<p dir="ltr">The emergence of infectious diseases like HIV, influenza, and COVID-19 highlights the urgent need for highly scalable testing methods that can be deployed outside traditional laboratory settings. Despite decades of research in point-of-care (POC) diagnostics, the main challenge remains the limited performance of assays, especially in terms of sensitivity. Furthermore, most POC assays originating from academic research struggle to transition beyond the laboratory due to manufacturability issues. This dissertation aims to enhance the effectiveness of viral molecular detection platforms for POC diagnostics by improving analytical and clinical sensitivity and facilitating the practical adaptation of academic-developed POC devices for use outside laboratory settings.</p><p dir="ltr">Each aim addresses a separate aspect of device development. The first aim addresses the need for clinical accuracy during test interpretation, especially in POC or at-home diagnostic tests, by developing an internal amplification control (IAC). Here, I develop a one-pot duplex reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) assay for detecting SARS-CoV- 2 along that incorporates a housekeeping gene as an IAC to ensure the quality of collected samples and the validity of assay reagents. The valid results can be easily visualized in triple-line lateral flow immunoassay (LFIA). The second aim makes progress towards overcoming the limited analytical sensitivity of existing rapid diagnostic tests for acute HIV infection screening. Here, I introduce a novel antibody-initiated LAMP assay targeting the HIV p24 capsid protein that combines LAMP sensitivity with the specificity of HIV p24 and its antibody. There are 3000 p24 capsid proteins present in the virion compared to only 2 viral RNA copies. In the assay, two DNA- conjugated antibody probes will each bind to p24 and their proximity will allow the DNA overlaps to generate a complete DNA target that acts as a trigger for the LAMP reaction. An LFIA is integrated into this design to enable simple result visualization. The third aim improves manufacturability and assembly of our existing nucleic acid detection platform by simplifying the platform components while maintaining the user-friendly sample-to-answer concept. Here, I validate material compatibility testing, assess chamber fabrication methods amenable to large-scale manufacturing, evaluate alternative heating units, and examine fluid flow control mechanisms of the redesigned wax valve. These combined aims demonstrate promising outcomes for practical implementation of molecular diagnostics to the POC.</p>

Através dos dispositivos e técnicas desenvolvidas na área da Instrumentação Biomédica é possível oferecer um tratamento ou de forma geral soluções que permitam uma vivência mais plena em sociedade para pessoas que possuem algum tipo de deficiência ou doença. Com o aumento da capacidade computacional nos últimos anos foi possível desenvolver muitas técnicas e dispositivos apoiados em processamento digital de sinais e há um grande interesse pelo desenvolvimento de interfaces mais naturais, como sinais biológicos para o controle de próteses e dispositivos. Este trabalho tem por objetivo apresentar o desenvolvimento de um método de Inteligência Computacional baseado em Regressão Logística capaz de classificar 17 movimentos do segmento mão-braço realizados pelos voluntários do estudo através do processamento do sinal mioelétrico (SME) adquiridos dos sujeitos em questão. Adicionalmente é realizada uma avaliação da influência de alguns dos canais, características do sinal e movimentos executados na taxa de acerto global. Para a realização do sistema foi utilizado um aparato experimental capaz de adquirir os SME através de 12 canais utilizando eletrodos não invasivos e posteriormente digitalizá-los. Logo após efetua-se a extração das três características utilizadas no trabalho, que servem de entrada para o método de Regressão Logística. Para este estudo foram processados três bancos de dados que perfazem um total de 50 voluntários. A taxa média de acerto alcançada foi de 70,1%, considerando todas as variações de testes realizados enquanto a média para os melhores casos de cada variação de entrada realizada foi de 92,5%. / Through the devices and techniques developed in the field of Biomedical Instrumentation commonly is possible to offer treatment or solutions to provide a more pleasurable experience in society for people who have a disability or illness. With increasing computing capability in recent years, it has been possible to develop many techniques and devices supported by digital signal processing, and there is a great interest in the development of more natural interfaces, such as biological signals for the control of devices and prostheses. This work aims to present the development of a computational intelligence method based on Logistic Regression able to classify 17 movements of the hand-arm segment performed by the subjects of this study through the processing of the myoelectric signal (SME) acquired from the subject in question. Additionally, an assessment of the influence of some of the combination of the channels, signal characteristics and movements performed in the overall hit rate is additionally performed. To conduct the system has built an experimental apparatus able to acquire the SME through 12 channels using non-invasive electrodes and scan them. Thereafter there is a three features extraction from the signal which serves as input to the Logistic Regression method. For this study were processed three databases that compose 50 volunteers. The average hit rate achieved was 70.1%, considering all tests variations while the average for the best cases for each input variation performed was 92,5 %.

Através dos dispositivos e técnicas desenvolvidas na área da Instrumentação Biomédica é possível oferecer um tratamento ou de forma geral soluções que permitam uma vivência mais plena em sociedade para pessoas que possuem algum tipo de deficiência ou doença. Com o aumento da capacidade computacional nos últimos anos foi possível desenvolver muitas técnicas e dispositivos apoiados em processamento digital de sinais e há um grande interesse pelo desenvolvimento de interfaces mais naturais, como sinais biológicos para o controle de próteses e dispositivos. Este trabalho tem por objetivo apresentar o desenvolvimento de um método de Inteligência Computacional baseado em Regressão Logística capaz de classificar 17 movimentos do segmento mão-braço realizados pelos voluntários do estudo através do processamento do sinal mioelétrico (SME) adquiridos dos sujeitos em questão. Adicionalmente é realizada uma avaliação da influência de alguns dos canais, características do sinal e movimentos executados na taxa de acerto global. Para a realização do sistema foi utilizado um aparato experimental capaz de adquirir os SME através de 12 canais utilizando eletrodos não invasivos e posteriormente digitalizá-los. Logo após efetua-se a extração das três características utilizadas no trabalho, que servem de entrada para o método de Regressão Logística. Para este estudo foram processados três bancos de dados que perfazem um total de 50 voluntários. A taxa média de acerto alcançada foi de 70,1%, considerando todas as variações de testes realizados enquanto a média para os melhores casos de cada variação de entrada realizada foi de 92,5%. / Through the devices and techniques developed in the field of Biomedical Instrumentation commonly is possible to offer treatment or solutions to provide a more pleasurable experience in society for people who have a disability or illness. With increasing computing capability in recent years, it has been possible to develop many techniques and devices supported by digital signal processing, and there is a great interest in the development of more natural interfaces, such as biological signals for the control of devices and prostheses. This work aims to present the development of a computational intelligence method based on Logistic Regression able to classify 17 movements of the hand-arm segment performed by the subjects of this study through the processing of the myoelectric signal (SME) acquired from the subject in question. Additionally, an assessment of the influence of some of the combination of the channels, signal characteristics and movements performed in the overall hit rate is additionally performed. To conduct the system has built an experimental apparatus able to acquire the SME through 12 channels using non-invasive electrodes and scan them. Thereafter there is a three features extraction from the signal which serves as input to the Logistic Regression method. For this study were processed three databases that compose 50 volunteers. The average hit rate achieved was 70.1%, considering all tests variations while the average for the best cases for each input variation performed was 92,5 %.

Através dos dispositivos e técnicas desenvolvidas na área da Instrumentação Biomédica é possível oferecer um tratamento ou de forma geral soluções que permitam uma vivência mais plena em sociedade para pessoas que possuem algum tipo de deficiência ou doença. Com o aumento da capacidade computacional nos últimos anos foi possível desenvolver muitas técnicas e dispositivos apoiados em processamento digital de sinais e há um grande interesse pelo desenvolvimento de interfaces mais naturais, como sinais biológicos para o controle de próteses e dispositivos. Este trabalho tem por objetivo apresentar o desenvolvimento de um método de Inteligência Computacional baseado em Regressão Logística capaz de classificar 17 movimentos do segmento mão-braço realizados pelos voluntários do estudo através do processamento do sinal mioelétrico (SME) adquiridos dos sujeitos em questão. Adicionalmente é realizada uma avaliação da influência de alguns dos canais, características do sinal e movimentos executados na taxa de acerto global. Para a realização do sistema foi utilizado um aparato experimental capaz de adquirir os SME através de 12 canais utilizando eletrodos não invasivos e posteriormente digitalizá-los. Logo após efetua-se a extração das três características utilizadas no trabalho, que servem de entrada para o método de Regressão Logística. Para este estudo foram processados três bancos de dados que perfazem um total de 50 voluntários. A taxa média de acerto alcançada foi de 70,1%, considerando todas as variações de testes realizados enquanto a média para os melhores casos de cada variação de entrada realizada foi de 92,5%. / Through the devices and techniques developed in the field of Biomedical Instrumentation commonly is possible to offer treatment or solutions to provide a more pleasurable experience in society for people who have a disability or illness. With increasing computing capability in recent years, it has been possible to develop many techniques and devices supported by digital signal processing, and there is a great interest in the development of more natural interfaces, such as biological signals for the control of devices and prostheses. This work aims to present the development of a computational intelligence method based on Logistic Regression able to classify 17 movements of the hand-arm segment performed by the subjects of this study through the processing of the myoelectric signal (SME) acquired from the subject in question. Additionally, an assessment of the influence of some of the combination of the channels, signal characteristics and movements performed in the overall hit rate is additionally performed. To conduct the system has built an experimental apparatus able to acquire the SME through 12 channels using non-invasive electrodes and scan them. Thereafter there is a three features extraction from the signal which serves as input to the Logistic Regression method. For this study were processed three databases that compose 50 volunteers. The average hit rate achieved was 70.1%, considering all tests variations while the average for the best cases for each input variation performed was 92,5 %.

<div>Due to reduced pain, shortened hospital stay and recovery, minimally invasive surgery (MIS) is becoming more and more popular in healthcare systems. MIS requires some devices for the motion and force transformation from outside to the inside of the body of a patient, and the strangled cables play a significant role in developing the instrumentations to serve for such purposes. However, current design and selection of a strangled cable is mostly intuitive that depend greatly on designers’ experiences and availability of experimental data, which leads to non-optimized designs and longer design cycles. In this thesis, both of analytical modelling and numerical simulation are proposed to build the relation of applied torque and deflection of part, so that a strangled cable with a given configuration can be characterized in term of its load-deflection relation. The defined relation has its great significance and application potential in the design optimization and precise controls of medical devices for MISs.Besides the various patterns of strangled cables, a Helical hollow strand (HHS®) tube is a special type of strangled cables with single- or multiple- layer configurations., In each layer, each of the helical wires touches its two neighboring helical wires, and it has a coreless hollow at the center. Its primary application is to carry a torsional load in a twisting mode. As an extreme, there is a possibility that all helical wires touch each other, and this forms a statically indeterminate contact obstacle in design analysis. Numerical simulation would predict that contacts occur simultaneously at all possible contacting points under the circumference that the strand is fixed at one end against rotation. In addition, the friction at contacts will affect the torsional deformation; therefore, these contacts must be taken into consideration in the development of analytical and numerical simulation models.This thesis reports the results of the investigation on the characteristics of Helical hollow strand tube (HHS®), more specifically, the relation of torsional deflection and the applied torque over a tube in the clockwise (CW) direction. The numerical simulation approach to predict the torsional deflection of HHS with various design parameters and configurations is emphasized. </div><div><br></div>

<p>Gastroparesis and functional dyspepsia are debilitating stomach disorders that together affect 10% of the world population. Modulating gastric function is an important target function for alternative therapies like gastric electrical stimulation (GES). The Enterra device is the only FDA approved implantable device currently available that can administer GES to entrain gastric slow wave activity. However, recent evidence has called into question the clinical utility of this system. In this work, I present the development and in vivo application of a new, closed loop, chronically implantable electroceutical device capable of continuously recording gastric motility and administering synchronous GES, that will form the needed foundation for neuromodulation protocols that can correct shortcomings in past, first-generation bioelectronic attempts to ameliorate and monitor gastric disorders. This system captures gastric serosal myoelectric activity using electrogastrography, as well as gastric contraction activity using strain gauge force transducers. I present data captured from anesthetized and freely behaving rats, demonstrating the ability of the device to capture physiologically relevant gastric motility patterns and changes, safely and effectively. I present a framework built on continuous wavelet transforms to analyze frequency and amplitude changes in captured data to inform potential therapies. I present data demonstrating the ability of the device to selectively stimulate enteric neurons in sync with gastric slow waves, resulting in a relaxation of the pyloric sphincter muscle, in a closed loop fashion. I present the development of a large animal preclinical proof-of-principle version of this system, and data captured from its implantation in freely behaving pigs, as a translational step to future human trials. In the future, this system will enable further studies into future closed loop therapies aimed at increasing gastric accommodation, stimulating physiological gastric emptying and/or pyloric opening with physiologically appropriate timing and extent. </p>

<p dir="ltr">Portable sensing enables an enhanced form of disease monitoring due to its accessible form-factors, low costs, and insights into user health, along with enhanced detection methods due to its many use cases for at-home or in-field applications. To that end, electrochemistry has been a widely used technique in characterization, detection, and diagnostics. Electrochemical Impedance Spectroscopy (EIS) is an electrochemical technique that enables electrode surface characterization through changes in impedance across a given frequency range making it sensitive to interactions at the electrode surface and enabling the detection and quantification of analytes. While EIS has been traditionally limited to benchtop potentiostats, advancements in integrated circuits (ICs) have since enabled the miniaturization of potentiostats for at-home or field applications. However, implementation of EIS in a portable format is still limited by discontinuous measurements, high cost, or designs not fit for portability. This work revolves around the development of a miniaturized potentiostat that can implement EIS to better accommodate the need for miniaturized sensing platforms. My design uses the AD5941 IC which is a single-chip potentiostat analog-front-end enabling a small form-factor that fits in the palm of the user’s hand. The device was able to characterize a resistor-capacitor circuit with errors as low as 0.33% and quantify the concentration of a redox active compound with a 6.2% error, providing agreeable results with a commercial benchtop potentiostat and demonstrating our device’s potential for diagnostic applications. Our working frequency range of 200 kHz – 0.15 Hz, coupled with high system configurability and a cost of $50 makes our device an accessible option for at-home and portable applications. Future work to implement truly wireless functionalities, such as WiFi or Bluetooth Low Energy, along with experimental testing of biological substances will create a truly robust platform for portable diagnostic and sensing applications.</p>

<p dir="ltr">Microfluidic techniques have been widely adopted in biomedical research due to the pre- cise control of fluids, small volume requirement, low cost and etc, and have boosted the development of biomolecular interaction analysis, point-of-care diagnostics, and biosensors.</p><p dir="ltr">Protein-protein interaction plays a key role in biological, biomedical and pharmaceutical research. The technical development of biosensors, new drugs and vaccines, and disease diagnostics heavily rely on the characterization of protein-protein interaction kinetics. The current gold standard assays for measuring protein-protein interaction are surface plasmon resonance (SPR), and bio-layer interferometry (BLI). These commercial devices are accurate but expensive, however.</p><p dir="ltr">Here, I have developed new microfluidic techniques and models in protein-protein in- teraction kinetics measurement, rotational diffusion coefficient modeling, electrochemical impedance spectroscopy-based biosensors, and two-phase porous media flow models. Firstly, I applied particle diffusometry (PD) in the streptavidin-biotin binding kinetics measurement, utilizing a Y-junction microchannel. Secondly, to reduce solution volumes used in an analysis experiment, I designed a low-volume chip and coupled it with PD to measure the binding kinetics of human immunodeficiency virus p24 antibody-antigen interactions. Thirdly, con- sidering the Brownian motion of the non-symmetric particles, I developed a new model to efficiently compute particles’ rotational diffusion coefficients. Fourthly, to make economic biosensors to detect multiple biomarkers, I created a new chip, enabling hundreds of tests in a single droplet (∼ 50 μL) on one chip. Finally, to understand the liquid flow in porous media, such as nitrocellulose in lateral flow assays, I built a new two-phase porous media flow model based on the Navier-Stokes equation and compared it with experiments. These techniques and models underwent rigorous experimental and computational validation, demonstrating their effectiveness and performance.</p>

La neuromodulation offre une possibilité de traitement pour des pathologies pharmoco-resistantes. Dans ce domaine, l'émergence de matrices d'électrodes à l'échelle microscopique ouvre la voie à des interfaces neurales sélectives. Cependant, leur fonctionnalité est réduite par le manque d'information sur l'anatomie fonctionnelle du nerf ciblé. L'objectif global de ce projet de thèse est d'explorer les possibilités d'imager un nerf de manière non-invasive par tomographie d'impédance électrique (EIT). Modalité d'imagerie des tissus mous, l'EIT déduit des cartes de conductivité à partir de mesures sur la frontière du domaine étudié. Une plateforme expérimentale a été mise en place et a permis de valider les développements des méthodes numériques effectués pour la prédiction des données et l'estimation des paramètres. Des tests in vivo ont été réalisés dans le contexte de la stimulation du nerf vague et du nerf sciatique. Des spécifications pour de futures expériences ont été déduites, avec l'utilisation d'électrodes plus robustes comprenant un plus grand nombre de contacts par section. / Neuromodulation offers treatments for drug resistant pathologies. In this field, the emergence of micro-scale multi-electrode arrays paves the way for selective neural interfaces. But they suffer from the lack of information on the nerve functional anatomy. The global aim of this PhD project is to explore the possibilities of imaging the inside of a nerve in a non-invasive way through electrical impedance tomography (EIT). As a soft-field imaging modality, EIT infers conductivity maps from boundary measurements. An experimental platform was built and allowed the validation of numerical methods developed for data prediction and parameter estimation. In vivo tests were performed in the context of vagus and sciatic nerve stimulation. Specifications were deduced for future experiments, with more reliable electrodes, embedding a higher number of contacts per cross-section.

A calorimetria indireta para análise do metabolismo humano tem se mostrado um método não invasivo de crescente importância no estudo mais preciso das respostas metabólicas e cardio-respiratórias. Entretanto, o alto custo dos equipamentos aliado à falta de conhecimento das técnicas tem impedido a disseminação do uso de analisadores metabólicos no Brasil. Este trabalho foi desenvolvido com o intuito de oferecer uma visão global dos procedimentos de calorimetria indireta aplicados à avaliação do metabolismo humano sob a abordagem de Engenharia Biomédica. Foram avaliados três transdutores tipicamente utilizados em equipamentos de calorimetria indireta para análise metabólica humana: um transdutor eletroquímico de oxigênio, um transdutor de dióxido de carbono por radiação infravermelha e um transdutor de fluxo em volume tipo turbina. O transdutor de oxigênio mostrou-se bastante confiável, mas com um tempo de resposta bastante elevado para aplicações em análises de respiração a respiração. A principal deficiência do transdutor de dióxido de carbono está no fato de sua configuração proporcionar um aumento significativo na resistência à respiração do indivíduo. Verificou-se que o transdutor de fluxo em volume apresenta um melhor comportamento para um fluxo em torno de 20 litros/minuto. Apesar das limitações apresentadas pelos transdutores, os resultados dos ensaios foram úteis e representativos para a compreensão do funcionamento de um analisador metabólico humano porcalorimetria indireta, evidenciando quais deficiências devem ser sanadas em pesquisas futuras e os cuidados a serem considerados na implantação do laboratório para análise metabólica/respiratória sendo instalado no Laboratório de Engenharia Biomédica da Escola Politécnica da Universidade de São Paulo. / The indirect calorimetry for analysis of the human metabolism has been a no invasive method of increasing importance in the study of metabolic and cardiorespiratory responses. However, the high cost of the equipment ally to the lack of knowledge of the techniques have been obstacles to the use of metabolic analysers in Brazil. This research was developed with the intention to offer a global vision of the applied procedures of indirect calorimetry to the evaluation of the human metabolism under the prism of the Biomedical Engineering. Three transducers typically used in equipment of indirect calorimetry for metabolic analysis had been evaluated: an electrochemical transducer of oxygen, a carbon dioxide transducer by infrared absorption and a turbine flowmeter. The oxygen transducer revealed sufficiently trustworthy, but a too high response time for applications in breath by breath analyses. The main deficiency of the transducer of carbon dioxide is to provide a significant increase in the flow resistance. It was verified that the flowmeter presents one better behavior for a stream around 20 litres/minute. Despite the limitations presented for the transducers, the results of the assays had been useful and representative for the understanding of the functioning of a human metabolic analyser for indirect calorimetry, evidencing which points have to be improved in future researches and to be considered in the implantation of the laboratory for metabolic and respiratory analysis being installed in the Laboratório de Engenharia Biomédica da Escola Politécnica da Universidade de São Paulo.