Predictive factors for voiding dysfunction after transobturator slings

PURPOSE: To identify the predictive factors for voiding dysfunction after transobturator slings. METHODS: We retrospectively reviewed the records of all patients who underwent a transobturator sling between March 2003 and December 2008. A total of 514 women had available data with at least a six-week follow-up. Patients' demographics, preoperative symptoms, urodynamic testing including multichannel voiding studies and surgical variables were tabulated. Voiding dysfunction was defined by a catheterized or ultrasonographic postvoid residual greater than 100 cc (≥six weeks after the procedure) associated with any complaints of abnormal voiding. Univariate logistic regression analysis was performed with respect to postoperative voiding dysfunction. RESULTS: The patient population had a mean age of 58.5±12.9 years. Thirty-three out of 514 patients (6.4%) had postoperative voiding dysfunction according to our definition, and 4 (0.78%) required sling transection. No differences were observed between normal and dysfunctional voiders in age, associated prolapse surgery, preoperative postvoid residual, preoperative urinary flow rate, prior pelvic surgery, and menopausal status. Valsalva efforts during the preoperative pressure flow study was the only predictive factor for postoperative voiding dysfunction, 72.4% dysfunctional versus 27.6% normal (p<0.001). CONCLUSION: Preoperative Valsalva maneuver during the micturition could identify those at risk for voiding dysfunction after transobturator sling, and it should be noted during preoperative counseling.

Predictive Capability of HPV and Pap Tests in Screening for Cervical Cancer over a Three-Year Follow-up

Purpose To compare the predictive capability of HPV and Pap smear tests for screening pre-cancerous lesions of the cervix over a three-year follow-up, in a population of users of the Brazilian National Health System (SUS). Methods This is a retrospective cohort study of 2,032 women with satisfactory results for Pap smear and HPV tests using second-generation hybrid capture,made in a previous study. We followed them for 36 months with data obtained from medical records, the Cervix Cancer Information System (SISCOLO), and the Mortality Information System (SIM). The outcome was a histological diagnosis of cervical intraepithelial neoplasia grade 2 or more advanced lesions (CIN2ş). We constructed progression curves of the baseline test results for the period, using the Kaplan-Meier method, and estimated sensitivity, specificity, positive and negative predictive value, and positive and negative likelihood ratios for each test. Results A total of 1,440 women had at least one test during follow-up. Progression curves of the baseline test results indicated differences in capability to detect CIN2ş (p < 0.001) with significantly greater capability when both tests were abnormal, followed by only a positive HPV test. The HPV test was more sensitive than the Pap smear (88.7% and 73.6%, respectively; p < 0.05) and had a better negative likelihood ratio (0.13 and 0.30, respectively). Specificity and positive likelihood ratio of the tests were similar. Conclusions These findings corroborate the importance of HPV test as a primary cervical cancer screening.

The prognostic and predictive value of selected biomarkers in colorectal cancer

Tissue-based biomarkers are studied to receive information about the pathologic processes and cancer outcome, and to enable development of patient-tailored treatments. The aim of this study was to investigate the potential prognostic and/or predictive value of selected biomarkers in colorectal cancer (CRC). Group IIA secretory phospholipase A2 (IIA PLA2) expression was assessed in 114 samples presenting different phases of human colorectal carcinogenesis. Securin, Ki-67, CD44 variant 6 (CD44v6), aldehyde dehydrogenase 1 (ALDH1) and β-catenin were studied in a material including 227 rectal carcinoma patients treated with short-course preoperative radiotherapy (RT), long-course preoperative (chemo)RT (CRT) or surgery only. Epidermal growth factor receptor (EGFR) gene copy number (GCN), its heterogeneity in CRC tissue, and association with response to EGFR-targeted antibodies cetuximab and panitumumab were analyzed in a cohort of 76 metastatic CRC. IIA PLA2 expression was decreased in invasive carcinomas compared to adenomas, but did not relate to patient survival. High securin expression after long-course (C)RT and high ALDH1 expression in node-negative rectal cancer were independent adverse prognostic factors, ALDH1 specifically in patients treated with adjuvant chemotherapy. The lack of membranous CD44v6 in the rectal cancer invasive front associated with infiltrative growth pattern and the risk of disease recurrence. Heterogeneous EGFR GCN increase predicted benefit from EGFR-targeted antibodies, also in the chemorefractory patient population. In summary, high securin and ALDH1 protein expression independently relate to poor outcome in subgroups of rectal cancer patients, potentially because of resistance to conventional chemotherapeutics. Heterogeneous increase in EGFR GCN was validated to be a promising predictive factor in the treatment of metastatic CRC.

Algorithm to determine the intersection curves between bezier surfaces by the solution of multivariable polynomial system and the differential marching method

The determination of the intersection curve between Bézier Surfaces may be seen as the composition of two separated problems: determining initial points and tracing the intersection curve from these points. The Bézier Surface is represented by a parametric function (polynomial with two variables) that maps a point in the tridimensional space from the bidimensional parametric space. In this article, it is proposed an algorithm to determine the initial points of the intersection curve of Bézier Surfaces, based on the solution of polynomial systems with the Projected Polyhedral Method, followed by a method for tracing the intersection curves (Marching Method with differential equations). In order to allow the use of the Projected Polyhedral Method, the equations of the system must be represented in terms of the Bernstein basis, and towards this goal it is proposed a robust and reliable algorithm to exactly transform a multivariable polynomial in terms of power basis to a polynomial written in terms of Bernstein basis .

Numerical Simulation of the Cavitation in the Hydrodynamic Lubrication of Journal Bearings: a Parallel Algorithm

In this paper we present an algorithm for the numerical simulation of the cavitation in the hydrodynamic lubrication of journal bearings. Despite the fact that this physical process is usually modelled as a free boundary problem, we adopted the equivalent variational inequality formulation. We propose a two-level iterative algorithm, where the outer iteration is associated to the penalty method, used to transform the variational inequality into a variational equation, and the inner iteration is associated to the conjugate gradient method, used to solve the linear system generated by applying the finite element method to the variational equation. This inner part was implemented using the element by element strategy, which is easily parallelized. We analyse the behavior of two physical parameters and discuss some numerical results. Also, we analyse some results related to the performance of a parallel implementation of the algorithm.

On Bayesian predictive modeling : with applications on the analysis of chromatography data in forensic investigations

Med prediktion avses att man skattar det framtida värdet på en observerbar storhet. Kännetecknande för det bayesianska paradigmet är att osäkerhet gällande okända storheter uttrycks i form av sannolikheter. En bayesiansk prediktiv modell är således en sannolikhetsfördelning över de möjliga värden som en observerbar, men ännu inte observerad storhet kan anta. I de artiklar som ingår i avhandlingen utvecklas metoder, vilka bl.a. tillämpas i analys av kromatografiska data i brottsutredningar. Med undantag för den första artikeln, bygger samtliga metoder på bayesiansk prediktiv modellering. I artiklarna betraktas i huvudsak tre olika typer av problem relaterade till kromatografiska data: kvantifiering, parvis matchning och klustring. I den första artikeln utvecklas en icke-parametrisk modell för mätfel av kromatografiska analyser av alkoholhalt i blodet. I den andra artikeln utvecklas en prediktiv inferensmetod för jämförelse av två stickprov. Metoden tillämpas i den tredje artik eln för jämförelse av oljeprover i syfte att kunna identifiera den förorenande källan i samband med oljeutsläpp. I den fjärde artikeln härleds en prediktiv modell för klustring av data av blandad diskret och kontinuerlig typ, vilken bl.a. tillämpas i klassificering av amfetaminprover med avseende på produktionsomgångar.

Hypomagnesemia in critically ill cancer patients: a prospective study of predictive factors

Hypomagnesemia is the most common electrolyte disturbance seen upon admission to the intensive care unit (ICU). Reliable predictors of its occurrence are not described. The objective of this prospective study was to determine factors predictive of hypomagnesemia upon admission to the ICU. In a single tertiary cancer center, 226 patients with different diagnoses upon entering were studied. Hypomagnesemia was defined by serum levels <1.5 mg/dl. Demographic data, type of cancer, cause of admission, previous history of arrhythmia, cardiovascular disease, renal failure, drug administration (particularly diuretics, antiarrhythmics, chemotherapy and platinum compounds), previous nutrition intake and presence of hypovolemia were recorded for each patient. Blood was collected for determination of serum magnesium, potassium, sodium, calcium, phosphorus, blood urea nitrogen and creatinine levels. Upon admission, 103 (45.6%) patients had hypomagnesemia and 123 (54.4%) had normomagnesemia. A normal dietary habit prior to ICU admission was associated with normal Mg levels (P = 0.007) and higher average levels of serum Mg (P = 0.002). Postoperative patients (N = 182) had lower levels of serum Mg (0.60 ± 0.14 mmol/l compared with 0.66 ± 0.17 mmol/l, P = 0.006). A stepwise multiple linear regression disclosed that only normal dietary habits (OR = 0.45; CI = 0.26-0.79) and the fact of being a postoperative patient (OR = 2.42; CI = 1.17-4.98) were significantly correlated with serum Mg levels (overall model probability = 0.001). These findings should be used to identify patients at risk for such disturbance, even in other critically ill populations.

Correlation of serum leptin and insulin levels of pregnant protein-restricted rats with predictive obesity variables

During pregnancy and protein restriction, changes in serum insulin and leptin levels, food intake and several metabolic parameters normally result in enhanced adiposity. We evaluated serum leptin and insulin levels and their correlations with some predictive obesity variables in Wistar rats (90 days), up to the 14th day of pregnancy: control non-pregnant (N = 5) and pregnant (N = 7) groups (control diet: 17% protein), and low-protein non-pregnant (N = 5) and pregnant (N = 6) groups (low-protein diet: 6%). Independent of the protein content of the diet, pregnancy increased total (F1,19 = 22.28, P < 0.001) and relative (F1,19 = 5.57, P < 0.03) food intake, the variation of weight (F1,19 = 49.79, P < 0.000) and final body weight (F1,19 = 19.52, P < 0.001), but glycemia (F1,19 = 9.02, P = 0.01) and the relative weight of gonadal adipose tissue (F1,19 = 17.11, P < 0.001) were decreased. Pregnancy (F1,19 = 18.13, P < 0.001) and low-protein diet (F1,19 = 20.35, P < 0.001) increased the absolute weight of brown adipose tissue. However, the relative weight of this tissue was increased only by protein restriction (F1,19 = 15.20, P < 0.001) and the relative lipid in carcass was decreased in low-protein groups (F1,19 = 4.34, P = 0.05). Serum insulin and leptin levels were similar among groups and did not correlate with food intake. However, there was a positive relationship between serum insulin levels and carcass fat depots in low-protein groups (r = 0.37, P < 0.05), while in pregnancy serum leptin correlated with weight of gonadal (r = 0.39, P < 0.02) and retroperitoneal (r = 0.41, P < 0.01) adipose tissues. Unexpectedly, protein restriction during 14 days of pregnancy did not alter the serum profile of adiposity signals and their effects on food intake and adiposity, probably due to the short term of exposure to low-protein diet.

Predictive significance of standardized uptake value parameters of FDG-PET in patients with non-small cell lung carcinoma

18F-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) is widely used to diagnose and stage non-small cell lung cancer (NSCLC). The aim of this retrospective study was to evaluate the predictive ability of different FDG standardized uptake values (SUVs) in 74 patients with newly diagnosed NSCLC. 18F-FDG PET/CT scans were performed and different SUV parameters (SUVmax, SUVavg, SUVT/L, and SUVT/A) obtained, and their relationship with clinical characteristics were investigated. Meanwhile, correlation and multiple stepwise regression analyses were performed to determine the primary predictor of SUVs for NSCLC. Age, gender, and tumor size significantly affected SUV parameters. The mean SUVs of squamous cell carcinoma were higher than those of adenocarcinoma. Poorly differentiated tumors exhibited higher SUVs than well-differentiated ones. Further analyses based on the pathologic type revealed that the SUVmax, SUVavg, and SUVT/L of poorly differentiated adenocarcinoma tumors were higher than those of moderately or well-differentiated tumors. Among these four SUV parameters, SUVT/Lwas the primary predictor for tumor differentiation. However, in adenocarcinoma, SUVmax was the determining factor for tumor differentiation. Our results showed that these four SUV parameters had predictive significance related to NSCLC tumor differentiation; SUVT/L appeared to be most useful overall, but SUVmax was the best index for adenocarcinoma tumor differentiation.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

Stability and predictive ability of beta

The purpose of this paper is to examine the stability and predictive abilities of the beta coefficients of individual equities in the Finnish stock market. As beta is widely used in several areas of finance, including risk management, asset pricing and performance evaluation among others, it is important to understand its characteristics and find out whether its estimates can be trusted and utilized.

Power and Performance Management Strategies on NoC-Based Multicore Platforms, Using DVFS, Adaptive Algorithm and Task Parallelism

This work presents synopsis of efficient strategies used in power managements for achieving the most economical power and energy consumption in multicore systems, FPGA and NoC Platforms. In this work, a practical approach was taken, in an effort to validate the significance of the proposed Adaptive Power Management Algorithm (APMA), proposed for system developed, for this thesis project. This system comprise arithmetic and logic unit, up and down counters, adder, state machine and multiplexer. The essence of carrying this project firstly, is to develop a system that will be used for this power management project. Secondly, to perform area and power synopsis of the system on these various scalable technology platforms, UMC 90nm nanotechnology 1.2v, UMC 90nm nanotechnology 1.32v and UMC 0.18 μmNanotechnology 1.80v, in order to examine the difference in area and power consumption of the system on the platforms. Thirdly, to explore various strategies that can be used to reducing system’s power consumption and to propose an adaptive power management algorithm that can be used to reduce the power consumption of the system. The strategies introduced in this work comprise Dynamic Voltage Frequency Scaling (DVFS) and task parallelism. After the system development, it was run on FPGA board, basically NoC Platforms and on these various technology platforms UMC 90nm nanotechnology1.2v, UMC 90nm nanotechnology 1.32v and UMC180 nm nanotechnology 1.80v, the system synthesis was successfully accomplished, the simulated result analysis shows that the system meets all functional requirements, the power consumption and the area utilization were recorded and analyzed in chapter 7 of this work. This work extensively reviewed various strategies for managing power consumption which were quantitative research works by many researchers and companies, it's a mixture of study analysis and experimented lab works, it condensed and presents the whole basic concepts of power management strategy from quality technical papers.

The Salmon Algorithm - A New Population Based Search Metaheuristic

This thesis introduces the Salmon Algorithm, a search meta-heuristic which can be used for a variety of combinatorial optimization problems. This algorithm is loosely based on the path finding behaviour of salmon swimming upstream to spawn. There are a number of tunable parameters in the algorithm, so experiments were conducted to find the optimum parameter settings for different search spaces. The algorithm was tested on one instance of the Traveling Salesman Problem and found to have superior performance to an Ant Colony Algorithm and a Genetic Algorithm. It was then tested on three coding theory problems - optimal edit codes, optimal Hamming distance codes, and optimal covering codes. The algorithm produced improvements on the best known values for five of six of the test cases using edit codes. It matched the best known results on four out of seven of the Hamming codes as well as three out of three of the covering codes. The results suggest the Salmon Algorithm is competitive with established guided random search techniques, and may be superior in some search spaces.