Evolution of anaesthesia care and related events between 1996 and 2010 in Switzerland.

BACKGROUND: Anaesthesia Databank Switzerland (ADS) is a voluntary data registry introduced in 1996. Its ultimate goal is to promote quality in anaesthesiology. METHODS: The ADS registry analyses routinely recorded adverse events and provides benchmark comparisons between anaesthesia departments. Data collection comprises a set of 31 variables organised into three modules, one mandatory and two optional. RESULTS: In 2010, the database included 2,158,735 anaesthetic procedures. Over time, the proportions of older patients have increased, the largest group being aged 50-64 years. The percentage of patients with American Society of Anesthesiologists (ASA) status 1 has decreased while the percentage of ASA status 2 or 3 patients has increased. The most frequent comorbidities recorded were hypertension (21%), smoking (16%), allergy (15%) and obesity (12%). Between 1996 and 2010, 125,579 adverse events were recorded, of which 34% were cardiovascular, 7% respiratory, 39% technical and 20% non-specific. The most severe events were resuscitation (50%), oliguria (22%), myocardial ischaemia (17%) and haemorrhage (10%). CONCLUSION: Routine ADS data collection contributes to the monitoring of trends in anaesthesia care in Switzerland. The ADS system has proved to be usable in daily practice, although this remains a constant challenge that is highly dependent on local quality management and quality culture. Nevertheless, success in developing routine regular feedback to users to initiate discussions about anaesthetic events would most likely help strengthen departmental culture regarding safety and quality of care.

Continuous sunitinib treatment in patients with unresectable hepatocellular carcinoma (HCC): A multicenter phase II trial (SAKK 77/06 and SASL 23)

Background: Sunitinib (SU) is a multitargeted tyrosine kinase inhibitor with antitumor and antiangiogenetic activity. Evidence for clinical activity in HCC was reported in 2 phase II trials [Zhu et al and Faivre et al, ASCO 2007] using either a 37.5 or a 50 mg daily dose in a 4 weeks on, 2 weeks off regimen. The objective of this trial was to demonstrate antitumor activity of continuous SU treatment in patients (pts) with HCC. Methods: Key eligibility criteria included unresectable or metastatic HCC, no prior systemic anticancer treatment, measurable disease and Child- Pugh A or B liver dysfunction. Pts received 37.5 mg SU daily until progression or unacceptable toxicity. The primary endpoint was progression free survival at 12 weeks (PFS12) defined as 'success' if the patient was alive and without tumor progression assessed by 12 weeks (±7 days) after registration. A PFS12 of _20% was considered uninteresting and promising if _40%. Using the Simon-two minimax stage design with 90% power and 5% significance the sample size was 45 pts. Secondary endpoints included safety assessments, measurement of serum cobalamin levels and tumor density. Results: From September 2007 to August 2008 45 pts, mostly male (87%), were enrolled in 10 centers. Median age was 63 years, 89% had Child-Pugh A and 47% had distant metastases. Median largest lesion diameter was 84mm (range: 18-280) and 18% had prior TACE. Reasons for stopping therapy were: PD 60%, symptomatic deterioration 16%, toxicity 11%, death 2% (due to tumor), and other reasons 4%; 7% remain on therapy. PFS12 was rated as success in 15 pts (33%) (95% CI: 20%, 49%) and failure in 27 (60%); 3 were not evaluable (due to refusal). Over the whole trial period 1 CR and 40% SD as best response were achieved. Median PFS, duration of disease stabilization, TTP and OS were 2.8, 3.2, 2.8 and 9.3 months, respectively. Grade 3 and 4 adverse events were infrequent and all deaths due to the tumor. Conclusions: Continuous SU treatment with 37.5 mg/d daily is feasible and demonstrates moderate activity in pts with advanced HCC and mild to moderately impaired liver dysfunction. Under this trial design the therapy is considered promising (>13 PFS12 successes).

De la perception à l'apprentissage [From perception to learning]

Dans le domaine de la perception, l'apprentissage est contraint par la présence d'une architecture fonctionnelle constituée d'aires corticales distribuées et très spécialisées. Dans le domaine des troubles visuels d'origine cérébrale, l'apprentissage d'un patient hémi-anopsique ou agnosique sera limité par ses capacités perceptives résiduelles, mais un déficit de reconnaissance visuelle de nature apparemment perceptive, peut également être associé à une altération des représentations en mémoire à long terme. Des réseaux neuronaux distincts pour la reconnaissance - cortex temporal - et pour la localisation des sons - cortex pariétal - ont été décrits chez l'homme. L'étude de patients cérébro-lésés confirme le rôle des indices spatiaux dans un traitement auditif explicite du « where » et dans la discrimination implicite du « what ». Cette organisation, similaire à ce qui a été décrit dans la modalité visuelle, faciliterait les apprentissages perceptifs. Plus généralement, l'apprentissage implicite fonde une grande partie de nos connaissances sur le monde en nous rendant sensible, à notre insu, aux règles et régularités de notre environnement. Il serait impliqué dans le développement cognitif, la formation des réactions émotionnelles ou encore l'apprentissage par le jeune enfant de sa langue maternelle. Le caractère inconscient de cet apprentissage est confirmé par l'étude des temps de réaction sériels de patients amnésiques dans l'acquisition d'une grammaire artificielle. Son évaluation pourrait être déterminante dans la prise en charge ré-adaptative. [In the field of perception, learning is formed by a distributed functional architecture of very specialized cortical areas. For example, capacities of learning in patients with visual deficits - hemianopia or visual agnosia - from cerebral lesions are limited by perceptual abilities. Moreover a visual deficit in link with abnormal perception may be associated with an alteration of representations in long term (semantic) memory. Furthermore, perception and memory traces rely on parallel processing. This has been recently demonstrated for human audition. Activation studies in normal subjects and psychophysical investigations in patients with focal hemispheric lesions have shown that auditory information relevant to sound recognition and that relevant to sound localisation are processed in parallel, anatomically distinct cortical networks, often referred to as the "What" and "Where" processing streams. Parallel processing may appear counterintuitive from the point of view of a unified perception of the auditory world, but there are advantages, such as rapidity of processing within a single stream, its adaptability in perceptual learning or facility of multisensory interactions. More generally, implicit learning mechanisms are responsible for the non-conscious acquisition of a great part of our knowledge about the world, using our sensitivity to the rules and regularities structuring our environment. Implicit learning is involved in cognitive development, in the generation of emotional processing and in the acquisition of natural language. Preserved implicit learning abilities have been shown in amnesic patients with paradigms like serial reaction time and artificial grammar learning tasks, confirming that implicit learning mechanisms are not sustained by the cognitive processes and the brain structures that are damaged in amnesia. In a clinical perspective, the assessment of implicit learning abilities in amnesic patients could be critical for building adapted neuropsychological rehabilitation programs.]

Adaptation to Abundant Low Quality Food Improves the Ability to Compete for Limited Rich Food in Drosophila melanogaster.

The rate of food consumption is a major factor affecting success in scramble competition for a limited amount of easy-to-find food. Accordingly, several studies report positive genetic correlations between larval competitive ability and feeding rate in Drosophila; both become enhanced in populations evolving under larval crowding. Here, we report the experimental evolution of enhanced competitive ability in populations of D. melanogaster previously maintained for 84 generations at low density on an extremely poor larval food. In contrast to previous studies, greater competitive ability was not associated with the evolution of higher feeding rate; if anything, the correlation between the two traits across lines tended to be negative. Thus, enhanced competitive ability may be favored by nutritional stress even when competition is not intense, and competitive ability may be decoupled from the rate of food consumption.

Opportunity for sexual selection and effective population size in the lek-breeding European treefrog (Hyla arborea).

Sexual selection in lek-breeding species might drastically lower male effective population size, with potentially important consequences for evolutionary and conservation biology. Using field-monitoring and parental-assignment methods, we analyzed sex-specific variances in breeding success in a population of European treefrogs, to (1) help understanding the dynamics of genetic variance at sex-specific loci, and (2) better quantify the risk posed by genetic drift in this species locally endangered by habitat fragmentation. The variance in male mating success turned out to be markedly lower than values obtained from other amphibian species with polygamous mating systems. The ratio of effective breeding size to census breeding size was only slightly lower in males (0.44) than in females (0.57), in line with the patterns of genetic diversity previously reported from H. arborea sex chromosomes. Combining our results with data on age at maturity and adult survival, we show that the negative effect of the mating system is furthermore compensated by the effect of delayed maturity, so that the estimated instantaneous effective size broadly corresponded to census breeding size. We conclude that the lek-breeding system of treefrogs impacts only weakly the patterns of genetic diversity on sex-linked genes and the ability of natural populations to resist genetic drift.

Being Female: a handicap for researchers in the competion for NCCR.

The Swiss National Science Foundation made a call for National Centers fo Competence in Research (NCCR) for the first time in 1999 and 2004. Together, these announcements concerned all disciplines and led to 126 preproposals, which were put forward by 2134 men and women researchers. It can be assumed that this operation mobilised Swiss researchers who regarded themselves as particularly well qualified to conduct high-level research in their field. The article uses network analysis and regression analysis methods to examine to what extend women had a lower success rate than men in the two selection rounds because of their sex. On the whole, the findings attest the gender neutrality of the National Science Foundation's selection procedures. However, they also confirm the well-known fact that women scientists are less represented in the higher echelons of academia and concentrated in the social sciences and humanities, as well as showing that this concentration reduces women's chances of success in scientific competition. The article shows that unequal gender-specific success rates prior to the NCCR funding contest play a fairly significant role.

Auditory cues support place navigation in rats when associated with a visual cue.

Rats, like other crepuscular animals, have excellent auditory capacities and they discriminate well between different sounds [Heffner HE, Heffner RS, Hearing in two cricetid rodents: wood rats (Neotoma floridana) and grasshopper mouse (Onychomys leucogaster). J Comp Psychol 1985;99(3):275-88]. However, most experimental literature concerning spatial orientation almost exclusively emphasizes the use of visual landmarks [Cressant A, Muller RU, Poucet B. Failure of centrally placed objects to control the firing fields of hippocampal place cells. J Neurosci 1997;17(7):2531-42; and Goodridge JP, Taube JS. Preferential use of the landmark navigational system by head direction cells in rats. Behav Neurosci 1995;109(1):49-61]. To address the important issue of whether rats are able to achieve a place navigation task relative to auditory beacons, we designed a place learning task in the water maze. We controlled cue availability by conducting the experiment in total darkness. Three auditory cues did not allow place navigation whereas three visual cues in the same positions did support place navigation. One auditory beacon directly associated with the goal location did not support taxon navigation (a beacon strategy allowing the animal to find the goal just by swimming toward the cue). Replacing the auditory beacons by one single visual beacon did support taxon navigation. A multimodal configuration of two auditory cues and one visual cue allowed correct place navigation. The deletion of the two auditory or of the one visual cue did disrupt the spatial performance. Thus rats can combine information from different sensory modalities to achieve a place navigation task. In particular, auditory cues support place navigation when associated with a visual one.

Dietary restriction affects lifespan but not cognitive aging in Drosophila melanogaster.

Summary Dietary restriction extends lifespan in a wide variety of animals, including Drosophila, but its relationship to functional and cognitive aging is unclear. Here, we study the effects of dietary yeast content on fly performance in an aversive learning task (association between odor and mechanical shock). Learning performance declined at old age, but 50-day-old dietary-restricted flies learned as poorly as equal-aged flies maintained on yeast-rich diet, even though the former lived on average 9 days (14%) longer. Furthermore, at the middle age of 21 days, flies on low-yeast diets showed poorer short-term (5 min) memory than flies on rich diet. In contrast, dietary restriction enhanced 60-min memory of young (5 days old) flies. Thus, while dietary restriction had complex effects on learning performance in young to middle-aged flies, it did not attenuate aging-related decline of aversive learning performance. These results are consistent with the hypothesis that, in Drosophila, dietary restriction reduces mortality and thus leads to lifespan extension, but does not affect the rate with which somatic damage relevant for cognitive performance accumulates with age.

Postnatal intracerebroventricular administrations of NGF alter spatial memory in adulthood

The present work assessed the effects of intracerebroventricular injections (2x5 mg/2.5 ml) of recombined human nerve growth factor (rhNGF) at postnatal days 2 and 3 upon the development of spatial learning capacities in rats. The treated rats were trained at the age of 22 days to escape onto an invisible platform at a fixed position in space in a Morris navigation task. For half of the subjects, the training position was also cued, a procedure aimed at facilitating escape and reducing attention to the distant spatial cues. At the age of 2 months all the rats were retrained in the same task. Treatment effects were found in both immature and adult rats. The injection of NGF induced a slight alteration of the immature rats' performance. In contrast, a marked impairment of spatial abilities was shown in the 2-month-old rats. The most consistent effects were a significant increase in the escape latency and a decrease bias towards the training platform area during probe trials. The reduction of spatial memory was particularly marked if the subjects had been trained in a cued condition. Taken together, these experiments reveal that an acute pharmacological treatment that leads to transient modifications during early development might induce a behavioural change long after treatment. Thus, the development and the maintenance of an accurate spatial representation are tightly related to the development of brain structures that could be altered by precocious NGF administrations.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Ibotenate Lesions of Hippocampus and/or Subiculum: Dissociating Components of Allocentric Spatial Learning

This study examined the effects of ibotenic acid-induced lesions of the hippocampus, subiculum and hippocampus +/- subiculum upon the capacity of rats to learn and perform a series of allocentric spatial learning tasks in an open-field water maze. The lesions were made by infusing small volumes of the neurotoxin at a total of 26 (hippocampus) or 20 (subiculum) sites intended to achieve complete target cell loss but minimal extratarget damage. The regional extent and axon-sparing nature of these lesions was evaluated using both cresyl violet and Fink - Heimer stained sections. The behavioural findings indicated that both the hippocampus and subiculum lesions caused impairment to the initial postoperative acquisition of place navigation but did not prevent eventual learning to levels of performance almost as effective as those of controls. However, overtraining of the hippocampus + subiculum lesioned rats did not result in significant place learning. Qualitative observations of the paths taken to find a hidden escape platform indicated that different strategies were deployed by hippocampal and subiculum lesioned groups. Subsequent training on a delayed matching to place task revealed a deficit in all lesioned groups across a range of sample choice intervals, but the subiculum lesioned group was less impaired than the group with the hippocampal lesion. Finally, unoperated control rats given both the initial training and overtraining were later given either a hippocampal lesion or sham surgery. The hippocampal lesioned rats were impaired during a subsequent retention/relearning phase. Together, these findings suggest that total hippocampal cell loss may cause a dual deficit: a slower rate of place learning and a separate navigational impairment. The prospect of unravelling dissociable components of allocentric spatial learning is discussed.

Demogenetics in metapopulations

SUMMARY : Human-induced habitat fragmentation constitutes a major threat to biodiversity. Small and isolated populations suffer from increased stochasticity and from limited rescue effects. These two factors may be sufficient to cause local extinctions but fragmentation induces some genetic consequences that can also contribute significantly to extinction risks. Increased genetic drift reduces the effectiveness of selection against deleterious mutations, leading to their progressive accumulation. Drift also decreases both the standing genetic variation and the rate of fixation of beneficial mutations, limiting the evolutionary potential of isolated populations. Demography and genetics further interact and feed back on each other, progressively driving fragmented populations into "extinction vortices". The aim of the thesis was to better understand the processes occurring in fragmented populations. For this, I combined simulation studies and empirical data from three species that live in structured habitats. Chapter 1 and 2 investigate the demography of two shrew species in fragmented habitats. I showed that connectivity and habitat quality strongly affect the demography of the greater white-tooted shrew, although demographic stochasticity was extremely high. I also demonstrated that habitat fragmentation is one of the leading factors allowing the local coexistence of two competing shrew species. Chapter 3 and 4 focus on measuring connectivity in fragmented populations based on genetic data. In particular, I showed that genetic data can be used to detect the landscape elements impeding dispersal. In Chapter 5 that deals with the accumulation of deleterious mutations in fragmented populations, I demonstrated that mutation accumulation, as well a time to extinction, can be predicted from simple demographic and genetic measures. In the last two chapters, I monitored individual reproductive success in an isolated tree frogs population. These data allowed quantifying the effective population size, a measure closely linked to population evolutionary potential. To conclude, this thesis brings some new insights into the processes occurring in fragmented populations, and I hope it will contribute to the improvement of the management and conservation of fragmented populations.

The incidence and selection of multiple mating in plants.

Mating with more than one pollen donor, or polyandry, is common in land plants. In flowering plants, polyandry occurs when the pollen from different potential sires is distributed among the fruits of a single individual, or when pollen from more than one donor is deposited on the same stigma. Because polyandry typically leads to multiple paternity among or within fruits, it can be indirectly inferred on the basis of paternity analysis using molecular markers. A review of the literature indicates that polyandry is probably ubiquitous in plants except those that habitually self-fertilize, or that disperse their pollen in pollen packages, such as polyads or pollinia. Multiple mating may increase plants' female component by alleviating pollen limitation or by promoting competition among pollen grains from different potential sires. Accordingly, a number of traits have evolved that should promote polyandry at the flower level from the female's point of view, e.g. the prolongation of stigma receptivity or increases in stigma size. However, many floral traits, such as attractiveness, the physical manipulation of pollinators and pollen-dispensing mechanisms that lead to polyandrous pollination, have probably evolved in response to selection to promote male siring success in general, so that polyandry might often best be seen as a by-product of selection to enhance outcross siring success. In this sense, polyandry in plants is similar to geitonogamy (selfing caused by pollen transfer among flowers of the same plant), because both polyandry and geitonogamy probably result from selection to promote outcross siring success, although geitonogamy is almost always deleterious while polyandry in plants will seldom be so.

Fungus ball in the urinary tract: A rare entity.

A fungal mass in the urinary tract (fungus ball), mainly occurring in compromised patients, is a rare and dangerous complication of candiduria. We report 2 cases of fungus ball associated with hydronephrosis and sepsis. As reported in the literature, we treated the first patient by prompt relief of obstruction by nephrostomy and local and systemic antifungal agent. The second patient failed to respond to this treatment due to a distal ureteral stenosis and required open surgery with fungus ball removal and ureteral reimplantation. Despite a large success in urinary tract drainage with antifungal treatments, some cases need a modified approach due to anatomical modification.

Dysfunctional SEMA3E signaling underlies gonadotropin-releasing hormone neuron deficiency in Kallmann syndrome.

Individuals with an inherited deficiency in gonadotropin-releasing hormone (GnRH) have impaired sexual reproduction. Previous genetic linkage studies and sequencing of plausible gene candidates have identified mutations associated with inherited GnRH deficiency, but the small number of affected families and limited success in validating candidates have impeded genetic diagnoses for most patients. Using a combination of exome sequencing and computational modeling, we have identified a shared point mutation in semaphorin 3E (SEMA3E) in 2 brothers with Kallmann syndrome (KS), which causes inherited GnRH deficiency. Recombinant wild-type SEMA3E protected maturing GnRH neurons from cell death by triggering a plexin D1-dependent (PLXND1-dependent) activation of PI3K-mediated survival signaling. In contrast, recombinant SEMA3E carrying the KS-associated mutation did not protect GnRH neurons from death. In murine models, lack of either SEMA3E or PLXND1 increased apoptosis of GnRH neurons in the developing brain, reducing innervation of the adult median eminence by GnRH-positive neurites. GnRH neuron deficiency in male mice was accompanied by impaired testes growth, a characteristic feature of KS. Together, these results identify SEMA3E as an essential gene for GnRH neuron development, uncover a neurotrophic function for SEMA3E in the developing brain, and elucidate SEMA3E/PLXND1/PI3K signaling as a mechanism that prevents GnRH neuron deficiency.