A malaria diagnostic system based on electric impedance spectroscopy

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 69-71).Malaria caused by Plasmodium falciparum infection is one of the major threats to world health and especially to the community without proper medical care. New approach to cost-efficient, portable, miniaturized diagnostic kit is needed. This work explores electric impedance spectroscopy (EIS) on a microfluidic device as a means of malaria diagnosis. This work introduces a microfabricated probe with microfluidic channel, and a high speed impedance analyzer circuit board. Combination of microfluidic device and circuit board resulted in a small-sized EIS system for micro-particles such as human red blood cell (RBC). After invasion by the parasites, RBC undergoes physiological changes including electrical property of cytoplasm and membrane. Detection of infected RBC is demonstrated as well as differentiation of micro-beads by surface charge density using EIS-based diagnostic system. Diagnosis based on EIS has merits over other diagnostic methods since it is label-free and quantitative test and applicable to whole blood, and also the test does not need bulky optical and electrical equipments.by Sungjae Ha.S.M

Electric impedance microflow cytometry for characterization of cell disease states

The electrical properties of biological cells have connections to their pathological states. Here we present an electric impedance microflow cytometry (EIMC) platform for the characterization of disease states of single cells. This platform entails a microfluidic device for a label-free and non-invasive cell-counting assay through electric impedance sensing. We identified a dimensionless offset parameter δ obtained as a linear combination of a normalized phase shift and a normalized magnitude shift in electric impedance to differentiate cells on the basis of their pathological states. This paper discusses a representative case study on red blood cells (RBCs) invaded by the malaria parasite Plasmodium falciparum. Invasion by P. falciparum induces physical and biochemical changes on the host cells throughout a 48-h multi-stage life cycle within the RBC. As a consequence, it also induces progressive changes in electrical properties of the host cells. We demonstrate that the EIMC system in combination with data analysis involving the new offset parameter allows differentiation of P. falciparum infected RBCs from uninfected RBCs as well as among different P. falciparum intraerythrocytic asexual stages including the ring stage. The representative results provided here also point to the potential of the proposed experimental and analysis platform as a valuable tool for non-invasive diagnostics of a wide variety of disease states and for cell separation.Singapore. National Research Foundation (Singapore-MIT Alliance for Research and Technology)Massachusetts Institute of Technology. Center for Integrated Circuits and SystemsNational Institutes of Health (U.S.) (Grant R01 HL094270

Electronic systems for interfacing with new materials and devices

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.Cataloged from PDF version of thesis.Includes bibliographical references (pages 163-173).The focus of this thesis is to explore and demonstrate electronics systems utilizing new materials and devices beyond the traditional ones solely based on Si CMOS technology. The first part of this thesis is to explore the combination of Bio-MEMS devices with traditional electronics as an effective diagnostic tool. In the case study of malaria, we report a microfluidic device as part of a continuous-flow cellular impedance spectroscopy system and a new data analysis method to differentiate Plasmodium falciparum-infected human erythrocytes including the early ring stage. The next parts of this thesis focus on two-dimensional (2D) materials which are believed to be a tool set for future electronics. In particular, graphene is explored as a new infrared sensitive material that can be used for sensors in mid- and long-wavelength infrared spectrum ([lambda] = 2- 15[mu]m) imaging systems. We demonstrate a Si CMOS-based readout IC and monolithic integration of an array of > 4000 electronically tunable graphene thermocouples. The prototype system shows that use of 2D material as add-on parts of the conventional technology can lead to development of new types of electronic applications. In addition to combinational uses with Si CMOS technology, 2D materials and their heterostructures have the potential to be used as stand-alone electronic systems. In the latter part of the thesis, we present a computer-aided design (CAD) flow for large-scale MoS₂ electronics. Combined with the state-of-the-art fabrication technology and the physics-based device model for MoS 2 FETs, a switched capacitor DC-DC converter, a half-wave rectifier, and a voltage doubler are implemented, and good agreement between simulation and measurement is observed. The presented CAD flow enables large-scale integrated circuit design on MoS₂ technology and paves the way for ubiquitous, flexible and possibly transparent electronics, such as printed RFID tags and transparent display drivers. Utilizing these design concepts, we push the capability of current electronics beyond its traditional boundaries.by Sungjae Ha.Ph. D

Neural Network for Metal Detection Based on Magnetic Impedance Sensor

The efficiency of the metal detection method using deep learning with data obtained from multiple magnetic impedance (MI) sensors was investigated. The MI sensor is a passive sensor that detects metal objects and magnetic field changes. However, when detecting a metal object, the amount of change in the magnetic field caused by the metal is small and unstable with noise. Consequently, there is a limit to the detectable distance. To effectively detect and analyze this distance, a method using deep learning was applied. The detection performances of a convolutional neural network (CNN) and a recurrent neural network (RNN) were compared from the data extracted from a self-impedance sensor. The RNN model showed better performance than the CNN model. However, in the shallow stage, the CNN model was superior compared to the RNN model. The performance of a deep-learning-based (DLB) metal detection network using multiple MI sensors was compared and analyzed. The network was detected using long short-term memory and CNN. The performance was compared according to the number of layers and the size of the metal sheet. The results are expected to contribute to sensor-based DLB detection technology

Electric impedance microflow cytometry for characterization of cell disease states.

<p>The electrical properties of biological cells have connections to their pathological states. Here we present an electric impedance microflow cytometry (EIMC) platform for the characterization of disease states of single cells. This platform entails a microfluidic device for a label-free and non-invasive cell-counting assay through electric impedance sensing. We identified a dimensionless offset parameter δ obtained as a linear combination of a normalized phase shift and a normalized magnitude shift in electric impedance to differentiate cells on the basis of their pathological states. This paper discusses a representative case study on red blood cells (RBCs) invaded by the malaria parasite Plasmodium falciparum. Invasion by P. falciparum induces physical and biochemical changes on the host cells throughout a 48-h multi-stage life cycle within the RBC. As a consequence, it also induces progressive changes in electrical properties of the host cells. We demonstrate that the EIMC system in combination with data analysis involving the new offset parameter allows differentiation of P. falciparum infected RBCs from uninfected RBCs as well as among different P. falciparum intraerythrocytic asexual stages including the ring stage. The representative results provided here also point to the potential of the proposed experimental and analysis platform as a valuable tool for non-invasive diagnostics of a wide variety of disease states and for cell separation.</p

NF-κB/AP-1-Targeted Inhibition of Macrophage-Mediated Inflammatory Responses by Depigmenting Compound AP736 Derived from Natural 1,3-Diphenylpropane Skeleton

AP736 was identified as an antimelanogenic drug that can be used for the prevention of melasma, freckles, and dark spots in skin by acting as a suppressor of melanin synthesis and tyrosinase expression. Since macrophage-mediated inflammatory responses are critical for skin health, here we investigated the potential anti-inflammatory activity of AP736. The effects of AP736 on various inflammatory events such as nitric oxide (NO)/prostaglandin (PG) E2 production, inflammatory gene expression, phagocytic uptake, and morphological changes were examined in RAW264.7 cells. AP736 was found to strongly inhibit the production of both NO and PGE2 in lipopolysaccharide- (LPS-) treated RAW264.7 cells. In addition, AP736 strongly inhibited both LPS-induced morphological changes and FITC-dextran-induced phagocytic uptake. Furthermore, AP736 also downregulated the expression of multiple inflammatory genes, such as inducible NO synthase (iNOS), cyclooxygenase- (COX-) 2, and interleukin- (IL-) 1β in LPS-treated RAW264.7 cells. Transcription factor analysis, including upstream signalling events, revealed that both NF-κB and AP-1 were targeted by AP736 via inhibition of the IKK/IκBα and IRAK1/TAK1 pathways. Therefore, our results strongly suggest that AP736 is a potential anti-inflammatory drug due to its suppression of NF-κB-IKK/IκBα and AP-1-IRAK1/TAK1 signalling, which may make AP736 useful for the treatment of macrophage-mediated skin inflammation

Graphene-Based Thermopile for Thermal Imaging Applications

In this work, we leverage graphene’s unique tunable Seebeck coefficient for the demonstration of a graphene-based thermal imaging system. By integrating graphene based photothermo-electric detectors with micromachined silicon nitride membranes, we are able to achieve room temperature responsivities on the order of ∼7–9 V/W (at λ = 10.6 μm), with a time constant of ∼23 ms. The large responsivities, due to the combination of thermal isolation and broadband infrared absorption from the underlying SiN membrane, have enabled detection as well as stand-off imaging of an incoherent blackbody target (300–500 K). By comparing the fundamental achievable performance of these graphene-based thermopiles with standard thermocouple materials, we extrapolate that graphene’s high carrier mobility can enable improved performances with respect to two main figures of merit for infrared detectors: detectivity (>8 × 10⁸ cm Hz^1/2 W^–1) and noise equivalent temperature difference (<100 mK). Furthermore, even average graphene carrier mobility (<1000 cm² V^–1 s^–1) is still sufficient to detect the emitted thermal radiation from a human target