Agents used for chemoprevention of prostate cancer may influence PSA secretion independently of cell growth in the LNCaP model of human prostate cancer progression

BACKGROUND: The aim of this study was to evaluate the inhibitory growth effects of different potential chemopreventive agents in vitro and to determine their influence on PSA mRNA and protein expression with an established screening platform. METHODS: LNCaP and C4-2 cells were incubated with genistein, seleno-L-methionine, lycopene, DL-alpha-tocopherol, and trans-beta-carotene at three different concentrations and cell growth was determined by the MTT assay. PSA mRNA expression was assessed by quantitative real-time RT-PCR and secreted PSA protein levels were quantified by the microparticle enzyme immunoassay. RESULTS: Genistein, seleno-l-methionine and lycopene inhibited LNCaP cell growth, and the proliferation of C4-2 cells was suppressed by seleno-L-methionine and lycopene. PSA mRNA expression was downregulated by genistein in LNCaP but not C4-2 cells. No other compound tested altered PSA mRNA expression. PSA protein expression was downregulated by genistein, seleno-L-methionine, DL-alpha-tocopherol in LNCaP cells. In C4-2 cells only genistein significantly reduced the secretion of PSA protein. CONCLUSIONS: In the LNCaP progression model PSA expression depends on the compound, its concentration and on the hormonal dependence of the cell line used and does not necessarily reflect cell growth or death. Before potential substances are evaluated in clinical trials using PSA as a surrogate end point marker, their effect on PSA mRNA and protein expression has to be considered to correctly assess treatment response by PSA.

HIGH PERFORMANCE, LOW COST SUBSPACE DECOMPOSITION AND POLYNOMIAL ROOTING FOR REAL TIME DIRECTION OF ARRIVAL ESTIMATION: ANALYSIS AND IMPLEMENTATION

This thesis develops high performance real-time signal processing modules for direction of arrival (DOA) estimation for localization systems. It proposes highly parallel algorithms for performing subspace decomposition and polynomial rooting, which are otherwise traditionally implemented using sequential algorithms. The proposed algorithms address the emerging need for real-time localization for a wide range of applications. As the antenna array size increases, the complexity of signal processing algorithms increases, making it increasingly difficult to satisfy the real-time constraints. This thesis addresses real-time implementation by proposing parallel algorithms, that maintain considerable improvement over traditional algorithms, especially for systems with larger number of antenna array elements. Singular value decomposition (SVD) and polynomial rooting are two computationally complex steps and act as the bottleneck to achieving real-time performance. The proposed algorithms are suitable for implementation on field programmable gated arrays (FPGAs), single instruction multiple data (SIMD) hardware or application specific integrated chips (ASICs), which offer large number of processing elements that can be exploited for parallel processing. The designs proposed in this thesis are modular, easily expandable and easy to implement. Firstly, this thesis proposes a fast converging SVD algorithm. The proposed method reduces the number of iterations it takes to converge to correct singular values, thus achieving closer to real-time performance. A general algorithm and a modular system design are provided making it easy for designers to replicate and extend the design to larger matrix sizes. Moreover, the method is highly parallel, which can be exploited in various hardware platforms mentioned earlier. A fixed point implementation of proposed SVD algorithm is presented. The FPGA design is pipelined to the maximum extent to increase the maximum achievable frequency of operation. The system was developed with the objective of achieving high throughput. Various modern cores available in FPGAs were used to maximize the performance and details of these modules are presented in detail. Finally, a parallel polynomial rooting technique based on Newton’s method applicable exclusively to root-MUSIC polynomials is proposed. Unique characteristics of root-MUSIC polynomial’s complex dynamics were exploited to derive this polynomial rooting method. The technique exhibits parallelism and converges to the desired root within fixed number of iterations, making this suitable for polynomial rooting of large degree polynomials. We believe this is the first time that complex dynamics of root-MUSIC polynomial were analyzed to propose an algorithm. In all, the thesis addresses two major bottlenecks in a direction of arrival estimation system, by providing simple, high throughput, parallel algorithms.

EXPLORATION OF THE MTSAT2 SATELLITE CAPABILITIES FOR REAL TIME DETECTION AND CHARACTERIZATION OF VOLCANIC EMISSIONS

In this report, we attempt to define the capabilities of the infrared satellite remote sensor, Multifunctional Transport Satellite-2 (MTSAT-2) (i.e. a geosynchronous instrument), in characterizing volcanic eruptive behavior in the highly active region of Indonesia. Sulfur dioxide data from NASA's Ozone Monitoring Instrument (OMI) (i.e. a polar orbiting instrument) are presented here for validation of the processes interpreted using the thermal infrared datasets. Data provided from two case studies are analyzed specifically for eruptive products producing large thermal anomalies (i.e. lava flows, lava domes, etc.), volcanic ash and SO2 clouds; three distinctly characteristic and abundant volcanic emissions. Two primary methods used for detection of heat signatures are used and compared in this report including, single-channel thermal radiance (4-µm) and the normalized thermal index (NTI) algorithm. For automated purposes, fixed thresholds must be determined for these methods. A base minimum detection limit (MDL) for single-channel thermal radiance of 2.30E+05 Wm- 2sr-1m-1 and -0.925 for NTI generate false alarm rates of 35.78% and 34.16%, respectively. A spatial comparison method, developed here specifically for use in Indonesia and used as a second parameter for detection, is implemented to address the high false alarm rate. For the single-channel thermal radiance method, the utilization of the spatial comparison method eliminated 100% of the false alarms while maintaining every true anomaly. The NTI algorithm showed similar results with only 2 false alarms remaining. No definitive difference is observed between the two thermal detection methods for automated use; however, the single-channel thermal radiance method coupled with the SO2 mass abundance data can be used to interpret volcanic processes including the identification of lava dome activity at Sinabung as well as the mechanism for the dome emplacement (i.e. endogenous or exogenous). Only one technique, the brightness temperature difference (BTD) method, is used for the detection of ash. Trends of ash area, water/ice area, and their respective concentrations yield interpretations of increased ice formation, aggregation, and sedimentation processes that only a high-temporal resolution instrument like the MTSAT-2 can analyze. A conceptual model of a secondary zone of aggregation occurring in the migrating Kelut ash cloud, which decreases the distal fine-ash component and hazards to flight paths, is presented in this report. Unfortunately, SO2 data was unable to definitively reinforce the concept of a secondary zone of aggregation due to the lack of a sufficient temporal resolution. However, a detailed study of the Kelut SO2 cloud is used to determine that there was no climatic impacts generated from this eruption due to the atmospheric residence times and e-folding rate of ~14 days for the SO2. This report applies the complementary assets offered by utilizing a high-temporal and a high-spatial resolution satellite, and it demonstrates that these two instruments can provide unparalleled observations of dynamic volcanic processes.

Vascular endothelial growth factors, angiogenesis, and survival in human ileal enterochromaffin cell carcinoids

BACKGROUND AND AIMS: Well-differentiated neuro-endocrine ileal carcinoids are composed of serotonin-producing enterochromaffin (EC) cells. Life expectancy is determined by metastatic spread to the liver because medical treatment options are still very limited. Selective inhibition of angiogenesis or lymphangiogenesis might prevent tumour growth and metastatic spread. We examined the role of the vascular endothelial growth factors (VEGFs) A, B, C, D, and their receptors (VEGFRs) 1, 2, 3 in angiogenesis and lymphangiogenesis of ileal EC cell carcinoids with and without liver metastases. METHODS: The expression of various VEGFs and VEGFRs was determined by quantitative real-time RT-PCR in healthy mucosa, primary tumour, lymph node metastases and liver metastases of 25 patients with ileal EC cell carcinoids. Microvessel density (MVD) was determined by CD-31 staining in primary tumours and lymphatic vessel density (LVD) by LYVE-1 staining. VEGF expression levels, MVD, LVD, and patients' survival time were correlated using logistic regression and Kaplan-Meier survival analysis. RESULTS: VEGF-A was highly expressed with no difference between normal mucosa and tumours. VEGF-B and -D as well as VEGFR-1 and -2 expression levels were significantly increased in the tumours when compared to normal mucosa. Patients with liver metastasis, however, had a significantly lower expression of the factors A, B, and C and the receptors 2 and 3. MVD in primary tumours positively correlated with the expression of VEGF ligands and their receptors, except for VEGF-D. LVD did not correlate with any VEGF ligand or receptor. Interestingly, low expression levels of VEGF-B were associated with poor survival. CONCLUSION: Patients with more aggressive metastatic spreading had relatively decreased expression levels of VEGF ligands and receptors. Thus, anti-angiogenic therapy may not be a suitable target in metastatic ileal EC cell carcinoids.

Selecting control genes for RT-QPCR using public microarray data

BACKGROUND: Gene expression analysis has emerged as a major biological research area, with real-time quantitative reverse transcription PCR (RT-QPCR) being one of the most accurate and widely used techniques for expression profiling of selected genes. In order to obtain results that are comparable across assays, a stable normalization strategy is required. In general, the normalization of PCR measurements between different samples uses one to several control genes (e.g. housekeeping genes), from which a baseline reference level is constructed. Thus, the choice of the control genes is of utmost importance, yet there is not a generally accepted standard technique for screening a large number of candidates and identifying the best ones. RESULTS: We propose a novel approach for scoring and ranking candidate genes for their suitability as control genes. Our approach relies on publicly available microarray data and allows the combination of multiple data sets originating from different platforms and/or representing different pathologies. The use of microarray data allows the screening of tens of thousands of genes, producing very comprehensive lists of candidates. We also provide two lists of candidate control genes: one which is breast cancer-specific and one with more general applicability. Two genes from the breast cancer list which had not been previously used as control genes are identified and validated by RT-QPCR. Open source R functions are available at http://www.isrec.isb-sib.ch/~vpopovic/research/ CONCLUSION: We proposed a new method for identifying candidate control genes for RT-QPCR which was able to rank thousands of genes according to some predefined suitability criteria and we applied it to the case of breast cancer. We also empirically showed that translating the results from microarray to PCR platform was achievable.

A communication and information technology infrastructure for real time monitoring and management of type 1 diabetes patients

This paper is focused on the integration of state-of-the-art technologies in the fields of telecommunications, simulation algorithms, and data mining in order to develop a Type 1 diabetes patient's semi to fully-automated monitoring and management system. The main components of the system are a glucose measurement device, an insulin delivery system (insulin injection or insulin pumps), a mobile phone for the GPRS network, and a PDA or laptop for the Internet. In the medical environment, appropriate infrastructure for storage, analysis and visualizing of patients' data has been implemented to facilitate treatment design by health care experts.

A real time simulation model of glucose-insulin metabolism for type 1 diabetes patients

In this paper, a simulation model of glucose-insulin metabolism for Type 1 diabetes patients is presented. The proposed system is based on the combination of Compartmental Models (CMs) and artificial Neural Networks (NNs). This model aims at the development of an accurate system, in order to assist Type 1 diabetes patients to handle their blood glucose profile and recognize dangerous metabolic states. Data from a Type 1 diabetes patient, stored in a database, have been used as input to the hybrid system. The data contain information about measured blood glucose levels, insulin intake, and description of food intake, along with the corresponding time. The data are passed to three separate CMs, which produce estimations about (i) the effect of Short Acting (SA) insulin intake on blood insulin concentration, (ii) the effect of Intermediate Acting (IA) insulin intake on blood insulin concentration, and (iii) the effect of carbohydrate intake on blood glucose absorption from the gut. The outputs of the three CMs are passed to a Recurrent NN (RNN) in order to predict subsequent blood glucose levels. The RNN is trained with the Real Time Recurrent Learning (RTRL) algorithm. The resulted blood glucose predictions are promising for the use of the proposed model for blood glucose level estimation for Type 1 diabetes patients.