U-Sleep's resilience to AASM guidelines

AASM guidelines are the result of decades of efforts aiming at standardizing sleep scoring procedure, with the final goal of sharing a worldwide common methodology. The guidelines cover several aspects from the technical/digital specifications,e.g., recommended EEG derivations, to detailed sleep scoring rules accordingly to age. Automated sleep scoring systems have always largely exploited the standards as fundamental guidelines. In this context, deep learning has demonstrated better performance compared to classical machine learning. Our present work shows that a deep learning based sleep scoring algorithm may not need to fully exploit the clinical knowledge or to strictly adhere to the AASM guidelines. Specifically, we demonstrate that U-Sleep, a state-of-the-art sleep scoring algorithm, can be strong enough to solve the scoring task even using clinically non-recommended or non-conventional derivations, and with no need to exploit information about the chronological age of the subjects. We finally strengthen a well-known finding that using data from multiple data centers always results in a better performing model compared with training on a single cohort. Indeed, we show that this latter statement is still valid even by increasing the size and the heterogeneity of the single data cohort. In all our experiments we used 28528 polysomnography studies from 13 different clinical studies

Fatigue in Post-COVID-19 Syndrome: Clinical Phenomenology, Comorbidities and Association With Initial Course of COVID-19.

Introduction Post-COVID-19 syndrome affects approximately 10-25% of people suffering from COVID-19 infection, irrespective of initial COVID-19 severity. Fatigue is one of the major symptoms, occurring in 30-90% of people with post-COVID-19 syndrome. This study aims at describing factors associated with fatigue in people with Post-COVID-19 seen in our newly established Post-Covid clinic. Methods This retrospective single center study included 42 consecutive patients suffering from Post-COVID-19 syndrome treated at the Department of Neurology, University Hospital Bern, between 11/2020 and05/2021. Clinical phenomenology of Post-COVID-19 syndrome with a special focus on fatigue and risk factor identification was performed using Mann-Whitney U Test, Pearson Correlation, and Chi-Quadrat-Test. Results Fatigue (90.5%) was the most prevalent Post-COVID-19 symptom followed by depressive mood (52.4%) and sleep disturbance (47.6%). Fatigue was in mean severe (Fatigue severity scale (FSS) mean 5.5 points (95% Confidence interval (95CI) 5.1 - 5.9, range .9 - 6.9, n = 40), and it was unrelated to age, COVID-19 severity or sex. The only related factors with fatigue severity were daytime sleepiness and depressed mood. Conclusion Fatigue is the main symptom of the Post-COVID-19 syndrome in our cohort. Further studies describing this syndrome are needed to prepare the healthcare systems for the challenge of treating patients with Post-COVID-19 syndrome

Memory CD8+ T Cells Balance Pro- and Anti-inflammatory Activity by Reprogramming Cellular Acetate Handling at Sites of Infection.

Serum acetate increases upon systemic infection. Acutely, assimilation of acetate expands the capacity of memory CD8+ T cells to produce IFN-γ. Whether acetate modulates memory CD8+ T cell metabolism and function during pathogen re-encounter remains unexplored. Here we show that at sites of infection, high acetate concentrations are being reached, yet memory CD8+ T cells shut down the acetate assimilating enzymes ACSS1 and ACSS2. Acetate, being thus largely excluded from incorporation into cellular metabolic pathways, now had different effects, namely (1) directly activating glutaminase, thereby augmenting glutaminolysis, cellular respiration, and survival, and (2) suppressing TCR-triggered calcium flux, and consequently cell activation and effector cell function. In vivo, high acetate abundance at sites of infection improved pathogen clearance while reducing immunopathology. This indicates that, during different stages of the immune response, the same metabolite-acetate-induces distinct immunometabolic programs within the same cell type

The Swiss Primary Hypersomnolence and Narcolepsy Cohort study (SPHYNCS): Study protocol for a prospective, multicentre cohort observational study.

Narcolepsy type 1 (NT1) is a disorder with well-established markers and a suspected autoimmune aetiology. Conversely, the narcoleptic borderland (NBL) disorders, including narcolepsy type 2, idiopathic hypersomnia, insufficient sleep syndrome and hypersomnia associated with a psychiatric disorder, lack well-defined markers and remain controversial in terms of aetiology, diagnosis and management. The Swiss Primary Hypersomnolence and Narcolepsy Cohort Study (SPHYNCS) is a comprehensive multicentre cohort study, which will investigate the clinical picture, pathophysiology and long-term course of NT1 and the NBL. The primary aim is to validate new and reappraise well-known markers for the characterization of the NBL, facilitating the diagnostic process. Seven Swiss sleep centres, belonging to the Swiss Narcolepsy Network (SNaNe), joined the study and will prospectively enrol over 500 patients with recent onset of excessive daytime sleepiness (EDS), hypersomnia or a suspected central disorder of hypersomnolence (CDH) during a 3-year recruitment phase. Healthy controls and patients with EDS due to severe sleep-disordered breathing, improving after therapy, will represent two control groups of over 50 patients each. Clinical and electrophysiological (polysomnography, multiple sleep latency test, maintenance of wakefulness test) information, and information on psychomotor vigilance and a sustained attention to response task, actigraphy and wearable devices (long-term monitoring), and responses to questionnaires will be collected at baseline and after 6, 12, 24 and 36 months. Potential disease markers will be searched for in blood, cerebrospinal fluid and stool. Analyses will include quantitative hypocretin measurements, proteomics/peptidomics, and immunological, genetic and microbiota studies. SPHYNCS will increase our understanding of CDH and the relationship between NT1 and the NBL. The identification of new disease markers is expected to lead to better and earlier diagnosis, better prognosis and personalized management of CDH

The Swiss Primary Hypersomnolence and Narcolepsy Cohort study (SPHYNCS): Study protocol for a prospective, multicentre cohort observational study

Narcolepsy type 1 (NT1) is a disorder with well-established markers and a suspected autoimmune aetiology. Conversely, the narcoleptic borderland (NBL) disorders, including narcolepsy type 2, idiopathic hypersomnia, insufficient sleep syndrome and hypersomnia associated with a psychiatric disorder, lack well-defined markers and remain controversial in terms of aetiology, diagnosis and management. The Swiss Primary Hypersomnolence and Narcolepsy Cohort Study (SPHYNCS) is a comprehensive multicentre cohort study, which will investigate the clinical picture, pathophysiology and long-term course of NT1 and the NBL. The primary aim is to validate new and reappraise well-known markers for the characterization of the NBL, facilitating the diagnostic process. Seven Swiss sleep centres, belonging to the Swiss Narcolepsy Network (SNaNe), joined the study and will prospectively enrol over 500 patients with recent onset of excessive daytime sleepiness (EDS), hypersomnia or a suspected central disorder of hypersomnolence (CDH) during a 3-year recruitment phase. Healthy controls and patients with EDS due to severe sleep-disordered breathing, improving after therapy, will represent two control groups of over 50 patients each. Clinical and electrophysiological (polysomnography, multiple sleep latency test, maintenance of wakefulness test) information, and information on psychomotor vigilance and a sustained attention to response task, actigraphy and wearable devices (long-term monitoring), and responses to questionnaires will be collected at baseline and after 6, 12, 24 and 36 months. Potential disease markers will be searched for in blood, cerebrospinal fluid and stool. Analyses will include quantitative hypocretin measurements, proteomics/peptidomics, and immunological, genetic and microbiota studies. SPHYNCS will increase our understanding of CDH and the relationship between NT1 and the NBL. The identification of new disease markers is expected to lead to better and earlier diagnosis, better prognosis and personalized management of CDH

Host cytoskeleton remodeling throughout the blood stages of Plasmodium falciparum

SUMMARYThe asexual intraerythrocytic development of; Plasmodium falciparum; , causing the most severe form of human malaria, is marked by extensive host cell remodeling. Throughout the processes of invasion, intracellular development, and egress, the erythrocyte membrane skeleton is remodeled by the parasite as required for each specific developmental stage. The remodeling is facilitated by a plethora of exported parasite proteins, and the erythrocyte membrane skeleton is the interface of most of the observed interactions between the parasite and host cell proteins. Host cell remodeling has been extensively described and there is a vast body of information on protein export or the description of parasite-induced structures such as Maurer's clefts or knobs on the host cell surface. Here we specifically review the molecular level of each host cell-remodeling step at each stage of the intraerythrocytic development of; P. falciparum; We describe key events, such as invasion, knob formation, and egress, and identify the interactions between exported parasite proteins and the host cell cytoskeleton. We discuss each remodeling step with respect to time and specific requirement of the developing parasite to explain host cell remodeling in a stage-specific manner. Thus, we highlight the interaction with the host membrane skeleton as a key event in parasite survival

Plasmodium Helical Interspersed Subtelomeric (PHIST) proteins, at the Center of Host Cell Remodeling

During the asexual cycle, Plasmodium falciparum extensively remodels the human erythrocyte to make it a suitable host cell. A large number of exported proteins facilitate this remodeling process, which causes erythrocytes to become more rigid, cytoadherent, and permeable for nutrients and metabolic products. Among the exported proteins, a family of 89 proteins, called the Plasmodium helical interspersed subtelomeric (PHIST) protein family, has been identified. While also found in other Plasmodium species, the PHIST family is greatly expanded in P. falciparum. Although a decade has passed since their first description, to date, most PHIST proteins remain uncharacterized and are of unknown function and localization within the host cell, and there are few data on their interactions with other host or parasite proteins. However, over the past few years, PHIST proteins have been mentioned in the literature at an increasing rate owing to their presence at various localizations within the infected erythrocyte. Expression of PHIST proteins has been implicated in molecular and cellular processes such as the surface display of PfEMP1, gametocytogenesis, changes in cell rigidity, and also cerebral and pregnancy-associated malaria. Thus, we conclude that PHIST proteins are central to host cell remodeling, but despite their obvious importance in pathology, PHIST proteins seem to be understudied. Here we review current knowledge, shed light on the definition of PHIST proteins, and discuss these proteins with respect to their localization and probable function. We take into consideration interaction studies, microarray analyses, or data from blood samples from naturally infected patients to combine all available information on this protein family

Plasmodium Helical Interspersed Subtelomeric (PHIST) proteins, at the Center of Host Cell Remodeling

During the asexual cycle, Plasmodium falciparum extensively remodels the human erythrocyte to make it a suitable host cell. A large number of exported proteins facilitate this remodeling process, which causes erythrocytes to become more rigid, cytoadherent, and permeable for nutrients and metabolic products. Among the exported proteins, a family of 89 proteins, called the Plasmodium helical interspersed subtelomeric (PHIST) protein family, has been identified. While also found in other Plasmodium species, the PHIST family is greatly expanded in P. falciparum. Although a decade has passed since their first description, to date, most PHIST proteins remain uncharacterized and are of unknown function and localization within the host cell, and there are few data on their interactions with other host or parasite proteins. However, over the past few years, PHIST proteins have been mentioned in the literature at an increasing rate owing to their presence at various localizations within the infected erythrocyte. Expression of PHIST proteins has been implicated in molecular and cellular processes such as the surface display of PfEMP1, gametocytogenesis, changes in cell rigidity, and also cerebral and pregnancy-associated malaria. Thus, we conclude that PHIST proteins are central to host cell remodeling, but despite their obvious importance in pathology, PHIST proteins seem to be understudied. Here we review current knowledge, shed light on the definition of PHIST proteins, and discuss these proteins with respect to their localization and probable function. We take into consideration interaction studies, microarray analyses, or data from blood samples from naturally infected patients to combine all available information on this protein family

U-Sleep's resilience to AASM guidelines.

AASM guidelines are the result of decades of efforts aiming at standardizing sleep scoring procedure, with the final goal of sharing a worldwide common methodology. The guidelines cover several aspects from the technical/digital specifications, e.g., recommended EEG derivations, to detailed sleep scoring rules accordingly to age. Automated sleep scoring systems have always largely exploited the standards as fundamental guidelines. In this context, deep learning has demonstrated better performance compared to classical machine learning. Our present work shows that a deep learning-based sleep scoring algorithm may not need to fully exploit the clinical knowledge or to strictly adhere to the AASM guidelines. Specifically, we demonstrate that U-Sleep, a state-of-the-art sleep scoring algorithm, can be strong enough to solve the scoring task even using clinically non-recommended or non-conventional derivations, and with no need to exploit information about the chronological age of the subjects. We finally strengthen a well-known finding that using data from multiple data centers always results in a better performing model compared with training on a single cohort. Indeed, we show that this latter statement is still valid even by increasing the size and the heterogeneity of the single data cohort. In all our experiments we used 28528 polysomnography studies from 13 different clinical studies