Disentangling the complex landscape of sleep-wake disorders with data-driven phenotyping: A study of the Bernese center.

BACKGROUND AND PURPOSE: The diagnosis of sleep-wake disorders (SWDs) is challenging because of the existence of only few accurate biomarkers and the frequent coexistence of multiple SWDs and/or other comorbidities. The aim of this study was to assess in a large cohort of well-characterized SWD patients the potential of a data-driven approach for the identification of SWDs. METHODS: We included 6958 patients from the Bernese Sleep Registry and 300 variables/biomarkers including questionnaires, results of polysomnography/vigilance tests, and final clinical diagnoses. A pipeline, based on machine learning, was created to extract and cluster the clinical data. Our analysis was performed on three cohorts: patients with central disorders of hypersomnolence (CDHs), a full cohort of patients with SWDs, and a clean cohort without coexisting SWDs. RESULTS: A first analysis focused on the cohort of patients with CDHs and revealed four patient clusters: two clusters for narcolepsy type 1 (NT1) but not for narcolepsy type 2 or idiopathic hypersomnia. In the full cohort of SWDs, nine clusters were found: four contained patients with obstructive and central sleep apnea syndrome, one with NT1, and four with intermixed SWDs. In the cohort of patients without coexisting SWDs, an additional cluster of patients with chronic insomnia disorder was identified. CONCLUSIONS: This study confirms the existence of clear clusters of NT1 in CDHs, but mainly intermixed groups in the full spectrum of SWDs, with the exception of sleep apnea syndromes and NT1. New biomarkers are needed for better phenotyping and diagnosis of SWDs.

Loss of a newly discovered microRNA in Chinese hamster ovary cells leads to upregulation of N-glycolylneuraminic acid sialylation on monoclonal antibodies.

Chinese hamster ovary (CHO) cells are known not to express appreciable levels of the sialic acid residue N-glycolylneuraminic acid (NGNA) on monoclonal antibodies. However, we actually have identified a recombinant CHO cell line expressing an IgG with unusually high levels of NGNA sialylation (>30%). Comprehensive multi-OMICs based experimental analyses unraveled the root cause of this atypical sialylation: (1) expression of the cytidine monophosphate-N-acetylneuraminic acid hydroxylase (CMAH) gene was spontaneously switched on, (2) CMAH mRNA showed an anti-correlated expression to the newly discovered Cricetulus griseus (cgr) specific microRNA cgr-miR-111 and exhibits two putative miR-111 binding sites, (3) miR-111 expression depends on the transcription of its host gene SDK1, and (4) a single point mutation within the promoter region of the sidekick cell adhesion molecule 1 (SDK1) gene generated a binding site for the transcriptional repressor histone H4 transcription factor HINF-P. The resulting transcriptional repression of SDK1 led to a downregulation of its co-expressed miR-111 and hence to a spontaneous upregulation of CMAH expression finally increasing NGNA protein sialylation.

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.

Meningitis, meningoencephalitis and encephalitis in Bern: an observational study of 258 patients.

BACKGROUND: Depending on geographic location, causes of encephalitis, meningoencephalitis and meningitis vary substantially. We aimed to identify the most frequent causes, clinical presentation and long-term outcome of encephalitis, meningoencephalitis and meningitis cases treated in the Inselspital University Hospital Bern, Switzerland. METHODS: In this monocentric, observational study, we performed a retrospective review of clinical patient records for all patients treated within a 3-year period. Patients were contacted for a telephone follow-up interview and to fill out questionnaires, especially related to disturbances of sleep and wakefulness. RESULTS: We included 258 patients with the following conditions: encephalitis (18%), nonbacterial meningoencephalitis (42%), nonbacterial meningitis (27%) and bacterial meningoencephalitis/meningitis (13%). Herpes simplex virus (HSV) was the most common cause of encephalitis (18%); tick-borne encephalitis virus (TBEV) was the most common cause of nonbacterial meningoencephalitis (46%), enterovirus was the most common cause of nonbacterial meningitis (21%) and Streptococcus pneumoniae was the most common cause of bacterial meningoencephalitis/meningitis (49%). Overall, 35% patients remained without a known cause. After a median time of 16 months, 162 patients participated in the follow-up interview; 56% reported suffering from neurological long-term sequelae such as fatigue and/or excessive daytime sleepiness (34%), cognitive impairment and memory deficits (22%), headache (14%) and epileptic seizures (11%). CONCLUSIONS: In the Bern region, Switzerland, TBEV was the overall most frequently detected infectious cause, with a clinical manifestation of meningoencephalitis in the majority of cases. Long-term neurological sequelae, most importantly cognitive impairment, fatigue and headache, were frequently self-reported not only in encephalitis and meningoencephalitis survivors but also in viral meningitis survivors up to 40 months after acute infection.

Comparative genetic and physiological characterisation of Pectinatus species reveals shared tolerance to beer-associated stressors but halotolerance specific to pickle-associated strains.

Obligate anaerobic bacteria from the genus Pectinatus have been known to cause beer spoilage for over 40 years. Whole genome sequencing was performed on eleven beer spoilage strains (nine Pectinatus frisingensis, one Pectinatus cerevisiiphilus and one Pectinatus haikarae isolate), as well as two pickle spoilage species (Pectinatus brassicae MB591 and Pectinatus sottacetonis MB620) and the tolerance of all species to a range of environmental conditions was tested. Exploration of metabolic pathways for carbohydrates, amino acids and vitamins showed little difference between beer spoilage- and pickle spoilage-associated strains. However, genes for certain carbohydrate- and sulphur-containing amino acid-associated enzymes were only present in the beer spoilage group and genes for specific transporters and regulatory genes were uniquely found in the pickle spoilage group. Transporters for compatible solutes, only present in pickle-associated strains, likely explain their experimentally observed higher halotolerance compared to the beer spoilers. Genes involved in biofilm formation and ATP Binding Cassette (ABC) transporters potentially capable of exporting hop-derived antimicrobial compounds were found in all strains. All species grew in the presence of alcohol up to 5% alcohol by volume (ABV) and hops extract up to 80 ppm of iso-α-acids. Therefore, the species isolated from pickle processes may pose novel hazards in brewing.

Bridging AI and Clinical Practice: Integrating Automated Sleep Scoring Algorithm with Uncertainty-Guided Physician Review.

Purpose: This study aims to enhance the clinical use of automated sleep-scoring algorithms by incorporating an uncertainty estimation approach to efficiently assist clinicians in the manual review of predicted hypnograms, a necessity due to the notable inter-scorer variability inherent in polysomnography (PSG) databases. Our efforts target the extent of review required to achieve predefined agreement levels, examining both in-domain (ID) and out-of-domain (OOD) data, and considering subjects' diagnoses. Patients and Methods: A total of 19,578 PSGs from 13 open-access databases were used to train U-Sleep, a state-of-the-art sleep-scoring algorithm. We leveraged a comprehensive clinical database of an additional 8832 PSGs, covering a full spectrum of ages (0-91 years) and sleep-disorders, to refine the U-Sleep, and to evaluate different uncertainty-quantification approaches, including our novel confidence network. The ID data consisted of PSGs scored by over 50 physicians, and the two OOD sets comprised recordings each scored by a unique senior physician. Results: U-Sleep demonstrated robust performance, with Cohen's kappa (K) at 76.2% on ID and 73.8-78.8% on OOD data. The confidence network excelled at identifying uncertain predictions, achieving AUROC scores of 85.7% on ID and 82.5-85.6% on OOD data. Independently of sleep-disorder status, statistical evaluations revealed significant differences in confidence scores between aligning vs discording predictions, and significant correlations of confidence scores with classification performance metrics. To achieve κ ≥ 90% with physician intervention, examining less than 29.0% of uncertain epochs was required, substantially reducing physicians' workload, and facilitating near-perfect agreement. Conclusion: Inter-scorer variability limits the accuracy of the scoring algorithms to ~80%. By integrating an uncertainty estimation with U-Sleep, we enhance the review of predicted hypnograms, to align with the scoring taste of a responsible physician. Validated across ID and OOD data and various sleep-disorders, our approach offers a strategy to boost automated scoring tools' usability in clinical settings.

SPHYNCS: Feasibility of long-term monitoring with Fitbit smartwatches in central disorders of hypersomnolence and extraction of digital biomarkers in narcolepsy.

The Swiss Primary Hypersomnolence and Narcolepsy Cohort Study (SPHYNCS) is a multicenter research initiative to identify new biomarkers in central disorders of hypersomnolence (CDH). Whereas narcolepsy type 1 (NT1) is well characterized, other CDH disorders lack precise biomarkers. In SPHYNCS, we utilized Fitbit smartwatches to monitor physical activity, heart rate, and sleep parameters over one year. We examined the feasibility of long-term ambulatory monitoring using the wearable device. We then explored digital biomarkers differentiating patients with NT1 from healthy controls (HC). A total of 115 participants received a Fitbit smartwatch. Using a compliance metric to evaluate the usability of the wearable device, we found an overall compliance rate of 80% over one year. We calculated daily physical activity, heart rate, and sleep parameters from two weeks of greatest compliance to compare NT1 (n=20) and HC (n=9) subjects. Compared to controls, NT1 patients demonstrated findings consistent with increased sleep fragmentation, including significantly greater wake-after-sleep onset (p=0.007) and awakening index (p=0.025), as well as standard deviation of time in bed (p=0.044). Moreover, NT1 patients exhibited a significantly shorter REM latency (p=0.019), and sleep latency (p=0.001), as well as a lower peak heart rate (p=0.008), heart rate standard deviation (p=0.039) and high-intensity activity (p=0.009) compared to HC. This ongoing study demonstrates the feasibility of long-term monitoring with wearable technology in patients with CDH and potentially identifies a digital biomarker profile for NT1. While further validation is needed in larger datasets, these data suggest that long-term wearable technology may play a future role in diagnosing and managing narcolepsy.

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.