samples successively was not cumulative, as the saturation current of each concentration matched closely with the peak current of corresponding samples when tested individually. This has an interesting implication that the devices biosensors also have a degree of reusability. A limit test was conducted by applying a sample with 3500 pg/mL to the sample and ran until the device burned out. Over several trials, compliance current ranged from ~ 13–17 nA over a period form 6–7 minutes, with rapid decay. The compliance current for a fixed voltage was heavily dependent on PANI bridge sizing and antibody concentration during surface modification. About 2.5 𝝁L of a 1:49 antibody-crosslinker solution (2 ng/mL antibody solute) was distributed across the entirety of a 3.5 𝝁m 2 functionalized area. Further testing can be done to observe the effects of different PANI topologies on sensitivity and specificity metrics. Ranges in compliance current and label destruction were likely due to small variances in production of the sensors. 3.3 Reagent Specificity System specificity was tested in a similar fashion. Two sequences of different solutions of biomarkers were applied to the sensor and the output measured and observed. The first sequence consisted of BNP, followed by myoglobin, creatine-kinase, and c-troponin in natural amounts, before ending with another higher concentration application of BNP (Fig. 2 c). The result showed strong specificity for BNP over other cardiovascular biomarkers. While each marker did correlate positively with magnitude of current response (~ 0.5 nA), the effect was largely negligible compared to that of BNP, which had approximately 6x the response for every 600 pg/mL added. Specifically, this test confirms that the sensor was able to capture how quickly the sensors responded to the BNP compared to the others. The small observable effect of the other biomarkers is therefore likely due to physical binding on the device rather than any chemical binding with the antibodies. An additional investigation would be needed to determine the effect of settling time on the current responses of each biomarker, but within the 30 s window, BNP far out responded the others. The second sequence included the addition of a PBS washing step in between each marker. The primary motive for this test was to further investigate why the binding seemed non-reversible under conditions of the previous test. Ordinarily, antigen-antibody interactions are governed by the law of mass action, the principle that describes the equilibria concentrations of a reaction. A corollary is each of those reactions is reversible. However, the results suggest that the antiBNP-BNP interaction is somewhat non-reversible (Fig. 2 d), as the washing procedure did not greatly overcome the Van der Waals forces of the complex. Although it is a well-known fact that BNP has a high affinity[ 30 ], the stability of the signal throughout the washing (pipette reapplications) is significant and shows high promise for the robustness of the sensor. As with the previous sequence, additional settling time testing would be required to extend results to larger time frames. However, the overall effect of the washing was in lines with expectations. There was a signal destabilization at each application (time points 20, 50, 80, and 110 s) that allowed for clearer observation of the strength of effect of the other biomarkers. Additionally, when compared with prior testing, the 1200 pg/mL sample resulted in an ~ 10% drop in current response, from 9nA down to 7.5 nA. Such a result indicates that some portion of the conformed sites were reversed due to the PBS, and it also strengthened the previous hypothesis that the majority of non-targets were physically bound and therefore easily washed away by the PBS. 3.3 Optical Readout Characterization All previous testing with the sensor was done while connected to an amplifier readout circuit, and corresponding voltage measurements were taken. The end goal was to utilize an LED threshold voltage as a switch powered directly by a biosensors output signal. Such a method, rather than directly modulating a transistor switch, allows for a small pseudo-binary signal. Rather than 2 states, this enables a third state by introducing a ramp up to full voltage. In terms of diagnostics, this is useful in separating determinations into healthy, caution, and at-risk categories. The states are defined: State 0 – LED off , State 1 – LED dim , State 2 – LED bright . (Fig. 3 a). The system was able to accurately reflect the sensor output for both the sensitivity and specificity tests, as LED voltage and state was measured concurrently with the BNP sensitivity test. With a standard threshold voltage V t = 0.65V, system gain was tuned such that V LED > V t at ~ 3 nA corresponding to 500 pg/mL. Accordingly, the left three LED states (25, 100, and 250 pg/mL, respectively) are in State 0. After enough antigen has bound to the antibodies when the 600 pg/mL sample is added, the output voltage exceeds the threshold voltage and the LED is able to conduct and enter State 1, lighting up but not at full brightness. Finally, as after the 1200 pg/mL sample is added, the LED reaches saturation voltage and emits maximum brightness, Stage 2. Samples within a 100 pg/mL range of the concentration threshold were unable to be consistently read correctly, and sometimes resulted in flickering. This may be the result of noise introduced by the circuitry, though many efforts were taken to maximize signal to noise ratio. However, a triage use case of this device calls for low resolution and as such the circuit performs satisfactorily. By simply tuning the gain of the third stage, the concentration at which the LED initially turns on can be controlled nearly linearly. When the half feedback resistance is used, the threshold concentration doubles to 1100 pg/mL. Likewise, when the gain is halved, only half of the base BNP concentration (250 pg/mL) is required to light the LED. This is a functionality that takes advantage of the direct link between LED state and BNP concentration as opposed to encoding the signal through different logic gates. Similar readings were taken during the specificity test. Initially the LED was in State 0 before application of a 750 pg/mL sample after which the LED is in State 1. Subsequent washing with PBS and adding other biomarkers has negligible effect on LED brightness, while adding a final 1200 pg/mL BNP sample fully pushes the LED from State 1 to State 2, as expected. This is a promising result as it confirms that non-specific binding has a very minor effect on the final output of the system. Because the transfer function of the system is tuned for linear gain, the overall characteristic expected is that of the limiting component, the LED (Fig. 3 b). Despite lower readings due to internal impedances, the measured data matches closely with the expected system characteristic. Though a longer current sweep would help establish a more accurate relationship, the tested range is enough to capture a large portion of patient BNP levels. Additionally, this presents a reliable method for both interpolation and extrapolation of bloodstream BNP concentration from LED voltage, building upon sensor current characterizations in previous work. 4. Conclusion This research has demonstrated a proof-of-concept for a lightweight biosensor-amplification readout circuit meant for trinary determination. By utilizing readout signals from flexible PANI-based ISFET biosensors built in lab, outputs were captured by an amplification circuit and raised to logical voltage levels to drive a standard LED indicator. Sample BNP concentration could be visually estimated as below threshold, within interest range, or above target saturation (LED off, dim, or full bright). The peripheral was able to successfully indicate a BNP concentration range from 25-1200 pg/mL, within range of typical physiological levels, with sensitivity error within ~ 100 pg/mL. When tested for specificity towards potentially confounding protein biomarkers, the PANI ISFET was able to accurately distinguish BNP among others, owing to the specificity of the monoclonal antibodies used. Furthermore, circuit gain was successfully finetuned to test for different concentrations, altering the sensitivity range of the LED. Declarations Declaration of competing interest Authors are required to disclose financial or non-financial interests that are directly or indirectly related to the work submitted for publication. Please refer to “Competing Interests and Funding” below for more information on how to complete this section. Acknowledgement The authors are grateful for financial support from the National Science Foundation (NSF), CBET 1706620. Author Contribution Seth So: Methodology, Validation, Formal Analysis, Investigation, Data Curation, Writing – Original Draft, Visualization. Jorge Torres Quiñones: Writing – Review and Editing, Visualization. Soonkon Kim: Methodology, Validation. Byoungdeog Choi: Methodology, Validation. Minhee Yun: Conceptualization, Supervision References Perera, G. S., Ahmed, T., Heiss, L., Walia, S., Bhaskaran, M., Sriram, S. 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DOI: 10.22489/CinC.2017.143-140. Sekine, T., Sugano, R., Tashiro, T., Sato, J., Takeda, Y., Matsui, H., Kumaki, D., Domingues Dos Santos, F., Miyabo, A., Tokito, S. Fully Printed Wearable Vital Sensor for Human Pulse Rate Monitoring using Ferroelectric Polymer. Sci. Rep. (2018) 8, 4442. DOI: 10.1038/s41598-018-22746-3 So, S., Khalaf, A., Yi, X., Herring, C., Zhang, Y., Simon, M.A., Akcakaya, M., Lee, S., Yun, M. Induced bioresistance via BNP detection for machine learning-based risk assessment. Biosens. Bioelectron. (2021) 175, 112903. DOI: 10.1016/j.bios.2020.112903 Bettencourt, P. M. Clinical usefulness of B-type natriuretic peptide measurement: present and future perspectives. Heart. (2005) 91, 11, 1489–1494. DOI: 10.1136/hrt.2005.063784 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3855022","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267074264,"identity":"bfc60d25-8e61-44a9-b5b1-3c1e5424722f","order_by":0,"name":"Seth So","email":"","orcid":"","institution":"University of Pittsburgh","correspondingAuthor":false,"prefix":"","firstName":"Seth","middleName":"","lastName":"So","suffix":""},{"id":267074265,"identity":"42894808-9c26-4bc8-8f7f-a95f609bc48d","order_by":1,"name":"Jorge Torres Quiñones","email":"","orcid":"","institution":"University of Pittsburgh","correspondingAuthor":false,"prefix":"","firstName":"Jorge","middleName":"Torres","lastName":"Quiñones","suffix":""},{"id":267074266,"identity":"b2e64cd2-bdbb-4492-924f-4132f3e62e52","order_by":2,"name":"Soonkon Kim","email":"","orcid":"","institution":"Sungkyunkwan University","correspondingAuthor":false,"prefix":"","firstName":"Soonkon","middleName":"","lastName":"Kim","suffix":""},{"id":267074267,"identity":"fd043224-1bed-467c-a3fe-812fe4b46cf4","order_by":3,"name":"Byoungdeog Choi","email":"","orcid":"","institution":"Sungkyunkwan University","correspondingAuthor":false,"prefix":"","firstName":"Byoungdeog","middleName":"","lastName":"Choi","suffix":""},{"id":267074268,"identity":"0598837e-80c5-4143-834f-04e6feaa2161","order_by":4,"name":"Minhee Yun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYDACZgaGAx8qGHjAHB4itTAenHGGgYeHjWgtQE2HOduAqonWotvOY3CYcd4dGXv5BsYHb9uI0GJ2GKilcNszkMOYDecSrWXmtsMgLWzSvERr4Z0D1sL+mwQtDRBbmInUwlZwcMYxoF+OJTZLzjlHjJbzhzd/+FBzx569+fDBD2/KiNDCwMBhACQOADFjA1HqgYD9AVTLKBgFo2AUjAIcAACmYDUgV/GuMwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Pittsburgh","correspondingAuthor":true,"prefix":"","firstName":"Minhee","middleName":"","lastName":"Yun","suffix":""}],"badges":[],"createdAt":"2024-01-11 23:29:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3855022/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3855022/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49712535,"identity":"a7e83242-f401-41ef-aeb0-2ed155e03fbb","added_by":"auto","created_at":"2024-01-16 20:30:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":314464,"visible":true,"origin":"","legend":"\u003cp\u003eSystem Visualizations: a) biosensor arrays vacuum mounted for testing with the soldered perf-board circuit and LED on the right. b) close up of 6 individual biosensors undergoing surface modification. c) CAD rending of biosensor in operation: Au/Cr electrodes on PET wafer with mAb-functionalized PANI bridge. A drop of human blood serum with BNP is placed directly on top of the functionalized area. Sample antigens bind to the antibodies, causing a charge accumulation biasing the PANI layer and increasing conductivity. A constant voltage is held across the electrodes and the resulting current is measured. d) light microscopy image of chemically synthesized PANI across main electrodes with scale.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3855022/v1/5d2f581603f02872b97ac097.jpg"},{"id":49712536,"identity":"b326eed4-262d-4fcf-8cb5-a1a05568a97b","added_by":"auto","created_at":"2024-01-16 20:30:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":796166,"visible":true,"origin":"","legend":"\u003cp\u003eSensor characterization of output current in response to different sequences of common cardiac biomarkers (BNP, c-troponin, creatine-kinase, and myoglobin): a) high level block schematic of system. 3 stages are: 1. Current generation through antigen sensing, 2. Transimpedance amplification for voltage control, and 3. Final voltage inversion and amplification. b) Concentration dependency, increasing levels of BNP (25, 100, 250, 750, 1200 pg/mL). For each application, current magnitude increases in agreement with prior characterization. Bulk reactions take several seconds to reach equilibrium, and appear to occur at a linear rate due to low sampling rate. c) Specificity towards target BNP vs non-target biomarkers. Application of each nontarget biomarker affects current response with less than ~10% of BNP sensitivity. The final BNP sample confirms proper sensor function. d) Specificity test with additional intermediate PBS0.1x washing steps to gauge reaction affinities. Washing procedure consisted of 3 rounds of PBS dilution and removal through pipetting. Results indicate that most non target biomarkers are physically bound to the PANI layer, but the BNP antigen-antibody complex is not washed away during the purging, showing high affinity relative to blocked biomarkers.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3855022/v1/e0f2dac61d27e7311f63b862.jpg"},{"id":49712537,"identity":"475a8a6a-4ec3-4248-83bd-d71c3dc9498f","added_by":"auto","created_at":"2024-01-16 20:30:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":483312,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of LED response during current response tests post amplification. a) LED states with corresponding visuals. Concentrations applied in order were: 25, 100, 250 (State0 – LED\u003csub\u003eoff\u003c/sub\u003e), 750 (State1 – LED\u003csub\u003edim\u003c/sub\u003e), and 1200 pg/mL (State2 – LED\u003csub\u003ebright\u003c/sub\u003e). Gain was tuned for concentration threshold of 500 pg/mL between State0 and State1. b) V-I characteristic of system mirrors that of LED, formed by plotting induced sensor current versus output LED voltage. ISFET generates a negative current but the signal voltage is inverted during amplification, resulting in a reflected. Additionally, note that in this circuit operation, LED voltage is a function of sensor current, opposite of standard I-V characteristic convention. Characterization matches expectations and validates holistic circuit design.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3855022/v1/6eb004592f03e4e81070239c.jpg"},{"id":51035388,"identity":"f3a5b012-a3b4-49ee-852f-2e4ccada1395","added_by":"auto","created_at":"2024-02-13 04:47:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":440421,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3855022/v1/07152103-8ff4-4be8-9d59-fb6a3a22eb5c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rapid Ultrasensitive and Specific BNP Biosensor with LED Readout","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eModern day biosensing technology is approaching an inflection point from research to clinical use. As a technology, biosensors are no longer in their infancy and are beginning to make large strides almost daily in both performance and understanding of mechanism. Sensing methodologies and morphologies are becoming more complex and varied without sacrificing precision, presenting exciting opportunities for new applications and inquiries.\u003c/p\u003e \u003cp\u003eOver the past decade, conductometric sensors have been significantly developed [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This sensing methodology has well established its broad range of capabilities and greatly expanded the repertoire of biosensors as a whole. Due to mechanistic advantages in sensitivity and specificity especially, conductometric biosensors have become of particular use in a plethora of biosensing applications. A major area of study currently is the detection of pathogens through use of functionalized electrodes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The method is to modulate the conductivity of an electrode bridge using either primary or secondary binding sites that accumulate or disperse charge in the presence of target molecules. Such a method can be easily accomplished with conjugate molecules, but utilizing coupling complexes to indirectly entrap targets inherently introduces a significant specificity to the sensing. This has been accomplished with bacterial entrapment for the detection of phenol [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Researchers were able to immobilize a strain of bacteria onto gold electrodes in order to detect down to 0.2 mg/L of phenol, an environmentally corrosive by pollutant. This is in contention for the first conductometric sensor to sense the compound. The method of indirect sensing creates potential to detect nearly any biological target with precision and accuracy, so long as there exists a target-coupling complex. One of the most studied topics is the use of antibodies since they provide great affinities with their associated antigens. Researchers from the University of Lyon demonstrated detection of the protein creatinine through entrapment of its metabolic enzyme, creatine deaminase. in a biosensor [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. They were able to achieve down to 2 \u0026#120641;M sensitivity and an ultimate range of 10\u0026ndash;600 \u0026#120641;M range with strong repeatability. There is still a long path left improving conductometric sensors; however, their inherent advantages over current biosensing methodologies in essentials like sensitivity, size, and response time display great promise in achieving the end goal of clinical use.\u003c/p\u003e \u003cp\u003eCurrent biosensing methodologies can be very powerful, but they lack in several key practical characteristics that reduce usability. First there is often a slow response time \u0026ndash; methods like mass or flow cytometry operate on a per cell basis. This enables great analysis depth, but the process is bottlenecked by sample size. The machines required to do this are also often expensive and large, unable to be housed in the hospitals themselves. As such, the machines, as powerful as they are, are often housed in specialized 3rd party laboratories \u0026ndash; the logistics of which introduce multiple potential failure points as well as weeks of lead time. There is also a trend of increasing complexity for other biosensors and established methods. In order to compensate for perceived shortcomings, auxiliary systems are being introduced.\u003c/p\u003e \u003cp\u003eA great deal of work has been done on augmenting biosensors with these systems. An international collaboration between Harvard, the Massachusetts Institute of Technology, Technical University Munich, and Institute for Basic Science (IBS) in Seoul garnered attention after creating a new architecture to greatly improve their heteronuclear magnetic resonance (NMR) biosensor[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. NMR systems have become a standard and are widely used for their powerful resolutions, but they suffer from long assay times and single channel detection. This issue of limited throughput often bottlenecks different procedures, affecting the technologies usefulness. The team aimed to by fix this problem by digitizing much of the hardware and adding multi-channel detection capabilities. Doing so greatly increases throughput and versatility, while also decreasing complexity and cost. The system, HERMES (heteronuclear resonance multichannel electronic system), was then used to tested for application in dengue fever detection and cancer cell profiling to prove successful realization. Aside from technical advancements, improvements to form factor are also a popular topic. Other works include creating much more integrated solutions into portable, handheld packages. Researchers from Greece created a capacitive sensor readout circuit for non-destructive analysis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The circuit fully digitizes capacitance readings to fempto regime resolution, and sends the information through USB and R232 connections for extremely high-speed sensing. Similarly, researchers from Germany and Vietnam made a miniaturized readout circuit for variable ISFET arrays, along with LabVIEW software for a comprehensive analysis of results, all housed in a case just inches in length [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While these are all fantastic works in increasing the power or portability and usability of biosensor systems, there can be many improvements made to reduce to the complexity of the system even further, such as removal of processing units. For biosensors to be more useful in point-of-care settings, they need to be smaller, maintain high standards of sensitivity and specificity, and above all be simpler.\u003c/p\u003e \u003cp\u003eThese are all areas where conductometric biosensors offer advantages. The simplistic sensing mechanism only requires 2 electrodes and a bridge. This enables a 2-dimensional topology suitable for both miniaturization and mass production. Consequently, operating procedure is also extremely simple, and output signals are single parameters that are easily interpreted. Bridge topology can be modified at the nano-level, through limitless surface textures (gratings, digitization, and others\u0026hellip;) or functionalization (nanoparticles, carbon nanotubes, graphene, antibodies, and so forth\u0026hellip;), in order to improve sensitivity and specificity measurements [\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. At certain stages of the clinical process, it is desirable to have as low complexity systems as possible. For instance, large scale batch testing requires high throughput, so any non-scalable design can dramatically slow down the process. Aside from time, in diagnostic scenarios, cost is also an important consideration. Biological reagents can often be extremely costly, so mismanagement of resources could lead to shortages. While high power machines have their uses, conductometric sensors offer to fill the void in triage-capacity devices for time-sensitive and high-risk applications, such as cardiovascular diagnosis.\u003c/p\u003e \u003cp\u003eCardiovascular disease (CVD) has the extremely high prevalence and is the leading cause of global mortality [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In pathologies like myocardial infarction where the ventricles undergo strain, the biomarker B-type natriuretic peptide (BNP) is released, and as such has proven to be an excellent indicator for CVDs [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However due to expensive testing and long result wait times, regular cardiovascular health testing has yet to be adopted as a standard practice, even though common indicators like BNP and cholesterol are known causes. Biosensors are becoming more viable options in order to change this and enable easier cardiovascular health monitoring. Recently a group from China pushed the bounds of cholesterol detection with a novel gold nanoparticle sensing system [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. They achieved a low limit of 2nM with an SNR of 3 and displayed linear characterization from 0.0004 to 15.36 mM (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9986), a range well-suited for early diagnosis of heart disease. In order to encourage regular testing and thereby mitigating overall risk of chronic CVDs, biosensor systems need usable, focusing on simplicity, speed, and diagnosis. Doing so will be a formative step in making biosensors a mainstay of modern medical clinical technology, and small sensor applications are being developed towards better point of care options with advantages in price, efficacy, form factor, and usability [\u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this research we have investigated a low-complexity system with high sensitivity, specificity, and fast response time for simple determination of BNP concentration in a blood sample as either low or high. Onboard processing is removed in favor of a facile near-binary optical output LED. Signals were generated by testing University of Pittsburgh Medical Center (UPMC) patient blood serum on in-house polyaniline-based (PANI) ion selective field effect transistor (ISFET) biosensors, and amplifying the output to logic voltage levels. Noise was minimized through circuit design to provide accurate and stable determinations.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Biosensors\u003c/h2\u003e \u003cp\u003eBiomarkers and monoclonal antibodies were purchased form Novus Biologicals. Chemicals (PBS, EDC, NHS, Ammonium Persulfate, BSA) were purchased from Sigma Aldrich.\u003c/p\u003e \u003cp\u003eProcedure for biosensor fabrication followed the steps outlined in So et al. (2021) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Sensors were fabricated in a cleanroom on a flexible polyethylene-terephthalate substrate, and PANI growth and surface modification were completed in a biological hood. Each wafer contained 150 devices in a 5 x 5 array of 6 device cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). A CAD closeup of a single sensor shows the sum structure of the device: a flexible PET substrate (gray) with Au/Cr electrodes (yellow) deposited on via e-beam evaporation. A thin film of PANI (green) is then chemically synthesized across the electrodes. The surface is functionalized overnight with antibodies, and then during testing a solution with target or nontarget molecules is deposited directly on top. The PANI bridge itself is 50 \u0026#120641;m x 70 \u0026#120641;m (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 PANI Growth\u003c/h2\u003e \u003cp\u003ePANI was chemically synthesized directly onto the wafer in a cold bath process to carefully control the film thickness. Prior, Au/Cr electrodes were deposited onto a PET wafer, followed by lithography templating and exposure to determine the growth region. The wafer was then submerged into a beaker filled with 200 mL of deionized water (\u0026gt;\u0026thinsp;13 g\u0026#120686;) set in an ice bath. The water was protonated with 6 mL of perchloric to stabilize synthesis and avoid unwanted products. After the wafer was submerged, 911 \u0026micro;g of aniline was mixed in, followed by 700 mg of ammonium per sulfate (aqueous) to serve as the oxidant. The solution was left to stir at 300 rpm via stir bar in the dark in order to promote polymerization of aniline monomer nucleation sites. After 90 minutes, the wafer was removed and dried with nitrogen to leave the aniline in a half-oxidized state and prevent over polymerization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Surface Modification\u003c/h2\u003e \u003cp\u003eBNP antibodies cannot directly bind to the PANI. Binding was achieved by utilizing a 2-step cross linking process with 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) as the main bridge molecule and N-Hydroxysuccinimide (NHS) as a reaction accelerant. A uniform mixture of EDC, NHS, and the antibody were prepared in a 2:49:49 ratio at room temperature, before depositing 2.5 \u0026#120641;L directly onto the PANI bridge and left to adhere over 6 hours, with care taken to prevent evaporation of the solution. Next, 2.5 \u0026#120641;L of 20 ng/mL reconstituted lyophilized bovine serum albumin was pipetted onto each sensor and left to settle for 30 minutes to reduce non-specific binding. Devices were tested immediately after in order to prevent antibody variation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Noise Reduction\u003c/h2\u003e \u003cp\u003eChipsets include AD8428 and TLE207X. All components including resistors and capacitors were acquired from Mouser Electronics. Testing was carried out utilizing a Rigol power supply and CH Instruments potentiostat.\u003c/p\u003e \u003cp\u003eThe circuit has 3 main stages: 1. Signal-voltage transimpedance amplification, 2. Low-noise logic amplification, and 3. optical readout with LED. Sequentially, the biosensor output is connected to stage 1, where the nanoampere signal is converted to a microvolt range. Then, the signal is simplified via low noise, ground-tie operational amplifiers up to small logic level.\u003c/p\u003e \u003cp\u003eOne of the largest challenges with the circuit was optimizing noise reduction, which was especially important due to regular single digit nanoampere measurements. For this, a PCB with a common ground plane was used, and any active components were ultra-low noise. The AD8428 chip is a low noise instrumentation amplifier with a gain of 2000 and stable temperature sensitivity and wide bandwidth. It has a 1.3 nV/\u0026radic;(Hz) at 1 kHz and an outstanding 40 nV\u003csub\u003epp\u003c/sub\u003e noise from 0.1\u0026ndash;10 Hz, making the chipset ideal for low range, high sensitivity applications. Another major feature of the chipset is custom filtering available on each amplifier, which allows for a number of system level modifications. One creative way is by daisy-chaining multiple AD8428 filters together. The method works by making use of the fact that the 1.3 nV/\u0026radic;(Hz) inherent noise is uncorrelated between devices, while input noise is positively correlated, thus causing a \u0026radic;2 factor reduction with each additional instrumentation amplifier chained.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Circuit Development and Testing\u003c/h2\u003e \u003cp\u003eAs stated previously, the circuit has 3 main stages: 1. ISFET input, 2. Transimpedance amplification, and 3. Low noise amplification and read out (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The gain of the system is tuned precisely so that samples of 500 pg/mL BNP concentration will exceed the LED threshold voltage V\u003csub\u003et\u003c/sub\u003e, thereby creating a trinary determination system, LED\u003csub\u003eoff\u003c/sub\u003e for typical levels of BNP and LED\u003csub\u003edim\u003c/sub\u003e for potentially concerning levels of BNP, and LED\u003csub\u003ebright\u003c/sub\u003e for predetermined high levels of BNP. This gain can be adjusted in order to alter the threshold concentration at which the LED begins to shine.\u003c/p\u003e \u003cp\u003eEvaluation of circuit performance involved 3 stages: simulation, secondary measuring, and final optical output. Simulation was used for tuning the final gain of the system, and an external potentiostat was used to monitor the input signal to verify proper function of the biosensor. During testing, the biosensors were biased at -0.4V, as optimized by prior testing, and a constant voltage of 0.4V was applied across electrodes to induce a current. Characterization involved measuring the current response of a sequence of solutions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 BNP Sensitivity\u003c/h2\u003e \u003cp\u003eSix blood serum aliquots from different UPMC patients with varying. BNP concentration samples were applied successively in 30 second intervals, cumulatively increasing the current response of the sensor. This resulted in a step response with increasing magnitude produced by the sensor over a 2.5-minute period (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The antibody-antigen reaction time can be observed as taking several seconds after the application of a higher graded sample. As sampling rate was low in order to accommodate for longer trials, the transitions appear to be abrupt. However, closer inspection shows a more gradual levelling, indicating that the binding occurs at relatively high speed. This confirms expectation from existing literature on antigen-antibody interaction speeds.\u003c/p\u003e \u003cp\u003eAs theorized, samples with higher BNP concentration produced a higher current response due to an increased immobilization of charged BNP molecules via antibody-antigen interactions. This accumulation of negative charge induces a positive charge over the PANI surface, increasing the conductivity of the p-type semiconducting material, and allowing increased carrier flow. Notably, the effect of adding samples successively was not cumulative, as the saturation current of each concentration matched closely with the peak current of corresponding samples when tested individually. This has an interesting implication that the devices biosensors also have a degree of reusability.\u003c/p\u003e \u003cp\u003eA limit test was conducted by applying a sample with 3500 pg/mL to the sample and ran until the device burned out. Over several trials, compliance current ranged from ~\u0026thinsp;13\u0026ndash;17 nA over a period form 6\u0026ndash;7 minutes, with rapid decay. The compliance current for a fixed voltage was heavily dependent on PANI bridge sizing and antibody concentration during surface modification. About 2.5 \u0026#120641;L of a 1:49 antibody-crosslinker solution (2 ng/mL antibody solute) was distributed across the entirety of a 3.5 \u0026#120641;m\u003csup\u003e2\u003c/sup\u003e functionalized area. Further testing can be done to observe the effects of different PANI topologies on sensitivity and specificity metrics. Ranges in compliance current and label destruction were likely due to small variances in production of the sensors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Reagent Specificity\u003c/h2\u003e \u003cp\u003eSystem specificity was tested in a similar fashion. Two sequences of different solutions of biomarkers were applied to the sensor and the output measured and observed. The first sequence consisted of BNP, followed by myoglobin, creatine-kinase, and c-troponin in natural amounts, before ending with another higher concentration application of BNP (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The result showed strong specificity for BNP over other cardiovascular biomarkers. While each marker did correlate positively with magnitude of current response (~\u0026thinsp;0.5 nA), the effect was largely negligible compared to that of BNP, which had approximately 6x the response for every 600 pg/mL added. Specifically, this test confirms that the sensor was able to capture how quickly the sensors responded to the BNP compared to the others. The small observable effect of the other biomarkers is therefore likely due to physical binding on the device rather than any chemical binding with the antibodies. An additional investigation would be needed to determine the effect of settling time on the current responses of each biomarker, but within the 30 s window, BNP far out responded the others.\u003c/p\u003e \u003cp\u003eThe second sequence included the addition of a PBS washing step in between each marker. The primary motive for this test was to further investigate why the binding seemed non-reversible under conditions of the previous test. Ordinarily, antigen-antibody interactions are governed by the law of mass action, the principle that describes the equilibria concentrations of a reaction. A corollary is each of those reactions is reversible. However, the results suggest that the antiBNP-BNP interaction is somewhat non-reversible (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003ed), as the washing procedure did not greatly overcome the Van der Waals forces of the complex. Although it is a well-known fact that BNP has a high affinity[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], the stability of the signal throughout the washing (pipette reapplications) is significant and shows high promise for the robustness of the sensor. As with the previous sequence, additional settling time testing would be required to extend results to larger time frames. However, the overall effect of the washing was in lines with expectations. There was a signal destabilization at each application (time points 20, 50, 80, and 110 s) that allowed for clearer observation of the strength of effect of the other biomarkers. Additionally, when compared with prior testing, the 1200 pg/mL sample resulted in an ~\u0026thinsp;10% drop in current response, from 9nA down to 7.5 nA. Such a result indicates that some portion of the conformed sites were reversed due to the PBS, and it also strengthened the previous hypothesis that the majority of non-targets were physically bound and therefore easily washed away by the PBS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Optical Readout Characterization\u003c/h2\u003e \u003cp\u003eAll previous testing with the sensor was done while connected to an amplifier readout circuit, and corresponding voltage measurements were taken. The end goal was to utilize an LED threshold voltage as a switch powered directly by a biosensors output signal. Such a method, rather than directly modulating a transistor switch, allows for a small pseudo-binary signal. Rather than 2 states, this enables a third state by introducing a ramp up to full voltage. In terms of diagnostics, this is useful in separating determinations into healthy, caution, and at-risk categories. The states are defined: State 0 \u0026ndash; LED\u003csub\u003eoff\u003c/sub\u003e, State 1 \u0026ndash; LED\u003csub\u003edim\u003c/sub\u003e, State 2 \u0026ndash; LED\u003csub\u003ebright\u003c/sub\u003e. (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The system was able to accurately reflect the sensor output for both the sensitivity and specificity tests, as LED voltage and state was measured concurrently with the BNP sensitivity test.\u003c/p\u003e \u003cp\u003eWith a standard threshold voltage V\u003csub\u003et\u003c/sub\u003e = 0.65V, system gain was tuned such that V\u003csub\u003eLED\u003c/sub\u003e \u0026gt; V\u003csub\u003et\u003c/sub\u003e at ~\u0026thinsp;3 nA corresponding to 500 pg/mL. Accordingly, the left three LED states (25, 100, and 250 pg/mL, respectively) are in State 0. After enough antigen has bound to the antibodies when the 600 pg/mL sample is added, the output voltage exceeds the threshold voltage and the LED is able to conduct and enter State 1, lighting up but not at full brightness. Finally, as after the 1200 pg/mL sample is added, the LED reaches saturation voltage and emits maximum brightness, Stage 2. Samples within a 100 pg/mL range of the concentration threshold were unable to be consistently read correctly, and sometimes resulted in flickering. This may be the result of noise introduced by the circuitry, though many efforts were taken to maximize signal to noise ratio. However, a triage use case of this device calls for low resolution and as such the circuit performs satisfactorily.\u003c/p\u003e \u003cp\u003eBy simply tuning the gain of the third stage, the concentration at which the LED initially turns on can be controlled nearly linearly. When the half feedback resistance is used, the threshold concentration doubles to 1100 pg/mL. Likewise, when the gain is halved, only half of the base BNP concentration (250 pg/mL) is required to light the LED. This is a functionality that takes advantage of the direct link between LED state and BNP concentration as opposed to encoding the signal through different logic gates.\u003c/p\u003e \u003cp\u003eSimilar readings were taken during the specificity test. Initially the LED was in State 0 before application of a 750 pg/mL sample after which the LED is in State 1. Subsequent washing with PBS and adding other biomarkers has negligible effect on LED brightness, while adding a final 1200 pg/mL BNP sample fully pushes the LED from State 1 to State 2, as expected. This is a promising result as it confirms that non-specific binding has a very minor effect on the final output of the system.\u003c/p\u003e \u003cp\u003eBecause the transfer function of the system is tuned for linear gain, the overall characteristic expected is that of the limiting component, the LED (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Despite lower readings due to internal impedances, the measured data matches closely with the expected system characteristic. Though a longer current sweep would help establish a more accurate relationship, the tested range is enough to capture a large portion of patient BNP levels. Additionally, this presents a reliable method for both interpolation and extrapolation of bloodstream BNP concentration from LED voltage, building upon sensor current characterizations in previous work.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis research has demonstrated a proof-of-concept for a lightweight biosensor-amplification readout circuit meant for trinary determination. By utilizing readout signals from flexible PANI-based ISFET biosensors built in lab, outputs were captured by an amplification circuit and raised to logical voltage levels to drive a standard LED indicator. Sample BNP concentration could be visually estimated as below threshold, within interest range, or above target saturation (LED off, dim, or full bright). The peripheral was able to successfully indicate a BNP concentration range from 25-1200 pg/mL, within range of typical physiological levels, with sensitivity error within ~\u0026thinsp;100 pg/mL. When tested for specificity towards potentially confounding protein biomarkers, the PANI ISFET was able to accurately distinguish BNP among others, owing to the specificity of the monoclonal antibodies used. Furthermore, circuit gain was successfully finetuned to test for different concentrations, altering the sensitivity range of the LED.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e\n\u003cp\u003eAuthors are required to disclose financial or non-financial interests that are directly or indirectly related to the work submitted for publication. Please refer to \u0026ldquo;Competing Interests and Funding\u0026rdquo; below for more information on how to complete this section.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors are grateful for financial support from the National Science Foundation (NSF), CBET 1706620.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eSeth So: Methodology, Validation, Formal Analysis, Investigation, Data Curation, Writing \u0026ndash; Original Draft, Visualization. Jorge Torres Qui\u0026ntilde;ones: Writing \u0026ndash; Review and Editing, Visualization. Soonkon Kim: Methodology, Validation. Byoungdeog Choi: Methodology, Validation. Minhee Yun: Conceptualization, Supervision\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePerera, G. S., Ahmed, T., Heiss, L., Walia, S., Bhaskaran, M., Sriram, S. Rapid and Selective Biomarker Detection with Conductometric Sensors. 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DOI: 10.22489/CinC.2017.143-140.\u003c/li\u003e\n\u003cli\u003eSekine, T., Sugano, R., Tashiro, T., Sato, J., Takeda, Y., Matsui, H., Kumaki, D., Domingues Dos Santos, F., Miyabo, A., Tokito, S. Fully Printed Wearable Vital Sensor for Human Pulse Rate Monitoring using Ferroelectric Polymer. Sci. Rep. (2018) 8, 4442. DOI: 10.1038/s41598-018-22746-3\u003c/li\u003e\n\u003cli\u003eSo, S., Khalaf, A., Yi, X., Herring, C., Zhang, Y., Simon, M.A., Akcakaya, M., Lee, S., Yun, M. Induced bioresistance via BNP detection for machine learning-based risk assessment. Biosens. Bioelectron. (2021) 175, 112903. DOI: 10.1016/j.bios.2020.112903\u003c/li\u003e\n\u003cli\u003eBettencourt, P. M. Clinical usefulness of B-type natriuretic peptide measurement: present and future perspectives. Heart. (2005) 91, 11, 1489\u0026ndash;1494. DOI: 10.1136/hrt.2005.063784 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"biosensor, circuit, amplification, optical, BNP, readout","lastPublishedDoi":"10.21203/rs.3.rs-3855022/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3855022/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBiosensing for diagnostics has risen rapidly in popularity over the past decades. With the discovery of new nanomaterials and morphologies, sensitivity is being constantly improved enough for reliable detection of trace biomarkers in human samples, like serum or sweat. This precision has enabled detailed research on the efficacy of biosensors. However, current biosensors suffer from reduced speed of operation. To make better use of this sensitivity, the development of a conductometric biosensor with in-situ use of an LED display can provide rapid determination of sample results, steadily pushing biosensors toward more clinical, point-of-care (POC) applications. In this research, a simple LED (laser emitting diode) was used for facile optical determination and visual output of an ultrasensitive bio-signal amplification circuit was made to interface with a b-type natriuretic peptide (BNP) biosensor. Tuning circuit gain enables an elegant method for adjustable separation of concentrations into 3 discrete categories: sub-threshold, analog, and saturation regions. These regions corresponded to 0 \u0026lt; [C]\u0026thinsp;\u0026lt;\u0026thinsp;500 pg/mL (LED off), 500 \u0026lt; [C]\u0026thinsp;\u0026lt;\u0026thinsp;1000 pg/mL (LED varying intensity), and 1000 pg/mL \u0026lt; [C] (LED full intensity). System efficacy was tested using human blood serum samples from University of Pittsburgh Medical Center patients, which were able to be accurately detected and sorted for rapid lo-fi. determination without need for complex digital elements. Additional specificity testing suggests insignificant impact of non-target biomarkers.\u003c/p\u003e","manuscriptTitle":"Rapid Ultrasensitive and Specific BNP Biosensor with LED Readout","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-16 20:30:04","doi":"10.21203/rs.3.rs-3855022/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d3aca531-7c3d-4d55-9a61-72e6e4471d2d","owner":[],"postedDate":"January 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-13T04:47:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-16 20:30:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3855022","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3855022","identity":"rs-3855022","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}