Leptin receptor (Ob-R) mRNA expression and serum leptin concentration in patients with colorectal and metastatic colorectal cancer

The objective of the present study was to investigate the effect of leptin on the progression of colorectal carcinoma to metastatic disease by analyzing the serum leptin concentration and Ob-R gene expression in colon cancer tissues. Tissue samples were obtained from 31 patients who underwent surgical resection for colon (18 cases) and metastatic colon (13 cases) cancer. Serum leptin concentration was determined by an enzyme-linked immunosorbent assay (ELISA) and Ob-R mRNA expression by real-time polymerase chain reaction (RT-PCR) for both groups. ELISA data were analyzed by the Student t-test and RT-PCR data were analyzed by the Mann-Whitney U-test. RT-PCR results demonstrated that mRNA expression of Ob-R in human metastatic colorectal cancer was higher than in local colorectal cancer tissues. On the other hand, mean serum leptin concentration was significantly higher in local colorectal cancer patients compared to patients with metastatic colorectal cancer. The results of the present study suggest a role for leptin in the progression of colon cancer to metastatic disease without weight loss. In other words, significantly increased Ob-R mRNA expression and decreased serum leptin concentration in patients with metastatic colon cancer indicate that sensitization to leptin activity may be a major indicator of metastasis to the colon tissue and the determination of leptin concentration and leptin gene expression may be used to aid the diagnosis.

Pharmacological characterization of the relaxant effect induced by adrenomedullin in rat cavernosal smooth muscle

The aim of the present study was to determine the mechanisms underlying the relaxant effect of adrenomedullin (AM) in rat cavernosal smooth muscle (CSM) and the expression of AM system components in this tissue. Functional assays using standard muscle bath procedures were performed in CSM isolated from male Wistar rats. Protein and mRNA levels of pre-pro-AM, calcitonin receptor-like receptor (CRLR), and Subtypes 1, 2 and 3 of the receptor activity-modifying protein (RAMP) family were assessed by Western immunoblotting and quantitative real-time polymerase chain reaction, respectively. Nitrate and 6-keto-prostaglandin F1α (6-keto-PGF1α; a stable product of prostacyclin) levels were determined using commercially available kits. Protein and mRNA of AM, CRLR, and RAMP 1, -2, and -3 were detected in rat CSM. Immunohistochemical assays demonstrated that AM and CRLR were expressed in rat CSM. AM relaxed CSM strips in a concentration-dependent manner. AM22-52, a selective antagonist for AM receptors, reduced the relaxation induced by AM. Conversely, CGRP8-37, a selective antagonist for calcitonin gene-related peptide receptors, did not affect AM-induced relaxation. Preincubation of CSM strips with NG-nitro-L-arginine-methyl-ester (L-NAME, nitric oxide synthase inhibitor), 1H-(1,2,4)oxadiazolo[4,3-a]quinoxalin-1-one (ODQ, quanylyl cyclase inhibitor), Rp-8-Br-PET-cGMPS (cGMP-dependent protein kinase inhibitor), SC560 [5-(4-chlorophenyl)-1-(4-methoxyphenyl)-3-trifluoromethyl pyrazole, selective cyclooxygenase-1 inhibitor], and 4-aminopyridine (voltage-dependent K+ channel blocker) reduced AM-induced relaxation. On the other hand, 7-nitroindazole (selective neuronal nitric oxide synthase inhibitor), wortmannin (phosphatidylinositol 3-kinase inhibitor), H89 (protein kinase A inhibitor), SQ22536 [9-(tetrahydro-2-furanyl)-9H-purin-6-amine, adenylate cyclase inhibitor], glibenclamide (selective blocker of ATP-sensitive K+ channels), and apamin (Ca2+-activated channel blocker) did not affect AM-induced relaxation. AM increased nitrate levels and 6-keto-PGF1α in rat CSM. The major new contribution of this research is that it demonstrated expression of AM and its receptor in rat CSM. Moreover, we provided evidence that AM-induced relaxation in this tissue is mediated by AM receptors by a mechanism that involves the nitric oxide-cGMP pathway, a vasodilator prostanoid, and the opening of voltage-dependent K+ channels.

Erythropoietin reduces the expression of myostatin in mdx dystrophic mice

Erythropoietin (EPO) has been well characterized as a renal glycoprotein hormone regulating red blood cell production by inhibiting apoptosis of erythrocyte progenitors in hematopoietic tissues. EPO exerts regulatory effects in cardiac and skeletal muscles. Duchenne muscular dystrophy is a lethal degenerative disorder of skeletal and cardiac muscle. In this study, we tested the possible therapeutic beneficial effect of recombinant EPO (rhEPO) in dystrophic muscles in mdx mice. Total strength was measured using a force transducer coupled to a computer. Gene expression for myostatin, transforming growth factor-β1 (TGF-β1), and tumor necrosis factor-α (TNF-α) was determined by quantitative real time polymerase chain reaction. Myostatin expression was significantly decreased in quadriceps from mdx mice treated with rhEPO (rhEPO=0.60±0.11, control=1.07±0.11). On the other hand, rhEPO had no significant effect on the expression of TGF-β1 (rhEPO=0.95±0.14, control=1.05±0.16) and TNF-α (rhEPO=0.73±0.20, control=1.01±0.09). These results may help to clarify some of the direct actions of EPO on skeletal muscle.

The consensus sequence of FAMLF alternative splice variants is overexpressed in undifferentiated hematopoietic cells

The familial acute myeloid leukemia related factor gene (FAMLF) was previously identified from a familial AML subtractive cDNA library and shown to undergo alternative splicing. This study used real-time quantitative PCR to investigate the expression of the FAMLF alternative-splicing transcript consensus sequence (FAMLF-CS) in peripheral blood mononuclear cells (PBMCs) from 119 patients with de novo acute leukemia (AL) and 104 healthy controls, as well as in CD34+cells from 12 AL patients and 10 healthy donors. A 429-bp fragment from a novel splicing variant of FAMLF was obtained, and a 363-bp consensus sequence was targeted to quantify total FAMLF expression. Kruskal-Wallis, Nemenyi, Spearman's correlation, and Mann-Whitney U-tests were used to analyze the data. FAMLF-CS expression in PBMCs from AL patients and CD34+ cells from AL patients and controls was significantly higher than in control PBMCs (P<0.0001). Moreover,FAMLF-CS expression in PBMCs from the AML group was positively correlated with red blood cell count (rs=0.317, P=0.006), hemoglobin levels (rs=0.210, P=0.049), and percentage of peripheral blood blasts (rs=0.256, P=0.027), but inversely correlated with hemoglobin levels in the control group (rs=–0.391, P<0.0001). AML patients with high CD34+ expression showed significantly higherFAMLF-CS expression than those with low CD34+ expression (P=0.041). Our results showed thatFAMLF is highly expressed in both normal and malignant immature hematopoietic cells, but that expression is lower in normal mature PBMCs.

Bayesian analysis of SEIR epidemic models

This thesis concerns the analysis of epidemic models. We adopt the Bayesian paradigm and develop suitable Markov Chain Monte Carlo (MCMC) algorithms. This is done by considering an Ebola outbreak in the Democratic Republic of Congo, former Zaïre, 1995 as a case of SEIR epidemic models. We model the Ebola epidemic deterministically using ODEs and stochastically through SDEs to take into account a possible bias in each compartment. Since the model has unknown parameters, we use different methods to estimate them such as least squares, maximum likelihood and MCMC. The motivation behind choosing MCMC over other existing methods in this thesis is that it has the ability to tackle complicated nonlinear problems with large number of parameters. First, in a deterministic Ebola model, we compute the likelihood function by sum of square of residuals method and estimate parameters using the LSQ and MCMC methods. We sample parameters and then use them to calculate the basic reproduction number and to study the disease-free equilibrium. From the sampled chain from the posterior, we test the convergence diagnostic and confirm the viability of the model. The results show that the Ebola model fits the observed onset data with high precision, and all the unknown model parameters are well identified. Second, we convert the ODE model into a SDE Ebola model. We compute the likelihood function using extended Kalman filter (EKF) and estimate parameters again. The motivation of using the SDE formulation here is to consider the impact of modelling errors. Moreover, the EKF approach allows us to formulate a filtered likelihood for the parameters of such a stochastic model. We use the MCMC procedure to attain the posterior distributions of the parameters of the SDE Ebola model drift and diffusion parts. In this thesis, we analyse two cases: (1) the model error covariance matrix of the dynamic noise is close to zero , i.e. only small stochasticity added into the model. The results are then similar to the ones got from deterministic Ebola model, even if methods of computing the likelihood function are different (2) the model error covariance matrix is different from zero, i.e. a considerable stochasticity is introduced into the Ebola model. This accounts for the situation where we would know that the model is not exact. As a results, we obtain parameter posteriors with larger variances. Consequently, the model predictions then show larger uncertainties, in accordance with the assumption of an incomplete model.

Aikaerotteisesti fluoresenssia mittaavan reaaliaikaisen PCR-laitteen kehitys

Kvantitatiivinen reaaliaikainen polymeraasiketjureaktio (engl. polymerase chain reaction, PCR) on osoittautunut käyttäjäystävällisimmäksi menetelmäksi nukleiinihapposekvenssien kvantitoimisessa. Tätä menetelmää voidaan herkistää pienempien DNA-pitoisuuksien havaitsemiseen käyttämällä hyväksi aikaerotteista fluorometriaa (engl. time-resolved fluorometry, TRF) ja luminoivia lantanidileimoja, joiden fluoresenssin pitkän eliniän ansiosta emission mittaus voidaan suorittaa vasta hetki virittävän valopulssin jälkeen, jolloin lyhytikäinen taustasäteily ehtii sammua. Tuloksena saadaan korkea signaali-taustasuhde. Tämän diplomityön tarkoituksena oli rakentaa TRF:än pystyvä reaaliaikainen PCR-laite, sillä tällaista laitetta ei ole markkinoilla tarjolla. Laite rakennettiin kehittämällä lämpökierrätin ja yhdistämällä se valmiiseen TRF:än kykenevään mittapäähän. Mittapään ja lämpökierrättimen hallitsemiseksi kehitettiin myös tietokoneohjelma. Valon tuottamiseksi ja mittaamiseksi haluttiin käyttää edullisia komponentteja, joten työssä käytettiin valmiin mittapään optiikkaa, jossa viritys tapahtuu hohtodiodilla (engl. light-emitting diode, LED) ja lantanidileiman emission mittaus fotodiodilla (engl. photodiode, PD) tai valomonistinputkella (engl. photomultiplier tube, PMT). Myös mittapään suorituskykyä tutkittiin. Työtä varten kehitettiin lämpökierrätin, joka koostui Peltier-elementillä lämmitettävästä PCR-putkitelineestä ja lämpökannesta. Mittalaitteen suorituskyvyn tutkimiseen käytettiin kelaattikomplementaatioon perustuvaa PCR-tuotteen havaitsemismenetelmää. Kelaattikomplementaatio perustuu kahteen erilliseen oligonukleotidimolekyyliin, joista toiseen on sidottu lantanidi-ioni ja toiseen valoa absorboiva ligandirakenne, jotka yhdessä muodostavat fluoresoivan kokonaisuuden. Kehitetyn lämpökierrättimen todettiin olevan tarpeeksi tarkka sekä tehokas ja sen lämmitys- ja jäähdytysnopeuden maksimeiksi saatiin 2,6 °C/sekunti. Detektorina käytetyn PD:n ei todettu olevan tarpeeksi herkkä emission havainnoimiseksi ja se korvattiin laitteessa PMT:llä. Käytetyllä PCR-määrityksellä kynnyssykleiksi (engl. threshold cycle, Ct) sekä kehitetylle että referenssilaitteelle saatiin 28,4 käyttämällä samaa 100 000 kopion DNA:n aloitusmäärää. Työssä osoitettiin, että on mahdollista kehittää edullisia komponentteja käyttävä, TRF:än pystyvä, reaaliaikainen PCR-laite, joka kykenee vastaavaan Ct-arvoon kuin vertailulaite. PD:n herkkyys ei kuitenkaan riittänyt. Tulokset olivat lupaavia, sillä LED- ja PD-teknologiat kehittyvät ja markkinoille on tullut myös muita komponentteja, joiden avulla on tulevaisuudessa mahdollista kehittää vielä herkempi laite.

An Eigenfunction Approach for Volatility Modeling.

In this paper, we introduce a new approach for volatility modeling in discrete and continuous time. We follow the stochastic volatility literature by assuming that the variance is a function of a state variable. However, instead of assuming that the loading function is ad hoc (e.g., exponential or affine), we assume that it is a linear combination of the eigenfunctions of the conditional expectation (resp. infinitesimal generator) operator associated to the state variable in discrete (resp. continuous) time. Special examples are the popular log-normal and square-root models where the eigenfunctions are the Hermite and Laguerre polynomials respectively. The eigenfunction approach has at least six advantages: i) it is general since any square integrable function may be written as a linear combination of the eigenfunctions; ii) the orthogonality of the eigenfunctions leads to the traditional interpretations of the linear principal components analysis; iii) the implied dynamics of the variance and squared return processes are ARMA and, hence, simple for forecasting and inference purposes; (iv) more importantly, this generates fat tails for the variance and returns processes; v) in contrast to popular models, the variance of the variance is a flexible function of the variance; vi) these models are closed under temporal aggregation.

Asymptotic Approximations in the Near-Integrated Model with a Non-Zero Initial Condition

This paper considers various asymptotic approximations in the near-integrated firstorder autoregressive model with a non-zero initial condition. We first extend the work of Knight and Satchell (1993), who considered the random walk case with a zero initial condition, to derive the expansion of the relevant joint moment generating function in this more general framework. We also consider, as alternative approximations, the stochastic expansion of Phillips (1987c) and the continuous time approximation of Perron (1991). We assess how these alternative methods provide or not an adequate approximation to the finite-sample distribution of the least-squares estimator in a first-order autoregressive model. The results show that, when the initial condition is non-zero, Perron's (1991) continuous time approximation performs very well while the others only offer improvements when the initial condition is zero.

Aggregations and Marginalization of GARCH and Stochastic Volatility Models

The GARCH and Stochastic Volatility paradigms are often brought into conflict as two competitive views of the appropriate conditional variance concept : conditional variance given past values of the same series or conditional variance given a larger past information (including possibly unobservable state variables). The main thesis of this paper is that, since in general the econometrician has no idea about something like a structural level of disaggregation, a well-written volatility model should be specified in such a way that one is always allowed to reduce the information set without invalidating the model. To this respect, the debate between observable past information (in the GARCH spirit) versus unobservable conditioning information (in the state-space spirit) is irrelevant. In this paper, we stress a square-root autoregressive stochastic volatility (SR-SARV) model which remains true to the GARCH paradigm of ARMA dynamics for squared innovations but weakens the GARCH structure in order to obtain required robustness properties with respect to various kinds of aggregation. It is shown that the lack of robustness of the usual GARCH setting is due to two very restrictive assumptions : perfect linear correlation between squared innovations and conditional variance on the one hand and linear relationship between the conditional variance of the future conditional variance and the squared conditional variance on the other hand. By relaxing these assumptions, thanks to a state-space setting, we obtain aggregation results without renouncing to the conditional variance concept (and related leverage effects), as it is the case for the recently suggested weak GARCH model which gets aggregation results by replacing conditional expectations by linear projections on symmetric past innovations. Moreover, unlike the weak GARCH literature, we are able to define multivariate models, including higher order dynamics and risk premiums (in the spirit of GARCH (p,p) and GARCH in mean) and to derive conditional moment restrictions well suited for statistical inference. Finally, we are able to characterize the exact relationships between our SR-SARV models (including higher order dynamics, leverage effect and in-mean effect), usual GARCH models and continuous time stochastic volatility models, so that previous results about aggregation of weak GARCH and continuous time GARCH modeling can be recovered in our framework.

ARMA Representation of Integrated and Realized Variances

This paper derives the ARMA representation of integrated and realized variances when the spot variance depends linearly on two autoregressive factors, i.e., SR SARV(2) models. This class of processes includes affine, GARCH diffusion, CEV models, as well as the eigenfunction stochastic volatility and the positive Ornstein-Uhlenbeck models. We also study the leverage effect case, the relationship between weak GARCH representation of returns and the ARMA representation of realized variances. Finally, various empirical implications of these ARMA representations are considered. We find that it is possible that some parameters of the ARMA representation are negative. Hence, the positiveness of the expected values of integrated or realized variances is not guaranteed. We also find that for some frequencies of observations, the continuous time model parameters may be weakly or not identified through the ARMA representation of realized variances.

A maximum likelihood framework for protein design

Affiliation: Claudia Kleinman, Nicolas Rodrigue & Hervé Philippe : Département de biochimie, Faculté de médecine, Université de Montréal

Stochastic Volatility.

This paper prepared for the Handbook of Statistics (Vol.14: Statistical Methods in Finance), surveys the subject of stochastic volatility. the following subjects are covered: volatility in financial markets (instantaneous volatility of asset returns, implied volatilities in option prices and related stylized facts), statistical modelling in discrete and continuous time and, finally, statistical inference (methods of moments, quasi-maximum likelihood, likelihood-based and bayesian methods and indirect inference).

Modélisation de l’observance et détermination de son impact biopharmaceutique

L’observance, qui décrit à quel degré le patient suit la prescription, est un facteur essentiel pour que le traitement réussisse. Les observances des patients varient beaucoup et l’efficacité du médicament varie parallèlement. Par conséquent, il faut avoir des paramètres sensibles et fiables pour mesurer l’observance. Dans la littérature, on trouve beaucoup de paramètres pour évaluer l’observance mais leurs avantages, limites et inconvénients, en ce qui concerne l’évaluation de l’impact de l’observance sur les effets des médicaments n’ont pas encore été étudiés en profondeur. L’évaluation de ces paramètres nécessite de les tester dans différentes situations. Comme les données disponibles sur l’observance ne concernent pas un ensemble exhaustif de situations, le recours à la simulation, en s’inspirant des cas réels ou plausibles, est très pertinent. On a ainsi réussi à développer un modèle dont les paramètres sont simples et compréhensibles et qui est pratique et flexible pour simuler les différents cas et même les cas extrêmes de l’observance. On a proposé de nouveaux paramètres pour mesurer l’impact biopharmaceutique de l’observance. Ensuite, on a comparé la performance, en termes de sensibilité et la fiabilité, des paramètres proposés et celles de paramètres déjà utilisés. En conclusion, on peut souligner qu’il n’y a pas de paramètre parfait étant donné que chacun a ses propres limites. Par exemple, pour les médicaments dont les effets sont directement liés aux leurs concentrations plasmatiques, le pourcentage des doses prises, qui est le paramètre le plus utilisé, offre la pire performance; par contre, le pourcentage des doses correctes nettes qui est un nouveau paramètre possède une bonne performance et des avantages prometteurs.

Statistical potentials for evolutionary studies

Les séquences protéiques naturelles sont le résultat net de l’interaction entre les mécanismes de mutation, de sélection naturelle et de dérive stochastique au cours des temps évolutifs. Les modèles probabilistes d’évolution moléculaire qui tiennent compte de ces différents facteurs ont été substantiellement améliorés au cours des dernières années. En particulier, ont été proposés des modèles incorporant explicitement la structure des protéines et les interdépendances entre sites, ainsi que les outils statistiques pour évaluer la performance de ces modèles. Toutefois, en dépit des avancées significatives dans cette direction, seules des représentations très simplifiées de la structure protéique ont été utilisées jusqu’à présent. Dans ce contexte, le sujet général de cette thèse est la modélisation de la structure tridimensionnelle des protéines, en tenant compte des limitations pratiques imposées par l’utilisation de méthodes phylogénétiques très gourmandes en temps de calcul. Dans un premier temps, une méthode statistique générale est présentée, visant à optimiser les paramètres d’un potentiel statistique (qui est une pseudo-énergie mesurant la compatibilité séquence-structure). La forme fonctionnelle du potentiel est par la suite raffinée, en augmentant le niveau de détails dans la description structurale sans alourdir les coûts computationnels. Plusieurs éléments structuraux sont explorés : interactions entre pairs de résidus, accessibilité au solvant, conformation de la chaîne principale et flexibilité. Les potentiels sont ensuite inclus dans un modèle d’évolution et leur performance est évaluée en termes d’ajustement statistique à des données réelles, et contrastée avec des modèles d’évolution standards. Finalement, le nouveau modèle structurellement contraint ainsi obtenu est utilisé pour mieux comprendre les relations entre niveau d’expression des gènes et sélection et conservation de leur séquence protéique.

Approximation de la distribution a posteriori d'un modèle Gamma-Poisson hiérarchique à effets mixtes

La méthode que nous présentons pour modéliser des données dites de "comptage" ou données de Poisson est basée sur la procédure nommée Modélisation multi-niveau et interactive de la régression de Poisson (PRIMM) développée par Christiansen et Morris (1997). Dans la méthode PRIMM, la régression de Poisson ne comprend que des effets fixes tandis que notre modèle intègre en plus des effets aléatoires. De même que Christiansen et Morris (1997), le modèle étudié consiste à faire de l'inférence basée sur des approximations analytiques des distributions a posteriori des paramètres, évitant ainsi d'utiliser des méthodes computationnelles comme les méthodes de Monte Carlo par chaînes de Markov (MCMC). Les approximations sont basées sur la méthode de Laplace et la théorie asymptotique liée à l'approximation normale pour les lois a posteriori. L'estimation des paramètres de la régression de Poisson est faite par la maximisation de leur densité a posteriori via l'algorithme de Newton-Raphson. Cette étude détermine également les deux premiers moments a posteriori des paramètres de la loi de Poisson dont la distribution a posteriori de chacun d'eux est approximativement une loi gamma. Des applications sur deux exemples de données ont permis de vérifier que ce modèle peut être considéré dans une certaine mesure comme une généralisation de la méthode PRIMM. En effet, le modèle s'applique aussi bien aux données de Poisson non stratifiées qu'aux données stratifiées; et dans ce dernier cas, il comporte non seulement des effets fixes mais aussi des effets aléatoires liés aux strates. Enfin, le modèle est appliqué aux données relatives à plusieurs types d'effets indésirables observés chez les participants d'un essai clinique impliquant un vaccin quadrivalent contre la rougeole, les oreillons, la rub\'eole et la varicelle. La régression de Poisson comprend l'effet fixe correspondant à la variable traitement/contrôle, ainsi que des effets aléatoires liés aux systèmes biologiques du corps humain auxquels sont attribués les effets indésirables considérés.