Browsing by Author "Prasad, Shalini"
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Item An Approach to Rapidly Assess Sepsis Using Machine Learning Approach(2021-12-01T06:00:00.000Z) Sardesai, Abha Umesh; Prasad, Shalini; Bhatia, Dinesh; Muthukumar, SriramSepsis is a life-threatening condition and understanding the disease pathophysiology using host immune response biomarkers is critical for patient stratification. Lack of accurate sepsis endotyping impedes clinicians to make timely decisions alongside insufficiencies in appropriate sepsis management. The objective of this work is to demonstrate the potential feasibility of a data-driven validation model for supporting clinical decision to predict sepsis host-immune response. Herein, we used machine learning approach to determine the predictive potential of identifying sepsis host immune response for patient stratification by combining multiple biomarker measurement from a single plasma sample. Results were obtained using the following cytokines and chemokines IL-6, IL-8, IL-10, IP-10, TRAIL, PCT and CRP where the test dataset was 70%. Supervised machine learning algorithm naïve Bayes and decision tree algorithm showed promising accuracies of 96.64% and 94.64% respectively. Using unsupervised clustering algorithms, we are able to achieve silhouette score of positive 0.5. These promising findings indicate the proposed AI approach could be a valuable testing resource for promoting clinical decision making.Item Characterization and Circuit Design of Soft Bend Sensors for Use in Robotic Hands and Orthotics(2022-05-01T05:00:00.000Z) Mohapatra, Sanjana; Tadesse, Yonas; Kang, Gu Eon; Prasad, ShaliniSmart materials such as shape memory alloy (SMA) and twisted and coiled polymer fishing line (TCPFL) are essential elements for the realization of novel smart robotic hands, orthotic hands, and prosthetic hands. These artificial muscle-actuated robotic hands need to be assessed extensively to understand the properties and efficiency of the designs. Cyclic movement of the fingers must be monitored to characterize the actuation frequency of the artificial muscles and the response due to the amplitude of stimuli. Flex sensors and strain gauges are commonly used to observe the bending action of robotic fingers by attaching them along the finger’s length. Such sensors are piezoresistive in nature and change their resistance due to stresses exerted on them which change the sensor’s dimensions. This piezoresistive property of some standard sensors available in the market is studied in this research to determine the angular position of the robotic finger during flexion or extension when the artificial muscles are triggered. A similar type of strain gauge sensor was designed in-house, which can be 3D printed and directly embedded into an orthotic finger. This sensor was fabricated using conductive and soft filament materials which consist of a composite of thermoplastic polyurethane (TPU) and carbon nanotubes (CNT). The purpose of this thesis is to characterize the sensor and design the optimized signal conditioning circuit of this sensor. A voltage divider circuit was utilized to characterize and understand the properties of the sensors. Various techniques including Wheatstone bridge, differential amplifiers, and active low pass filters have been implemented for designing the signal conditioning circuit of the strain gauge sensor, and several simulations results were obtained. This optimization converts the variable resistance of this strain gauge into a linearized voltage signal that is easier to monitor through common interfaces. In general, soft smart materials, actuators, and sensors can transform the existing robotic hands to achieve more elegant, lightweight, and modular designs.Item CLASP (Continuous Lifestyle Awareness Through Sweat Platform): A Novel Sensor for Simultaneous Detection of Alcohol and Glucose from Passive Perspired Sweat(Elsevier Ltd) Bhide, Ashlesha; Muthukumar, S.; Prasad, Shalini; Bhide, Ashlesha; Prasad, ShaliniWearable- IOT based low- cost platforms can enable dynamic lifestyle monitoring through enabling promising and exciting opportunities for wellness and chronic- disease management in personalized environments. Diabetic and pre- diabetic populations can modulate their alcohol intake by tracking their glycemic content continuously to prevent health risks through these platforms. We demonstrate the first technological proof of a combinatorial biosensor for continuous, dynamic monitoring of alcohol and glucose in ultra- low volumes (1–5 µL) of passive perspired sweat towards developing a wearable- IOT based platform. Non-invasive biosensing in sweat is achieved by a unique gold- zinc oxide (ZnO) thin film electrode stack fabricated on a flexible substrate suitable for wearable applications. The active ZnO sensing region is immobilized with enzyme complexes specific for the detection of alcohol and glucose through non- faradaic electrochemical impedance spectroscopy (EIS) and chronoamperometry (CA). Biomolecular interactions occurring at the electrode- sweat interface are represented by the impedance and capacitive current changes in response to charge modulations arising in the double layer. We also report the detection of alcohol concentrations of 0.01–100 mg/dl and glucose concentrations of 0.01–50 mg/dl present in synthetic sweat and perspired human sweat. The limit of detection obtained for alcohol and glucose was found to be 0.1 mg/dl in perspired human sweat. Cross- reactivity studies revealed that glucose and alcohol did not show any signal response to cross- reactive molecules. Furthermore, the stable temporal response of the combinatorial biosensor on continuous exposure to passive perspired human sweat spiked with alcohol and glucose over a 120-min duration was demonstrated. © 2018 Elsevier B.V.Item Computational Modeling of FGF10 Mediated Buckling Morphogenesis Within the Embryonic Airway Epithelium(2021-12-01T06:00:00.000Z) Rajaguru, Poornacharanya; Varner, Victor; Prasad, Shalini; Schmidtke, David; Ferruzzi, JacopoThe embryonic lung is an excellent model system to study mechanisms of branching morphogenesis, since the embryonic airways undergo a series of recursive branching events to build the bronchial tree. This process involves reciprocal signaling interactions between the branching airway epithelium and a surrounding layer of pulmonary mesenchyme. Focal regions of fibroblast growth factor (FGF10) expression within the pulmonary mesenchyme are thought to provide a biochemical template for the overall airway branching pattern. Recent work, however, has shown that mechanical forces can also impact this process, and it remains unclear how patterns of FGF10 expression interact with biophysical cues in the developing lung to sculpt incipient branches. This is, in part, owing a lack of computational models that incorporate both growth-factor diffusion and the mechanics of growth and remodeling. Several previous studies have used computational modeling to suggest how specific spatial patterns of FGF10 expression might arise spontaneously within the pulmonary mesenchyme, while others have used continuum mechanics to identify the forces involved in bud initiation, but no computational framework has been developed which unite these different approaches. Here, we developed a finite-element model in COMSOL Multiphysics, which couples growth-factor diffusion to the mechanics of epithelial morphogenesis. We first consider the axial growth of a constrained bar, in which the components of the growth tensor depend on the local concentration of a diffusible molecule. This framework is then extended to determine how growth factor diffusion from a focal source within the pulmonary mesenchyme elicits patterns of epithelial growth and budding morphogenesis along the embryonic airway epithelium. This work highlights the importance of both mechanical forces and growth factor diffusion during airway branching morphogenesis.Item Demonstration of POC Biosensor Toward Clinical Translation for Patient Bed-side Monitoring(December 2021) Tanak, Ambalika Sanjeev; Prasad, Shalini; Walker, Amy V.; Muthukumar, Sriram; Sirsi, Shashank; Ardestani Khoubrouy, SoudehThe research presented in this dissertation focuses on developing and characterizing a multiplexed affinity based electrochemical biosensing device toward clinical translation. The goal of this work is to establish a portable POC device for early disease detection across diverse healthcare applications using low sample volume, rapid response time and usability amongst minimally trained individual relying on ASSURED (Affordable, Sensitive, Specific, User friendly, rapid, and Robust, Equipment free and Deliverable to end users) criteria. Primarily, we designed a robust, non-faradaic electrochemical affinity biosensing platform for the rapid assessment of parathyroid hormone (PTH) as a single biosensing system. Unique high density semiconducting nanostructured arrays on a flexible sensing surface were used to create the analytical nanobiosensor. The surface modification technique was specifically designed to improve the interaction of the nanostructure–biological interface to capture the desired PTH level in HS and plasma. This was followed by evaluating the analytical performance of the developed biosensor with clinical rigor. The assay validation results were compared with laboratory standard as reference with results that demonstrated comparable performance with higher accuracy. Next, the scope of the biosensor was expanded to solve a clinically challenging problem of detecting host immune markers for life-threatening sepsis infection. Herein, we demonstrate a first-of-a-kind multiplexed POC biosensing device that simultaneously detects a panel of eight key immune response cytokine biomarkers in sample volume equivalent to two drops of plasma and whole blood within 5 minutes without sample dilution. Moreover, this work focuses on validating the developed biosensing device with LUMINEX standard reference method for clinical translation using nearly 200 patient samples. The DeTecT (Direct Electrochemical Technique Targeting) Sepsis biosensing device is surface engineered with specific capture probes that utilizes EIS to measure the capacitive impedance change reflecting binding interactions between the capture probe and target biomarker enabling multiplexed detection. Specificity of the biosensor was validated using cross-reactive studies, which displayed insignificant interference from non-specific biomarkers. The biosensor also displays stable and repeatable performance. The novelty presented in this research combines the effectiveness of choosing specific host immune response biomarkers for detection of sepsis combined with unique surface modification strategy coupled with EIS technique to enable efficient clinical decision-making process. This unique sensor technology would allow medical practitioners to facilitate targeted interventions for septic patients as a rapid prognostic approach, preventing complications arriving from sepsis.Item Developing a Flexible, Affinity-based, Electrochemical Sensing Platform Towards Multiplexed Detection of Apocrine and Eccrine Sweat Based Biomarkers; SLOCK: Sensor for Circadian Clock(December 2021) Upasham, Sayali; Prasad, Shalini; Gupta, Gopal; Rodrigues, Danieli; Polk, Todd; Sirsi, ShashankChronobiology is defined as the temporal fluctuations occurring in the human physiology due to the circadian cycle. These fluctuations are good indicators of the functioning of the HypothalamicPituitary-Adrenal axis (HPA axis) and can be tracked by using biomarkers: Cortisol and Dehydroepiandrosteron (DHEA). Low volume tracking systems are beneficial for patients exposed to chronic stress, patients suffering from endocrine conditions manifested by circadian disruption and act as a lifestyle monitoring tool. An estimate of 50-70 million Americans suffer from chronic sleep disorders (NHLBI, 2003) which hinders routine functioning and has deleterious health implications including elevated cortisol levels, increased risk of hypertension, diabetes, depression, cardiac conditions, and stroke. Currently available technologies for monitoring the circadian rhythm (e.g., Polysomnography (Ambulatory monitoring of nocturnal sleep) or Actigraphy (Multiday ambulatory assessment)) do not provide an accurate estimation of the extent of circadian disruption, as they are superficial. Biomarker assays are the diagnostic gold standard techniques used for the diagnosis of circadian dysregulation caused by adrenal disorders. These assays usually rely on the use of blood or serum and take about 3-4 hours for the test results which proves to be an ineffective solution, from the point of view of developing a circadian profile for the user. Adrenal steroids like cortisol and DHEA are expressed in sweat in the nanogram range and can be used as biomarkers to facilitate self-monitoring. SLOCK is a sweat based chronobiology tracking system that works on the principles of electrochemically transduced affinity-based systems. The sensor can detect cortisol and DHEA in the physiologically relevant ranges i.e., 8-200 ng/ml and 2-131 ng/ml respectively. The sensor was also tested through human subject-based studies and is able to capture rise and fall in biomarker levels on-body. In addition to this, the use of serpentine interdigitated electrodes provides mechanical stability when exposed to oscillations caused due to a wearable form factor. Based on the cross-reactivity studies, the response for target biomarkers is highly specific to the biomarker of interest. Towards the last part of this work, the SLOCK platform was tuned for detection of a protein biomarker, interleukin-31 (IL-31) for the purpose of creating a chronic disease diagnosis and management platform. The benchmarking for this was carried out by modelling it for detection of atopic dermatitis related flares. The platform is able to sensitively detect IL-31 over the dynamic range of 50-1000 pg/mL. The platform was successfully coupled with portable electronics and was able to record biomarker fluctuations on-body through human subject-based testing. The SLOCK platforms offers highly sensitive, non-invasive monitoring of circadian related or chronic disorders in a point-of-need setting.Item Development of a Gaseous Ammonia Sensor(2022-12-01T06:00:00.000Z) Rice, Paul Andrew; Prasad, Shalini; Basu, Kanad; Bhatia, DineshAmmonia is a commonly found volatile organic compound which can be present in both liquid and gaseous forms. For many years, researchers have been interested in understanding the human body’s generation and excretion of ammonia as a byproduct of natural bodily functions. One method of excretion comes through outgassing from one’s skin. Many have hypothesized that this concentration may fluctuate as a result of changes in a subject’s health. This research provides the groundwork for two non-invasive sensor approaches for the detection of ammonia in a range relevant to the typical emanated gas concentration range. The developed sensors use electrochemical methods and two thin film approaches as interfaces for ammonia chemisorption. Ultimately, this work may provide an avenue towards the development a sensor capable of detecting outgassed ammonia from the skin of human subjects.Item Development of a Multifunctional Biosensing Platform with Applications Across Various Consumer Markets and Industries(2019-01-14) Stevenson, Hunter Stanley; Prasad, ShaliniThe purpose of this work is to develop a point-of-use affinity-based biosensing platform capable of detecting biomarkers across complex matrices – ranging in electrolyte and macromolecule composition – with applications in consumer industries and regulatory marketspaces. This work focuses on the sensor and hardware development process to promote highly sensitive biomarker detection. First, this work explored how to enhance sensor performance while maintaining a compact design. The ideal placement of the electrodes was determined to stabilize the biosensor's baseline electrochemical characteristics. Additionally, the signal response was monitored with varying input signals and electrolyte constituency. Next, interactions between the sensor and target biomarker were explored in the presence of complex matrices. Both capacitive and resistive responses were recorded to identify the best detection modality for each biomarker/matrix combination. Last, the sensor performance was evaluated to predict the presence of the biomarker in each complex matrix. A limit-of-detection was identified for each biomarker/matrix combination, and a model was erected to accurately predict the presence of the biomarker. The sensor platform demonstrated stable faradaic detection of GMO proteins in plant and food extracts; non-faradaic detection of two strains of wheat viruses in reconstituted plant samples; non-faradaic detection of antibiotics in meat; as well as non-faradaic detection of a psychoactive marijuana compound within human saliva. This platform displayed excellent translatability when exploring numerous biomarkers in an array of complex matrices by leveraging a more intuitive understanding of the electrode-electrolyte interface and defining a systematic approach for evaluating key optimization parameters of electrochemical biosensors.Item Electrochemical impedance spectroscopy wearable systems for reporting biomarker modulation in sweat(December 2021) Sankhala, Devangsingh; Prasad, Shalini; Bhatia, Dinesh; Zhang, Jie; Balsara, Poras T.; Muthukumar, SriramThe commercial wearable device market today majorly consists of activity trackers and smartwatches: that enable the monitoring of user states such as walking, sleeping, and exercising using sensors relying on physically measurable quantities. These devices are the ones that make a huge impact on the lives of people suffering from chronic illnesses and their quality of life. Integrating a sweat-based electrochemical biosensor with a wearable device opens new avenues in health management and decision support systems for healthcare providers as they can provide a physiologically relevant and clinically acceptable output. Integrating a glucose-sensing sweat biosensor adds more value in the lives of diabetics, who require support in terms of balancing quality of life using good diet and exercise routines. This work is a methodology of understanding the aspects of making such a wearable platform, starting from understanding the needs of the wearable device user population. The current market technology is thoroughly studied to pick relevant aspects and an electronic front end is designed within the bounds of good design practice to enable good accuracy, ease of use, and 1-week battery life. This in turn is utilized to collect human subject data to get an understanding of the performance of the sensors in varying environmental conditions and user states. Finally, mathematical modeling approaches are used to build correlations between the outcome to be presented to the user against change in the recorded data features as per the human subject experimentation.Item An Electrochemical Sensor for the Detection of Antibiotic Contaminants in WaterJacobs, Michael; Nagaraj, V. J.; Mertz, T.; Selvam, Anjan Panneer; Ngo, T.; Prasad, ShaliniA nanochannel-based electrochemical sensor for the detection of trace amounts of erythromycin has been developed. The sensor is capable of specifically detecting erythromycin, at a sensitivity of 0.001 parts per trillion, in various water samples and has potential utility in the assessment of environmental water quality.Item Electrochemsense : Electrochemical Real-Time Pesticide Sensing System(2019-05-07) Dhamu, Vikram Narayanan; Prasad, ShaliniPesticide contamination of produce and water in the United States has been a drastic problem to address partly due to the high overhead costs of screening in produce samples, while the other being the inability to report data in real-time-accurately. In 2007, Glyphosate was the most widely used pesticide in the US agricultural sector. More recently, the use of Glyphosate-based herbicides (GBH) has been reported to have increased more than 100-fold with the emergence of glyphosate resistant weeds. The WHO has classified it as being possibly carcinogenic to humans with more current reports suggesting that a few days of GBH use per year-increases the risk of cancer twofold. In addition to this, it has been known to cause chromosomal damage to cells. But, while there is high interest and a necessity to detect this compound-the challenge however lies in designing a sensitive, low volume sensor that can screen for glyphosate with high specificity directly from produce run-off/extract and report it as useful concentration information. In this study, we have developed a portable real-time electrochemical sensing system that can identify and report trace levels of Glyphosate as below or above the Maximum Residue Limit (MRL) of that particular produce type. Through an optimized label-free assay protocol developed in our laboratory, we have built and characterized an electrochemical sensor device that utilizes Chronoamperometry as the detection modality to characterize concentration of glyphosate pesticide as a measure of current-It gauges the chemical interactions occurring at the active sensing region to modulations in electrical signal response. Using this system makes it possible to detect concentrations as low as 0.01ppm (1ng/mL), which is the sensor’s limit of detection (LOD). The device currently in the form factor of a small box can report contaminant concentration of glyphosate in the produce sample in under a minute (rapid) using a GREEN or RED light for SAFE (low) or UNSAFE (high) threshold ranges based on MRL/residue tolerance values of the produce in question. Hence, we have built an effective, low-cost, portable tool eliminating time consuming laboratory analysis that can be used by consumers and industry alike to keep a check on food safety and quality.Item Electromagnetic Optimization of Switched Reluctance Motor for Torque Ripple and Vibration Mitigation(2022-12-01T06:00:00.000Z) Movahed Mohammadi, Seyed Ehsan; Fahimi, Babak; Prasad, Shalini; Nourani, Mehrdad; Balsara, Poras T.; Akin, BilalSwitched reluctance motor (SRM) generates torque based on the principle of reluctance torque using a discontinuous rotating magnetic field. Double saliency of SRM causes magnetic reluctance to change with respect to rotor position. SRM is singly excited on the stator and it does not need magnetic excitation on its rotor. This feature makes SRM to be a simple, low cost, and robust configuration that makes it desirable for high speed and harsh applications. However, SRM exhibits high levels of torque ripple contributing to its acoustic response. The main contributing factor to this behavior is the non-uniform distribution of the flux and force density in SRM. To elaborate, SRM experiences a sudden rise in the flux density, in the airgap when rotor and stator poles start to overlap. This causes a sudden rise in the force density in both tangential and radial components of force at points close to the stator slot and that leads to the vibration and torque ripple. To address this problem, a novel rotor geometry with optimally designed flux barriers has been proposed in this dissertation to be used along with a conventional SRM stator. An optimization algorithm comprised of Genetic Algorithm (GA) and Finite Element Analysis (FEA) has been used to identify the best rotor geometry for maintaining average torque while minimizing torque ripple and tangential vibration of the stator. The performance of the optimized motor is then compared with a conventional SRM of the same size through experiments. The results show significant improvement in torque ripple as well as vibration for the new topology with no tangible drop in efficiency at high speeds.Item Empirical Investigation of CO₂ Utilizing Room Temperature Ionic Liquids(2018-05) Graef, Edward William, Jr.; Prasad, ShaliniSince the start of the industrial revolution, atmospheric carbon dioxide levels have been on the rise. CO₂ detection has been made possible through a number of different techniques though they all suffer one way or the other from cross-sensitivity, narrow temperature operation range, high temperature or power operation, or limited lifetimes due to exposure to CO₂. Room temperature ionic liquids are a purely ionic solution consisting of tunable inorganic anions and organic cations. Salt like in nature with below room temperature melting points, these novel materials express tunable physical and chemical properties that can be selected to make them highly sensitive to CO₂. Through the selection of different fluorinated anions, this work shows the empirical development of a CO₂ gas sensor as part of a SRC sponsored research project from initial evaluation of some of the RTILs physical properties, electrode design evaluation, through to the examination of down selected RTILs on gold interdigitated electrodes at different humidity and temperature conditions. EMIM[TF2N] is shown as the front runner as the future RTIL for integration into a prototype CO₂ gas sensing system.Item Enzymatic Low Volume Passive Sweat Based Assays for Multi-Biomarker Detection(MDPI, 2019-01-16) Bhide, Ashlesha; Cheeran, Sarah; Muthukumar, Sriram; Prasad, Shalini; Bhide, Ashlesha; Cheeran, Sarah; Prasad, ShaliniSimultaneous detection of correlated multi-biomarkers on a single low-cost platform in ultra-low fluid volumes with robustness is in growing demand for the development of wearable diagnostics. A non-faradaic biosensor for the simultaneous detection of alcohol, glucose, and lactate utilizing low volumes (1-5 μL) of sweat is demonstrated. Biosensing is implemented using nanotextured ZnO films integrated on a flexible porous membrane to achieve enhanced sensor performance. The ZnO sensing region is functionalized with enzymes specific for the detection of alcohol, glucose, and lactate in the ranges encompassing their physiologically relevant levels. A non-faradaic chronoamperometry technique is used to measure the current changes associated with interactions of the target biomarkers with their specific enzyme. The specificity performance of the biosensing platform was established in the presence of cortisol as the non-specific molecule. Biosensing performance of the platform in a continuous mode performed over a 1.5-h duration showed a stable current response to cumulative lifestyle biomarker concentrations with capability to distinguish reliably between low, mid, and high concentration ranges of alcohol (0.1, 25, 100 mg/dL), glucose (0.1, 10, 50 mg/dL), and lactate (1, 50, 100 mM). The low detection limits and a broader dynamic range for the lifestyle biomarker detection are quantified in this research demonstrating its suitability for translation into a wearable device.Item Evaluation of Molybdenum as an Electrode Material for Affinity Based Urine Dipstick Biosensing(2017-08) Kamakoti, Vikramshankar; 0000 0001 2765 4678 (Prasad, S); Prasad, ShaliniThe work presented in this dissertation focuses on evaluation and characterization of molybdenum (Mo) as an electrode material for affinity based biosensing applications. The material properties of the electrode material dictate the performance of electrochemical biosensors. Mo demonstrates electrochemical properties upon its interaction with electrolytes. Here, we have evaluated the electrochemical properties of Mo for use in affinity based biosensors. Surface characterization of deposited Mo electrode helps us to evaluate the efficiency of fabrication process conditions. The deposition profile of Mo on the flexible polyamide (PA) substrate was characterized through Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM) techniques. A label-free immunoassay was designed for detection of target biomolecules. A monolayer of crosslinker was formed on the Mo electrode surface. Thiol and carbodiimide crosslinker chemistries were evaluated with Mo electrode. The characterization of chemical affinity between Mo and crosslinker molecules is required to understand the effectiveness of crosslinker monolayer formation on the electrode surface. The affinity between electrode and crosslinker molecules were characterized through Fourier Transform Infrared Spectroscopy (FTIR), Fluorescence microscopy and X-ray photoelectron spectroscopy (XPS). The binding affinity between antibody and crosslinker molecules was also characterized using FTIR technique. The form factor of the biosensor was modified as a dipstick to detect inflammatory biomarkers namely Interleukin (IL-6) and C-reactive protein (CRP). The effect of variable pH of the synthetic urine on the detection of CRP and Il-6 was evaluated using Electrochemical Impedance Spectroscopy (EIS) technique. The portability of the biosensor was demonstrated using customized electronics hardware assembly. The impedance response from the electronics hardware was compared against standard potentiostat systems.Item Flexible Nanoporous Tunable Electrical Double Layer Biosensors for Sweat DiagnosticsMunje, Rujuta D.; Muthukumar, Sriram; Selvam, Anjan Panneer; Prasad, Shalini; 0000 0001 2765 4678 (Prasad, S); Munje, Rujuta D.; Selvam, Anjan Panneer; Prasad, ShaliniAn ultra-sensitive and highly specific electrical double layer (EDL) modulated biosensor, using nanoporous flexible substrates for wearable diagnostics is demonstrated with the detection of the stress biomarker cortisol in synthetic and human sweat. Zinc oxide thin film was used as active region in contact with the liquid i.e. synthetic and human sweat containing the biomolecules. Cortisol detection in sweat was accomplished by measuring and quantifying impedance changes due to modulation of the double layer capacitance within the electrical double layer through the application of a low orthogonally directed alternating current (AC) electric field. The EDL formed at the liquid-semiconductor interface was amplified in the presence of the nanoporous flexible substrate allowing for measuring the changes in the alternating current impedance signal due to the antibody-hormone interactions at diagnostically relevant concentrations. High sensitivity of detection of 1 pg/mL or 2.75 pmol cortisol in synthetic sweat and 1 ng/mL in human sweat is demonstrated with these novel biosensors. Specificity in synthetic sweat was demonstrated using a cytokine IL-1β. Cortisol detection in human sweat was demonstrated over a concentration range from 10–200 ng/mL.Item Human Hydration Monitoring(2020-05-01T05:00:00.000Z) Abraham, Alejandro; Prasad, Shalini; Pacheco, Joe; Winter, PatrickChronic dehydration is the usual state at which most people keep their bodies through their entire lives. Dehydration states lead to many unnoticed detrimental health issues that can end up reducing the life quality of entire societies. Water is the most important nutrient of human life, and because of this it is important to monitor it. To my present knowledge, currently there are no commercial wearable dehydration monitors that could help the public in keeping well hydrated and healthy. This is the reason why finding a low-cost way to design a dehydration sensor system in a wearable format is of paramount importance. In this work, the basis for the design of a hydration monitor are established, and this could lead in the future, to a wearable device that consumers could reach to so as to keep their hydration status well screened. In order to achieve such goal, multiple sensors were designed, simulated using Finite Element Analysis (FEA) software, fabricated in The University of Texas at Dallas Cleanroom, tested in-vitro, and those that performed best were tested in Human Trials. Moreover, a custom Analog Front End linked to a microcontroller was designed and fabricated to interface the sensors and provide measurement readings.Item Human Milk Rheology: a Path Toward Understanding the Physiology of Breastfeeding(December 2021) Alatalo, Diana Lynn; Hassanipour, Fatemeh; Bleris, Leonidas; Iungo, Giacomo Valerio; Fadda, Dani; Prasad, ShaliniBreastfeeding is the recommended method of feeding infants and requires coordinated mechanical activities from both the mother and infant to succeed. Previous studies on breastfeeding biomechanics have been limited to infant-applied forces that are easily obtained. The flow of milk within the human mammary ducts plays a critical role in breastfeeding but lacks extensive study. The rheological profile of raw human milk provides a medium to facilitate further study on the mechanics of breastfeeding with greater emphasis on the contribution of the mammary gland in response to infant-applied forces. The following work presents a new understanding in infant-applied pressures, an expanded exploration of milk rheological behavior, and the response of milk to specific suckling patterns. The results of these experimental studies provide greater insight into breastfeeding mechanics.Item Implementation of Machine Learning for Analysis of an on Demand Passive Sweat Cortisol Sensor(2021-08-01T05:00:00.000Z) Shahub, Sarah; Prasad, Shalini; Ardestani Khoubrouy, Soudeh; Bian, FangCortisol is a steroid hormone produced by the adrenal glands for the purpose of regulating the body’s response to stress. Stress, as a physiological condition, can be caused by a wide variety of factors, such as mental exertion, diet, sleep, exercise, etc. For this reason, cortisol has the potential to serve as a biomarker for general health, as it relates to the everyday habits of patients. With the development of wearable technologies such as the smartwatch, increased attention has been focused on the development of noninvasive sensors for on demand testing that can integrated with wearable technologies. Current biosensing technologies for monitoring of chemical biomarkers such as cortisol depend on blood or salivary testing, which is invasive, costly, and time consuming. For this reason, the focus of this research is on the detection of cortisol through passive sweat, which contains many of the biomarkers present in blood at concentrations sufficient for detection. We have developed a noninvasive sensor on a flexible, nano porous substrate that has the capability to detect cortisol passively through sweat. The sensor data was then processed and input into a machine learning algorithm to analyze the rising and falling trend of cortisol concentration with time. The use of machine learning to analyze cortisol trends can be used to inform the wearer of rising or falling cortisol levels, which can enable them to make informed decisions about their health and lifestyle. Sensor response was measured by conducting Electrochemical Impedance Spectroscopy (EIS) assays of synthetic sweat dosed with concentrations of cortisol within the physiological range, from which the responses for low, medium, and high concentrations of cortisol were found to be significant. Similar assays were performed within the frequency region of maximum capacitance with dosing regimens ranging from high to low and low to high concentrations of cortisol, to simulate the rise and fall of cortisol levels of a human patient over a short period of time. The assay data was analyzed to find the rate of the change of the sensor response to a shift in cortisol concentration, which was then used to train a weighted KNN supervised machine learning algorithm to detect and classify increasing and decreasing cortisol concentrations in sweat. Algorithm accuracy was validated to be 100% by k means cross validation, showing that a passive, wearable sweat sensor can successfully be used with machine learning to detect rising and falling trends in cortisol concentration for on demand, noninvasive cortisol sensing.Item Innovative Biosensing Strategies: Electrochemical Profiling of vWFA2 for Early Sepsis Detection(December 2023) David, Bianca Elizabeth 1995-; Prasad, Shalini; Porter, Benjamin; Muthukumar, SriramIn the dynamic intersection of technologically advancing medicine and engineering, rapid diagnostic methods are imperative for prognostic evaluation of critically ill - upon multimer assembly patients presenting with clinical complications from a dysregulated host response. Evaluation of biomolecular markers in inflammatory conditions, specifically sepsis, is of peak interest in research to develop a highly rapid, robust, and effective diagnostic option. The complexity of sepsis, a life-threatening inflammatory condition arising from the systemic response to an infectious agent, presents a grave challenge in detection and treatment. Following vascular injury, endothelial cells, strategically placed at the blood-tissue interface, respond to inflammatory stimuli by releasing circulating co-factors, including Von Willebrand Factor (vWF). Von Willebrand Factor-Like 2 Domain (vWFA2), the A2 domain of the heterogenous multimeric glycoprotein, plays a central role in primary hemostasis by promoting platelet adhesion to the subendothelial matrix of damaged vessels and protecting FVIII from proteolytic degradation. vWFA2 responds to shear stress by balancing bleeding and clotting mechanisms. Reduced functionality of vWFA2 leads to severe hemorrhagic consequences following defective formation of a platelet-rich thrombi and fibrin network. The immunethrombotic role of vWFA2 in sepsis is understudied due to the emphasis on its narrowed role in thrombotic events, but it is critically important to understand the developing biological relationship between both physiological processes. Elevated levels of vWF are seen in septic conditions, thus the marker is a vital for disease severity assessment. Current gold-standard diagnostic methods include blood cultures for accurate microbial diagnosis but is faced with compromised sensitivity, prolonged processing time, and a large sample volume requirement. This gap paves the way for development of a prompt and accurate diagnostic protocol, particularly one that targets inflammatory biomarkers. The goal of this research is to develop a biosensor device for rapid detection of biomarkers in septic and inflammatory states. The transformative potential of sensors in revolutionizing detection guides refinement of sensitive assays that addresses the shortcomings of conventional detection methods. This label-free biomolecular assay is fabricated on a flexible hybrid electrode surface and leverages electrochemical impedance spectroscopy (EIS) to measure the capacitive change in impedance, unveiling the binding effects of the target, vWFA2, to the capture probe. The device aims for high sensitivity and specificity in the targeted assay development of a wide dynamic range of 500-32,000 pg/mL. These methods offer a promising solution in point-of-care diagnostic methods, offering a high sensitivity, rapid response time and minimal biofluid volume requirement. The vision behind this research stems from the desire to develop this electrochemical sensor to detect disease states rapidly and accurately, thus creating a pivotal prognostic tool in sepsis treatment, and ultimately mitigating severe mortality and morbidity.