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Nerve Fiber Diameter Measurements Using Hematoxylin and Eosin Staining and Brightfield Microscopy to Assess the Novel Method of Characterizing Peripheral Nerve Fiber Distributions by Group DelayVazquez, Jorge Arturo 01 August 2014 (has links) (PDF)
Peripheral neuropathies are a set of common diseases that affect the peripheral nervous system, causing damage to vital connections between various parts of the body and the brain and spinal cord. Different clinical conditions are known to selectively impact various size nerve fibers, which often makes it difficult to diagnose which peripheral neuropathy a patient might have. The nerve conduction velocity diagnostic test provides clinically useful information in the diagnosis of some peripheral neuropathies. This method is advantageous because it tends to be minimally invasive yet it provides valuable diagnostic information. However, this test does not determine characteristics of peripheral nerve fiber size distributions, and therefore does not show any detailed information regarding the nerve fibers within the nerve trunk. Being able to determine which nerve fibers are contributing to the evoked potential within a nerve trunk could provide additional information to clinicians for the diagnosis of specific pathologies of the peripheral nervous system, such as chronic inflammatory demyelinating polyneuropathy or early diabetic peripheral neuropathy. In this study, three rat sciatic nerves are sectioned and stained with hematoxylin and eosin in order to measure the nerve fiber diameters within the nerve trunk. Stained samples are viewed using brightfield microscopy and images are analyzed using ImageJ. Histograms were created to show the frequency of various nerve fiber diameters. The nerve fiber diameters measured during this research are consistent with the range of previously published diameter values and will be used to support continuing research for a novel method to characterize peripheral nerve fiber size distributions using group delay.
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Computer-Aided Diagnoses (CAD) System: An Artificial Neural Network Approach to MRI Analysis and Diagnosis of Alzheimer's Disease (AD)Padilla Cerezo, Berizohar 01 December 2017 (has links) (PDF)
Alzheimer’s disease (AD) is a chronic and progressive, irreversible syndrome that deteriorates the cognitive functions. Official death certificates of 2013 reported 84,767 deaths from Alzheimer’s disease, making it the 6th leading cause of death in the United States. The rate of AD is estimated to double by 2050. The neurodegeneration of AD occurs decades before symptoms of dementia are evident. Therefore, having an efficient methodology for the early and proper diagnosis can lead to more effective treatments.
Neuroimaging techniques such as magnetic resonance imaging (MRI) can detect changes in the brain of living subjects. Moreover, medical imaging techniques are the best diagnostic tools to determine brain atrophies; however, a significant limitation is the level of training, methodology, and experience of the diagnostician. Thus, Computer aided diagnosis (CAD) systems are part of a promising tool to help improve the diagnostic outcomes. No publications addressing the use of Feedforward Artificial Neural Networks (ANN), and MRI image attributes for the classification of AD were found.
Consequently, the focus of this study is to investigate if the use of MRI images, specifically texture and frequency attributes along with a feedforward ANN model, can lead to the classification of individuals with AD. Moreover, this study compared the use of a single view versus a multi-view of MRI images and their performance. The frequency, texture, and MRI views in combination with the feedforward artificial neural network were tested to determine if they were comparable to the clinician’s performance. The clinician’s performances used were 78 percent accuracy, 87 percent sensitivity, 71 percent specificity, and 78 percent precision from a study with 1,073 individuals.
The study found that the use of the Discrete Wavelet Transform (DWT) and Fourier Transform (FT) low frequency give comparable results to the clinicians; however, the FT outperformed the clinicians with an accuracy of 85 percent, precision of 87 percent, sensitivity of 90 percent and specificity of 75 percent. In the case of texture, a single texture feature, and the combination of two or more features gave results comparable to the clinicians. However, the Gray level co-occurrence matrix (GLCOM), which is the combination of texture features, was the highest performing texture method with 82 percent accuracy, 86 percent sensitivity, 76 percent specificity, and 86 percent precision. Combination CII (energy and entropy) outperformed all other combinations with 78 percent accuracy, 88 percent sensitivity, 72 percent specificity, and 78 percent precision. Additionally, a combination of views can increase performance for certain texture attributes; however, the axial view outperformed the sagittal and coronal views in the case of frequency attributes. In conclusion, this study found that both texture and frequency characteristics in combinations with a feedforward backpropagation neural network can perform at the level of the clinician and even higher depending on the attribute and the view or combination of views used.
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A Low Power Electrical Method for Cell Accumulation and Lysis Using MicrofluidicsIslam, Md. Shehadul 10 1900 (has links)
<p>Microbiological contamination from bacteria such as <em>Escherichia coli</em> and Salmonella is one of the main reasons for waterborne illness. Real time and accurate monitoring of water is needed in order to alleviate this human health concern. Performing multiple and parallel analysis of biomarkers such as DNA and mRNA that targets different regions of pathogen functionality provides a complete picture of its presence and viability in the shortest possible time. These biomarkers are present inside the cell and need to be extracted for analysis and detection. Hence, lysis of these pathogenic bacteria is an important part in the sample preparation for rapid detection. In addition, collecting a small amount of bacteria present in a large volume of sample and concentrating them before lysing is important as it facilitates the downstream assay. Various techniques, categorized as mechanical, chemical, thermal and electrical, have been used for lysing cells. In the electrical method, cells are lysed by exposure to an external electric field. The advantage of this method, in contrast to other methods, is that it allows lysis without the introduction of any chemical and biological reagents and permits rapid recovery of intercellular organelles. Despite the advantages, issues such as high voltage requirement, bubble generation and Joule heating are associated with the electrical method.</p> <p>To alleviate the issues associated with electrical lysis, a new design and associated fabrication process for a microfluidic cell lysis device is described in this thesis. The device consists of a nanoporous polycarbonate (PCTE) membrane sandwiched between two PDMS microchannels with electrodes embedded at the reservoirs of the microchannels. Microcontact printing is used to attach this PCTE membrane with PDMS.</p> <p>By using this PCTE membrane, it was possible to intensify the electric field at the interface of two channels while maintaining it low in the other sections of the device. Furthermore, the device also allowed electrophoretic trapping of cells before lysis at a lower applied potential. For instance, it could trap bacteria such as <em>E. coli</em> from a continuous flow into the intersection between two channels for lower electric field (308 V/cm) and lyse the cell when electric field was increased more than 1000 V/cm into that section.</p> <p>Application of lower DC voltage with pressure driven flow alleviated adverse effect from Joule heating. Moreover, gas evolution and bubble generation was not observed during the operation of this device.</p> <p>Accumulation and lysis of bacteria were studied under a fluorescence microscope and quantified by using intensity measurement. To observe the accumulation and lysis, LIVE/DEAD BacLight Bacterial Viability Kit consisting of two separate components of SYTO 9 and propidium iodide (PI) into the cell suspension in addition to GFP expressed <em>E. coli</em> were used. Finally, plate counting was done to determine the efficiency of the device and it was observed that the device could lyse 90% of bacteria for an operation voltage of 300V within 3 min.</p> <p>In conclusion, a robust, reliable and flexible microfluidic cell lysis device was proposed and analyzed which is useful for sample pretreatment in a Micro Total Analysis System.</p> / Master of Applied Science (MASc)
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Development of Sparse Recovery Based Optimized Diffuse Optical and Photoacoustic Image Reconstruction MethodsShaw, Calvin B January 2014 (has links) (PDF)
Diffuse optical tomography uses near infrared (NIR) light as the probing media to re-cover the distributions of tissue optical properties with an ability to provide functional information of the tissue under investigation. As NIR light propagation in the tissue is dominated by scattering, the image reconstruction problem (inverse problem) is non-linear and ill-posed, requiring usage of advanced computational methods to compensate this.
Diffuse optical image reconstruction problem is always rank-deficient, where finding the independent measurements among the available measurements becomes challenging problem. Knowing these independent measurements will help in designing better data acquisition set-ups and lowering the costs associated with it. An optimal measurement selection strategy based on incoherence among rows (corresponding to measurements) of the sensitivity (or weight) matrix for the near infrared diffuse optical tomography is proposed. As incoherence among the measurements can be seen as providing maximum independent information into the estimation of optical properties, this provides high level of optimization required for knowing the independency of a particular measurement on its counterparts. The utility of the proposed scheme is demonstrated using simulated and experimental gelatin phantom data set comparing it with the state-of-the-art methods.
The traditional image reconstruction methods employ ℓ2-norm in the regularization functional, resulting in smooth solutions, where the sharp image features are absent. The sparse recovery methods utilize the ℓp-norm with p being between 0 and 1 (0 ≤ p1), along with an approximation to utilize the ℓ0-norm, have been deployed for the reconstruction of diffuse optical images. These methods are shown to have better utility in terms of being more quantitative in reconstructing realistic diffuse optical images compared to traditional methods.
Utilization of ℓp-norm based regularization makes the objective (cost) function non-convex and the algorithms that implement ℓp-norm minimization utilizes approximations to the original ℓp-norm function. Three methods for implementing the ℓp-norm were con-sidered, namely Iteratively Reweigthed ℓ1-minimization (IRL1), Iteratively Reweigthed Least-Squares (IRLS), and Iteratively Thresholding Method (ITM). These results in-dicated that IRL1 implementation of ℓp-minimization provides optimal performance in terms of shape recovery and quantitative accuracy of the reconstructed diffuse optical tomographic images.
Photoacoustic tomography (PAT) is an emerging hybrid imaging modality combining optics with ultrasound imaging. PAT provides structural and functional imaging in diverse application areas, such as breast cancer and brain imaging. A model-based iterative reconstruction schemes are the most-popular for recovering the initial pressure in limited data case, wherein a large linear system of equations needs to be solved. Often, these iterative methods requires regularization parameter estimation, which tends to be a computationally expensive procedure, making the image reconstruction process to be performed off-line. To overcome this limitation, a computationally efficient approach that computes the optimal regularization parameter is developed for PAT. This approach is based on the least squares-QR (LSQR) decomposition, a well-known dimensionality reduction technique for a large system of equations. It is shown that the proposed framework is effective in terms of quantitative and qualitative reconstructions of initial pressure distribution.
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A Stochastic Search Approach to Inverse ProblemsVenugopal, Mamatha January 2016 (has links) (PDF)
The focus of the thesis is on the development of a few stochastic search schemes for inverse problems and their applications in medical imaging. After the introduction in Chapter 1 that motivates and puts in perspective the work done in later chapters, the main body of the thesis may be viewed as composed of two parts: while the first part concerns the development of stochastic search algorithms for inverse problems (Chapters 2 and 3), the second part elucidates on the applicability of search schemes to inverse problems of interest in tomographic imaging (Chapters 4 and 5). The chapter-wise contributions of the thesis are summarized below.
Chapter 2 proposes a Monte Carlo stochastic filtering algorithm for the recursive estimation of diffusive processes in linear/nonlinear dynamical systems that modulate the instantaneous rates of Poisson measurements. The same scheme is applicable when the set of partial and noisy measurements are of a diffusive nature. A key aspect of our development here is the filter-update scheme, derived from an ensemble approximation of the time-discretized nonlinear Kushner Stratonovich equation, that is modified to account for Poisson-type measurements. Specifically, the additive update through a gain-like correction term, empirically approximated from the innovation integral in the filtering equation, eliminates the problem of particle collapse encountered in many conventional particle filters that adopt weight-based updates. Through a few numerical demonstrations, the versatility of the proposed filter is brought forth, first with application to filtering problems with diffusive or Poisson-type measurements and then to an automatic control problem wherein the exterminations of the associated cost functional is achieved simply by an appropriate redefinition of the innovation process.
The aim of one of the numerical examples in Chapter 2 is to minimize the structural response of a duffing oscillator under external forcing. We pose this problem of active control within a filtering framework wherein the goal is to estimate the control force that minimizes an appropriately chosen performance index. We employ the proposed filtering algorithm to estimate the control force and the oscillator displacements and velocities that are minimized as a result of the application of the control force. While Fig. 1 shows the time histories of the uncontrolled and controlled displacements and velocities of the oscillator, a plot of the estimated control force against the external force applied is given in Fig. 2.
(a) (b)
Fig. 1. A plot of the time histories of the uncontrolled and controlled (a) displacements and (b) velocities.
Fig. 2. A plot of the time histories of the external force and the estimated control force
Stochastic filtering, despite its numerous applications, amounts only to a directed search and is best suited for inverse problems and optimization problems with unimodal solutions. In view of general optimization problems involving multimodal objective functions with a priori unknown optima, filtering, similar to a regularized Gauss-Newton (GN) method, may only serve as a local (or quasi-local) search. In Chapter 3, therefore, we propose a stochastic search (SS) scheme that whilst maintaining the basic structure of a filtered martingale problem, also incorporates randomization techniques such as scrambling and blending, which are meant to aid in avoiding the so-called local traps. The key contribution of this chapter is the introduction of yet another technique, termed as the state space splitting (3S) which is a paradigm based on the principle of divide-and-conquer. The 3S technique, incorporated within the optimization scheme, offers a better assimilation of measurements and is found to outperform filtering in the context of quantitative photoacoustic tomography (PAT) to recover the optical absorption field from sparsely available PAT data using a bare minimum ensemble. Other than that, the proposed scheme is numerically shown to be better than or at least as good as CMA-ES (covariance matrix adaptation evolution strategies), one of the best performing optimization schemes in minimizing a set of benchmark functions.
Table 1 gives the comparative performance of the proposed scheme and CMA-ES in minimizing a set of 40-dimensional functions (F1-F20), all of which have their global minimum at 0, using an ensemble size of 20. Here, 10 5 is the tolerance limit to be attained for the objective function value and MAX is the maximum number of iterations permissible to the optimization scheme to arrive at the global minimum.
Table 1. Performance of the SS scheme and
Chapter 4 gathers numerical and experimental evidence to support our conjecture in the previous chapters that even a quasi-local search (afforded, for instance, by the filtered martingale problem) is generally superior to a regularized GN method in solving inverse problems. Specifically, in this chapter, we solve the inverse problems of ultrasound modulated optical tomography (UMOT) and diffraction tomography (DT). In UMOT, we perform a spatially resolved recovery of the mean-squared displacements, p r of the scattering centres in a diffusive object by measuring the modulation depth in the decaying autocorrelation of the incident coherent light. This modulation is induced by the input ultrasound focussed to a specific region referred to as the region of interest (ROI) in the object. Since the ultrasound-induced displacements are a measure of the material stiffness, in principle, UMOT can be applied for the early diagnosis of cancer in soft tissues. In DT, on the other hand, we recover the real refractive index distribution, n r of an optical fiber from experimentally acquired transmitted intensity of light traversing through it. In both cases, the filtering step encoded within the optimization scheme recovers superior reconstruction images vis-à-vis the GN method in terms of quantitative accuracies.
Fig. 3 gives a comparative cross-sectional plot through the centre of the reference and reconstructed p r images in UMOT when the ROI is at the centre of the object. Here, the anomaly is presented as an increase in the displacements and is at the centre of the ROI.
Fig. 4 shows the comparative cross-sectional plot of the reference and reconstructed refractive index distributions, n r of the optical fiber in DT.
Fig. 3. Cross-sectional plot through the center of the reference and reconstructed p r images.
Fig. 4. Cross-sectional plot through the center of the reference and reconstructed n r distributions.
In Chapter 5, the SS scheme is applied to our main application, viz. photoacoustic tomography (PAT) for the recovery of the absorbed energy map, the optical absorption coefficient and the chromophore concentrations in soft tissues. Nevertheless, the main contribution of this chapter is to provide a single-step method for the recovery of the optical absorption field from both simulated and experimental time-domain PAT data. A single-step direct recovery is shown to yield better reconstruction than the generally adopted two-step method for quantitative PAT. Such a quantitative reconstruction maybe converted to a functional image through a linear map. Alternatively, one could also perform a one-step recovery of the chromophore concentrations from the boundary pressure, as shown using simulated data in this chapter. Being a Monte Carlo scheme, the SS scheme is highly parallelizable and the availability of such a machine-ready inversion scheme should finally enable PAT to emerge as a clinical tool in medical diagnostics.
Given below in Fig. 5 is a comparison of the optical absorption map of the Shepp-Logan phantom with the reconstruction obtained as a result of a direct (1-step) recovery.
Fig. 5. The (a) exact and (b) reconstructed optical absorption maps of the Shepp-Logan phantom. The x- and y-axes are in m and the colormap is in mm-1.
Chapter 6 concludes the work with a brief summary of the results obtained and suggestions for future exploration of some of the schemes and applications described in this thesis.
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Label-free surface-enhanced Raman spectroscopy-linked immunosensor assay (SLISA) for environmental surveillancebhardwaj, vinay 02 October 2015 (has links)
The contamination of the environment, accidental or intentional, in particular with chemical toxins such as industrial chemicals and chemical warfare agents has increased public fear. There is a critical requirement for the continuous detection of toxins present at very low levels in the environment. Indeed, some ultra-sensitive analytical techniques already exist, for example chromatography and mass spectroscopy, which are approved by the US Environmental Protection Agency for the detection of toxins. However, these techniques are limited to the detection of known toxins. Cellular expression of genomic and proteomic biomarkers in response to toxins allows monitoring of known as well as unknown toxins using Polymerase Chain Reaction and Enzyme Linked Immunosensor Assays. However, these molecular assays allow only the endpoint (extracellular) detection and use labels such as fluorometric, colorimetric and radioactive, which increase chances of uncertainty in detection. Additionally, they are time, labor and cost intensive. These technical limitations are unfavorable towards the development of a biosensor technology for continuous detection of toxins. Federal agencies including the Departments of Homeland Security, Agriculture, Defense and others have urged the development of a detect-to-protect class of advanced biosensors, which enable environmental surveillance of toxins in resource-limited settings.
In this study a Surface-Enhanced Raman Spectroscopy (SERS) immunosensor, aka a SERS-linked immunosensor assay (SLISA), has been developed. Colloidal silver nanoparticles (Ag NPs) were used to design a flexible SERS immunosensor. The SLISA proof-of-concept biosensor was validated by the measurement of a dose dependent expression of RAD54 and HSP70 proteins in response to H2O2 and UV. A prototype microchip, best suited for SERS acquisition, was fabricated using an on-chip SLISA to detect RAD54 expression in response to H2O2. A dose-response relationship between H2O2 and RAD54 is established and correlated with EPA databases, which are established for human health risk assessment in the events of chemical exposure. SLISA outperformed ELISA by allowing RISE (rapid, inexpensive, simple and effective) detection of proteins within 2 hours and 3 steps. It did not require any label and provided qualitative information on antigen-antibody binding. SLISA can easily be translated to a portable assay using a handheld Raman spectrometer and it can be used in resource-limited settings. Additionally, this is the first report to deliver Ag NPs using TATHA2, a fusogenic peptide with cell permeability and endosomal rupture release properties, for rapid and high levels of Ag NPs uptake into yeast without significant toxicity, prerequisites for the development of the first intracellular SERS immunosensor.
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Developent of a Phospholipid Encapsulation Process for Quantum Dots to Be Used in Biologic ApplicationsGrimes, Logan 01 June 2014 (has links) (PDF)
The American Cancer Society predicts that 1,665,540 people will be diagnosed with cancer, and 585,720 people will die from cancer in 2014. One of the most common types of cancer in the United States is skin cancer. Melanoma alone is predicted to account for 10,000 of the cancer related deaths in 2014. As a highly mobile and aggressive form of cancer, melanoma is difficult to fight once it has metastasized through the body. Early detection in such varieties of cancer is critical in improving survival rates in afflicted patients. Present methods of detection rely on visual examination of suspicious regions of tissue via various forms of biopsies. Accurate assessment of cancerous cells via this method are subjective, and often unreliable in the early stages of cancer formation when only few cancer cells are forming. With fewer cancer cells, it is less likely that a cancer cell will appear in a biopsied tissue. This leads to a lower detection rate, even when cancer is present. This lack of detection when cancer is in fact present is referred to as a false negative. False negatives can have a highly detrimental effect on treating the cancer as soon as possible. More accurate methods of detecting cancer in early stages, in a nonsubjective form would alleviate these problems. A proposed alternative to visual examination of biopsied legions is to utilize fluorescent nanocrystalline biomarker constructs to directly attach to the abnormal markers found on cancerous tissues.
Quantum dots (QDs) are hydrophobic nanoscale crystals composed of semiconducting materials which fluoresce when exposed to specific wavelengths of radiation, most commonly in the form of an ultraviolet light source. The QD constructs generated were composed of cadmium-selenium (CdSe) cores encapsulated with zinc-sulfide (ZnS) shells. These QDs were then encapsulated with phospholipids in an effort to create a hydrophilic particle which could interact with polar fluids as found within the human body. The goal of this thesis is to develop a method for the solubilization, encapsulation, and initial functionalization of CdSe/ZnS QDs. The first stage of this thesis focused on the generation of CdSe/ZnS QDs and the fluorescence differences between unshelled and shelled QDs. The second stage focused on utilizing the shelled QDs to generate hydrophilic constructs by utilizing phospholipids to bind with the QDs. Analysis via spectroscopy was performed in an effort to characterize the difference in QDs both prior to and after the encapsulation process. The method generated provides insight on fluorescence trends and the encapsulation of QDs in polar substances. Future research focusing on the repeatability of the process, introducing the QD constructs to a biological material, and eventual interaction with cancer cells are the next steps in generating a new technique to target and reveal skin cancer cells in the earliest possible stages without using a biopsy.
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The Effects of Emerging Technology on Healthcare and the Difficulties of IntegrationPavlish-Carpenter, Skyler J 01 January 2018 (has links)
Background: Disruptive technology describes technology that is significantly more advanced than previous iterations, such as: 3D printing, genetic manipulation, stem cell research, innovative surgical procedures, and computer-based charting software. These technologies often require extensive overhauls to implement into older systems and must overcome many difficult financial and societal complications before they can be widely used. In a field like healthcare that makes frequent advancements, these difficulties can mean that the technology will not be utilized to its full potential or implemented at all.
Objective: To determine the inhibiting factors that prevent disruptive technology from being implemented in conventional healthcare.
Methods: Peer reviewed articles were gathered from Cumulative Index to Nursing and Allied Health Literature (CINAHL), Educational Resources Information Center (ERIC), Elton B. Stephens Co. Host (Ebsco Host), Medical Literature On-line (Medline), and Psychological Information Database (PsychINFO). Articles were included if written in English and focusing on technology that was or is difficult to implement.
Results: Research suggests that the primary reason disruptive technology is not implemented sooner is the cost versus benefit ratio. Those technologies with extremely high benefits that greatly improve efficiency, safety, or expense are integrated relatively quickly, especially if their cost is reasonable. Secondary reasons for difficulty with integration include ethical dilemmas, extreme complexity, technical limitations, maintenance, security, and fallibility.
Conclusion: Research indicates that a decrease in production cost and selling price along with removing any issues that may depreciate the technology will provide better incentives for healthcare systems to integrate disruptive technologies on a wider scale.
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Vision Beyond Optics: Standardization, Evaluation and Innovation for Fluorescence Microscopy in Life SciencesHuisman, Maximiliaan 01 April 2019 (has links)
Fluorescence microscopy is an essential tool in biomedical sciences that allows specific molecules to be visualized in the complex and crowded environment of cells. The continuous introduction of new imaging techniques makes microscopes more powerful and versatile, but there is more than meets the eye. In addition to develop- ing new methods, we can work towards getting the most out of existing data and technologies. By harnessing unused potential, this work aims to increase the richness, reliability, and power of fluorescence microscopy data in three key ways: through standardization, evaluation and innovation.
A universal standard makes it easier to assess, compare and analyze imaging data – from the level of a single laboratory to the broader life sciences community. We propose a data-standard for fluorescence microscopy that can increase the confidence in experimental results, facilitate the exchange of data, and maximize compatibility with current and future data analysis techniques.
Cutting-edge imaging technologies often rely on sophisticated hardware and multi-layered algorithms for reconstruction and analysis. Consequently, the trustworthiness of new methods can be difficult to assess. To evaluate the reliability and limitations of complex methods, quantitative analyses – such as the one present here for the 3D SPEED method – are paramount.
The limited resolution of optical microscopes prevents direct observation of macro- molecules like DNA and RNA. We present a multi-color, achromatic, cryogenic fluorescence microscope that has the potential to produce multi-color images with sub-nanometer precision. This innovation would move fluorescence imaging beyond the limitations of optics and into the world of molecular resolution.
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