In-vivo brain neuroimaging provides a gateway for integrating biological and clinical biomarkers of Alzheimer's disease.

PURPOSE OF REVIEW: Only 5% of the Alzheimer's cases are explained by genetic mutations, whereas the remaining 95% are sporadic. The pathophysiological mechanisms underlying sporadic Alzheimer's disease are not well understood, suggesting a complex multifactorial cause. This review summarizes the recent findings on research aiming to show how biomarkers can be used for revealing the underlying mechanisms of preclinical stage Alzheimer's disease and help in their diagnosis. RECENT FINDINGS: The undisputed successful publicly accessible repositories provide longitudinal brain images, clinical, genetic and proteomic information of Alzheimer's disease. By combining with increasingly sophisticated data analysis methods, it is a great opportunity for searching new biomarkers. Innovative studies validated theoretical models of disease progression demonstrating the sequential ordering of well-established biomarkers. Novel observations shed light on the interaction between biomarkers to confirm that disease progression is related to multiple pathological factors. A typical example is the tau-associated neuronal toxicity that can be additionally potentiated by amyloid β peptides. To increase further the complexity, studies report specific impact of common genetic variants that can be traced from childhood through middle age up to the symptomatic onset of Alzheimer's disease. SUMMARY: The discovery of efficient therapies to prevent the disease or modify the progression of disease requires a more thorough understanding of the underlying biological processes. Neuroimaging, genetic and proteomic biomarkers for Alzheimer's disease are critically discussed and proposed to be included in clinical descriptions and diagnostic guidelines.

Surgery Versus Radiotherapy for Early Oropharyngeal Tumors: a Never-Ending Debate.

OPINION STATEMENT: Therapeutic options for early stage oropharyngeal squamous cell carcinoma (OPSCC) include both surgery and radiotherapy as single treatment modality. Retrospective data reporting on locoregional control and survival rates in early stage OPSCC have shown equivalent efficacy, although no prospective randomized trials are available to confirm these results. Given the assumed comparable oncologic results in both groups, complication rates and functional outcomes associated with each modality play a major role when making treatment decisions. Radiotherapy is used preferentially in many centers because few trials have reported higher complication rates in surgical patients. However, these adverse effects were mainly due to traditional invasive open surgical approaches used for access to the oropharynx. In order to decrease the morbidity of these techniques, transoral surgical (TOS) approaches have been developed progressively. They include transoral laser microsurgery (TLM), transoral robotic surgery (TORS), and conventional transoral techniques. Meta-analysis comparing these new approaches with radiotherapy showed equivalent efficacy in terms of oncologic results. Furthermore, studies reporting on functional outcomes in patients undergoing TOS for OPSCC did not show major long-term functional impairment following treatment. Given the abovementioned statements, it is our practice to treat early stage OPSCC as follows: whenever a single modality treatment seems feasible (T1-2 and N0-1), we advocate TOS resection of the primary tumor associated with selective neck dissection, as indicated. In our opinion, the advantage of this approach relies on the possibility to stratify the risk of disease progression based on the pathological features of the tumor. Depending on the results, adjuvant radiation treatment or chemoradiotherapy can be chosen for high-risk patients. For tumors without adverse features, no adjuvant treatment is given. This approach also allows prevention of potential radiation-induced late complications while keeping radiotherapy as an option for any second primary lesions whenever needed. Definitive radiotherapy is generally reserved for selected patients with specific anatomical location associated with poor functional outcome following surgery, such as tumor of the soft palate, or for patients with severe comorbidities that do not allow surgical treatment.

2nd ESMO Consensus Conference in Lung Cancer: locally advanced stage III non-small-cell lung cancer.

To complement the existing treatment guidelines for all tumour types, ESMO organises consensus conferences to focus on specific issues in each type of tumour. The 2nd ESMO Consensus Conference on Lung Cancer was held on 11-12 May 2013 in Lugano. A total of 35 experts met to address several questions on non-small-cell lung cancer (NSCLC) in each of four areas: pathology and molecular biomarkers, first-line/second and further lines of treatment in advanced disease, early-stage disease and locally advanced disease. For each question, recommendations were made including reference to the grade of recommendation and level of evidence. This consensus paper focuses on locally advanced disease.

A spatial modelling framework for assessing climate change impacts on freshwater ecosystems: Response of brown trout (Salmo trutta L.) biomass to warming water temperature

Mountain regions worldwide are particularly sensitive to on-going climate change. Specifically in the Alps in Switzerland, the temperature has increased twice as fast than in the rest of the Northern hemisphere. Water temperature closely follows the annual air temperature cycle, severely impacting streams and freshwater ecosystems. In the last 20 years, brown trout (Salmo trutta L) catch has declined by approximately 40-50% in many rivers in Switzerland. Increasing water temperature has been suggested as one of the most likely cause of this decline. Temperature has a direct effect on trout population dynamics through developmental and disease control but can also indirectly impact dynamics via food-web interactions such as resource availability. We developed a spatially explicit modelling framework that allows spatial and temporal projections of trout biomass using the Aare river catchment as a model system, in order to assess the spatial and seasonal patterns of trout biomass variation. Given that biomass has a seasonal variation depending on trout life history stage, we developed seasonal biomass variation models for three periods of the year (Autumn-Winter, Spring and Summer). Because stream water temperature is a critical parameter for brown trout development, we first calibrated a model to predict water temperature as a function of air temperature to be able to further apply climate change scenarios. We then built a model of trout biomass variation by linking water temperature to trout biomass measurements collected by electro-fishing in 21 stations from 2009 to 2011. The different modelling components of our framework had overall a good predictive ability and we could show a seasonal effect of water temperature affecting trout biomass variation. Our statistical framework uses a minimum set of input variables that make it easily transferable to other study areas or fish species but could be improved by including effects of the biotic environment and the evolution of demographical parameters over time. However, our framework still remains informative to spatially highlight where potential changes of water temperature could affect trout biomass. (C) 2015 Elsevier B.V. All rights reserved.-

Induction chemoradiation in stage IIIA/N2 non-small-cell lung cancer: a phase 3 randomised trial.

BACKGROUND: One of the standard options in the treatment of stage IIIA/N2 non-small-cell lung cancer is neoadjuvant chemotherapy and surgery. We did a randomised trial to investigate whether the addition of neoadjuvant radiotherapy improves outcomes. METHODS: We enrolled patients in 23 centres in Switzerland, Germany and Serbia. Eligible patients had pathologically proven, stage IIIA/N2 non-small-cell lung cancer and were randomly assigned to treatment groups in a 1:1 ratio. Those in the chemoradiotherapy group received three cycles of neoadjuvant chemotherapy (100 mg/m(2) cisplatin and 85 mg/m(2) docetaxel) followed by radiotherapy with 44 Gy in 22 fractions over 3 weeks, and those in the control group received neoadjuvant chemotherapy alone. All patients were scheduled to undergo surgery. Randomisation was stratified by centre, mediastinal bulk (less than 5 cm vs 5 cm or more), and weight loss (5% or more vs less than 5% in the previous 6 months). The primary endpoint was event-free survival. Analyses were done by intention to treat. This trial is registered with ClinicalTrials.gov, number NCT00030771. FINDINGS: From 2001 to 2012, 232 patients were enrolled, of whom 117 were allocated to the chemoradiotherapy group and 115 to the chemotherapy group. Median event-free survival was similar in the two groups at 12·8 months (95% CI 9·7-22·9) in the chemoradiotherapy group and 11·6 months (8·4-15·2) in the chemotherapy group (p=0·67). Median overall survival was 37·1 months (95% CI 22·6-50·0) with radiotherapy, compared with 26·2 months (19·9-52·1) in the control group. Chemotherapy-related toxic effects were reported in most patients, but 91% of patients completed three cycles of chemotherapy. Radiotherapy-induced grade 3 dysphagia was seen in seven (7%) patients. Three patients died in the control group within 30 days after surgery. INTERPRETATION: Radiotherapy did not add any benefit to induction chemotherapy followed by surgery. We suggest that one definitive local treatment modality combined with neoadjuvant chemotherapy is adequate to treat resectable stage IIIA/N2 non-small-cell lung cancer. FUNDING: Swiss State Secretariat for Education, Research and Innovation (SERI), Swiss Cancer League, and Sanofi.

Ribavirin Concentrations Do Not Predict Sustained Virological Response in HIV/HCV-Coinfected Patients Treated with Ribavirin and Pegylated Interferon in the Swiss HIV Cohort Study.

BACKGROUND: Ribavirin (RBV) is an essential component of most current hepatitis C (HCV) treatment regimens and still standard of care in the combination with pegylated interferon (pegIFN) to treat chronic HCV in resource limited settings. Study results in HIV/HCV-coinfected patients are contradicting as to whether RBV concentration correlates with sustained virological response (SVR). METHODS: We included 262 HCV treatment naïve HIV/HCV-coinfected Swiss HIV Cohort Study (SHCS) participants treated with RBV and pegIFN between 01.01.2001-01.01.2010, 134 with HCV genotype (GT) 1/4, and 128 with GT 2/3 infections. RBV levels were measured retrospectively in stored plasma samples obtained between HCV treatment week 4 and end of therapy. Uni- and multivariable logistic regression analyses were used to evaluate the association between RBV concentration and SVR in GT 1/4 and GT 2/3 infections. The analyses were repeated stratified by treatment phase (week 4-12, 13-24, >24) and IL28B genotype (CC versus CT/TT). RESULTS: SVR rates were 35.1% in GT 1/4 and 70.3% in GT 2/3 infections. Overall, median RBV concentration was 2.0 mg/L in GT 1/4, and 1.9 mg/L in GT 2/3, and did not change significantly across treatment phases. Patients with SVR had similar RBV concentrations compared to patients without SVR in both HCV genotype groups. SVR was not associated with RBV levels ≥2.0 mg/L (GT 1/4, OR 1.19 [0.5-2.86]; GT 2/3, 1.94 [0.78-4.80]) and ≥2.5 mg/L (GT 1/4, 1.56 [0.64-3.84]; GT 2/3 2.72 [0.85-8.73]), regardless of treatment phase, and IL28B genotype. CONCLUSION: In HIV/HCV-coinfected patients treated with pegIFN/RBV, therapeutic drug monitoring of RBV concentrations does not enhance the chance of HCV cure, regardless of HCV genotype, treatment phase and IL28B genotype.

Error models in hydrogeology applications

Notre consommation en eau souterraine, en particulier comme eau potable ou pour l'irrigation, a considérablement augmenté au cours des années. De nombreux problèmes font alors leur apparition, allant de la prospection de nouvelles ressources à la remédiation des aquifères pollués. Indépendamment du problème hydrogéologique considéré, le principal défi reste la caractérisation des propriétés du sous-sol. Une approche stochastique est alors nécessaire afin de représenter cette incertitude en considérant de multiples scénarios géologiques et en générant un grand nombre de réalisations géostatistiques. Nous rencontrons alors la principale limitation de ces approches qui est le coût de calcul dû à la simulation des processus d'écoulements complexes pour chacune de ces réalisations. Dans la première partie de la thèse, ce problème est investigué dans le contexte de propagation de l'incertitude, oú un ensemble de réalisations est identifié comme représentant les propriétés du sous-sol. Afin de propager cette incertitude à la quantité d'intérêt tout en limitant le coût de calcul, les méthodes actuelles font appel à des modèles d'écoulement approximés. Cela permet l'identification d'un sous-ensemble de réalisations représentant la variabilité de l'ensemble initial. Le modèle complexe d'écoulement est alors évalué uniquement pour ce sousensemble, et, sur la base de ces réponses complexes, l'inférence est faite. Notre objectif est d'améliorer la performance de cette approche en utilisant toute l'information à disposition. Pour cela, le sous-ensemble de réponses approximées et exactes est utilisé afin de construire un modèle d'erreur, qui sert ensuite à corriger le reste des réponses approximées et prédire la réponse du modèle complexe. Cette méthode permet de maximiser l'utilisation de l'information à disposition sans augmentation perceptible du temps de calcul. La propagation de l'incertitude est alors plus précise et plus robuste. La stratégie explorée dans le premier chapitre consiste à apprendre d'un sous-ensemble de réalisations la relation entre les modèles d'écoulement approximé et complexe. Dans la seconde partie de la thèse, cette méthodologie est formalisée mathématiquement en introduisant un modèle de régression entre les réponses fonctionnelles. Comme ce problème est mal posé, il est nécessaire d'en réduire la dimensionnalité. Dans cette optique, l'innovation du travail présenté provient de l'utilisation de l'analyse en composantes principales fonctionnelles (ACPF), qui non seulement effectue la réduction de dimensionnalités tout en maximisant l'information retenue, mais permet aussi de diagnostiquer la qualité du modèle d'erreur dans cet espace fonctionnel. La méthodologie proposée est appliquée à un problème de pollution par une phase liquide nonaqueuse et les résultats obtenus montrent que le modèle d'erreur permet une forte réduction du temps de calcul tout en estimant correctement l'incertitude. De plus, pour chaque réponse approximée, une prédiction de la réponse complexe est fournie par le modèle d'erreur. Le concept de modèle d'erreur fonctionnel est donc pertinent pour la propagation de l'incertitude, mais aussi pour les problèmes d'inférence bayésienne. Les méthodes de Monte Carlo par chaîne de Markov (MCMC) sont les algorithmes les plus communément utilisés afin de générer des réalisations géostatistiques en accord avec les observations. Cependant, ces méthodes souffrent d'un taux d'acceptation très bas pour les problèmes de grande dimensionnalité, résultant en un grand nombre de simulations d'écoulement gaspillées. Une approche en deux temps, le "MCMC en deux étapes", a été introduite afin d'éviter les simulations du modèle complexe inutiles par une évaluation préliminaire de la réalisation. Dans la troisième partie de la thèse, le modèle d'écoulement approximé couplé à un modèle d'erreur sert d'évaluation préliminaire pour le "MCMC en deux étapes". Nous démontrons une augmentation du taux d'acceptation par un facteur de 1.5 à 3 en comparaison avec une implémentation classique de MCMC. Une question reste sans réponse : comment choisir la taille de l'ensemble d'entrainement et comment identifier les réalisations permettant d'optimiser la construction du modèle d'erreur. Cela requiert une stratégie itérative afin que, à chaque nouvelle simulation d'écoulement, le modèle d'erreur soit amélioré en incorporant les nouvelles informations. Ceci est développé dans la quatrième partie de la thèse, oú cette méthodologie est appliquée à un problème d'intrusion saline dans un aquifère côtier. -- Our consumption of groundwater, in particular as drinking water and for irrigation, has considerably increased over the years and groundwater is becoming an increasingly scarce and endangered resource. Nofadays, we are facing many problems ranging from water prospection to sustainable management and remediation of polluted aquifers. Independently of the hydrogeological problem, the main challenge remains dealing with the incomplete knofledge of the underground properties. Stochastic approaches have been developed to represent this uncertainty by considering multiple geological scenarios and generating a large number of realizations. The main limitation of this approach is the computational cost associated with performing complex of simulations in each realization. In the first part of the thesis, we explore this issue in the context of uncertainty propagation, where an ensemble of geostatistical realizations is identified as representative of the subsurface uncertainty. To propagate this lack of knofledge to the quantity of interest (e.g., the concentration of pollutant in extracted water), it is necessary to evaluate the of response of each realization. Due to computational constraints, state-of-the-art methods make use of approximate of simulation, to identify a subset of realizations that represents the variability of the ensemble. The complex and computationally heavy of model is then run for this subset based on which inference is made. Our objective is to increase the performance of this approach by using all of the available information and not solely the subset of exact responses. Two error models are proposed to correct the approximate responses follofing a machine learning approach. For the subset identified by a classical approach (here the distance kernel method) both the approximate and the exact responses are knofn. This information is used to construct an error model and correct the ensemble of approximate responses to predict the "expected" responses of the exact model. The proposed methodology makes use of all the available information without perceptible additional computational costs and leads to an increase in accuracy and robustness of the uncertainty propagation. The strategy explored in the first chapter consists in learning from a subset of realizations the relationship between proxy and exact curves. In the second part of this thesis, the strategy is formalized in a rigorous mathematical framework by defining a regression model between functions. As this problem is ill-posed, it is necessary to reduce its dimensionality. The novelty of the work comes from the use of functional principal component analysis (FPCA), which not only performs the dimensionality reduction while maximizing the retained information, but also allofs a diagnostic of the quality of the error model in the functional space. The proposed methodology is applied to a pollution problem by a non-aqueous phase-liquid. The error model allofs a strong reduction of the computational cost while providing a good estimate of the uncertainty. The individual correction of the proxy response by the error model leads to an excellent prediction of the exact response, opening the door to many applications. The concept of functional error model is useful not only in the context of uncertainty propagation, but also, and maybe even more so, to perform Bayesian inference. Monte Carlo Markov Chain (MCMC) algorithms are the most common choice to ensure that the generated realizations are sampled in accordance with the observations. Hofever, this approach suffers from lof acceptance rate in high dimensional problems, resulting in a large number of wasted of simulations. This led to the introduction of two-stage MCMC, where the computational cost is decreased by avoiding unnecessary simulation of the exact of thanks to a preliminary evaluation of the proposal. In the third part of the thesis, a proxy is coupled to an error model to provide an approximate response for the two-stage MCMC set-up. We demonstrate an increase in acceptance rate by a factor three with respect to one-stage MCMC results. An open question remains: hof do we choose the size of the learning set and identify the realizations to optimize the construction of the error model. This requires devising an iterative strategy to construct the error model, such that, as new of simulations are performed, the error model is iteratively improved by incorporating the new information. This is discussed in the fourth part of the thesis, in which we apply this methodology to a problem of saline intrusion in a coastal aquifer.

Biomarkers of Host Response Predict Primary End-Point Radiological Pneumonia in Tanzanian Children with Clinical Pneumonia: A Prospective Cohort Study.

BACKGROUND: Diagnosing pediatric pneumonia is challenging in low-resource settings. The World Health Organization (WHO) has defined primary end-point radiological pneumonia for use in epidemiological and vaccine studies. However, radiography requires expertise and is often inaccessible. We hypothesized that plasma biomarkers of inflammation and endothelial activation may be useful surrogates for end-point pneumonia, and may provide insight into its biological significance. METHODS: We studied children with WHO-defined clinical pneumonia (n = 155) within a prospective cohort of 1,005 consecutive febrile children presenting to Tanzanian outpatient clinics. Based on x-ray findings, participants were categorized as primary end-point pneumonia (n = 30), other infiltrates (n = 31), or normal chest x-ray (n = 94). Plasma levels of 7 host response biomarkers at presentation were measured by ELISA. Associations between biomarker levels and radiological findings were assessed by Kruskal-Wallis test and multivariable logistic regression. Biomarker ability to predict radiological findings was evaluated using receiver operating characteristic curve analysis and Classification and Regression Tree analysis. RESULTS: Compared to children with normal x-ray, children with end-point pneumonia had significantly higher C-reactive protein, procalcitonin and Chitinase 3-like-1, while those with other infiltrates had elevated procalcitonin and von Willebrand Factor and decreased soluble Tie-2 and endoglin. Clinical variables were not predictive of radiological findings. Classification and Regression Tree analysis generated multi-marker models with improved performance over single markers for discriminating between groups. A model based on C-reactive protein and Chitinase 3-like-1 discriminated between end-point pneumonia and non-end-point pneumonia with 93.3% sensitivity (95% confidence interval 76.5-98.8), 80.8% specificity (72.6-87.1), positive likelihood ratio 4.9 (3.4-7.1), negative likelihood ratio 0.083 (0.022-0.32), and misclassification rate 0.20 (standard error 0.038). CONCLUSIONS: In Tanzanian children with WHO-defined clinical pneumonia, combinations of host biomarkers distinguished between end-point pneumonia, other infiltrates, and normal chest x-ray, whereas clinical variables did not. These findings generate pathophysiological hypotheses and may have potential research and clinical utility.

High c-Met expression in stage I-II pancreatic adenocarcinoma: proposal for an immunostaining scoring method and correlation with poor prognosis.

AIMS: c-Met is an emerging biomarker in pancreatic ductal adenocarcinoma (PDAC); there is no consensus regarding the immunostaining scoring method for this marker. We aimed to assess the prognostic value of c-Met overexpression in resected PDAC, and to elaborate a robust and reproducible scoring method for c-Met immunostaining in this setting. METHODS AND RESULTS: c-Met immunostaining was graded according to the validated MetMab score, a classic visual scale combining surface and intensity (SI score), or a simplified score (high c-Met: ≥20% of tumour cells with strong membranous staining), in stage I-II PDAC. A computer-assisted classification method (Aperio software) was developed. Clinicopathological parameters were correlated with disease-free survival (DFS) and overall survival(OS). One hundred and forty-nine patients were analysed retrospectively in a two-step process. Thirty-seven samples (whole slides) were analysed as a pre-run test. Reproducibility values were optimal with the simplified score (kappa = 0.773); high c-Met expression (7/37) was associated with shorter DFS [hazard ratio (HR) 3.456, P = 0.0036] and OS (HR 4.257, P = 0.0004). c-Met expression was concordant on whole slides and tissue microarrays in 87.9% of samples, and quantifiable with a specific computer-assisted algorithm. In the whole cohort (n = 131), patients with c-Met(high) tumours (36/131) had significantly shorter DFS (9.3 versus 20.0 months, HR 2.165, P = 0.0005) and OS (18.2 versus 35.0 months, HR 1.832, P = 0.0098) in univariate and multivariate analysis. CONCLUSIONS: Simplified c-Met expression is an independent prognostic marker in stage I-II PDAC that may help to identify patients with a high risk of tumour relapse and poor survival.

Overexpression of GPC6 and TMEM132D in Early Stage Ovarian Cancer Correlates with CD8+ T-Lymphocyte Infiltration and Increased Patient Survival.

Infiltration of cytotoxic T-lymphocytes in ovarian cancer is a favorable prognostic factor. Employing a differential expression approach, we have recently identified a number of genes associated with CD8+ T-cell infiltration in early stage ovarian tumors. In the present study, we validated by qPCR the expression of two genes encoding the transmembrane proteins GPC6 and TMEM132D in a cohort of early stage ovarian cancer patients. The expression of both genes correlated positively with the mRNA levels of CD8A, a marker of T-lymphocyte infiltration [Pearson coefficient: 0.427 (p = 0.0067) and 0.861 (p < 0.0001), resp.]. GPC6 and TMEM132D expression was also documented in a variety of ovarian cancer cell lines. Importantly, Kaplan-Meier survival analysis revealed that high mRNA levels of GPC6 and/or TMEM132D correlated significantly with increased overall survival of early stage ovarian cancer patients (p = 0.032). Thus, GPC6 and TMEM132D may serve as predictors of CD8+ T-lymphocyte infiltration and as favorable prognostic markers in early stage ovarian cancer with important consequences for diagnosis, prognosis, and tumor immunobattling.

Stereotactic body radiation therapy with helical tomotherapy for medical inoperable early-stage primary and second-primary non-small cell lung neoplasm : one-year outcome and toxicity analysis

Le cancer du poumon est la première cause de mortalité associée au cancer dans le monde. Le traitement curatif des tumeurs pulmonaires non-à-petites-cellules (NSCLC) diagnostiquées à un stade précoce se base sur une approche chirurgicale. Cependant, étant donné les comorbidités liées à la consommation de tabac, dont la bronchopneumopathie chronique occupe la première place, l'éligibilité chirurgicale pour ce type de cancer se trouve fréquemment limitée. Dans ce contexte, l'emploi de la radiothérapie stéréotaxique (SBRT) est une alternative valable chez les patients atteints d'un NSCLC primaire de stade précoce, et qui sont considérés inopérables à cause de leurs comorbidités. Depuis peu seulemement, le spectre de la SBRT a été élargi aux patients atteints d'un deuxième NSCLC primaire (SPLC), faisant suite à un premier NSCLC, traité avec un but curatif. Ils concernent donc des patients ayant déjà subits une intervention chirurgicale au préalable et qui présentent une réserve fonctionnelle pulmonaire extrêmement réduite. Le succès croissant de la SBRT résulte soit d'une efficacité thérapeutique comparables à la chirurgie, soit de sa toxicité qui semble limitée. À notre connaissance, seulement une étude a reporté des issues cliniques de patients affectés par des NSCLC primaires traités par SBRT. Cette dernière a utilisé la tomothérapie comme système d'irradiation (T-SBRT), sur un faible échantillon de patients (n = 27). Concernant l'irradiation des patients présentant des SPLC, la littérature disponible est pauvre et aucune publication a décrit l'utilisation de la T-SBRT. Ces éléments innovants ont donc motivé la rédaction d'un travail de thèse concernant les premières données cliniques de l'expérience faite au CHUV. Du point de vue des effets secondaires, si la pneumonie actinique précoce et tardive survenant au niveau du champ d'irradiation est désormais une complication iatrogène bien connue de la SBRT, une seule étude s'est intéressée à ce sujet dans le cadre de la T-SBRT. De plus, une entité bénigne et transitoire de pneumonie ( ?) a été reconnue depuis peu : la pneumonie organisée radio-induite (OP). Celle-ci semble se chevaucher comme un autre effet iatrogène à l'extérieur du champ d'irradiation. Originellement, cette dernière avait été rapportée dans les suites de la radiothérapie pour les cancer du sein. Elle a été décrite comme étant initialement limitée au champ d'irradiation et successivement pouvant s'étendre dynamiquement en dehors de celui-ci. Nous avons donc supposé que des infiltrats de OP peuvent être présents chez des patients asymptomatiques, et que ce dynamisme pourrait être identifié déjà au sein du champ d'irradiation. Notre étude a démontré que le traitement par T-SBRT garde des issues cliniques très encourageantes, aussi bien pour les tumeurs primaires que pour les SPLC. Entre autre, ce traitement semble avoir une toxicité limitée, et l'existence vraisemblable de la OP, déjà au sein du champ d'irradiation, peut aider les radiologues à différencier les infiltrats radio-induits d'une une récidive tumorale.

Assessing the use of very high resolution data to predict species distribution in a mountain environment

Nowadays, Species Distribution Models (SDMs) are a widely used tool. Using different statistical approaches these models reconstruct the realized niche of a species using presence data and a set of variables, often topoclimatic. There utilization range is quite large from understanding single species requirements, to the creation of nature reserve based on species hotspots, or modeling of climate change impact, etc... Most of the time these models are using variables at a resolution of 50km x 50km or 1 km x 1 km. However in some cases these models are used with resolutions below the kilometer scale and thus called high resolution models (100 m x 100 m or 25 m x 25 m). Quite recently a new kind of data has emerged enabling precision up to lm x lm and thus allowing very high resolution modeling. However these new variables are very costly and need an important amount of time to be processed. This is especially the case when these variables are used in complex calculation like models projections over large areas. Moreover the importance of very high resolution data in SDMs has not been assessed yet and is not well understood. Some basic knowledge on what drive species presence-absences is still missing. Indeed, it is not clear whether in mountain areas like the Alps coarse topoclimatic gradients are driving species distributions or if fine scale temperature or topography are more important or if their importance can be neglected when balance to competition or stochasticity. In this thesis I investigated the importance of very high resolution data (2-5m) in species distribution models using either very high resolution topographic, climatic or edaphic variables over a 2000m elevation gradient in the Western Swiss Alps. I also investigated more local responses of these variables for a subset of species living in this area at two precise elvation belts. During this thesis I showed that high resolution data necessitates very good datasets (species and variables for the models) to produce satisfactory results. Indeed, in mountain areas, temperature is the most important factor driving species distribution and needs to be modeled at very fine resolution instead of being interpolated over large surface to produce satisfactory results. Despite the instinctive idea that topographic should be very important at high resolution, results are mitigated. However looking at the importance of variables over a large gradient buffers the importance of the variables. Indeed topographic factors have been shown to be highly important at the subalpine level but their importance decrease at lower elevations. Wether at the mountane level edaphic and land use factors are more important high resolution topographic data is more imporatant at the subalpine level. Finally the biggest improvement in the models happens when edaphic variables are added. Indeed, adding soil variables is of high importance and variables like pH are overpassing the usual topographic variables in SDMs in term of importance in the models. To conclude high resolution is very important in modeling but necessitate very good datasets. Only increasing the resolution of the usual topoclimatic predictors is not sufficient and the use of edaphic predictors has been highlighted as fundamental to produce significantly better models. This is of primary importance, especially if these models are used to reconstruct communities or as basis for biodiversity assessments. -- Ces dernières années, l'utilisation des modèles de distribution d'espèces (SDMs) a continuellement augmenté. Ces modèles utilisent différents outils statistiques afin de reconstruire la niche réalisée d'une espèce à l'aide de variables, notamment climatiques ou topographiques, et de données de présence récoltées sur le terrain. Leur utilisation couvre de nombreux domaines allant de l'étude de l'écologie d'une espèce à la reconstruction de communautés ou à l'impact du réchauffement climatique. La plupart du temps, ces modèles utilisent des occur-rences issues des bases de données mondiales à une résolution plutôt large (1 km ou même 50 km). Certaines bases de données permettent cependant de travailler à haute résolution, par conséquent de descendre en dessous de l'échelle du kilomètre et de travailler avec des résolutions de 100 m x 100 m ou de 25 m x 25 m. Récemment, une nouvelle génération de données à très haute résolution est apparue et permet de travailler à l'échelle du mètre. Les variables qui peuvent être générées sur la base de ces nouvelles données sont cependant très coûteuses et nécessitent un temps conséquent quant à leur traitement. En effet, tout calcul statistique complexe, comme des projections de distribution d'espèces sur de larges surfaces, demande des calculateurs puissants et beaucoup de temps. De plus, les facteurs régissant la distribution des espèces à fine échelle sont encore mal connus et l'importance de variables à haute résolution comme la microtopographie ou la température dans les modèles n'est pas certaine. D'autres facteurs comme la compétition ou la stochasticité naturelle pourraient avoir une influence toute aussi forte. C'est dans ce contexte que se situe mon travail de thèse. J'ai cherché à comprendre l'importance de la haute résolution dans les modèles de distribution d'espèces, que ce soit pour la température, la microtopographie ou les variables édaphiques le long d'un important gradient d'altitude dans les Préalpes vaudoises. J'ai également cherché à comprendre l'impact local de certaines variables potentiellement négligées en raison d'effets confondants le long du gradient altitudinal. Durant cette thèse, j'ai pu monter que les variables à haute résolution, qu'elles soient liées à la température ou à la microtopographie, ne permettent qu'une amélioration substantielle des modèles. Afin de distinguer une amélioration conséquente, il est nécessaire de travailler avec des jeux de données plus importants, tant au niveau des espèces que des variables utilisées. Par exemple, les couches climatiques habituellement interpolées doivent être remplacées par des couches de température modélisées à haute résolution sur la base de données de terrain. Le fait de travailler le long d'un gradient de température de 2000m rend naturellement la température très importante au niveau des modèles. L'importance de la microtopographie est négligeable par rapport à la topographie à une résolution de 25m. Cependant, lorsque l'on regarde à une échelle plus locale, la haute résolution est une variable extrêmement importante dans le milieu subalpin. À l'étage montagnard par contre, les variables liées aux sols et à l'utilisation du sol sont très importantes. Finalement, les modèles de distribution d'espèces ont été particulièrement améliorés par l'addition de variables édaphiques, principalement le pH, dont l'importance supplante ou égale les variables topographique lors de leur ajout aux modèles de distribution d'espèces habituels.

Importance of early weight changes to predict long-term weight gain during psychotropic drug treatment.

BACKGROUND: Psychotropic drugs can induce substantial weight gain, particularly during the first 6 months of treatment. The authors aimed to determine the potential predictive power of an early weight gain after the introduction of weight gain-inducing psychotropic drugs on long-term weight gain. METHOD: Data were obtained from a 1-year longitudinal study ongoing since 2007 including 351 psychiatric (ICD-10) patients, with metabolic parameters monitored (baseline and/or 1, 3, 6, 9, 12 months) and with compliance ascertained. International Diabetes Federation and World Health Organization definitions were used to define metabolic syndrome and obesity, respectively. RESULTS: Prevalences of metabolic syndrome and obesity were 22% and 17%, respectively, at baseline and 32% and 24% after 1 year. Receiver operating characteristic analyses indicated that an early weight gain > 5% after a period of 1 month is the best predictor for important long-term weight gain (≥ 15% after 3 months: sensitivity, 67%; specificity, 88%; ≥ 20% after 12 months: sensitivity, 47%; specificity, 89%). This analysis identified most patients (97% for 3 months, 93% for 12 months) who had weight gain ≤ 5% after 1 month as continuing to have a moderate weight gain after 3 and 12 months. Its predictive power was confirmed by fitting a longitudinal multivariate model (difference between groups in 1 year of 6.4% weight increase as compared to baseline, P = .0001). CONCLUSION: Following prescription of weight gain-inducing psychotropic drugs, a 5% threshold for weight gain after 1 month should raise clinician concerns about weight-controlling strategies.

Mir-21-Sox2 Axis Delineates Glioblastoma Subtypes with Prognostic Impact.

UNLABELLED: Glioblastoma (GBM) is the most aggressive human brain tumor. Although several molecular subtypes of GBM are recognized, a robust molecular prognostic marker has yet to be identified. Here, we report that the stemness regulator Sox2 is a new, clinically important target of microRNA-21 (miR-21) in GBM, with implications for prognosis. Using the MiR-21-Sox2 regulatory axis, approximately half of all GBM tumors present in the Cancer Genome Atlas (TCGA) and in-house patient databases can be mathematically classified into high miR-21/low Sox2 (Class A) or low miR-21/high Sox2 (Class B) subtypes. This classification reflects phenotypically and molecularly distinct characteristics and is not captured by existing classifications. Supporting the distinct nature of the subtypes, gene set enrichment analysis of the TCGA dataset predicted that Class A and Class B tumors were significantly involved in immune/inflammatory response and in chromosome organization and nervous system development, respectively. Patients with Class B tumors had longer overall survival than those with Class A tumors. Analysis of both databases indicated that the Class A/Class B classification is a better predictor of patient survival than currently used parameters. Further, manipulation of MiR-21-Sox2 levels in orthotopic mouse models supported the longer survival of the Class B subtype. The MiR-21-Sox2 association was also found in mouse neural stem cells and in the mouse brain at different developmental stages, suggesting a role in normal development. Therefore, this mechanism-based classification suggests the presence of two distinct populations of GBM patients with distinguishable phenotypic characteristics and clinical outcomes. SIGNIFICANCE STATEMENT: Molecular profiling-based classification of glioblastoma (GBM) into four subtypes has substantially increased our understanding of the biology of the disease and has pointed to the heterogeneous nature of GBM. However, this classification is not mechanism based and its prognostic value is limited. Here, we identify a new mechanism in GBM (the miR-21-Sox2 axis) that can classify ∼50% of patients into two subtypes with distinct molecular, radiological, and pathological characteristics. Importantly, this classification can predict patient survival better than the currently used parameters. Further, analysis of the miR-21-Sox2 relationship in mouse neural stem cells and in the mouse brain at different developmental stages indicates that miR-21 and Sox2 are predominantly expressed in mutually exclusive patterns, suggesting a role in normal neural development.

Pédiatrie. (2.) Consultation de médecine du sport dédiée à l'adolescent [Sport medicine consultation for adolescents].

Sport activity is a determinant of health and well-being for adolescents' psychological and physic development. Early detection of traumatic lesions or pathological condition among sportive adolescent in the light of their developmental stage is of outmost importance and is best done by an interdisciplinary team. This clinical management also aims at preventing consequences of inappropriate training. The CHUV has set-up a specific sports medicine outpatient consultation clinic for adolescents in the order to provide the best integrative management of young athletes.