Twenty-four-hour esophageal pH monitoring in children and adolescents with chronic and/or recurrent rhinosinusitis

Gastroesophageal reflux (GER) disorder was studied in children and adolescents with chronic and/or recurrent rhinosinusitis not associated with bronchial asthma. Ten children with a clinical and radiological diagnosis of chronic and/or recurrent rhinosinusitis, consecutively attended at the Pediatric Otolaryngology Outpatient Clinic, Federal University of São Paulo, were evaluated. Prolonged esophageal pH monitoring was used to investigate GER disorder. The mean age of the ten patients evaluated (eight males) was 7.4 ± 2.4 years. Two patients presented vomiting as a clinical manifestation and one patient presented retrosternal pain with a burning sensation. Twenty-four-hour esophageal pH monitoring was performed using the Sandhill apparatus. An antimony probe electrode was placed in the lower third of the esophagus, confirmed by fluoroscopy and later by a chest X-ray. The parameters analyzed by esophageal pH monitoring included: total percent time of the presence of acid esophageal pH, i.e., pH below 4 (<4.2%); total number of acid episodes (<50 episodes); number of reflux episodes longer than 5 min (3 or less), and duration of the longest reflux episode (<9.2 min). One patient (1/10, 10%) presented a 24-h esophageal pH profile compatible with GER disorder. This data suggest that an association between chronic rhinosinusitis not associated with bronchial asthma and GER disorder may exist in children and adolescents, especially in those with compatible GER disorder symptoms. In these cases, 24-h esophageal pH monitoring should be performed before indicating surgery, since the present data suggest that 10% of chronic rhinosinusitis surgeries can be eliminated.

Connections: can the 20th century coronary heart disease epidemic reveal something about the 1918 influenza lethality?

This essay proposes that the ecologic association shown between the 20th century coronary heart disease epidemic and the 1918 influenza pandemic could shed light on the mechanism associated with the high lethality of the latter. It suggests that an autoimmune interference at the apoB-LDL interface could explain both hypercholesterolemia and inflammation (through interference with the cellular metabolism of arachidonic acid). Autoimmune inflammation, then, would explain the 1950s-60s acute coronary events (coronary thrombosis upon influenza re-infection) and the respiratory failure seen among young adults in 1918. This hypothesis also argues that the lethality of the 1918 pandemic may have not depended so much on the 1918 virus as on an immune vulnerability to it, possibly resulting from an earlier priming of cohorts born around 1890 by the 1890 influenza pandemic virus.

Anxiety and depression symptoms in recurrent painful renal lithiasis colic

Several studies have reported that symptoms of anxiety and depression are significantly associated with diseases characterized by painful crises. However, there is little information about the psychological aspects of recurrent painful episodes of renal stone disease. Our objective was to evaluate the association of symptoms of anxiety, depression and recurrent painful renal colic in a case-control study involving 64 subjects (32 cases/32 controls) matched for age and sex. Cases were outpatients with a confirmed diagnosis of nephrolithiasis as per their case history, physical examination, image examination and other laboratory exams. Patients had a history of at least two episodes within a 3-year period, and were currently in an intercrisis interval. The control group consisted of subjects seen at the Ophthalmology Outpatient Clinic of this University Hospital with only eye refraction symptoms, and no other associated disease. Symptoms of anxiety were evaluated by the State-Trait Anxiety Inventory and symptoms of depression by the Beck Depression Inventory. Statistically significant differences were observed between patients with nephrolithiasis and controls for anxiety state (P = 0.001), anxiety trait (P = 0.005) and symptoms of depression (odds ratio = 3.74; 95%CI = 1.31-10.62). The Beck Depression Inventory showed 34.5% of respondents with moderate and 6% with severe levels of depression. There was a significant linear correlation between symptoms of anxiety (P = 0.002) and depression (P < 0.001) and the number of recurrent colic episodes (anxiety-state: P = 0.016 and anxiety-trait: P < 0.001). These data suggest an association between recurrent renal colic and symptoms of both anxiety and depression.

Epidemiology of recurrent venous thrombosis

Venous thrombosis, including deep vein thrombosis and pulmonary embolism, is a common disease that frequently recurs. Recurrence can be prevented by anticoagulants, but this comes at the risk of bleeding. Therefore, assessment of the risk of recurrence is important to balance the risks and benefits of anticoagulant treatment. This review briefly outlines what is currently known about the epidemiology of recurrent venous thrombosis, and focuses in more detail on potential new risk factors for venous recurrence. The general implications of these findings in patient management are discussed.

Capecitabine maintenance therapy in patients with recurrent or metastatic breast cancer

Our objective was to investigate the efficacy and safety of capecitabine maintenance therapy (CMT) after capecitabine-based combination chemotherapy in patients with metastatic breast cancer. The clinical data of 139 metastatic breast cancer patients treated from March 2008 to May 2012 with capecitabine-based combination chemotherapy were retrospectively analyzed. When initial disease control was achieved by the combination chemotherapy, we used CMT for 50 patients, while 37 patients were treated with a different (non-CMT) maintenance therapy. We compared time to progression (TTP), objective response rate, disease control rate, clinical benefit rate, and safety of the two groups, and a sub-group analysis was performed according to pathological characteristics. Sixty-four percent of the patients received a median of six cycles of a docetaxel+capecitabine combination chemotherapy regimen (range 1-45); the median TTP (MTTP) for the complete treatment was 9.43 months (95%CI=8.38-10.48 months) for the CMT group and 4.5 months (95%CI=4.22-4.78 months; P=0.004) for the non-CMT group. The MTTPs for the maintenance therapies administered after the initial capecitabine combined chemotherapy were 4.11 months (95%CI=3.34-4.87 months) for the CMT group and 2.0 months (95%CI=1.63-2.38 months) for the non-CMT group. Gastrointestinal side effects, decreased white blood cells and palmar-plantar erythrodysesthesia were the main adverse reactions experienced with the combination chemotherapies, CMT and non-CMT treatments. No significant differences in the incidence of adverse reactions were detected in the CMT and non-CMT patients. After initial disease control was achieved with the capecitabine-based combination chemotherapy, CMT can significantly prolong TTP rates with a favorable safety profile.

Oxidative stress and cellular immunity in patients with recurrent aphthous ulcers

Recurrent aphthous ulcer (RAU) is an inflammatory condition of the oral mucosa characterized by painful, well-circumscribed, single or multiple round or ovoid ulcerations. The exact etiologic factor(s) of these ulcerations are not yet understood. The objective of this study was to evaluate inflammatory processes and free radical metabolism of 25 patients with RAUs compared to 25 healthy controls. The levels of malondialdehyde (MDA) and glutathione (GSH) were determined by high-performance liquid chromatography. Tumor necrosis factor-alpha (TNF-α), interleukin-2 (IL-2), IL-10, and IL-12 were determined by ELISA. Nitric oxide (NO), myeloperoxidase (MPO), total antioxidant status (TAS), and total oxidant status (TOS) levels were measured spectroscopically in serum. The levels of MDA, GSH, TNF-α, IL-2, IL-12, MPO, and TOS, and oxidative stress index (OSI) were higher, and the levels of NO, IL-10, and TAS were lower in patients with RAU than in controls. Statistical analysis showed that GSH, TNF-α, IL-2, IL-10, and OSI differed significantly in patients with RAU compared to controls. These parameters have important roles in oxidant/antioxidant defense.

Overview of Zika virus (ZIKV) infection in regards to the Brazilian epidemic

Zika virus (ZIKV), a mosquito-borne flavivirus, belongs to the Flaviviridae family, genus Flavivirus. ZIKV was initially isolated in 1947 from a sentinel monkey in the Zika forest, Uganda. Little clinical importance was attributed to ZIKV, once only few symptomatic cases were reported in some African and Southeast Asiatic countries. This situation changed in 2007, when a large outbreak was registered on the Yap Island, Micronesia, caused by the Asian ZIKV lineage. Between 2013 and 2014, ZIKV spread explosively and caused many outbreaks in different islands of the Southern Pacific Ocean and in 2015 autochthonous transmission was reported in Brazil. Currently, Brazil is the country with the highest number of ZIKV-positive cases in Latin America. Moreover, for the first time after the discovery of ZIKV, the Brazilian scientists are studying the possibility for the virus to cause severe congenital infection related to microcephaly and serious birth defects due to the time-spatial coincidence of the alarming increase of newborns with microcephaly and the Brazilian ZIKV epidemic. The present review summarizes recent information for ZIKV epidemiology, clinical picture, transmission, diagnosis and the consequences of this emerging virus in Brazil.

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.

Central auditory processing and the acquisition of phonology in 2-year-old children with recurrent acute otitis media

Middle ear infections (acute otitis media, AOM) are among the most common infectious diseases in childhood, their incidence being greatest at the age of 6–12 months. Approximately 10–30% of children undergo repetitive periods of AOM, referred to as recurrent acute otitis media (RAOM). Middle ear fluid during an AOM episode causes, on average, 20–30 dB of hearing loss lasting from a few days to as much as a couple of months. It is well known that even a mild permanent hearing loss has an effect on language development but so far there is no consensus regarding the consequences of RAOM on childhood language acquisition. The results of studies on middle ear infections and language development have been partly discrepant and the exact effects of RAOM on the developing central auditory nervous system are as yet unknown. This thesis aims to examine central auditory processing and speech production among 2-year-old children with RAOM. Event-related potentials (ERPs) extracted from electroencephalography can be used to objectively investigate the functioning of the central auditory nervous system. For the first time this thesis has utilized auditory ERPs to study sound encoding and preattentive auditory discrimination of speech stimuli, and neural mechanisms of involuntary auditory attention in children with RAOM. Furthermore, the level of phonological development was studied by investigating the number and the quality of consonants produced by these children. Acquisition of consonant phonemes, which are harder to hear than vowels, is a good indicator of the ability to form accurate memory representations of ambient language and has not been studied previously in Finnish-speaking children with RAOM. The results showed that the cortical sound encoding was intact but the preattentive auditory discrimination of multiple speech sound features was atypical in those children with RAOM. Furthermore, their neural mechanisms of auditory attention differed from those of their peers, thus indicating that children with RAOM are atypically sensitive to novel but meaningless sounds. The children with RAOM also produced fewer consonants than their controls. Noticeably, they had a delay in the acquisition of word-medial consonants and the Finnish phoneme /s/, which is acoustically challenging to perceive compared to the other Finnish phonemes. The findings indicate the immaturity of central auditory processing in the children with RAOM, and this might also emerge in speech production. This thesis also showed that the effects of RAOM on central auditory processing are long-lasting because the children had healthy ears at the time of the study. An effective neural network for speech sound processing is a basic requisite of language acquisition, and RAOM in early childhood should be considered as a risk factor for language development.

The lived experience of women with recurrent ovarian cancer /

The purpose of this study was to understand the Hved experience of 6 women with recurrent ovarian cancer. Six women were interviewed 2-20 weeks after the recurrence of their ovarian cancer. Interview questions focused on the meaning of the recurrence and their communication with others. Women were asked about the information and support that they felt they needed at that time, van Manen's method of reflection and writing guided the inquiry. Analysis of the data revealed the themes of: my cancer is back; it means that I will die; talking about it; we are people, we are not a disease; information; and life has changed/life hasn't changed. This study revealed the perspectives of these 6 women with recurrent ovarian cancer. It provided an understanding and knowledge about the lives of these women. Future research should explore the experiences of a larger group of women with recurrent ovarian cancer in order to address their unique needs.

Haplotype analysis suggest common founders in carriers of the recurrent BRCA2 mutation, 3398delAAAAG, in French Canadian hereditary breast and/ovarian cancer families

Affiliation: Faculté de médicine, Université de Montréal

RNA recurrent motifs : identification and characterization

La détermination de la structure tertiaire du ribosome fut une étape importante dans la compréhension du mécanisme de la synthèse des protéines. Par contre, l’élucidation de la structure du ribosome comme tel ne permet pas une compréhension de sa fonction. Pour mieux comprendre la nature des relations entre la structure et la fonction du ribosome, sa structure doit être étudiée de manière systématique. Au cours des dernières années, nous avons entrepris une démarche systématique afin d’identifier et de caractériser de nouveaux motifs structuraux qui existent dans la structure du ribosome et d’autres molécules contenant de l’ARN. L’analyse de plusieurs exemples d’empaquetage de deux hélices d’ARN dans la structure du ribosome nous a permis d’identifier un nouveau motif structural, nommé « G-ribo ». Dans ce motif, l’interaction d’une guanosine dans une hélice avec le ribose d’un nucléotide d’une autre hélice donne naissance à un réseau d’interactions complexes entre les nucléotides voisins. Le motif G-ribo est retrouvé à 8 endroits dans la structure du ribosome. La structure du G-ribo possède certaines particularités qui lui permettent de favoriser la formation d’un certain type de pseudo-nœuds dans le ribosome. L’analyse systématique de la structure du ribosome et de la ARNase P a permis d’identifier un autre motif structural, nommé « DTJ » ou « Double-Twist Joint motif ». Ce motif est formé de trois courtes hélices qui s’empilent l’une sur l’autre. Dans la zone de contact entre chaque paire d’hélices, deux paires de bases consécutives sont surenroulées par rapport à deux paires de bases consécutives retrouvées dans l’ARN de forme A. Un nucléotide d’une paire de bases est toujours connecté directement à un nucléotide de la paire de bases surenroulée, tandis que les nucléotides opposés sont connectés par un ou plusieurs nucléotides non appariés. L’introduction d’un surenroulement entre deux paires de bases consécutives brise l’empilement entre les nucléotides et déstabilise l’hélice d’ARN. Dans le motif DTJ, les nucléotides non appariés qui lient les deux paires de bases surenroulées interagissent avec une des trois hélices qui forment le motif, offrant ainsi une stratégie élégante de stabilisation de l’arrangement. Pour déterminer les contraintes de séquences imposées sur la structure tertiaire d’un motif récurrent dans le ribosome, nous avons développé une nouvelle approche expérimentale. Nous avons introduit des librairies combinatoires de certains nucléotides retrouvés dans des motifs particuliers du ribosome. Suite à l’analyse des séquences alternatives sélectionnées in vivo pour différents représentants d’un motif, nous avons été en mesure d’identifier les contraintes responsables de l’intégrité d’un motif et celles responsables d’interactions avec les éléments qui forment le contexte structural du motif. Les résultats présentés dans cette thèse élargissent considérablement notre compréhension des principes de formation de la structure d’ARN et apportent une nouvelle façon d’identifier et de caractériser de nouveaux motifs structuraux d’ARN.

Modeling High-Dimensional Audio Sequences with Recurrent Neural Networks

Cette thèse étudie des modèles de séquences de haute dimension basés sur des réseaux de neurones récurrents (RNN) et leur application à la musique et à la parole. Bien qu'en principe les RNN puissent représenter les dépendances à long terme et la dynamique temporelle complexe propres aux séquences d'intérêt comme la vidéo, l'audio et la langue naturelle, ceux-ci n'ont pas été utilisés à leur plein potentiel depuis leur introduction par Rumelhart et al. (1986a) en raison de la difficulté de les entraîner efficacement par descente de gradient. Récemment, l'application fructueuse de l'optimisation Hessian-free et d'autres techniques d'entraînement avancées ont entraîné la recrudescence de leur utilisation dans plusieurs systèmes de l'état de l'art. Le travail de cette thèse prend part à ce développement. L'idée centrale consiste à exploiter la flexibilité des RNN pour apprendre une description probabiliste de séquences de symboles, c'est-à-dire une information de haut niveau associée aux signaux observés, qui en retour pourra servir d'à priori pour améliorer la précision de la recherche d'information. Par exemple, en modélisant l'évolution de groupes de notes dans la musique polyphonique, d'accords dans une progression harmonique, de phonèmes dans un énoncé oral ou encore de sources individuelles dans un mélange audio, nous pouvons améliorer significativement les méthodes de transcription polyphonique, de reconnaissance d'accords, de reconnaissance de la parole et de séparation de sources audio respectivement. L'application pratique de nos modèles à ces tâches est détaillée dans les quatre derniers articles présentés dans cette thèse. Dans le premier article, nous remplaçons la couche de sortie d'un RNN par des machines de Boltzmann restreintes conditionnelles pour décrire des distributions de sortie multimodales beaucoup plus riches. Dans le deuxième article, nous évaluons et proposons des méthodes avancées pour entraîner les RNN. Dans les quatre derniers articles, nous examinons différentes façons de combiner nos modèles symboliques à des réseaux profonds et à la factorisation matricielle non-négative, notamment par des produits d'experts, des architectures entrée/sortie et des cadres génératifs généralisant les modèles de Markov cachés. Nous proposons et analysons également des méthodes d'inférence efficaces pour ces modèles, telles la recherche vorace chronologique, la recherche en faisceau à haute dimension, la recherche en faisceau élagué et la descente de gradient. Finalement, nous abordons les questions de l'étiquette biaisée, du maître imposant, du lissage temporel, de la régularisation et du pré-entraînement.

On Recurrent and Deep Neural Networks

L'apprentissage profond est un domaine de recherche en forte croissance en apprentissage automatique qui est parvenu à des résultats impressionnants dans différentes tâches allant de la classification d'images à la parole, en passant par la modélisation du langage. Les réseaux de neurones récurrents, une sous-classe d'architecture profonde, s'avèrent particulièrement prometteurs. Les réseaux récurrents peuvent capter la structure temporelle dans les données. Ils ont potentiellement la capacité d'apprendre des corrélations entre des événements éloignés dans le temps et d'emmagasiner indéfiniment des informations dans leur mémoire interne. Dans ce travail, nous tentons d'abord de comprendre pourquoi la profondeur est utile. Similairement à d'autres travaux de la littérature, nos résultats démontrent que les modèles profonds peuvent être plus efficaces pour représenter certaines familles de fonctions comparativement aux modèles peu profonds. Contrairement à ces travaux, nous effectuons notre analyse théorique sur des réseaux profonds acycliques munis de fonctions d'activation linéaires par parties, puisque ce type de modèle est actuellement l'état de l'art dans différentes tâches de classification. La deuxième partie de cette thèse porte sur le processus d'apprentissage. Nous analysons quelques techniques d'optimisation proposées récemment, telles l'optimisation Hessian free, la descente de gradient naturel et la descente des sous-espaces de Krylov. Nous proposons le cadre théorique des méthodes à région de confiance généralisées et nous montrons que plusieurs de ces algorithmes développés récemment peuvent être vus dans cette perspective. Nous argumentons que certains membres de cette famille d'approches peuvent être mieux adaptés que d'autres à l'optimisation non convexe. La dernière partie de ce document se concentre sur les réseaux de neurones récurrents. Nous étudions d'abord le concept de mémoire et tentons de répondre aux questions suivantes: Les réseaux récurrents peuvent-ils démontrer une mémoire sans limite? Ce comportement peut-il être appris? Nous montrons que cela est possible si des indices sont fournis durant l'apprentissage. Ensuite, nous explorons deux problèmes spécifiques à l'entraînement des réseaux récurrents, à savoir la dissipation et l'explosion du gradient. Notre analyse se termine par une solution au problème d'explosion du gradient qui implique de borner la norme du gradient. Nous proposons également un terme de régularisation conçu spécifiquement pour réduire le problème de dissipation du gradient. Sur un ensemble de données synthétique, nous montrons empiriquement que ces mécanismes peuvent permettre aux réseaux récurrents d'apprendre de façon autonome à mémoriser des informations pour une période de temps indéfinie. Finalement, nous explorons la notion de profondeur dans les réseaux de neurones récurrents. Comparativement aux réseaux acycliques, la définition de profondeur dans les réseaux récurrents est souvent ambiguë. Nous proposons différentes façons d'ajouter de la profondeur dans les réseaux récurrents et nous évaluons empiriquement ces propositions.