Targeted Multistage Delivery of Nanoparticles to the Bone Marrow

Bone marrow is a target organ site involved in multiple diseases including myeloproliferative disorders and hematologic malignancies and metastases from breast and prostate. Most of these diseases are characterized with poor quality of life, and the treatment options are only palliative due to lack of delivery mechanisms for systemically injected drugs which results in dose limitation to protect the healthy hematopoietic cells. Therefore, there is a critical need to develop effective therapeutic strategies that allow for selective delivery of therapeutic payload to the bone marrow. Nanotechnology-based drug delivery systems provide the opportunity to deliver drugs to the target tissue while decreasing exposure to normal tissues. E-selectin is constitutively expressed on the bone marrow vasculature, but almost absent in normal vessels, and therefore, E-selectin targeted drug delivery presents an ideal strategy for the delivery of therapeutic nanoparticles to the bone marrow. The objective of this study was to develop a novel bone marrow targeted multistage vector (MSV) via E-selectin for delivery of therapeutics and imaging agents. To achieve this goal, Firstly, an E-selectin thioaptamer (ESTA) ligand was identified through a two-step screening from a combinatorial thioaptamer library. Next, ESTA-conjugated MSV (ESTA-MSV) were developed and evaluated for their stability and binding to E-selectin expressing endothelial cells. Different types of nanoparticles including liposomes, quantum dots, and iron oxide nanoparticles were loaded into the porous structure of ESTA-MSV. In vivo targeting experiments demonstrated 8-fold higher accumulation of ESTA-MSV in the mouse bone marrow as compared to non-targeted MSV Furthermore, intravenous injection of liposomes loaded ESTA-MSV resulted in a significantly higher accumulation of liposome in the bone marrow space as compared to injection of non-targeted MSV or liposomes alone. Overall this study provides first evidence that E-selectin targeted multistage vector preferentially targets to bone marrow vasculature and delivers larger amounts of nanoparticles. This delivery strategy holds potential for the selective delivery of large amounts of therapeutic payload to the vascular niches in the bone marrow for the treatment of bone marrow associated diseases.

Anti-PSMA antibody-coupled gold nanorods detection by optical and electron microscopies

While cancer is one of the greatest challenges to public health care, prostate cancer was chosen as cancer model to develop a more accurate imaging assessment than those currently available. Indeed, an efficient imaging technique which considerably improves the sensitivity and specificity of the diagnostic and predicting the cancer behavior would be extremely valuable. The concept of optoacoustic imaging using home-made functionalized gold nanoparticles coupled to an antibody targeting PSMA (prostate specific membrane antigen) was evaluated on different cancer cell lines to demonstrate the specificity of the designed platform. Two commonly used microscopy techniques (indirect fluorescence and scanning electron microscopy) showed their straightforwardness and versatility for the nanoparticle binding investigations regardless the composition of the investigated nanoobjects. Moreover most of the research laboratories and centers are equipped with fluorescence microscopes, so indirect fluorescence using Quantum dots can be used for any active targeting nanocarriers (polymers, ceramics, metals, etc.). The second technique based on backscattered electron is not only limited to gold nanoparticles but also suits for any study of metallic nanoparticles as the electronic density difference between the nanoparticles and binding surface stays high enough. Optoacoustic imaging was finally performed on a 3D cellular model to assess and prove the concept of the developed platform.

Efficient luminescent solar concentrators based on self-absorption free, Tm2+ doped halides

The optical and luminescence properties of CaI2 and NaCl doped with divalent thulium are reported for solar energy applications. These halides strongly absorb solar light from the UV up to 900 nm due to the intense Tm2+ 4f13→4f125d1 electronic transitions. Absorption is followed by emission of 1140 nm light due to the 2F5/2→2F7/2 transition of the 4f13 configuration that can be efficiently converted to electric power by thin film CuInSe2 (CIS) solar cells. Because of a negligible spectral overlap between absorption and emission spectra, a luminescent solar concentrator (LSC) based on these black luminescent materials would not suffer from self-absorption losses. The Tm2+ doped halides may therefore lead to efficient semi-transparent power generating windows that absorb solar light over the whole visible spectrum. It will be shown that the power efficiency of the Tm2+ based LSCs can be up to four times higher compared to LSCs based on organic dyes or quantum dots.

Expanded Clinical Spectrum of Multiple Evanescent White Dot Syndrome with Multimodal Imaging

PURPOSE: To evaluate and characterize multiple evanescent white dot syndrome abnormalities with modern multimodal imaging modalities. METHODS: This retrospective cohort study evaluated fundus photography, fluorescein angiography, indocyanine green angiography, optical coherence tomography, enhanced depth imaging optical coherence tomography, short-wavelength autofluorescence, and near-infrared autofluorescence. RESULTS: Thirty-four multiple evanescent white dot syndrome patients with mean age of 28.7 years were studied (range, 14-49 years). Twenty-six patients were women, and eight were men. Initial mean visual acuity was 0.41 logMAR. Final mean visual acuity was 0.03 logMAR. Fluorescein angiography shows a variable number of mid retinal early fluorescent dots distributed in a wreathlike pattern, which correlate to fundus photography, fundus autofluorescence, and indocyanine green angiography. Indocyanine green angiography imaging shows the dots and also hypofluorescent, deeper, and larger spots, which are occasionally confluent, demonstrating a large plaque of deep retinal hypofluorescence. Optical coherence tomography imaging shows multifocal debris centered at and around the ellipsoid layer, corresponding to the location of spots seen with photography, indocyanine green angiography, and fluorescein angiography. Protrusions of the hyperreflectant material from the ellipsoid layer toward the outer nuclear layer correspond to the location of dots seen with photography, indocyanine green angiography, and fluorescein angiography. CONCLUSION: Multimodal imaging analysis of the retina in patients with multiple evanescent white dot syndrome shows additional features that may help in the diagnosis of the disease and in further understanding its etiology. Multiple evanescent white dot syndrome is predominantly a disease of the outer retina, centered at the ellipsoid zone, but also involving the interdigitation zone and the outer nuclear layer.

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Weather Inputs to a Warning System for Sooty Blotch and Flyspeck of Apple

Sooty blotch and flyspeck (SBFS) is a mid- to late-season disease of apple. SBFS fungi show up as dark smudges and clusters of black dots on the fruit surface. Since blemished fruit are downgraded, crop losses can exceed 90 percent of the fresh market value.

Desarrollo de estructuras nanofotonicas de campo cercano para intensificacion localizada de luz en celulas solares de banda intermedia - Near-field nanophotonic structures for localized light enhancement in intermediate band solar cells

This doctoral thesis explores some of the possibilities that near-field optics can bring to photovoltaics, and in particular to quantum-dot intermediate band solar cells (QD-IBSCs). Our main focus is the analytical optimization of the electric field distribution produced in the vicinity of single scattering particles, in order to produce the highest possible absorption enhancement in the photovoltaic medium in their surroundings. Near-field scattering structures have also been fabricated in laboratory, allowing the application of the previously studied theoretical concepts to real devices. We start by looking into the electrostatic scattering regime, which is only applicable to sub-wavelength sized particles. In this regime it was found that metallic nano-spheroids can produce absorption enhancements of about two orders of magnitude on the material in their vicinity, due to their strong plasmonic resonance. The frequency of such resonance can be tuned with the shape of the particles, allowing us to match it with the optimal transition energies of the intermediate band material. Since these metallic nanoparticles (MNPs) are to be inserted inside the cell photovoltaic medium, they should be coated by a thin insulating layer to prevent electron-hole recombination at their surface. This analysis is then generalized, using an analytical separation-of-variables method implemented in Mathematica7.0, to compute scattering by spheroids of any size and material. This code allowed the study of the scattering properties of wavelengthsized particles (mesoscopic regime), and it was verified that in this regime dielectric spheroids perform better than metallic. The light intensity scattered from such dielectric spheroids can have more than two orders of magnitude than the incident intensity, and the focal region in front of the particle can be shaped in several ways by changing the particle geometry and/or material. Experimental work was also performed in this PhD to implement in practice the concepts studied in the analysis of sub-wavelength MNPs. A wet-coating method was developed to self-assemble regular arrays of colloidal MNPs on the surface of several materials, such as silicon wafers, amorphous silicon films, gallium arsenide and glass. A series of thermal and chemical tests have been performed showing what treatments the nanoparticles can withstand for their embedment in a photovoltaic medium. MNPs arrays are then inserted in an amorphous silicon medium to study the effect of their plasmonic near-field enhancement on the absorption spectrum of the material. The self-assembled arrays of MNPs constructed in these experiments inspired a new strategy for fabricating IBSCs using colloidal quantum dots (CQDs). Such CQDs can be deposited in self-assembled monolayers, using procedures similar to those developed for the patterning of colloidal MNPs. The use of CQDs to form the intermediate band presents several important practical and physical advantages relative to the conventional dots epitaxially grown by the Stranski-Krastanov method. Besides, this provides a fast and inexpensive method for patterning binary arrays of QDs and MNPs, envisioned in the theoretical part of this thesis, in which the MNPs act as antennas focusing the light in the QDs and therefore boosting their absorption

Radiative thermal escape in intermediate band solar cells

To achieve high efficiency, the intermediate band (IB) solar cell must generate photocurrent from sub-bandgap photons at a voltage higher than that of a single contributing sub-bandgap photon. To achieve the latter, it is necessary that the IB levels be properly isolated from the valence and conduction bands. We prove that this is not the case for IB cells formed with the confined levels of InAs quantum dots (QDs) in GaAs grown so far due to the strong density of internal thermal photons at the transition energies involved. To counteract this, the QD must be smaller.

Room temperature absorption in laterally biased Quantum Infrared Detectors fabricated by MBE regrowth

In this paper, we show room temperature operation of a quantum well infrared photodetector (QWIP) using lateral conduction through ohmic contacts deposited at both sides of two n-doped quantum wells. To reduce the dark current due to direct conduction in the wells, we apply an electric field between the quantum wells and two pinch-off Schottky gates, in a fashion similar to a field effect device. Since the normal incidence absorption is strongly reduced in intersubband transitions in quantum wells, we first analyze the response of a detector based on quantum dots (QD). This QD device shows photocurrent signal up to 150 K when it is processed in conventional vertical detector. However, it is possible to observe room temperature signal when it is processed in a lateral structure. Finally, the room temperature photoresponse of the QWIP is demonstrated, and compared with theory. An excellent agreement between the estimated and measured characteristics of the device is found

Near infrared high efficiency InAs/GaAsSb QDLEDs: band alignment and carrier recombination mechanisms

The development of high efficiency laser diodes (LD) and light emitting diodes (LED) covering the 1.0 to 1.55 μm region of the spectra using GaAs heteroepitaxy has been long pursued. Due to the lack of materials that can be grown lattice-macthed to GaAs with bandgaps in the 1.0 to 1.55 μm region, quantum wells (QW) or quantum dots (QD) need be used. The most successful approach with QWs has been to use InGaAs, but one needs to add another element, such as N, to be able to reach 1.3/1.5μm. Even though LDs have been successfully demonstrated with the QW approach, using N leads to problems with compositional homogeneity across the wafer, and limited efficiency due to strong non-radiative recombination. The alternative approach of using InAs QDs is an attractive option, but once again, to reach the longest wavelengths one needs very large QDs and control over the size distribution and band alignment. In this work we demonstrate InAs/GaAsSb QDLEDs with high efficiencies, emitting from 1.1 to 1.52 μm, and we analyze the band alignment and carrier loss mechanisms that result from the presence of Sb in the capping layer.

Gas sensor based on room temperature optical properties of Surface QDs

Self-organized InGaAs QDs are intensively studied for optoelectronic applications. Several approaches are in study to reach the emission wavelengths needed for these applications. The use of antimony (Sb) in either the capping layer or into the dots is one example. However, these studies are normally focused on buried QD (BQD) where there are still different controversial theories concerning the role of Sb. Ones suggest that Sb incorporates into the dot [1], while others support the hypothesis that the Sb occupies positions surrounding the dot [2] thus helping to keep their shape during the capping growth.

Aplicación de representación gráfica de datos y/o señales en entorno GNU/LINUX

El contenido de este PFC supone el desarrollo de una aplicación gráfica en el entorno GNU/LINUX. La aplicación debe representar en la pantalla gráficos procedentes de una fuente externa (un fichero, una tarjeta de adquisición de datos, otra aplicación, etc.) Debe permitir las siguientes funcionalidades: Representar la señal uniendo puntos o marcándolos como símbolos. Debe gestionar de forma adecuada el zoom. Debe permitir exportar la gráfica a formatos SVG y PDF. Debe permitir mostrar varias señales superpuestas para poder compararlas. Opcionalmente debe permitir arrastrar y soltar (“drag and drop”). ABSTRACT The content of the thesis is the development of a graphical application in GNU / Linux environment. The application must render graphics on the screen from an external source (a file, a data acquisition card, other applications, etc..) It must allow the following functionality: Joining dots represent the signal or marking them as symbols. Must properly manage the zoom. Allow export the graph to SVG and PDF formats. Allow display multiple signals superimposed for comparison. Optionally should allow drag and drop.

Absorption coefficient for the intraband transitions in quantum dot materials

In this paper, we present calculations of the absorption coefficient for transitions between the bound states of quantum dots grown within a semiconductor and the extended states of the conduction band. For completeness, transitions among bound states are also presented. In the separation of variables, single band k·p model is used in which most elements may be expressed analytically. The analytical formulae are collected in the appendix of this paper. It is concluded that the transitions are strong enough to provide a quick path to the conduction band for electrons pumped from the valence to the intermediate band

The influence of quantum dot size on the sub-bandgap intraband photocurrent in intermediate band solar cells

The effect of quantum dot (QD) size on the performance of quantum dot intermediate band solar cells is investigated. A numerical model is used to calculate the bound state energy levels and the absorption coefficient of transitions from the ground state to all other states in the conduction band. Comparing with the current state of the art, strong absorption enhancements are found for smaller quantum dots, as well as a better positioning of the energy levels, which is expected to reduce thermal carrier escape. It is concluded that reducing the quantum dot size can increase sub-bandgap photocurrent and improve voltage preservation.

Site-controlled Ag nanocrystals grown by molecular beam epitaxy-Towards plasmonic integration technology

We demonstrate site-controlled growth of epitaxial Ag nanocrystals on patterned GaAs substrates by molecular beam epitaxy with high degree of long-range uniformity. The alignment is based on lithographically defined holes in which position controlled InAs quantum dots are grown. The Ag nanocrystals self-align preferentially on top of the InAs quantum dots. No such ordering is observed in the absence of InAs quantum dots, proving that the ordering is strain-driven. The presented technique facilitates the placement of active plasmonic nanostructures at arbitrarily defined positions enabling their integration into complex devices and plasmonic circuits.