Preserved capillary density of dorsal finger skin in treated hypertensive patients with or without type 2 diabetes

RESUME : La raréfaction des vaisseaux capillaires est une caractéristique de l'hypertension artérielle non traitée. Des données récentes indiquent que cette raréfaction peut être renversée par un traitement antihypertenseur chez les patients hypertendus non diabétiques. Malgré la fréquente association du diabète et de l'hypertension, on ne sait rien de la densité capillaire de patients diabétiques traités, souffrant d'hypertension artérielle. Nous avons dès lors recruté 21 patients normotendus (groupe contrôle), 25 patients souffrant uniquement d'hypertension artérielle , et 21 patients diabétiques (Diabète de type 2) souffrant également d'hypertension artérielle. Tous les patients hypertendus ont été traités avec un inhibiteur du système rénine-angiotensine, et une majorité présentait une tension artérielle moyenne en auto-contrôle à domicile de 135/85 mmHg ou moins. La densité capillaire a été évaluée par vidéomicroscopie sur la peau du dos des doigts et avec laser Doppler sur la peau de l'avant-bras (vasodilatation maximale induite par le chauffage local). Au final, il n'y avait pas de différence entre les groupes de l'étude, que ce soit lors des mesures de la densité capillaire sur le dos du doigt (groupe contrôle 101 ±11 capillaires, groupe des patients non- diabétiques hypertendus 99 ± 16, groupe des patients hypertendus et diabétiques 96 ± 18, p>0,5) ou lors des mesures de débit sanguin maximal sur la peau de l'avant-bras, un témoin indirect de la densité capillaire dans ce territoire (contrôles 666 ±114 unités de perfusion, non diabétique hypertendu 612 ± 126, hypertendus diabétiques 620 ±103, p> 0,5). En conclusion, notre étude est la première à démontrer que indépendamment de la présence ou non d'un diabète de type 2, la densité capillaire est normale chez les patients hypertendus présentant un contrôle raisonnable de la pression artérielle obtenue avec un bloqueur du système rénine-angiotensine

Artificial intelligence techniques for studying neural functions in coma and sleep disorders

The use of artificial intelligence in computational neuroscience has increased within the last years. In the field of electroencephalography (EEG) research machine and deep learning, models show huge potential. EEG data is high dimensional, and complex models are well suited for their analysis. However, the use of artificial intelligence in EEG research and clinical applications is not yet established, and multiple challenges remain to be addressed. This thesis is focused on analyzing neurological EEG signals for clinical applications with artificial intelligence and is split into three sub-projects. The first project is a methodological contribution, presenting a proof of concept that deep learning on EEG signals can be used as a multivariate pattern analysis technique for research. Even though the field of deep learning for EEG has produced many publications, the use of these algorithms in research for the analysis of EEG signals is not established. Therefore for my first project, I developed an analysis pipeline based on a deep learning architecture, data augmentation techniques, and feature extraction method that is class and trial-specific. In summary, I present a novel multivariate pattern analysis pipeline for EEG data based on deep learning that can extract in a data-driven way trial-by-trial discriminant activity. In the second part of this thesis, I present a clinical application of predicting the outcome of comatose patients after cardiac arrest. Outcome prediction of patients in a coma is today still an open challenge, that depends on subjective clinical evaluations. Importantly, current clinical markers can leave up to a third of patients without a clear prognosis. To address this challenge, I trained a convolutional neural network on EEG signals of coma patients that were exposed to standardized auditory stimulations. This work showed a high predictive power of the trained deep learning model, also on patients that were without a established prognosis based on existing clinical criteria. These results emphasize the potential of deep learning models for predicting outcome of coma and assisting clinicians. In the last part of my thesis, I focused on sleep-wake disorders and studied whether unsupervised machine learning techniques could improve diagnosis. The field of sleep-wake disorders is convoluted, as they can cooccur within patients, and only a few disorders have clear diagnostic biomarkers. Thus I developed a pipeline based on an unsupervised clustering algorithm to disentangle the full landscape of sleep-wake disorders. First I reproduced previous results in a sub-cohort of patients with central disorders of hypersomnolence. The verified pipeline was then used on the full landscape of sleep-wake disorders, where I identified clear clusters of disorders with clear diagnostic biomarkers. My results call for new biomarkers, to improve patient phenotyping

No evidence for general intelligence in a fish

The average mammal or bird has a roughly ten times larger brain relative to body size than the average ectotherm vertebrate. It has been surprisingly challenging to determine how this translates into increased cognitive performance. In particular, it is unclear whether the brain size differences translate into qualitative differences in specific cognitive abilities. Here, we provide a first exploratory study to examine the possibility that the larger brains of endotherms support a different organisation of information processing, rather than specific differences in cognitive processes. In mammals, individual performance across domain-general cognitive tasks is positively correlated, resulting in the psychometric factor g. The value of g is positively correlated with brain size. We tested wild-caught female cleaner fish Labroides dimidiatus, known for its highly sophisticated social behaviour, in four ecologically nonrelevant cognitive tasks that have been used to varying degrees to assess g in mammals. Cleaner fish solved three of these four tasks, flexibility (reversal learning), self-control (detour around an obstacle) and numerical competence (simultaneous two-choice task), while also providing enough interindividual variation to test for g. They did not perform above chance levels in the fourth task, which tested for object permanence. For the three retained tasks, individual performance did not load positively on one principal component. Furthermore, all pairwise correlation coefficients were close to zero. These negative results contradict a frequent criticism of g studies, which proposes that g is a default result of how brains are designed. Rather, the results provide a first indication that endotherm and ectotherm vertebrates may process cognitive tasks in fundamentally different ways due to differences in brain organisation. Our relatively low number of experiments compared to mammalian studies enhances this hypothesis, as the probability of finding a g factor by chance would have been higher

Molecular determinants of HPV- induced oropharyngeal squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) arises in the oral cavity,oropharynx,larynx and hypopharynx.Recent works highlighted a subset of HNSCC related to Human Papilloma Virus (HPV) with complete differet molecular progression and associated with a better prognosis than HPV-negative HNSCC. The aim of this work is to culture HPV-positive tumor cells and transfect them with shRNA-expressing plasmids, to prepare RNA from the control and shRNA-treated cells and to measure gene expression...