Comparison of baseline data between chronic kidney disease patients starting hemodialysis who live near and far from the dialysis center

Introduction: The treatment offered to chronic kidney disease (CKD) patients before starting hemodialysis (HD) impacts prognosis. Objective: We seek differences among incident HD patients according to the distance between home and the dialysis center. Methods: We included 179 CKD patients undergoing HD. Patients were stratified in two groups: "living near the dialysis center" (patients whose hometown was in cities up to 100 km from the dialysis center) or as "living far from the dialysis center" (patients whose hometown was more than 100 km from the dialysis center). Socioeconomic status, laboratory results, awareness of CKD before starting HD, consultation with nephrologist before the first HD session, and type of vascular access when starting HD were compared between the two groups. Comparisons of continuous and categorical variables were performed using Student's t-test and the Chi-square test, respectively. Results: Ninety (50.3%) patients were classified as "living near the dialysis center" and 89 (49.7%) as "living far from the dialysis center". Patients living near the dialysis center were more likely to know about their condition of CKD than those living far from the dialysis center, respectively 46.6% versus 28.0% (p = 0.015). Although without statistical significance, patients living near the dialysis center had more frequent previous consultation with nephrologists (55.5% versus 42.6%; p = 0.116) and first HD by fistula (30.0% versus 19.1%; p = 0.128) than those living far from the dialysis center. Conclusion: There are potential advantages of CKD awareness, referral to nephrologists and starting HD through fistula among patients living near the dialysis center.

Bearing-spesific complications of total hip arthroplasty: characterization and treatment

Hip resurfacing arthroplasty (HRA) and large head metal-on-metal total arthroplasty (LDH MoM THA) gained popularity during the last decade. Adverse reaction to metal debris (ARMD) is a unique complication of metal bearings. ARMD is a complex reaction caused by metal debris from metal-on- metal bearing surfaces and from trunnion corrosion of modular junctions. We analyzed survivorship of 8059 LDH MoM THAs based on data of the Finnish Arthroplasty Register. We found relatively high short-term survivorship for some LDH MoM THAs, but there were remarkable differences between the devices studied. After some alarming reports of failing MoM THAs, we studied the first 80 patients who had received a ReCap-M2a-Magnum implant at our institution and evaluated the prevalence of ARMD. We found a high prevalence of pseudotumors, and, because of this, we discontinued the use of MoM bearings and followed up all patients with a MoM THA. Bone loss due infection, osteolysis or fracture poses a great challenge for reconstructive and fracture surgery. Onlay allografting for both revision and fracture surgery provides mechanical stability and increases bone stock. Bone loss and implant stability must be assessed preoperatively and adequately classified; this provides guidelines for the operative treatment of periprosthetic fractures and revision THA. In our studies on structural allografts union rates were high, although the rates of infections and dislocations were marked. In summary, early results of the use of LDH MoM devices were encouraging. However, the survival of the LDH MoMs varied. The prevalence of adverse reaction to metal debris was high after application of the ReCap-Magnum THA. New implants should be introduced carefully and under close surveillance by University clinics and arthroplasty registers.

Adherence to Treatment in Psychotic Disorders - Development of user-centered mobile health intervention

The aim of this study was to explore adherence to treatment among people with psychotic disorders through the development of user-centered mobile technology (mHealth) intervention. More specifically, this study investigates treatment adherence as well as mHealth intervention and the factors related to its possible usability. The data were collected from 2010 to 2013. First, patients’ and professionals’ perceptions of adherence management and restrictive factors of adherence were described (n = 61). Second, objectives and methods of the intervention were defined based on focus group interviews and previously used methods. Third, views of patients and professionals about barriers and requirements of the intervention were described (n = 61). Fourth, mHealth intervention was evaluated based on a literature review (n = 2) and patients preferences regarding the intervention (n = 562). Adherence management required support in everyday activities, social networks and maintaining a positive outlook. The factors restricting adherence were related to illness, behavior and the environment. The objective of the intervention was to support the intention to follow the treatment guidelines and recommendations with mHealth technology. The barriers and requirements for the use of the mHealth were related to technology, organizational issues and the users themselves. During the course of the intervention, 33 (6%) out of 562 participants wanted to edit the content, timing or amount of the mHealth tool, and 23 (4%) quit the intervention or study before its conclusion. According to the review, mHealth interventions were ineffective in promoting adherence. Prior to the intervention, participants perceived that adherence could be supported, and the use of mHealth as a part of treatment was seen as an acceptable and efficient method for doing so. In conclusion, the use of mHealth may be feasible among people with psychotic disorders. However, clear evidence for its effectiveness in regards to adherence is still currently inconclusive.

Identifying the psychosexual implications of radical radiation treatment in prostate cancer

The purpose of this study was to determine whether there was any evidence of psychosexual morbidity among men who experienced radical radiation treatment for prostate cancer. With relatively little known or available retrospective data on the psychosexual implications of radical radiation treatment in men with prostate cancer, this study posited eight research questions which provided the basis for the research. Fifty men from Southern Ontario, between the ages of 52 to 78 years, were included in the study. They had been previously randomized to a clinical trial comparing radical radiation therapy by external beam radiation, or radical radiation using a combination of a temporary iridium implant plus external beam radiation, for localized or locally advanced prostate cancer. Assessment of sexual functioning, drive, attitudes, body image, and sexual satisfaction was drawn from a multidimensional approach, since psychosexuality was viewed as having an impact on biological, psychological, and sociological domains of functioning. Medical chart reviews, semi-structured interviews, demographical profiles of each participant, and the Derogatis Sexual Functioning Inventory (DSFI) were the methods used to collect data over a four-month period. Both quantitative and qualitative research methods were incorporated in the design and evaluation of the study. Frequencies, contingency analysis, Pearson's coefficient of correlation, t-tests, and ANOVA comprised the quantitative analysis. Data obtained from audio-taped interviews were analyzed qualitatively, and used for offering further insight and for facilitating the quantitative aspect of the analysis. Overall, there was sufficient evidence to suggest psychosexual morbidity among men who were treated with radiation therapy for prostate cancer. As well,there were a number of significant findings available to answer all of the posited research questions. The most significant findings were noted in post-treatment erectile ability and sexual activity. A post-treatment change in erectile ability was reported by eighty percent of men. Sixty percent of men noted a decrease in their ability to achieve an erection by reporting some morning stiffness only, penile rigidity insufficient for penetration, decreased control of erection, and loss of spontaneous erection. Other contributing factors associated with change in erectile status were: pain or altering sensation of orgasm, blood in ejaculate, pain and decreased amount of ejaculate, and penile numbness or pain. Eighty-two percent of men experienced a post-treatment change in sexual function, primarily due to the impact of decreasing erectile status. Only seven men reported that they experienced a decrease in desire mentally, whereas the vast majority did not experience any change in desire. Changes in foreplay, stress with optimal sexual positioning, and reduced spontaneity of sex, were other factors reported with the changes in sexual activity. The findings in this study broaden our understanding of what middle- to later-aged men feel and experience as they venture onward following treatment. This was the first study that evaluated available prospective data on pre-treatment erectile status and sexual activity. As well, this study was the first (with participant compliance rates of 100 percent) to have included an interview format to capture the views of such a large number of men. This study concluded with recommendations and implications for future research and practice as we move in the direction of understanding what is necessary for preserving psychosexual well being and enhancing quality of life in men treated with radiation therapy for prostate cancer.

Solid state NMR chemical shifts as an alternative to diffraction data in the determination of SIC polytypic structures

Silicon carbide, which has many polytypic modifications of a very simple and very symmetric structure, is an excellent model system for exploring, the relationship between chemical shift, long-range dipolar shielding, and crystal structure in network solids. A simple McConnell equation treatment of bond anisotropy effects in a poly type predicts chemical shifts for silicon and carbon sites which agree well with the experiment, provided that contributions from bonds up to 100 A are included in the calculation. The calculated chemical shifts depend on three factors: the layer stacking sequence, electrical centre of gravity, and the spacings between silicon and carbon layers. The assignment of peaks to lattice sites is proved possible for three polytypes (6H, 15R, and 3C). The fact that the calculated chemical shifts are very sensitive to layer spacings provides us a potential way to detennine and refine a crystal structure. In this work, the layer spacings of 6H SiC have been calculated and are within X-ray standard deviations. Under this premise, the layer spacings of 15R have been detennined. 29Si and 13C single crystal nmr studies of 6H SiC polytype indicate that all silicons and carbons are magnetically anisotropic. The relationship between a magnetic shielding tensor component and layer spacings has been derived. The comparisons between experimental and semi-empirical chemical shielding tensor components indicate that the paramagnetic shielding of silicon should be included in the single crystal chemical shift calculation.

The self-management approach of health education in the treatment of chronic illnesses

Health education is essential to the successful treatment of individuals with chronic illnesses. Self-management is a philosophical model of health education that has been shown to be effective in teaching individuals with chronic arthritis to manage their illness as part of their daily lives. Despite the proven results of arthritis self-management programs, some limitations of this form of health education were apparent in the literature. The present study attempted to address the problems of the self-management approach of health education such as reasons for lack of participation in programs and poor course outcomes. In addition, the study served to investigate the relationship between course outcomes and participation in programs with the theory upon which arthritis self-management programs are based, known as self-efficacy theory. Through a combination of qualitative and quantitative methodologies, data collection, and analysis, a deeper understanding of the self-management phenomenon in the treatment of chronic arthritic conditions was established. Findings of the study confirm findings of previous studies that suggest that arthritis self-management programs result in enhanced levels of self-efficacy and are effective in teaching individuals with arthritis to self-manage their health and health care. Findings of the study suggest that there are many factors that determine the choice of participants to participate in programs and the outcomes for the individuals who do choose to participate in programs. Some of the major determinants of enrollment and outcomes of programs include: the participant's personality, beliefs, attitudes and abilities, and the degree of emotional acceptance of the illness. Other determinants of course enrollment and outcomes included class size and length of time, timing of participation, and ongoing support after the program. The results of the study are consistent with the self-management literature and confirm the relationship between the underlying philosophies of adult education and Freire's model of education and self-management.

The effects of plants(Typha Latifolia)and root-bed medium on the treatment of domestic sewage within a vertical flow constructed wetland /

The effect that plants {Typha latifolia) as well as root-bed medium physical and chemical characteristics have on the treatment of primary treated domestic wastewater within a vertical flow constructed wetland system was investigated. Five sets of cells, with two cells in each set, were used. Each cell was made of concrete and measured 1 .0 m X 1 .0 m and was 1.3 m deep. Four different root-bed media were tested : Queenston Shale, Fonthill Sand, Niagara Shale and a Michigan Sand. Four of the sets contained plants and a single type of root-bed medium. The influence of plants was tested by operating a Queenston Shale set without plants. Due to budget constraints no replicates were constructed. All of the sets were operated independently and identically for twenty-eight months. Twelve months of data are presented here, collected after 16 months of continuous operation. Root-bed medium type did not influence BOD5 removal. All of the sets consistently met Ontario Ministry of Environment (MOE) requirements (<25 mg/L) for BOD5 throughout the year. The 12 month average BOD5 concentration from all sets with plants was below 2.36 mg/L. All of the sets were within MOE discharge requirements (< 25 mg/L) for suspended solids with set effluent concentrations ranging from 1.53 to 14.80 mg/L. The Queenston Shale and Fonthill Sand media removed the most suspended solids while the Niagara Shale set produced suspended solids. The set containing Fonthill Sand was the only series to meet MOE discharge requirements (< Img/L) for total phosphorus year-round with a twelve month mean effluent concentration of 0.23 mg/L. Year-round all of the root-bed media were well below MOE discharge requirements (< 20mg/L in winter and < 10 mg/L in sumnner) for ammonium. The Queenston Shale and Fonthill Sand sets removed the most total nitrogen. Plants had no effect on total nitrogen removal, but did influence how nitrogen was cycled within the system. Plants increased the removal of suspended solids by 14%, BOD5 by 10% and total phosphorus by 22%. Plants also increased the amount of dissolved oxygen that entered the system. During the plant growing season removal of total phosphorus was better in all sets with plants regardless of media type. The sets containing Queenston Shale and Fonthill Sand media achieved the best results and plants in the Queenston Shale set increased treatment efficiency for every parameter except nitrogen. Vertical flow wetland sewage treatment systems can be designed and built to consistently meet MOE discharge requirements year-round for BOD5, suspended solids, total phosphorus and ammonium. This system Is generally superior to the free water systems and sub-surface horizontal flow systems in cold climate situations.

I Believe in me, not OCB: Evaluation of Group Function-Based CBT as a Treatment for Obsessive Compulsive Behaviour in Four Children with HFA

Researchers have conceptualized repetitive behaviours in individuals with Autism Spectrum Disorder (ASD) on a continuum oflower-Ievel, motoric, repetitive behaviours and higher-order, repetitive behaviours that include symptoms ofOCD (Hollander, Wang, Braun, & Marsh, 2009). Although obsessional, ritualistic, and stereotyped behaviours are a core feature of ASD, individuals with ASD frequently experience obsessions and compulsions that meet DSM-IV-TR (American Psychiatric Association, 2000) criteria for Obsessive-Compulsive Disorder (OCD). Given the acknowledged difficulty in differentiating between OCD and Autism-related obsessive-compulsive phenomena, the present study uses the term Obsessive Compulsive Behaviour (OCB) to represent both phenomena. This study used a multiple baseline design across behaviours and ABC designs (Cooper, Heron, & Heward, 2007) to investigate if a 9-week Group Function-Based Cognitive Behavioural Therapy (CBT) decreased OCB in four children (ages 7 - 11 years) with High Functioning Autism (HFA). Key treatment components included traditional CBT components (awareness training, cognitive-behavioural skills training, exposure and response prevention) as well as function-based assessment and intervention. Time series data indicated significant decreases in OCBs. Standardized assessments showed decreases in symptom severity, and increases in quality of life for the participants and their families. Issues regarding symptom presentation, assessment, and treatment of a dually diagnosed child are discussed.

Bone mineral density measurement and osteoporosis treatment after a fragility fracture in older adults: regional variation and determinants of use in Quebec

Affiliation: Pierre Dagenais : Hôpital Maisonneuve-Rosemont, Faculté de médecine, Université de Montréal

Bootstrap for panel data models with an application to the evaluation of public policies

Le but de cette thèse est d étendre la théorie du bootstrap aux modèles de données de panel. Les données de panel s obtiennent en observant plusieurs unités statistiques sur plusieurs périodes de temps. Leur double dimension individuelle et temporelle permet de contrôler l 'hétérogénéité non observable entre individus et entre les périodes de temps et donc de faire des études plus riches que les séries chronologiques ou les données en coupe instantanée. L 'avantage du bootstrap est de permettre d obtenir une inférence plus précise que celle avec la théorie asymptotique classique ou une inférence impossible en cas de paramètre de nuisance. La méthode consiste à tirer des échantillons aléatoires qui ressemblent le plus possible à l échantillon d analyse. L 'objet statitstique d intérêt est estimé sur chacun de ses échantillons aléatoires et on utilise l ensemble des valeurs estimées pour faire de l inférence. Il existe dans la littérature certaines application du bootstrap aux données de panels sans justi cation théorique rigoureuse ou sous de fortes hypothèses. Cette thèse propose une méthode de bootstrap plus appropriée aux données de panels. Les trois chapitres analysent sa validité et son application. Le premier chapitre postule un modèle simple avec un seul paramètre et s 'attaque aux propriétés théoriques de l estimateur de la moyenne. Nous montrons que le double rééchantillonnage que nous proposons et qui tient compte à la fois de la dimension individuelle et la dimension temporelle est valide avec ces modèles. Le rééchantillonnage seulement dans la dimension individuelle n est pas valide en présence d hétérogénéité temporelle. Le ré-échantillonnage dans la dimension temporelle n est pas valide en présence d'hétérogénéité individuelle. Le deuxième chapitre étend le précédent au modèle panel de régression. linéaire. Trois types de régresseurs sont considérés : les caractéristiques individuelles, les caractéristiques temporelles et les régresseurs qui évoluent dans le temps et par individu. En utilisant un modèle à erreurs composées doubles, l'estimateur des moindres carrés ordinaires et la méthode de bootstrap des résidus, on montre que le rééchantillonnage dans la seule dimension individuelle est valide pour l'inférence sur les coe¢ cients associés aux régresseurs qui changent uniquement par individu. Le rééchantillonnage dans la dimen- sion temporelle est valide seulement pour le sous vecteur des paramètres associés aux régresseurs qui évoluent uniquement dans le temps. Le double rééchantillonnage est quand à lui est valide pour faire de l inférence pour tout le vecteur des paramètres. Le troisième chapitre re-examine l exercice de l estimateur de différence en di¤érence de Bertrand, Duflo et Mullainathan (2004). Cet estimateur est couramment utilisé dans la littérature pour évaluer l impact de certaines poli- tiques publiques. L exercice empirique utilise des données de panel provenant du Current Population Survey sur le salaire des femmes dans les 50 états des Etats-Unis d Amérique de 1979 à 1999. Des variables de pseudo-interventions publiques au niveau des états sont générées et on s attend à ce que les tests arrivent à la conclusion qu il n y a pas d e¤et de ces politiques placebos sur le salaire des femmes. Bertrand, Du o et Mullainathan (2004) montre que la non-prise en compte de l hétérogénéité et de la dépendance temporelle entraîne d importantes distorsions de niveau de test lorsqu'on évalue l'impact de politiques publiques en utilisant des données de panel. Une des solutions préconisées est d utiliser la méthode de bootstrap. La méthode de double ré-échantillonnage développée dans cette thèse permet de corriger le problème de niveau de test et donc d'évaluer correctement l'impact des politiques publiques.

Essays in partial identification and applications to treatment effects and policy evaluation.

Dans cette thèse, je me suis interessé à l’identification partielle des effets de traitements dans différents modèles de choix discrets avec traitements endogènes. Les modèles d’effets de traitement ont pour but de mesurer l’impact de certaines interventions sur certaines variables d’intérêt. Le type de traitement et la variable d’intérêt peuvent être défini de manière générale afin de pouvoir être appliqué à plusieurs différents contextes. Il y a plusieurs exemples de traitement en économie du travail, de la santé, de l’éducation, ou en organisation industrielle telle que les programmes de formation à l’emploi, les techniques médicales, l’investissement en recherche et développement, ou l’appartenance à un syndicat. La décision d’être traité ou pas n’est généralement pas aléatoire mais est basée sur des choix et des préférences individuelles. Dans un tel contexte, mesurer l’effet du traitement devient problématique car il faut tenir compte du biais de sélection. Plusieurs versions paramétriques de ces modèles ont été largement étudiées dans la littérature, cependant dans les modèles à variation discrète, la paramétrisation est une source importante d’identification. Dans un tel contexte, il est donc difficile de savoir si les résultats empiriques obtenus sont guidés par les données ou par la paramétrisation imposée au modèle. Etant donné, que les formes paramétriques proposées pour ces types de modèles n’ont généralement pas de fondement économique, je propose dans cette thèse de regarder la version nonparamétrique de ces modèles. Ceci permettra donc de proposer des politiques économiques plus robustes. La principale difficulté dans l’identification nonparamétrique de fonctions structurelles, est le fait que la structure suggérée ne permet pas d’identifier un unique processus générateur des données et ceci peut être du soit à la présence d’équilibres multiples ou soit à des contraintes sur les observables. Dans de telles situations, les méthodes d’identifications traditionnelles deviennent inapplicable d’où le récent développement de la littérature sur l’identification dans les modèles incomplets. Cette littérature porte une attention particuliere à l’identification de l’ensemble des fonctions structurelles d’intérêt qui sont compatibles avec la vraie distribution des données, cet ensemble est appelé : l’ensemble identifié. Par conséquent, dans le premier chapitre de la thèse, je caractérise l’ensemble identifié pour les effets de traitements dans le modèle triangulaire binaire. Dans le second chapitre, je considère le modèle de Roy discret. Je caractérise l’ensemble identifié pour les effets de traitements dans un modèle de choix de secteur lorsque la variable d’intérêt est discrète. Les hypothèses de sélection du secteur comprennent le choix de sélection simple, étendu et généralisé de Roy. Dans le dernier chapitre, je considère un modèle à variable dépendante binaire avec plusieurs dimensions d’hétérogéneité, tels que les jeux d’entrées ou de participation. je caractérise l’ensemble identifié pour les fonctions de profits des firmes dans un jeux avec deux firmes et à information complète. Dans tout les chapitres, l’ensemble identifié des fonctions d’intérêt sont écrites sous formes de bornes et assez simple pour être estimées à partir des méthodes d’inférence existantes.

The use of artificial intelligence algorithms to guide surgical treatment of adolescent idiopathic scoliosis

La scoliose idiopathique de l’adolescent (SIA) est une déformation tri-dimensionelle du rachis. Son traitement comprend l’observation, l’utilisation de corsets pour limiter sa progression ou la chirurgie pour corriger la déformation squelettique et cesser sa progression. Le traitement chirurgical reste controversé au niveau des indications, mais aussi de la chirurgie à entreprendre. Malgré la présence de classifications pour guider le traitement de la SIA, une variabilité dans la stratégie opératoire intra et inter-observateur a été décrite dans la littérature. Cette variabilité s’accentue d’autant plus avec l’évolution des techniques chirurgicales et de l’instrumentation disponible. L’avancement de la technologie et son intégration dans le milieu médical a mené à l’utilisation d’algorithmes d’intelligence artificielle informatiques pour aider la classification et l’évaluation tridimensionnelle de la scoliose. Certains algorithmes ont démontré être efficace pour diminuer la variabilité dans la classification de la scoliose et pour guider le traitement. L’objectif général de cette thèse est de développer une application utilisant des outils d’intelligence artificielle pour intégrer les données d’un nouveau patient et les évidences disponibles dans la littérature pour guider le traitement chirurgical de la SIA. Pour cela une revue de la littérature sur les applications existantes dans l’évaluation de la SIA fut entreprise pour rassembler les éléments qui permettraient la mise en place d’une application efficace et acceptée dans le milieu clinique. Cette revue de la littérature nous a permis de réaliser que l’existence de “black box” dans les applications développées est une limitation pour l’intégration clinique ou la justification basée sur les évidence est essentielle. Dans une première étude nous avons développé un arbre décisionnel de classification de la scoliose idiopathique basé sur la classification de Lenke qui est la plus communément utilisée de nos jours mais a été critiquée pour sa complexité et la variabilité inter et intra-observateur. Cet arbre décisionnel a démontré qu’il permet d’augmenter la précision de classification proportionnellement au temps passé à classifier et ce indépendamment du niveau de connaissance sur la SIA. Dans une deuxième étude, un algorithme de stratégies chirurgicales basé sur des règles extraites de la littérature a été développé pour guider les chirurgiens dans la sélection de l’approche et les niveaux de fusion pour la SIA. Lorsque cet algorithme est appliqué à une large base de donnée de 1556 cas de SIA, il est capable de proposer une stratégie opératoire similaire à celle d’un chirurgien expert dans prêt de 70% des cas. Cette étude a confirmé la possibilité d’extraire des stratégies opératoires valides à l’aide d’un arbre décisionnel utilisant des règles extraites de la littérature. Dans une troisième étude, la classification de 1776 patients avec la SIA à l’aide d’une carte de Kohonen, un type de réseaux de neurone a permis de démontrer qu’il existe des scoliose typiques (scoliose à courbes uniques ou double thoracique) pour lesquelles la variabilité dans le traitement chirurgical varie peu des recommandations par la classification de Lenke tandis que les scolioses a courbes multiples ou tangentielles à deux groupes de courbes typiques étaient celles avec le plus de variation dans la stratégie opératoire. Finalement, une plateforme logicielle a été développée intégrant chacune des études ci-dessus. Cette interface logicielle permet l’entrée de données radiologiques pour un patient scoliotique, classifie la SIA à l’aide de l’arbre décisionnel de classification et suggère une approche chirurgicale basée sur l’arbre décisionnel de stratégies opératoires. Une analyse de la correction post-opératoire obtenue démontre une tendance, bien que non-statistiquement significative, à une meilleure balance chez les patients opérés suivant la stratégie recommandée par la plateforme logicielle que ceux aillant un traitement différent. Les études exposées dans cette thèse soulignent que l’utilisation d’algorithmes d’intelligence artificielle dans la classification et l’élaboration de stratégies opératoires de la SIA peuvent être intégrées dans une plateforme logicielle et pourraient assister les chirurgiens dans leur planification préopératoire.

Women with provoked vestibulodynia experience clinically significant reductions in pain regardless of treatment : results from a 2-year follow up study.

Introduction Provoked vestibulodynia (PVD) is a prevalent genital pain syndrome that has been assumed to be chronic, with little spontaneous remission. Despite this assumption, there is a dearth of empirical evidence regarding the progression of PVD in a natural setting. Although many treatments are available, there is no single treatment that has demonstrated efficacy above others. Aims The aims of this secondary analysis of a prospective study were to (i) assess changes over a 2-year period in pain, depressive symptoms, and sexual outcomes in women with PVD; and (ii) examine changes based on treatment(s) type. Methods Participants completed questionnaire packages at Time 1 and a follow-up package 2 years later. Main Outcome Measures Visual analog scale of genital pain, Global Measure of Sexual Satisfaction, Female Sexual Function Index, Beck Depression Inventory, Dyadic Adjustment Scale, and sexual intercourse attempts over the past month. Results Two hundred thirty-nine women with PVD completed both time one and two questionnaires. For the sample as a whole, there was significant improvement over 2 years on pain ratings, sexual satisfaction, sexual function, and depressive symptoms. The most commonly received treatments were physical therapy, sex/psychotherapy, and medical treatment, although 41.0% did not undergo any treatment. Women receiving no treatment also improved significantly on pain ratings. No single treatment type predicted better outcome for any variable except depressive symptoms, in which women who underwent surgery were more likely to improve. Discussion These results suggest that PVD may significantly reduce in severity over time. Participants demonstrated clinically significant pain improvement, even when they did not receive treatment. Furthermore, the only single treatment type predicting better outcomes was surgery, and only for depressive symptoms, accounting for only 2.3% of the variance. These data do not demonstrate the superiority of any one treatment and underscore the need to have control groups in PVD treatment trials, otherwise improvements may simply be the result of natural progression.

Mean Squared Residue Based Biclustering Algorithms for the Analysis of Gene Expression Data

Computational Biology is the research are that contributes to the analysis of biological data through the development of algorithms which will address significant research problems.The data from molecular biology includes DNA,RNA ,Protein and Gene expression data.Gene Expression Data provides the expression level of genes under different conditions.Gene expression is the process of transcribing the DNA sequence of a gene into mRNA sequences which in turn are later translated into proteins.The number of copies of mRNA produced is called the expression level of a gene.Gene expression data is organized in the form of a matrix. Rows in the matrix represent genes and columns in the matrix represent experimental conditions.Experimental conditions can be different tissue types or time points.Entries in the gene expression matrix are real values.Through the analysis of gene expression data it is possible to determine the behavioral patterns of genes such as similarity of their behavior,nature of their interaction,their respective contribution to the same pathways and so on. Similar expression patterns are exhibited by the genes participating in the same biological process.These patterns have immense relevance and application in bioinformatics and clinical research.Theses patterns are used in the medical domain for aid in more accurate diagnosis,prognosis,treatment planning.drug discovery and protein network analysis.To identify various patterns from gene expression data,data mining techniques are essential.Clustering is an important data mining technique for the analysis of gene expression data.To overcome the problems associated with clustering,biclustering is introduced.Biclustering refers to simultaneous clustering of both rows and columns of a data matrix. Clustering is a global whereas biclustering is a local model.Discovering local expression patterns is essential for identfying many genetic pathways that are not apparent otherwise.It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.A biclusters is a submatrix of the gene expression data matrix.The rows and columns in the submatrix need not be contiguous as in the gene expression data matrix.Biclusters are not disjoint.Computation of biclusters is costly because one will have to consider all the combinations of columans and rows in order to find out all the biclusters.The search space for the biclustering problem is 2 m+n where m and n are the number of genes and conditions respectively.Usually m+n is more than 3000.The biclustering problem is NP-hard.Biclustering is a powerful analytical tool for the biologist.The research reported in this thesis addresses the problem of biclustering.Ten algorithms are developed for the identification of coherent biclusters from gene expression data.All these algorithms are making use of a measure called mean squared residue to search for biclusters.The objective here is to identify the biclusters of maximum size with the mean squared residue lower than a given threshold. All these algorithms begin the search from tightly coregulated submatrices called the seeds.These seeds are generated by K-Means clustering algorithm.The algorithms developed can be classified as constraint based,greedy and metaheuristic.Constarint based algorithms uses one or more of the various constaints namely the MSR threshold and the MSR difference threshold.The greedy approach makes a locally optimal choice at each stage with the objective of finding the global optimum.In metaheuristic approaches particle Swarm Optimization(PSO) and variants of Greedy Randomized Adaptive Search Procedure(GRASP) are used for the identification of biclusters.These algorithms are implemented on the Yeast and Lymphoma datasets.Biologically relevant and statistically significant biclusters are identified by all these algorithms which are validated by Gene Ontology database.All these algorithms are compared with some other biclustering algorithms.Algorithms developed in this work overcome some of the problems associated with the already existing algorithms.With the help of some of the algorithms which are developed in this work biclusters with very high row variance,which is higher than the row variance of any other algorithm using mean squared residue, are identified from both Yeast and Lymphoma data sets.Such biclusters which make significant change in the expression level are highly relevant biologically.

“Reconstruction of Gene Regulatory Network from Expression Profile of Plasma RNA Data of Colorectal Cancer Patients using Soft Computing Techniques”

Microarray data analysis is one of data mining tool which is used to extract meaningful information hidden in biological data. One of the major focuses on microarray data analysis is the reconstruction of gene regulatory network that may be used to provide a broader understanding on the functioning of complex cellular systems. Since cancer is a genetic disease arising from the abnormal gene function, the identification of cancerous genes and the regulatory pathways they control will provide a better platform for understanding the tumor formation and development. The major focus of this thesis is to understand the regulation of genes responsible for the development of cancer, particularly colorectal cancer by analyzing the microarray expression data. In this thesis, four computational algorithms namely fuzzy logic algorithm, modified genetic algorithm, dynamic neural fuzzy network and Takagi Sugeno Kang-type recurrent neural fuzzy network are used to extract cancer specific gene regulatory network from plasma RNA dataset of colorectal cancer patients. Plasma RNA is highly attractive for cancer analysis since it requires a collection of small amount of blood and it can be obtained at any time in repetitive fashion allowing the analysis of disease progression and treatment response.