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Abstract 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 1 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 adherence metric to evaluate the usability of the wearable device, we found an overall adherence rate of 80% over 1 year. We calculated daily physical activity, heart rate, and sleep parameters from 2 weeks of greatest adherence to compare NT1 (n = 20) and HC (n = 9) participants. Compared to controls, NT1 patients demonstrated findings consistent with increased sleep fragmentation, including significantly greater wake-after-sleep onset (p = .007) and awakening index (p = .025), as well as standard deviation of time in bed (p = .044). Moreover, NT1 patients exhibited a significantly shorter REM latency (p = .019), and sleep latency (p = .001), as well as a lower peak heart rate (p = .008), heart rate standard deviation (p = .039) and high-intensity activity (p = .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. Graphical Abstract Graphical Abstract

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.

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.

Introduction Post‐COVID‐19 syndrome affects approximately 10–25% of people after a COVID‐19 infection, irrespective of initial COVID‐19 severity. The aim of this project was to assess the clinical characteristics, course, and prognosis of post‐COVID‐19 syndrome using a systematic multidimensional approach. Patients and Methods An online survey of people with suspected and confirmed COVID‐19 and post‐COVID‐19 syndrome, distributed via Swiss COVID‐19 support groups, social media, and our post‐COVID‐19 consultation, was performed. A total of 8 post‐infectious domains were assessed with 120 questions. Data were collected from October 15 to December 12, 2021, and 309 participants were included. Analysis of clinical phenomenology of post‐COVID‐19 syndrome was performed using comparative statistics. Results The three most prevalent post‐COVID‐19 symptoms in our survey cohort were fatigue (288/309, 93.2%), pain including headache (218/309, 70.6%), and sleep–wake disturbances (mainly insomnia and excessive daytime sleepiness, 145/309, 46.9%). Post‐COVID‐19 syndrome had an impact on work ability, as more than half of the respondents (168/268, 62.7%) reported an inability to work, which lasted on average 26.6 weeks (95% CI 23.5–29.6, range 1–94, n = 168). Quality of life measured by WHO‐5 Well‐being Index was overall low in respondents with post‐COVID‐19 syndrome (mean, 95% CI 9.1 [8.5–9.8], range 1–25, n = 239). Conclusion Fatigue, pain, and sleep–wake disturbances were the main symptoms of the post‐COVID‐19 syndrome in our cohort and had an impact on the quality of life and ability to work in a majority of patients. However, survey respondents reported a significant reduction in symptoms over 12 months. Post‐COVID‐19 syndrome remains a significant challenge. Further studies to characterize this syndrome and to explore therapeutic options are therefore urgently needed. Fatigue, pain, and sleep–wake disturbances were the main symptoms of the post‐COVID‐19 syndrome in our cohort and had an impact on quality of life and ability to work in a majority of patients. However, survey respondents reported a significant reduction in symptoms over 12 months.

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.

We constrain the coupling between axionlike particles (ALPs) and photons, measured with the superconducting resonant detection circuit of a cryogenic Penning trap. By searching the noise spectrum of our fixed-frequency resonant circuit for peaks caused by dark matter ALPs converting into photons in the strong magnetic field of the Penning-trap magnet, we are able to constrain the coupling of ALPs with masses around \\(2.7906-2.7914\\,\\textrm{neV/c}^2\\) to \\(g_{a\\gamma}< 1 \\times 10^{-11}\\,\\textrm{GeV}^{-1}\\). This is more than one order of magnitude lower than the best laboratory haloscope and approximately 5 times lower than the CERN axion solar telescope (CAST), setting limits in a mass and coupling range which is not constrained by astrophysical observations. Our approach can be extended to many other Penning-trap experiments and has the potential to provide broad limits in the low ALP mass range.

IntroductionACL injury is one of the most common injuries of the knee joint in sports. As accompanying osseous injuries of the ACL rupture a femoral impression the so-called lateral femoral notch sign and a posterolateral fracture of the tibial plateau are described. However, frequency, concomitant ligament injuries and when and how to treat these combined injuries are not clear. There is still a lack of understanding with which ligamentous concomitant injuries besides the anterior cruciate ligament injury these bony injuries are associated.Materials and methodsOne hundred fifteen MRI scans with proven anterior cruciate ligament rupture performed at our center were retrospectively evaluated for the presence of a meniscus, collateral ligament injury, a femoral impression, or a posterolateral impression fracture. Femoral impressions were described according to their local appearance and posterolateral tibial plateau fractures were described using the classification of Menzdorf et al.ResultsIn 29 cases a significant impression in the lateral femoral condyle was detected. There was a significantly increased number of lateral meniscal (41.4% vs. 18.6% p = 0.023) and medial ligament (41.4% vs. 22.1%; p = 0.040) injuries in the group with a lateral femoral notch sign. 104 patients showed a posterolateral bone bruise or fracture of the tibial plateau. Seven of these required an intervention according to Menzdorf et al. In the group of anterior cruciate ligament injuries with posterolateral tibial plateau fracture significantly more lateral meniscus injuries were seen (p = 0.039).ConclusionIn the preoperative planning of ACL rupture accompanied with a positive femoral notch sign, attention should be paid to possible medial collateral ligament and lateral meniscus injuries. As these are more likely to occur together. A posterolateral impression fracture of the tibial plateau is associated with an increased likelihood of the presence of a lateral meniscal injury. This must be considered in surgical therapy and planning and may be the indication for necessary early surgical treatment.

Purpose The pediatric intensive care unit admissions (PIA) network was initiated to interconnect pediatric intensive care units (PICUs) and establish a research infrastructure for pediatric intensive care in Germany. The primary aim is to collect data on pediatric critical illness to answer clinical and epidemiological research questions. Secondary goals are the implementation of quality indicators to allow benchmarking for German PICUs. Methods PIA is a hospital-based, pediatric intensive care registry. Patients admitted to a PICU with a corrected gestational age of ≥ 28 days and > 41 + 0 weeks are eligible for a basic survey; a more detailed survey is conducted for patients under 18 years with a PICU stay of more than 48 h or for patients who died within 48 h. Results We report on the first results from 8196 patients admitted from 05/2023–12/2024 from 23 PICUs. 65.03% of admissions were non-elective. 2895 (35.32%) children were eligible for the detailed survey. Emergency re-admissions within 24 h of discharge were 0.90%. Children < 5 years of age accounted for 49.44% of all admissions. The leading symptoms for PICU admissions were mainly of respiratory nature (38.41%), with 55.20% of children receiving respiratory support within the first hour of admission. During PICU stay, 68.70% received respiratory support, 38.89% cardiovascular support and 49.53% of children required sedation. The mortality rate was 1.85%. Conclusion The findings presented here and future findings from the PIA network will allow data comparison, disease surveillance and benchmarking and help advance intensive care research and clinical care of critically ill children.

Deficiency of adenosine deaminase 2 (DADA2) is a severe, congenital syndrome, which manifests with hematologic, immunologic and inflammatory pathologies. DADA2 is caused by biallelic mutations in ADA2, but the function of ADA2, and the mechanistic link between ADA2 deficiency and the severe inflammatory phenotype remains unclear. Here, we show that monocyte-derived proteomes from DADA2 patients are highly enriched in interferon response proteins. Using immunohistochemistry and detailed glycan analysis we demonstrate that ADA2 is post-translationally modified for sorting to the lysosomes. At acidic, lysosomal pH, ADA2 acts as a novel DNase that degrades cGAS/Sting-activating ligands. Furthermore, we define a clear structure-function relationship for this acidic DNase activity. Deletion of ADA2 increased the production of cGAMP and type I interferons upon exposure to dsDNA, which was reverted by ADA2 overexpression or deletion of STING. Our results identify a new level of control in the nucleic acid sensing machinery and provide a mechanistic explanation for the pathophysiology of autoinflammation in DADA2. ADA2 is a lysosomal nuclease controlling nucleic acid sensing and type I interferon production.