Characterization of nanoparticle mediated laser transfection by femtosecond laser pulses for applications in molecular medicine

Background: In molecular medicine, the manipulation of cells is prerequisite to evaluate genes as therapeutic targets or to transfect cells to develop cell therapeutic strategies. To achieve these purposes it is essential that given transfection techniques are capable of handling high cell numbers in reasonable time spans. To fulfill this demand, an alternative nanoparticle mediated laser transfection method is presented herein. The fs-laser excitation of cell-adhered gold nanoparticles evokes localized membrane permeabilization and enables an inflow of extracellular molecules into cells. Results: The parameters for an efficient and gentle cell manipulation are evaluated in detail. Efficiencies of 90% with a cell viability of 93% were achieved for siRNA transfection. The proof for a molecular medical approach is demonstrated by highly efficient knock down of the oncogene HMGA2 in a rapidly proliferating prostate carcinoma in vitro model using siRNA. Additionally, investigations concerning the initial perforation mechanism are conducted. Next to theoretical simulations, the laser induced effects are experimentally investigated by spectrometric and microscopic analysis. The results indicate that near field effects are the initial mechanism of membrane permeabilization. Conclusion: This methodical approach combined with an automated setup, allows a high throughput targeting of several 100,000 cells within seconds, providing an excellent tool for in vitro applications in molecular medicine. NIR fs lasers are characterized by specific advantages when compared to lasers employing longer (ps/ns) pulses in the visible regime. The NIR fs pulses generate low thermal impact while allowing high penetration depths into tissue. Therefore fs lasers could be used for prospective in vivo applications.

Learning Binary Code Representations for Effective and Efficient Image Retrieval

The size of online image datasets is constantly increasing. Considering an image dataset with millions of images, image retrieval becomes a seemingly intractable problem for exhaustive similarity search algorithms. Hashing methods, which encodes high-dimensional descriptors into compact binary strings, have become very popular because of their high efficiency in search and storage capacity. In the first part, we propose a multimodal retrieval method based on latent feature models. The procedure consists of a nonparametric Bayesian framework for learning underlying semantically meaningful abstract features in a multimodal dataset, a probabilistic retrieval model that allows cross-modal queries and an extension model for relevance feedback. In the second part, we focus on supervised hashing with kernels. We describe a flexible hashing procedure that treats binary codes and pairwise semantic similarity as latent and observed variables, respectively, in a probabilistic model based on Gaussian processes for binary classification. We present a scalable inference algorithm with the sparse pseudo-input Gaussian process (SPGP) model and distributed computing. In the last part, we define an incremental hashing strategy for dynamic databases where new images are added to the databases frequently. The method is based on a two-stage classification framework using binary and multi-class SVMs. The proposed method also enforces balance in binary codes by an imbalance penalty to obtain higher quality binary codes. We learn hash functions by an efficient algorithm where the NP-hard problem of finding optimal binary codes is solved via cyclic coordinate descent and SVMs are trained in a parallelized incremental manner. For modifications like adding images from an unseen class, we propose an incremental procedure for effective and efficient updates to the previous hash functions. Experiments on three large-scale image datasets demonstrate that the incremental strategy is capable of efficiently updating hash functions to the same retrieval performance as hashing from scratch.

Calculation of sample size for stroke trials assessing functional outcome: comparison of binary and ordinal approaches

Background Many acute stroke trials have given neutral results. Sub-optimal statistical analyses may be failing to detect efficacy. Methods which take account of the ordinal nature of functional outcome data are more efficient. We compare sample size calculations for dichotomous and ordinal outcomes for use in stroke trials. Methods Data from stroke trials studying the effects of interventions known to positively or negatively alter functional outcome – Rankin Scale and Barthel Index – were assessed. Sample size was calculated using comparisons of proportions, means, medians (according to Payne), and ordinal data (according to Whitehead). The sample sizes gained from each method were compared using Friedman 2 way ANOVA. Results Fifty-five comparisons (54 173 patients) of active vs. control treatment were assessed. Estimated sample sizes differed significantly depending on the method of calculation (Po00001). The ordering of the methods showed that the ordinal method of Whitehead and comparison of means produced significantly lower sample sizes than the other methods. The ordinal data method on average reduced sample size by 28% (inter-quartile range 14–53%) compared with the comparison of proportions; however, a 22% increase in sample size was seen with the ordinal method for trials assessing thrombolysis. The comparison of medians method of Payne gave the largest sample sizes. Conclusions Choosing an ordinal rather than binary method of analysis allows most trials to be, on average, smaller by approximately 28% for a given statistical power. Smaller trial sample sizes may help by reducing time to completion, complexity, and financial expense. However, ordinal methods may not be optimal for interventions which both improve functional outcome

Controlling Pattern Formation in Nanoparticle Assemblies via Directed Solvent Dewetting

We have achieved highly localised control of pattern formation in two dimensional nanoparticle assemblies by direct modification of solvent dewetting dynamics. A striking dependence of nanoparticle organisation on the size of atomic force microscope-generated surface heterogeneities is observed and reproduced in numerical simulations. Nanoscale features induce rupture of the solvent-nanoparticle film, causing the local flow of solvent to carry nanoparticles into confinement. Microscale heterogeneities instead slow the evaporation of the solvent, producing a remarkably abrupt interface between different nanoparticle patterns.

Use of ordinal outcomes in vascular prevention trials: comparison with binary outcomes in published trials

Background and Purpose—Vascular prevention trials mostly count “yes/no” (binary) outcome events, eg, stroke/no stroke. Analysis of ordered categorical vascular events (eg, fatal stroke/nonfatal stroke/no stroke) is clinically relevant and could be more powerful statistically. Although this is not a novel idea in the statistical community, ordinal outcomes have not been applied to stroke prevention trials in the past. Methods—Summary data on stroke, myocardial infarction, combined vascular events, and bleeding were obtained by treatment group from published vascular prevention trials. Data were analyzed using 10 statistical approaches which allow comparison of 2 ordinal or binary treatment groups. The results for each statistical test for each trial were then compared using Friedman 2-way analysis of variance with multiple comparison procedures. Results—Across 85 trials (335 305 subjects) the test results differed substantially so that approaches which used the ordinal nature of stroke events (fatal/nonfatal/no stroke) were more efficient than those which combined the data to form 2 groups (P0.0001). The most efficient tests were bootstrapping the difference in mean rank, Mann–Whitney U test, and ordinal logistic regression; 4- and 5-level data were more efficient still. Similar findings were obtained for myocardial infarction, combined vascular outcomes, and bleeding. The findings were consistent across different types, designs and sizes of trial, and for the different types of intervention. Conclusions—When analyzing vascular events from prevention trials, statistical tests which use ordered categorical data are more efficient and are more likely to yield reliable results than binary tests. This approach gives additional information on treatment effects by severity of event and will allow trials to be smaller. (Stroke. 2008;39:000-000.)

Vapor–liquid equilibrium of binary mixtures containing isopropyl acetate and alkanols at 101.32 kPa

Isobaric vapor-liquid equilibria of binary mixtures of isopropyl acetate plus an alkanol (1-propanol, 2-propanol, 1-butanol, or 2-butanol) were measured at 101.32 kPa, using a dynamic recirculating still. An azeotropic behavior was observed only in the mixtures of isopropyl acetate + 2-propanol and isopropyl acetate + 1-propanol. The application of four thermodynamic consistency tests (the Herington test, the Van Ness test, the infinite dilution test, and the pure component test) showed the high quality of the experimental data. Finally, both NRTL and UNIQUAC activity coefficient models were successfully applied in the correlation of the measured data, with the average absolute deviations in vapor phase composition and temperature of 0.01 and 0.16 K, respectively.

Testing the link specification in binary choice models. A semiparametric approach.

Doutoramento em Matemática.

Methotrexate nanoparticle delivery system for treatment of inflammatory bowel disease in pediatric patients

Purpose: To evaluate the efficacy and safety of methotrexate (MTX) nanoparticles in pediatric patients with inflammatory bowel disease (IBD). Methods: In this randomized, open-label clinical study, 28 pediatric patients with moderate to severe IBD were randomly assigned to treatment (MTX nanoparticles,15 mg/week) or control (azathioprine, AZA, 2 mg/kg/day) group. Nanoparticles were synthesized by adding calcium chloride to sodium alginate solution containing MTX, and was further treated with poly-L-lysine aqueous solution. The nanoparticles were evaluated for particle size, zeta potential and drug encapsulation efficacy. Erythrocyte sedimentation rate, C-reactive protein, aspartate aminotransferase, alanine transaminase, and disease activity scores were used to assess IBD remission. Results: Nanoparticle size, zeta potential and encapsulation efficacy were 164.4 ± 6.9 nm, -32.6 ± 3.7 mV, and 97.8 ± 4.2 %, respectively. After 12 weeks of therapy, the mean Pediatric Crohn\'s Disease Activity Index (PCDAI) scores for control and treatment groups were 22.3 ± 2.14 and 16.8 ± 1.87, respectively, while mean Pediatric Ulcerative Colitis Activity (PUCAI) Index scores were 24.3 ± 1.47 and 18.7 ± 1.92, respectively. Eight patients in the treatment and five patients in the control group achieved remission. Biochemical parameters varied significantly between the groups. Conclusion: MTX nanoparticles are safe and more effective than standard first-line IBD therapy. However, further studies are required to determine the suitability of the formulation for therapeutic use.

Magnetic Nanoparticle-based Targeted Drug Delivery for Treatment of Neuro-AIDS and Drug Addiction

Brain is one of the safe sanctuaries for HIV and, in turn, continuously supplies active viruses to the periphery. Additionally, HIV infection in brain results in several mild-to-severe neuro-immunological complications termed neuroAIDS. One-tenth of HIV-infected population is addicted to recreational drugs such as opiates, alcohol, nicotine, marijuana, etc. which share common target-areas in the brain with HIV. Interestingly, intensity of neuropathogenesis is remarkably enhanced due to exposure of recreational drugs during HIV infection. Current treatments to alleviate either the individual or synergistic effects of abusive drugs and HIV on neuronal modulations are less effective at CNS level, basically due to impermeability of therapeutic molecules across blood-brain barrier (BBB). Despite exciting advancement of nanotechnology in drug delivery, existing nanovehicles such as dendrimers, polymers, micelles, etc. suffer from the lack of adequate BBB penetrability before the drugs are engulfed by the reticuloendothelial system cells as well as the uncertainty that if and when the nanocarrier reaches the brain. Therefore, in order to develop a fast, target-specific, safe, and effective approach for brain delivery of anti-addiction, anti-viral and neuroprotective drugs, we exploited the potential of magnetic nanoparticles (MNPs) which, in recent years, has attracted significant importance in biomedical applications. We hypothesize that under the influence of external (non-invasive) magnetic force, MNPs can deliver these drugs across BBB in most effective manner. Accordingly, in this dissertation, I delineated the pharmacokinetics and dynamics of MNPs bound anti-opioid, anti-HIV and neuroprotective drugs for delivery in brain. I have developed a liposome-based novel magnetized nanovehicle which, under the influence of external magnetic forces, can transmigrate and effectively deliver drugs across BBB without compromising its integrity. It is expected that the developed nanoformulations may be of high therapeutic significance for neuroAIDS and for drug addiction as well.

Niobic acid nanoparticle catalysts for the aqueous phase transformation of glucose and fructose to 5-hydroxymethylfurfural

A family of bulk and SBA-15 supported peroxo niobic acid sols were prepared by peptisation of niobic acid precipitates with H2O2 as heterogeneous catalysts for aqueous phase glucose and fructose conversion to 5-hydroxymethylfurfural (5-HMF). Niobic acid nanoparticles possess a high density of Brønsted and Lewis acid sites, conferring good activity towards glucose and fructose conversion, albeit with modest 5-HMF yields under mild reaction conditions (100 °C). Thermally-induced niobia crystallisation suppresses solid acidity and activity. Nanoparticulate niobic acid dispersed over SBA-15 exhibits pure Brønsted acidity and an enhanced Turnover Frequency for fructose dehydration.

Medium-Chain-Length Poly (3-Hydroxyalkanoate) Nanoparticle Suspensions

Thesis (Master, Chemical Engineering) -- Queen's University, 2016-08-16 04:58:55.749

Cálculo de constantes ópticas de películas delgadas de Cu3BiS3 a través del método de Wolfe

Se calculó la obtención de las constantes ópticas usando el método de Wolfe. Dichas contantes: coeficiente de absorción (α), índice de refracción (n) y espesor de una película delgada (d ), son de importancia en el proceso de caracterización óptica del material. Se realizó una comparación del método del Wolfe con el método empleado por R. Swanepoel. Se desarrolló un modelo de programación no lineal con restricciones, de manera que fue posible estimar las constantes ópticas de películas delgadas semiconductoras, a partir únicamente, de datos de transmisión conocidos. Se presentó una solución al modelo de programación no lineal para programación cuadrática. Se demostró la confiabilidad del método propuesto, obteniendo valores de α = 10378.34 cm−1, n = 2.4595, d =989.71 nm y Eg = 1.39 Ev, a través de experimentos numéricos con datos de medidas de transmitancia espectral en películas delgadas de Cu3BiS3.

Assessing the fit of unidimensional IRT models for binary data under model misspecification

Model misspecification affects the classical test statistics used to assess the fit of the Item Response Theory (IRT) models. Robust tests have been derived under model misspecification, as the Generalized Lagrange Multiplier and Hausman tests, but their use has not been largely explored in the IRT framework. In the first part of the thesis, we introduce the Generalized Lagrange Multiplier test to detect differential item response functioning in IRT models for binary data under model misspecification. By means of a simulation study and a real data analysis, we compare its performance with the classical Lagrange Multiplier test, computed using the Hessian and the cross-product matrix, and the Generalized Jackknife Score test. The power of these tests is computed empirically and asymptotically. The misspecifications considered are local dependence among items and non-normal distribution of the latent variable. The results highlight that, under mild model misspecification, all tests have good performance while, under strong model misspecification, the performance of the tests deteriorates. None of the tests considered show an overall superior performance than the others. In the second part of the thesis, we extend the Generalized Hausman test to detect non-normality of the latent variable distribution. To build the test, we consider a seminonparametric-IRT model, that assumes a more flexible latent variable distribution. By means of a simulation study and two real applications, we compare the performance of the Generalized Hausman test with the M2 limited information goodness-of-fit test and the Likelihood-Ratio test. Additionally, the information criteria are computed. The Generalized Hausman test has a better performance than the Likelihood-Ratio test in terms of Type I error rates and the M2 test in terms of power. The performance of the Generalized Hausman test and the information criteria deteriorates when the sample size is small and with a few items.