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Understanding black box models has become paramount as systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper highlights the advancements in XAI and its application in real-world scenarios and addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. We aim to develop a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 28 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders.

Accurate measurement of habitual sleep duration (HSD) is crucial for understanding the relationship between sleep and health. This study aimed to assess the bias and agreement limits between two commonly used short HSD self-report methods, considering sleep quality (SQ) and social jetlag (SJL) as potential predictors of bias. Data from 10,268 participants in the International COVID Sleep Study-II (ICOSS-II) were used. Method-Self and Method-MCTQ were compared. Method-Self involved a single question about average nightly sleep duration (HSDself), while Method-MCTQ estimated HSD from reported sleep times on workdays (HSDMCTQwork) and free days (HSDMCTQfree). Sleep quality was evaluated using a Likert scale and the Insomnia Severity Index (ISI) to explore its influence on estimation bias. HSDself was on average 42.41 ± 67.42 min lower than HSDMCTQweek, with an agreement range within ± 133 min. The bias and agreement range between methods increased with poorer SQ. HSDMCTQwork showed less bias and better agreement with HSDself compared to HSDMCTQfree. Sleep duration irregularity was - 43.35 ± 78.26 min on average. Subjective sleep quality predicted a significant proportion of variance in HSDself and estimation bias. The two methods showed very poor agreement and a significant systematic bias, both worsening with poorer SQ. Method-MCTQ considered sleep intervals without adjusting for SQ issues such as wakefulness after sleep onset but accounted for sleep irregularity and sleeping in on free days, while Method-Self reflected respondents' interpretation of their sleep, focusing on their sleep on workdays. Including an SQ-related question in surveys may help bidirectionally adjust the possible bias and enhance the accuracy of sleep-health studies.

STUDY OBJECTIVES: Preliminary evidence suggests that the risk of Long COVID is higher among people with pre-existing medical conditions. Based on its proven adjuvant role in immunity, habitual sleep duration may alter the risk for developing Long COVID. The objective of this study was to determine whether the odds of Long COVID are higher amongst those with pre-existing medical conditions, and whether the strength of this association varies by habitual sleep duration. METHODS: Using data from 13,461 respondents from 16 countries who participated in the 2021 survey based International COVID Sleep Study II (ICOSS II), we studied the associations between habitual sleep duration, pre-existing medical conditions, and Long COVID. RESULTS: Of 2,508 individuals who had COVID-19, 61% reported at least one Long COVID symptom. Multivariable logistic regression analysis showed that the risk of having Long COVID was 1.8-fold higher for average-length sleepers (6-9h/night) with pre-existing medical conditions compared to those without pre-existing medical conditions [aOR 1.84 (1.18-2.90), P=0.008]. The risk of Long COVID was 3-fold higher for short sleepers with pre-existing medical conditions [aOR 2.95 (1.04-8.4), P=0.043] and not significantly higher for long sleepers with pre-existing conditions [aOR 2.11 (0.93-4.77), P=0.073] compared to average-length sleepers without pre-existing conditions. CONCLUSIONS: Habitual short nighttime sleep duration exacerbated the risk of Long COVID in individuals with pre-existing conditions. Restoring nighttime sleep to average duration represents a potentially modifiable behavioral factor to lower the odds of Long COVID for at-risk patients.

The association between nightmare frequency (NMF) and suicidal ideation (SI) is well known, yet the impact of the COVID-19 pandemic on this relation is inconsistent. This study aimed to investigate changes in NMF, SI, and their association during the COVID-19 pandemic. Data were collected in 16 countries using a harmonised questionnaire. The sample included 9328 individuals (4848 women; age M[SD] = 46.85 [17.75] years), and 17.60% reported previous COVID-19. Overall, SI was significantly 2% lower during the pandemic vs. before, and this was consistent across genders and ages. Most countries/regions demonstrated decreases in SI during this pandemic, with Austria (-9.57%), Sweden (-6.18%), and Bulgaria (-5.14%) exhibiting significant declines in SI, but Italy (1.45%) and Portugal (2.45%) demonstrated non-significant increases. Suicidal ideation was more common in participants with long-COVID (21.10%) vs. short-COVID (12.40%), though SI did not vary by COVID-19 history. Nightmare frequency increased by 4.50% during the pandemic and was significantly higher in those with previous COVID-19 (14.50% vs. 10.70%), during infection (23.00% vs. 8.10%), and in those with long-COVID (18.00% vs. 8.50%). The relation between NMF and SI was not significantly stronger during the pandemic than prior (rs = 0.18 vs. 0.14; z = 2.80). Frequent nightmares during the pandemic increased the likelihood of reporting SI (OR = 1.57, 95% CI 1.20-2.05), while frequent dream recall during the pandemic served a protective effect (OR = 0.74, 95% CI 0.59-0.94). These findings have important implications for identifying those at risk of suicide and may offer a potential pathway for suicide prevention.

We report atmospheric fine micro- and nanoplastics concentrations from particulate matter (PM) samples of two size fractions (PM10, fine micro- and nanoplastics, and PM1, nanoplastics), which were collected at the remote high alpine station Sonnblick Observatory, Austria. Active sampling was performed from June 2021 until April 2022. Analysis was done using TD-PTR-MS to detect 6 different plastic types. Polyethylene terephthalate (PET), polyethylene (PE) and polypropylene/polypropylene carbonate (PP/PPC) were found to be the dominating species. PET was detected in almost all samples, while the other plastic types occurred more episodically. Furthermore, polyvinyl chloride (PVC), polystyrene (PS) and tire wear particles were detected in single samples. Considering the three main plastic types, average plastics concentrations were 35 and 21 ng m−³ with maximum concentrations of 165 and 113 ng m−³ for PM10 and PM1, respectively. Average polymer concentrations were higher in the summer/fall period than in winter/spring. In summer/fall, PM10 plastics concentrations were higher by a factor of 2 compared to PM1, while concentrations of both size classes were comparable in the winter/spring period. This suggests that in the colder season plastic particles arriving at the Eastern Alpine crests are mainly present as nanoplastics. The contribution of micro- and nanoplastics to organic matter at the remote site was found to be comparable to data determined at an urban site. We found significant correlations between the PET concentration and tracers originating from anthropogenic activities such as elemental carbon, nitrate, ammonium, and sulphate as well as organic carbon and arabitol.

Microplastics and nanoplastics are hazardous to ecosystems, wildlife, and through seafood, also for human health. Due to biological, chemical, and physical characteristics, nanoplastics can slip through cell membranes, being even more toxicologically important than microplastics. Thermal Desorption - Proton Transfer Reaction - Mass Spectrometry was used to analyze mussels from the Apulian region, Italy. All the analyzed organisms have plastics (values ranging from 10 to 187 ng of microplastics and nanoplastics per dry weight mg). The highest mass of plastics was detected in the size groups >2.2 µm (218 ng per dry weight mg) and 20–200 nm (187 ng per dry weight mg). Upscaling data we estimated that people in Europe could ingest more than 2 mg of nanoplastics per year through seafood consumption. The detected presence of nanoplastics in farmed mussels here presented contributes to establishing a baseline for monitoring these pollutants.

Zygnematophyceae are the algal sisters of land plants. Here we sequenced four genomes of filamentous Zygnematophyceae, including chromosome-scale assemblies for three strains of Zygnema circumcarinatum. We inferred traits in the ancestor of Zygnematophyceae and land plants that might have ushered in the conquest of land by plants: expanded genes for signaling cascades, environmental response, and multicellular growth. Zygnematophyceae and land plants share all the major enzymes for cell wall synthesis and remodifications, and gene gains shaped this toolkit. Co-expression network analyses uncover gene cohorts that unite environmental signaling with multicellular developmental programs. Our data shed light on a molecular chassis that balances environmental response and growth modulation across more than 600 million years of streptophyte evolution.

Proton-transfer-reaction mass spectrometry (PTR-MS) is an analytical technique used to monitor volatile organic compounds in real-time. For quantitative analysis, compounds of interest are typically calibrated using gas standards, but PTR-MS is quantitative to uncalibrated compounds if the mass-dependent transmission is well defined. However, long-term measurements are challenging due to the drift in transmission over time. Performing frequent calibrations helps, but the methods are time-consuming and tedious, often leading to instruments being under-calibrated. Here we show the use of long-lived and globally monitored compounds in the atmosphere as a tool to constrain the transmission between calibrations. The major ion of trichlorofluromethane (CFC-11) and carbon tetrachloride (CCl4) is found at the mass-to-charge ratio (m/z) 116.9, which we propose using to retrieve the transmission of a PTR-MS. We determined the pseudo-reaction rate constants of CFC-11 and CCl4 to be 0.82 × 10−9 ± 0.05 × 10−9 cm3 s−1 molecule−1, and 1.65 × 10−9 ± 0.08 × 10−9 cm3 s−1 molecule−1, respectively. The method introduced here can improve data quality and accuracy, especially for long-term atmospheric measurements.

BACKGROUND: Preliminary data suggests that obesity might hasten the decline in mRNA vaccine-induced immunity against SARS-CoV-2. However, whether this renders individuals with obesity more susceptible to long COVID symptoms post-vaccination remains uncertain. Given sleep's critical role in immunity, exploring the associations between obesity, probable long COVID symptoms, and sleep disturbances is essential. METHODS: We analyzed data from a survey of 5919 adults aged 18 to 89, all of whom received two SARS-CoV-2 mRNA vaccinations. Participants were categorized into normal weight, overweight, and obesity groups based on ethnicity-specific BMI cutoffs. The probability of long COVID was evaluated using the Post-Acute Sequelae of SARS-CoV-2 (PASC) score, as our survey did not permit confirmation of acute SARS-CoV-2 infection through methods such as antibody testing. Additionally, sleep patterns were assessed through questionnaires. RESULTS: Participants with obesity exhibited a significantly higher adjusted odds ratio (OR) of having a PASC score of 12 or higher, indicative of probable long COVID in our study, compared to those with normal weight (OR: 1.55, 95% CI: 1.05, 2.28). No significant difference was observed for overweight individuals (OR: 0.92 [95% CI: 0.63, 1.33]). Both obesity and probable long COVID were associated with increased odds of experiencing a heightened sleep burden, such as the presence of obstructive sleep apnea or insomnia (P < 0.001). However, no significant interaction between BMI and probable long COVID status was found. CONCLUSIONS: Even post-vaccination, individuals with obesity may encounter a heightened risk of experiencing prolonged COVID-19 symptoms. However, confirming our observations necessitates comprehensive studies incorporating rigorous COVID infection testing, such as antibody assays - unavailable in our anonymous survey. Additionally, it is noteworthy that the correlation between probable long COVID and sleep disturbances app

Microplastics and nanoplastics are hazardous to ecosystems, wildlife, and through seafood, also for human health. Due to biological, chemical, and physical characteristics, nanoplastics can slip through cell membranes, being even more toxicologically important than microplastics. Thermal Desorption - Proton Transfer Reaction - Mass Spectrometry was used to analyze mussels from the Apulian region, Italy. All the analyzed organisms have plastics (values ranging from 10 to 187 ng of microplastics and nanoplastics per dry weight mg). The highest mass of plastics was detected in the size groups >2.2 µm (218 ng per dry weight mg) and 20–200 nm (187 ng per dry weight mg). Upscaling data we estimated that people in Europe could ingest more than 2 mg of nanoplastics per year through seafood consumption. The detected presence of nanoplastics in farmed mussels here presented contributes to establishing a baseline for monitoring these pollutants.

Understanding black box models has become paramount as systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper highlights the advancements in XAI and its application in real-world scenarios and addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. We aim to develop a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 28 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders.

We report atmospheric fine micro- and nanoplastics concentrations from particulate matter (PM) samples of two size fractions (PM10, fine micro- and nanoplastics, and PM1, nanoplastics), which were collected at the remote high alpine station Sonnblick Observatory, Austria. Active sampling was performed from June 2021 until April 2022. Analysis was done using TD-PTR-MS to detect 6 different plastic types. Polyethylene terephthalate (PET), polyethylene (PE) and polypropylene/polypropylene carbonate (PP/PPC) were found to be the dominating species. PET was detected in almost all samples, while the other plastic types occurred more episodically. Furthermore, polyvinyl chloride (PVC), polystyrene (PS) and tire wear particles were detected in single samples. Considering the three main plastic types, average plastics concentrations were 35 and 21 ng m−³ with maximum concentrations of 165 and 113 ng m−³ for PM10 and PM1, respectively. Average polymer concentrations were higher in the summer/fall period than in winter/spring. In summer/fall, PM10 plastics concentrations were higher by a factor of 2 compared to PM1, while concentrations of both size classes were comparable in the winter/spring period. This suggests that in the colder season plastic particles arriving at the Eastern Alpine crests are mainly present as nanoplastics. The contribution of micro- and nanoplastics to organic matter at the remote site was found to be comparable to data determined at an urban site. We found significant correlations between the PET concentration and tracers originating from anthropogenic activities such as elemental carbon, nitrate, ammonium, and sulphate as well as organic carbon and arabitol.

Background: Preliminary data suggests that obesity might hasten the decline in mRNA vaccine-induced immunity against SARS-CoV-2. However, whether this renders individuals with obesity more susceptible to long COVID symptoms post-vaccination remains uncertain. Given sleep's critical role in immunity, exploring the associations between obesity, probable long COVID symptoms, and sleep disturbances is essential. Methods: We analyzed data from a survey of 5919 adults aged 18 to 89, all of whom received two SARS-CoV-2 mRNA vaccinations. Participants were categorized into normal weight, overweight, and obesity groups based on ethnicity-specific BMI cutoffs. The probability of long COVID was evaluated using the Post-Acute Sequelae of SARS-CoV-2 (PASC) score, as our survey did not permit confirmation of acute SARS-CoV-2 infection through methods such as antibody testing. Additionally, sleep patterns were assessed through questionnaires. Results: Participants with obesity exhibited a significantly higher adjusted odds ratio (OR) of having a PASC score of 12 or higher, indicative of probable long COVID in our study, compared to those with normal weight (OR: 1.55, 95% CI: 1.05, 2.28). No significant difference was observed for overweight individuals (OR: 0.92 [95% CI: 0.63, 1.33]). Both obesity and probable long COVID were associated with increased odds of experiencing a heightened sleep burden, such as the presence of obstructive sleep apnea or insomnia (P < 0.001). However, no significant interaction between BMI and probable long COVID status was found. Conclusions: Even post-vaccination, individuals with obesity may encounter a heightened risk of experiencing prolonged COVID-19 symptoms. However, confirming our observations necessitates comprehensive studies incorporating rigorous COVID infection testing, such as antibody assays - unavailable in our anonymous survey. Additionally, it is noteworthy that the correlation between probable long COVID and sleep disturbances, Funding Agencies|Novo Nordisk Foundation [NNF23OC0081873]; Swedish Brain Research Foundation [FO2023-0292]; Ake Wiberg Foundation [M23-0040]; Tore Nilsson Foundation [2023-079]; Signe and Arne Gyllenberg Foundation [5815, 5961]; Uppsala University

Study Objectives: Preliminary evidence suggests that the risk of Long COVID is higher among people with pre-existing medical conditions. Based on its proven adjuvant role in immunity, habitual sleep duration may alter the risk of developing Long COVID. The objective of this study was to determine whether the odds of Long COVID are higher among those with pre-existing medical conditions, and whether the strength of this association varies by habitual sleep duration. Methods: Using data from 13,461 respondents from 16 countries who participated in the 2021 survey -based International COVID Sleep Study II (ICOSS II), we studied the associations between habitual sleep duration, pre-existing medical conditions, and Long COVID. Results: Of 2,508 individuals who had COVID-19, 61% reported at least 1 Long COVID symptom. Multivariable logistic regression analysis showed that the risk of having Long COVID was 1.8 -fold higher for average -length sleepers (6-9 h/night) with pre-existing medical conditions compared with those without pre-existing medical conditions (adjusted odds ratio [aOR] 1.84 [1.18-2.90]; P = .008). The risk of Long COVID was 3 -fold higher for short sleepers with pre-existing medical conditions (aOR 2.95 [1.04-8.4]; P = .043) and not significantly higher for long sleepers with pre-existing conditions (aOR 2.11 [0.93-4.77]; P = .073) compared with average -length sleepers without pre-existing conditions. Conclusions: Habitual short nighttime sleep duration exacerbated the risk of Long COVID in individuals with pre-existing conditions. Restoring nighttime sleep to average duration represents a potentially modifiable behavioral factor to lower the odds of Long COVID for at -risk patients.

The association between nightmare frequency (NMF) and suicidal ideation (SI) is well known, yet the impact of the COVID-19 pandemic on this relation is inconsistent. This study aimed to investigate changes in NMF, SI, and their association during the COVID-19 pandemic. Data were collected in 16 countries using a harmonised questionnaire. The sample included 9328 individuals (4848 women; age M[SD] = 46.85 [17.75] years), and 17.60% reported previous COVID-19. Overall, SI was significantly 2% lower during the pandemic vs. before, and this was consistent across genders and ages. Most countries/regions demonstrated decreases in SI during this pandemic, with Austria (-9.57%), Sweden (-6.18%), and Bulgaria (-5.14%) exhibiting significant declines in SI, but Italy (1.45%) and Portugal (2.45%) demonstrated non-significant increases. Suicidal ideation was more common in participants with long-COVID (21.10%) vs. short-COVID (12.40%), though SI did not vary by COVID-19 history. Nightmare frequency increased by 4.50% during the pandemic and was significantly higher in those with previous COVID-19 (14.50% vs. 10.70%), during infection (23.00% vs. 8.10%), and in those with long-COVID (18.00% vs. 8.50%). The relation between NMF and SI was not significantly stronger during the pandemic than prior (rs = 0.18 vs. 0.14; z = 2.80). Frequent nightmares during the pandemic increased the likelihood of reporting SI (OR = 1.57, 95% CI 1.20-2.05), while frequent dream recall during the pandemic served a protective effect (OR = 0.74, 95% CI 0.59-0.94). These findings have important implications for identifying those at risk of suicide and may offer a potential pathway for suicide prevention., Funding Agencies|South Texas Veterans Health Care System in San Antonio, TX, USA

Accurate measurement of habitual sleep duration (HSD) is crucial for understanding the relationship between sleep and health. This study aimed to assess the bias and agreement limits between two commonly used short HSD self-report methods, considering sleep quality (SQ) and social jetlag (SJL) as potential predictors of bias. Data from 10,268 participants in the International COVID Sleep Study-II (ICOSS-II) were used. Method-Self and Method-MCTQ were compared. Method-Self involved a single question about average nightly sleep duration (HSDself), while Method-MCTQ estimated HSD from reported sleep times on workdays (HSDMCTQwork) and free days (HSDMCTQfree). Sleep quality was evaluated using a Likert scale and the Insomnia Severity Index (ISI) to explore its influence on estimation bias. HSDself was on average 42.41 +/- 67.42 min lower than HSDMCTQweek, with an agreement range within +/- 133 min. The bias and agreement range between methods increased with poorer SQ. HSDMCTQwork showed less bias and better agreement with HSDself compared to HSDMCTQfree. Sleep duration irregularity was - 43.35 +/- 78.26 min on average. Subjective sleep quality predicted a significant proportion of variance in HSDself and estimation bias. The two methods showed very poor agreement and a significant systematic bias, both worsening with poorer SQ. Method-MCTQ considered sleep intervals without adjusting for SQ issues such as wakefulness after sleep onset but accounted for sleep irregularity and sleeping in on free days, while Method-Self reflected respondents' interpretation of their sleep, focusing on their sleep on workdays. Including an SQ-related question in surveys may help bidirectionally adjust the possible bias and enhance the accuracy of sleep-health studies., Funding Agencies|South Texas Veterans Health Care System in San Antonio, TX, USA

The emergence of the COVID-19 pandemic, followed by policy measures to combat the virus, evoked public protest movements world-wide. These movements emerged through virtual social networks as well as local protest gatherings. Prior research has studied such movements solely in the virtual space through social network analysis, thereby disregarding the role of local interaction for protest. This study, however, recognizes the importance of the geo-spatial dimension in protest movements. We therefore introduce a large-scale spatial-social network analysis of a georeferenced Twitter user network to understand the regional connections and transnational influences of the Austrian COVID-19 protest movement through the social network. Our findings reveal that the virtual network is distinctly structured along geographic and linguistic boundaries. We further find that the movement is clearly organized along national protest communities. These results highlight the importance of regional and local influencing factors over the impact of transnational influences for the protest movement.

As machine learning (ML) has emerged as the predominant technological paradigm for artificial intelligence (AI), complex black box models such as GPT-4 have gained widespread adoption. Concurrently, explainable AI (XAI) has risen in significance as a counterbalancing force. But the rapid expansion of this research domain has led to a proliferation of terminology and an array of diverse definitions, making it increasingly challenging to maintain coherence. This confusion of languages also stems from the plethora of different perspectives on XAI, e.g. ethics, law, standardization and computer science. This situation threatens to create a “tower of Babel” effect, whereby a multitude of languages impedes the establishment of a common (scientific) ground. In response, this paper first maps different vocabularies, used in ethics, law and standardization. It shows that despite a quest for standardized, uniform XAI definitions, there is still a confusion of languages. Drawing lessons from these viewpoints, it subsequently proposes a methodology for identifying a unified lexicon from a scientific standpoint. This could aid the scientific community in presenting a more unified front to better influence ongoing definition efforts in law and standardization, often without enough scientific representation, which will shape the nature of AI and XAI in the future.

The release of ChatGPT to the general public has sparked discussions about the dangers of artificial intelligence (AI) among the public. The European Commission’s draft of the AI Act has further fueled these discussions, particularly in relation to the definition of AI and the assignment of risk levels to different technologies. Security concerns in AI systems arise from the need to protect against potential adversaries and to safeguard individuals from AI decisions that may harm their well-being. However, ensuring secure and trustworthy AI systems is challenging, especially with deep learning models that lack explainability. This paper proposes the concept of Controllable AI as an alternative to Trustworthy AI and explores the major differences between the two. The aim is to initiate discussions on securing complex AI systems without sacrificing practical capabilities or transparency. The paper provides an overview of techniques that can be employed to achieve Controllable AI. It discusses the background definitions of explainability, Trustworthy AI, and the AI Act. The principles and techniques of Controllable AI are detailed, including detecting and managing control loss, implementing transparent AI decisions, and addressing intentional bias or backdoors. The paper concludes by discussing the potential applications of Controllable AI and its implications for real-world scenarios.

In this paper, we present a fundamental framework for defining different types of explanations of AI systems and the criteria for evaluating their quality. Starting from a structural view of how explanations can be constructed, i.e., in terms of an explanandum (what needs to be explained), multiple explanantia (explanations, clues, or parts of information that explain), and a relationship linking explanandum and explanantia, we propose an explanandum-based typology and point to other possible typologies based on how explanantia are presented and how they relate to explanandia. We also highlight two broad and complementary perspectives for defining possible quality criteria for assessing explainability: epistemological and psychological (cognitive). These definition attempts aim to support the three main functions that we believe should attract the interest and further research of XAI scholars: clear inventories, clear verification criteria, and clear validation methods.

OBJECTIVE: There is evidence of a strong association between insomnia and COVID-19, yet few studies have examined the relationship between insomnia and long COVID. This study aimed to investigate whether COVID-19 patients with pre-pandemic insomnia have a greater risk of developing long COVID and whether long COVID is in turn associated with higher incident rates of insomnia symptoms after infection. METHODS: Data were collected cross-sectionally (May-Dec 2021) as part of an international collaborative study involving participants from 16 countries. A total of 2311 participants (18-99 years old) with COVID-19 provided valid responses to a web-based survey about sleep, insomnia, and health-related variables. Log-binomial regression was used to assess bidirectional associations between insomnia and long COVID. Analyses were adjusted for age, sex, and health conditions, including sleep apnea, attention and memory problems, chronic fatigue, depression, and anxiety. RESULTS: COVID-19 patients with pre-pandemic insomnia showed a higher risk of developing long COVID than those without pre-pandemic insomnia (70.8% vs 51.4%; adjusted relative risk [RR]: 1.33, 95% confidence interval [CI]: 1.07-1.65). Among COVID-19 cases without pre-pandemic insomnia, the rates of incident insomnia symptoms after infection were 24.1% for short COVID cases and 60.6% for long COVID cases (p < .001). Compared with short COVID cases, long COVID cases were associated with an increased risk of developing insomnia symptoms (adjusted RR: 2.00; 95% CI: 1.50-2.66). CONCLUSIONS: The findings support a bidirectional relationship between insomnia and long COVID. These findings highlight the importance of addressing sleep and insomnia in the prevention and management of long COVID.

Background: Differential diagnosis in children with signs of unprovoked inflammation can be challenging. In particular, differentiating systemic juvenile idiopathic arthritis (SJIA) from other diagnoses is difficult. We have recently validated the complex of myeloid-related proteins 8/14 (MRP8/14, also known as S100A8/A9 complex or serum calprotectin) as a helpful biomarker supporting the diagnosis of SJIA. The results were subsequently confirmed with a commercial ELISA. However, further optimization of the analytical technology is important to ensure its feasibility for large-scale use in routine laboratory settings. Methods: To evaluate the accuracy in identifying children with SJIA, the performance of a particle-enhanced immuno-turbidimetric assay for serum calprotectin (sCAL turbo) on an automated laboratory instrument was analyzed. Samples from 615 children were available with the diagnoses SJIA (n = 99), non-systemic JIA (n = 169), infections (n = 51), other inflammatory diseases (n = 126), and acute lymphoblastic leukemia (ALL, n = 147). In addition, samples from 23 healthy controls were included. Results: The sCAL turbo assay correlated well with the MRP8/14 ELISA used in previous validation studies (r = 0.99, p < 0.001). It could reliably differentiate SJIA from all other diagnoses with significant accuracy (cutoff at 10,500 ng/ml, sensitivity 84%, specificity 94%, ROC area under curve 0.960, p < 0.001). Conclusions: Serum calprotectin analyses are a helpful tool supporting the diagnosis of SJIA in children with prolonged fever or inflammatory disease. Here, we show that an immuno-turbidimetric assay for detection of serum calprotectin on an automated laboratory instrument can be implemented in clinical laboratory settings to facilitate its use as a diagnostic routine test in clinical practice.

Background: Differential diagnosis in children with signs of unprovoked inflammation can be challenging. In particular, differentiating systemic juvenile idiopathic arthritis (SJIA) from other diagnoses is difficult. We have recently validated the complex of myeloid-related proteins 8/14 (MRP8/14, also known as S100A8/A9 complex or serum calprotectin) as a helpful biomarker supporting the diagnosis of SJIA. The results were subsequently confirmed with a commercial ELISA. However, further optimization of the analytical technology is important to ensure its feasibility for large-scale use in routine laboratory settings. Methods: To evaluate the accuracy in identifying children with SJIA, the performance of a particle-enhanced immuno-turbidimetric assay for serum calprotectin (sCAL turbo) on an automated laboratory instrument was analyzed. Samples from 615 children were available with the diagnoses SJIA (n = 99), non-systemic JIA (n = 169), infections (n = 51), other inflammatory diseases (n = 126), and acute lymphoblastic leukemia (ALL, n = 147). In addition, samples from 23 healthy controls were included. Results: The sCAL turbo assay correlated well with the MRP8/14 ELISA used in previous validation studies (r = 0.99, p < 0.001). It could reliably differentiate SJIA from all other diagnoses with significant accuracy (cutoff at 10,500 ng/ml, sensitivity 84%, specificity 94%, ROC area under curve 0.960, p < 0.001). Conclusions: Serum calprotectin analyses are a helpful tool supporting the diagnosis of SJIA in children with prolonged fever or inflammatory disease. Here, we show that an immuno-turbidimetric assay for detection of serum calprotectin on an automated laboratory instrument can be implemented in clinical laboratory settings to facilitate its use as a diagnostic routine test in clinical practice.

Background: Differential diagnosis in children with signs of unprovoked inflammation can be challenging. In particular, differentiating systemic juvenile idiopathic arthritis (SJIA) from other diagnoses is difficult. We have recently validated the complex of myeloid-related proteins 8/14 (MRP8/14, also known as S100A8/A9 complex or serum calprotectin) as a helpful biomarker supporting the diagnosis of SJIA. The results were subsequently confirmed with a commercial ELISA. However, further optimization of the analytical technology is important to ensure its feasibility for large-scale use in routine laboratory settings. Methods: To evaluate the accuracy in identifying children with SJIA, the performance of a particle-enhanced immuno-turbidimetric assay for serum calprotectin (sCAL turbo) on an automated laboratory instrument was analyzed. Samples from 615 children were available with the diagnoses SJIA (n = 99), non-systemic JIA (n = 169), infections (n = 51), other inflammatory diseases (n = 126), and acute lymphoblastic leukemia (ALL, n = 147). In addition, samples from 23 healthy controls were included. Results: The sCAL turbo assay correlated well with the MRP8/14 ELISA used in previous validation studies (r = 0.99, p < 0.001). It could reliably differentiate SJIA from all other diagnoses with significant accuracy (cutoff at 10,500 ng/ml, sensitivity 84%, specificity 94%, ROC area under curve 0.960, p < 0.001). Conclusions: Serum calprotectin analyses are a helpful tool supporting the diagnosis of SJIA in children with prolonged fever or inflammatory disease. Here, we show that an immuno-turbidimetric assay for detection of serum calprotectin on an automated laboratory instrument can be implemented in clinical laboratory settings to facilitate its use as a diagnostic routine test in clinical practice.

In polar regions, the microphytobenthos has important ecological functions in shallow-water habitats, such as on top of coastal sediments. This community is dominated by benthic diatoms, which contribute significantly to primary production and biogeochemical cycling while also being an important component of polar food webs. Polar diatoms are able to cope with markedly changing light conditions and prolonged periods of darkness during the polar night in Antarctica. However, the underlying mechanisms are poorly understood. In this study, five strains of Antarctic benthic diatoms were isolated in the field, and the resulting unialgal cultures were identified as four distinct species, of which one is described as a new species, Planothidium wetzelii sp. nov. All four species were thoroughly examined using physiological, cell biological, and biochemical methods over a fully controlled dark period of 3 months. The results showed that the utilization of storage lipids is one of the key mechanisms in Antarctic benthic diatoms to survive the polar night, although different fatty acids were involved in the investigated taxa. In all tested species, the storage lipid content declined significantly, along with an ultrastructurally observable degradation of the chloroplasts. Surprisingly, photosynthetic performance did not change significantly despite chloroplasts decreasing in thylakoid membranes and an increased number of plastoglobules. Thus, a combination of biochemical and cell biological mechanisms allows Antarctic benthic diatoms to survive the polar night.

Aerosol formation acts as a sink for gas-phase atmospheric species that controls their atmospheric lifetime and environmental effects. To investigate aerosol formation and evolution in the Netherlands, a hybrid positive matrix factorization (PMF) analysis has been conducted using observations from May, June, and September 2021 collected in a rural site of Cabauw in Central Netherlands. The hybrid input matrix consists of the full organic mass spectrum acquired from a time-of-flight aerosol chemical speciation monitor (ToF-ACSM), ACSM species concentrations, and binned particle size distribution concentrations from a scanning mobility particle sizer (SMPS). These hybrid PMF analyses discerned six factors that describe aerosol composition variations: four size-driven factors that are related to new particle formation and growth (F6, F5, F4, and F3), and two bulk factors driven by composition, not size (F2, F1). The smallest-diameter size factor (F6) contains ammonium sulfate and organics, and typically occurs during the daytime. Newly formed particles, represented by F6, are correlated with wind from the southwesterly-westerly, northerly, and easterly sectors that transport sulfur oxides (SOx), ammonia (NH3), and organic precursors to Cabauw. As the particles grow from F6 to F3, nitrate plays an increasing role, and the particle loading diurnal cycle shifts from daytime to a nighttime maximum. The inorganic ion balance and organics composition in the bulk atmosphere affects the chemical composition variation across factors and seasons. Changing ammonium-sulfate-nitrate equilibrium shifts inorganic species among factors, and greater organics availability makes secondary organic aerosol (SOA) more influential in summertime aerosol growth, principally due to volatility differences produced by seasonal variation in photooxidation and temperature.

Microplastics and nanoplastics have been reported in nearly all environmental compartments, the dataset about their contributions and their role as a sink and carrier for other hydrophobic compounds in the atmosphere however is limited. This study presents, for the first time, atmospheric concentrations of ultrafine microplastics and nanoplastics (UFMNP) on the single polymer level and their correlations with atmospheric concentrations of 23 polycyclic aromatic hydrocarbons (PAHs). Measurements of UFMNP in ambient particulate matter (PM2.5) were performed for an urban sampling site in Graz, Austria. During the sampling period of 02.01. - 31.03.2017 the average UFMNP concentration was 238 ng/m3, reaching up to 557 ng/m3. This accounts for an average contribution of 0.67% to PM2.5mass and 1.7% of organic matter. The individual polymer types were polyethylene terephthalate (PET), polypropylene (PP) and polyethylene (PE), which sum up the most demanded polymer types in Europe. PET was found to be the most prominent polymer in the urban samples contributing to 50% of the overall UFMNP mass, followed by PP (27%) and PE (23%). However, the relative contributions vary with time. PET was observed during the entire time period, while PP and PE were quite variable and linked to the origin of the air masses. A possible carrier function of PP and PE for selected micropollutants can be deduced from the significant monotonic correlations between these polymers and selected PAHs. High correlations were predominantly found for low molecular weight congeners and correlations were more pronounced than those investigated for PAHs and other constituents of PM2.5, i.e. elemental carbon and organic matter again pointing to special interactions of those emerging pollutants.

A new Continuous Flow Analysis (CFA) system coupled with Fast Liquid Chromatography - tandem Mass Spectrometry (FLC-MS/MS) has been recently developed for determining organic markers in ice cores. In this work we present an upgrade of this innovative technique, optimized for the detection of levoglucosan in ice cores, a crucial tracer for reconstructing past fires. The upgrade involved a specific optimization of the chro-matographic and mass spectrometric parameters, allowing for a higher sampling resolution (down to 1 cm) and the simultaneous collection of discrete samples, for off-line analysis of water stable isotopes and additional chemical markers. The robustness and repeatability of the method has been tested by the analysis of multiple sticks of ice cut from the same shallow alpine ice core, and running the system for several hours on different days. The results show similar and comparable trends between the ice sticks. With this upgraded system, a higher sensitivity and a lower limit of detection (LOD) was achieved compared to discrete analysis of alpine samples for levoglucosan measurements. The new LOD was as low as 66 ng L-1, a net improvement over the previous LOD of 600 ng L-1.

Since its inception more than 2 decades ago, proton-transfer-reaction mass spectrometry (PTR-MS) has established itself as a powerful technique for the measurements of a wide range of volatile organic compounds (VOCs) with high time resolution and low detection limits and without the need for any sample pre-treatment. As this technology has matured and its application become more widespread, there is a growing need for accurate and traceable calibration to ensure measurement comparability. As a result of the large number of VOCs detectable with PTR-MS, it is impractical to have a calibration standard or standards that cover all observable compounds. However, recent work has demonstrated that quantitative measurements of uncalibrated compounds are possible provided that the transmission curve is accurately constrained. To enable this, a novel traceable multi-component gas reference material containing 20 compounds spanning a mass range of 32 to 671 has been developed. The development and compositional evolution of this reference material are described along with an evaluation of its accuracy and stability. This work demonstrates that for the majority of components the accuracy is < 5 % (most < 3 %; < 10 % for hexamethylcyclotrisiloxane (D3-siloxane) and 1,2,4-trichlorobenzene - 1,2,4-TCB) with stabilities of > 2 years (> 1 year for acetonitrile, methanol and perfluorotributylamine - PFTBA).

During the PAMARCMiP 2018 campaign (March and April 2018) a proton-transfer-reaction mass spectrometer (PTR-MS) was deployed onboard the POLAR 5 research aircraft and sampled the high Arctic atmosphere under Arctic haze conditions. More than 100 compounds exhibited levels above 1 pmol/mol in at least 25% of the measurements. We used acetone mixing ratios, ozone concentrations, and back trajectories to identify periods with and without long-range transport from continental sources. During two flights, surface ozone depletion events (ODE) were observed that coincided with enhanced levels of acetone, and methylethylketone, and ice nucleating particles (INP).Air masses with continental influence contained elevated levels of compounds associated with aged biogenic emissions and anthropogenic pollution (e.g., methanol, peroxyacetylnitrate (PAN), acetone, acetic acid, meth-ylethylketone (MEK), proprionic acid, and pentanone). Almost half of all positively detected compounds (>100) in the high Arctic atmosphere can be associated with terpene oxidation products, likely produced from mono-terpenes and sesquiterpenes emitted from boreal forests. We speculate that the transport of biogenic terpene emissions may constitute an important control of the High Arctic aerosol burden. The sum concentration of the detected aerosol forming vapours is-12 pmol/mol, which is of the same order than measured dimethylsulfide (DMS) mixing ratios and their mass density corresponds to approximately one fifth of the measured non-black -carbon particles.

Methylsiloxanes have gained growing attention as emerging pollutants due to their toxicity to organisms. As man-made chemicals with no natural source, most research to date has focused on volatile methylsiloxanes from personal care or household products and industrial processes. Here, we show that methylsiloxanes can be found in primary aerosol particles emitted by vehicles based on aerosol samples collected in two tunnels in São Paulo, Brazil. The aerosol samples were analyzed with thermal desorption-proton transfer reaction-mass spectrometry (TD-PTR-MS), and methylsiloxanes were identified and quantified in the mass spectra based on the natural abundance of silicon isotopes. Various methylsiloxanes and derivatives were found in aerosol particles from both tunnels. The concentrations of methylsiloxanes and derivatives ranged 37.7-377 ng m-3, and the relative fractions in organic aerosols were 0.78-1.9%. The concentrations of methylsiloxanes exhibited a significant correlation with both unburned lubricating oils and organic aerosol mass. The emission factors of methylsiloxanes averaged 1.16 ± 0.59 mg kg-1 of burned fuel for light-duty vehicles and 1.53 ± 0.37 mg kg-1 for heavy-duty vehicles. Global annual emissions of methylsiloxanes in vehicle-emitted aerosols were estimated to range from 0.0035 to 0.0060 Tg, underscoring the significant yet largely unknown potential for health and climate impacts.

Based on our longitudinal, in‐depth qualitative research focusing on the social construction of deskilling among highly educated migrants from Central and Eastern European (CEE) member states of the European Union, we will discuss in this article the positioning of the interview partners within the interview situation as interrelated to societal racialised power asymmetries. In this contribution, we exemplify that critical migration research can only be carried out when we reflect on our methods accordingly. To do so, we discuss actual evidence from this ongoing research project: While we see that many of our interview partners from new EU member states are reluctant to point to negative experiences in our conversations, we want to highlight that the potentiality of discrimination is part of the interview setting in our research and thus co‐constructs the empirical data. By analysing a variety of discursive positioning strategies employed by our interview partners that can be understood as strategies to avoid anticipated discrimination, we aim to fulfil the promise of methodological reflexivity and thus contribute to the quality of interview research in the context of migration studies. The aim of this contribution is thus twofold: We want to contribute to methodological discussions as well as refine current research focussing on the racist experiences of CEE migrants.

Schriftsprache ist für hörgesunde Menschen ein sekundäres Medium. Hörgesunde Kinder erlernen die Schriftsprache, wenn im Regelfalle die wichtigsten Stadien des Erstspracherwerbes weitgehend abgeschlossen sind. Die Kinder kennen dann das Lautsystem ihrer Muttersprache, sie sind dabei, die phonologische Kodierung zu erwerben, sie verfügen über die grundlegenden syntaktischen Prinzipien und einen Wortschatz von 2500 Wörtern und mehr, wenn sie erste schriftliche Symbole kennenlernen.

Short nighttime sleep duration impairs the immune response to virus vaccination, and long nighttime sleep duration is associated with poor health status. Thus, we hypothesized that short (<6 h) and long (>9 h) nighttime sleepers have a higher post-COVID risk than normal nighttime sleepers, despite two doses of mRNA vaccine (which has previously been linked to lower odds of long-lasting COVID-19 symptoms). Post-COVID was defined as experiencing at least one core COVID-19 symptom for at least three months (e.g., shortness of breath). Multivariate logistic regression adjusting for age, sex, BMI, and other factors showed in 9717 respondents (age span 18-99) that two mRNA vaccinations lowered the risk of suffering from post-COVID by about 21% (p < 0.001). When restricting the analysis to double-vaccinated respondents (n = 5918), short and long sleepers exhibited a greater post-COVID risk than normal sleepers (adjusted OR [95%-CI], 1.56 [1.29, 1.88] and 1.87 [1.32, 2.66], respectively). Among respondents with persistent sleep duration patterns during the pandemic compared to before the pandemic, short but not long sleep duration was significantly associated with the post-COVID risk (adjusted OR [95%-CI], 1.59 [1.24, 2.03] and 1.18 [0.70, 1.97], respectively). No significant association between sleep duration and post-COVID symptoms was observed in those reporting positive SARS-CoV-2 test results (n = 538). Our findings suggest that two mRNA vaccinations against SARS-CoV-2 are associated with a lower post-COVID risk. However, this protection may be less pronounced among those sleeping less than 6 h per night. Our findings warrant replication in cohorts with individuals with confirmed SARS-CoV-2 infection.

Many people report suffering from post-acute sequelae of COVID-19 or "long-COVID", but there are still open questions on what actually constitutes long-COVID and how prevalent it is. The current definition of post-acute sequelae of COVID-19 is based on voting using the Delphi-method by the WHO post-COVID-19 working group. It emphasizes long-lasting fatigue, shortness of breath and cognitive dysfunction as the core symptoms of post-acute sequelae of COVID-19. In this international survey study consisting of 13,628 subjects aged 18-99 years from 16 countries of Asia, Europe, North America and South America (May-Dec 2021), we show that post-acute sequelae of COVID-19 symptoms were more prevalent amongst the more severe COVID-19 cases, i.e. those requiring hospitalisation for COVID-19. We also found that long-lasting sleep symptoms are at the core of post-acute sequelae of COVID-19 and associate with the COVID-19 severity when COVID-19 cases are compared with COVID-negative cases. Specifically, fatigue (61.3%), insomnia symptoms (49.6%) and excessive daytime sleepiness (35.8%) were highly prevalent amongst respondents reporting long-lasting symptoms after hospitalisation for COVID-19. Understanding the importance of sleep-related symptoms in post-acute sequelae of COVID-19 has a clinical relevance when diagnosing and treating long-COVID.

Many people report suffering from post-acute sequelae of COVID-19 or "long-COVID", but there are still open questions on what actually constitutes long-COVID and how prevalent it is. The current definition of post-acute sequelae of COVID-19 is based on voting using the Delphi-method by the WHO post-COVID-19 working group. It emphasizes long-lasting fatigue, shortness of breath and cognitive dysfunction as the core symptoms of post-acute sequelae of COVID-19. In this international survey study consisting of 13,628 subjects aged 18-99 years from 16 countries of Asia, Europe, North America and South America (May-Dec 2021), we show that post-acute sequelae of COVID-19 symptoms were more prevalent amongst the more severe COVID-19 cases, i.e. those requiring hospitalisation for COVID-19. We also found that long-lasting sleep symptoms are at the core of post-acute sequelae of COVID-19 and associate with the COVID-19 severity when COVID-19 cases are compared with COVID-negative cases. Specifically, fatigue (61.3%), insomnia symptoms (49.6%) and excessive daytime sleepiness (35.8%) were highly prevalent amongst respondents reporting long-lasting symptoms after hospitalisation for COVID-19. Understanding the importance of sleep-related symptoms in post-acute sequelae of COVID-19 has a clinical relevance when diagnosing and treating long-COVID.

Short nighttime sleep duration impairs the immune response to virus vaccination, and long nighttime sleep duration is associated with poor health status. Thus, we hypothesized that short (<6 h) and long (>9 h) nighttime sleepers have a higher post-COVID risk than normal nighttime sleepers, despite two doses of mRNA vaccine (which has previously been linked to lower odds of long-lasting COVID-19 symptoms). Post-COVID was defined as experiencing at least one core COVID-19 symptom for at least three months (e.g., shortness of breath). Multivariate logistic regression adjusting for age, sex, BMI, and other factors showed in 9717 respondents (age span 18-99) that two mRNA vaccinations lowered the risk of suffering from post-COVID by about 21% (p < 0.001). When restricting the analysis to double-vaccinated respondents (n = 5918), short and long sleepers exhibited a greater post-COVID risk than normal sleepers (adjusted OR [95%-CI], 1.56 [1.29, 1.88] and 1.87 [1.32, 2.66], respectively). Among respondents with persistent sleep duration patterns during the pandemic compared to before the pandemic, short but not long sleep duration was significantly associated with the post-COVID risk (adjusted OR [95%-CI], 1.59 [1.24, 2.03] and 1.18 [0.70, 1.97], respectively). No significant association between sleep duration and post-COVID symptoms was observed in those reporting positive SARS-CoV-2 test results (n = 538). Our findings suggest that two mRNA vaccinations against SARS-CoV-2 are associated with a lower post-COVID risk. However, this protection may be less pronounced among those sleeping less than 6 h per night. Our findings warrant replication in cohorts with individuals with confirmed SARS-CoV-2 infection.

Short nighttime sleep duration impairs the immune response to virus vaccination, and long nighttime sleep duration is associated with poor health status. Thus, we hypothesized that short (<6 h) and long (>9 h) nighttime sleepers have a higher post-COVID risk than normal nighttime sleepers, despite two doses of mRNA vaccine (which has previously been linked to lower odds of long-lasting COVID-19 symptoms). Post-COVID was defined as experiencing at least one core COVID-19 symptom for at least three months (e.g., shortness of breath). Multivariate logistic regression adjusting for age, sex, BMI, and other factors showed in 9717 respondents (age span 18-99) that two mRNA vaccinations lowered the risk of suffering from post-COVID by about 21% (p < 0.001). When restricting the analysis to double-vaccinated respondents (n = 5918), short and long sleepers exhibited a greater post-COVID risk than normal sleepers (adjusted OR [95%-CI], 1.56 [1.29, 1.88] and 1.87 [1.32, 2.66], respectively). Among respondents with persistent sleep duration patterns during the pandemic compared to before the pandemic, short but not long sleep duration was significantly associated with the post-COVID risk (adjusted OR [95%-CI], 1.59 [1.24, 2.03] and 1.18 [0.70, 1.97], respectively). No significant association between sleep duration and post-COVID symptoms was observed in those reporting positive SARS-CoV-2 test results (n = 538). Our findings suggest that two mRNA vaccinations against SARS-CoV-2 are associated with a lower post-COVID risk. However, this protection may be less pronounced among those sleeping less than 6 h per night. Our findings warrant replication in cohorts with individuals with confirmed SARS-CoV-2 infection.

The present study had two main aims. First, to investigate whether shift/night workers had a higher prevalence and severity of COVID-19 compared with day workers. Second, to investigate whether people regularly working in face-to-face settings during the pandemic exhibited a higher prevalence and severity of COVID-19 compared with those having no need to be in close contact with others at work. Data consisted of 7141 workers from 15 countries and four continents who participated in the International COVID Sleep Study-II (ICOSS-II) between May and December 2021. The associations between work status and a positive COVID-19 test and several indications of disease severity were tested with chi-square tests and logistic regressions adjusted for relevant confounders. In addition, statistical analyses were conducted for the associations between face-to-face work and COVID-19 status. Results showed that shift/night work was not associated with an increased risk of COVID-19 compared to day work. Still, shift/night workers reported higher odds for moderate to life-threatening COVID-19 (adjusted odds ratio (aOR) = 2.71, 95%-confidence interval = 1.23-5.95) and need for hospital care (aOR = 5.66, 1.89-16.95). Face-to-face work was associated with an increased risk of COVID-19 (aOR = 1.55, 1.12-2.14) but not with higher disease severity. In conclusion, shift/night work was not associated with an increased risk of COVID-19, but when infected, shift/night workers reported more severe disease. Impaired sleep and circadian disruption commonly seen among shift/night workers may be mediating factors. Working face-to-face increased the risk of COVID-19, likely due to increased exposure to the virus. However, face-to-face work was not associated with increased disease severity.

Many people report suffering from post-acute sequelae of COVID-19 or "long-COVID", but there are still open questions on what actually constitutes long-COVID and how prevalent it is. The current definition of post-acute sequelae of COVID-19 is based on voting using the Delphi-method by the WHO post-COVID-19 working group. It emphasizes long-lasting fatigue, shortness of breath and cognitive dysfunction as the core symptoms of post-acute sequelae of COVID-19. In this international survey study consisting of 13,628 subjects aged 18-99 years from 16 countries of Asia, Europe, North America and South America (May-Dec 2021), we show that post-acute sequelae of COVID-19 symptoms were more prevalent amongst the more severe COVID-19 cases, i.e. those requiring hospitalisation for COVID-19. We also found that long-lasting sleep symptoms are at the core of post-acute sequelae of COVID-19 and associate with the COVID-19 severity when COVID-19 cases are compared with COVID-negative cases. Specifically, fatigue (61.3%), insomnia symptoms (49.6%) and excessive daytime sleepiness (35.8%) were highly prevalent amongst respondents reporting long-lasting symptoms after hospitalisation for COVID-19. Understanding the importance of sleep-related symptoms in post-acute sequelae of COVID-19 has a clinical relevance when diagnosing and treating long-COVID., Funding Agencies|Academy of Finland [322312]; Finnish Cultural Foundation; Signe and Ane Gyllenberg Foundation

Many people report suffering from post-acute sequelae of COVID-19 or "long-COVID", but there are still open questions on what actually constitutes long-COVID and how prevalent it is. The current definition of post-acute sequelae of COVID-19 is based on voting using the Delphi-method by the WHO post-COVID-19 working group. It emphasizes long-lasting fatigue, shortness of breath and cognitive dysfunction as the core symptoms of post-acute sequelae of COVID-19. In this international survey study consisting of 13,628 subjects aged 18-99 years from 16 countries of Asia, Europe, North America and South America (May-Dec 2021), we show that post-acute sequelae of COVID-19 symptoms were more prevalent amongst the more severe COVID-19 cases, i.e. those requiring hospitalisation for COVID-19. We also found that long-lasting sleep symptoms are at the core of post-acute sequelae of COVID-19 and associate with the COVID-19 severity when COVID-19 cases are compared with COVID-negative cases. Specifically, fatigue (61.3%), insomnia symptoms (49.6%) and excessive daytime sleepiness (35.8%) were highly prevalent amongst respondents reporting long-lasting symptoms after hospitalisation for COVID-19. Understanding the importance of sleep-related symptoms in post-acute sequelae of COVID-19 has a clinical relevance when diagnosing and treating long-COVID., Funding Agencies|Academy of Finland [322312]; Finnish Cultural Foundation; Signe and Ane Gyllenberg Foundation

BACKGROUND: The COVID-19 pandemic and related restriction measures have affected our daily life, sleep, and circadian rhythms worldwide. Their effects on hypersomnolence and fatigue remain unclear. METHODS: The International COVID-19 Sleep Study questionnaire which included items on hypersomnolence such as excessive daytime sleepiness (EDS), and excessive quantity of sleep (EQS), as well as sociodemographic factors, sleep patterns, psychological symptoms, and quality of life was distributed in 15 countries across the world from May to September in 2020. RESULTS: Altogether responses from 18,785 survey participants (65% women, median age 39 years) were available for analysis. Only 2.8% reported having had COVID-19. Compared to before the pandemic, the prevalence of EDS, EQS, and fatigue increased from 17.9% to 25.5%, 1.6%-4.9%, and 19.4%-28.3% amid the pandemic, respectively. In univariate logistic regression models, reports of having a COVID-19 were associated with EQS (OR 5.3; 95%-CI 3.6-8.0), EDS (2.6; 2.0-3.4), and fatigue (2.8; 2.1-3.6). In adjusted multivariate logistic regression, sleep duration shorter than desired (3.9; 3.2-4.7), depressive symptoms (3.1; 2.7-3.5), use of hypnotics (2.3; 1.9-2.8), and having reported COVID-19 (1.9; 1.3-2.6) remained strong predictors of EDS. Similar associations emerged for fatigue. In the multivariate model, depressive symptoms (4.1; 3.6-4.6) and reports of having COVID-19 (2.0; 1.4-2.8) remained associated with EQS. CONCLUSIONS: A large increase in EDS, EQS, and fatigue occurred due to the COVID-19 pandemic, and especially in self-reported cases of COVID-19. These findings warrant a thorough understanding of their pathophysiology to target prevention and treatment strategies for long COVID condition.

Many people report suffering from post-acute sequelae of COVID-19 or "long-COVID", but there are still open questions on what actually constitutes long-COVID and how prevalent it is. The current definition of post-acute sequelae of COVID-19 is based on voting using the Delphi-method by the WHO post-COVID-19 working group. It emphasizes long-lasting fatigue, shortness of breath and cognitive dysfunction as the core symptoms of post-acute sequelae of COVID-19. In this international survey study consisting of 13,628 subjects aged 18-99 years from 16 countries of Asia, Europe, North America and South America (May-Dec 2021), we show that post-acute sequelae of COVID-19 symptoms were more prevalent amongst the more severe COVID-19 cases, i.e. those requiring hospitalisation for COVID-19. We also found that long-lasting sleep symptoms are at the core of post-acute sequelae of COVID-19 and associate with the COVID-19 severity when COVID-19 cases are compared with COVID-negative cases. Specifically, fatigue (61.3%), insomnia symptoms (49.6%) and excessive daytime sleepiness (35.8%) were highly prevalent amongst respondents reporting long-lasting symptoms after hospitalisation for COVID-19. Understanding the importance of sleep-related symptoms in post-acute sequelae of COVID-19 has a clinical relevance when diagnosing and treating long-COVID.

Many people report suffering from post-acute sequelae of COVID-19 or "long-COVID", but there are still open questions on what actually constitutes long-COVID and how prevalent it is. The current definition of post-acute sequelae of COVID-19 is based on voting using the Delphi-method by the WHO post-COVID-19 working group. It emphasizes long-lasting fatigue, shortness of breath and cognitive dysfunction as the core symptoms of post-acute sequelae of COVID-19. In this international survey study consisting of 13,628 subjects aged 18-99 years from 16 countries of Asia, Europe, North America and South America (May-Dec 2021), we show that post-acute sequelae of COVID-19 symptoms were more prevalent amongst the more severe COVID-19 cases, i.e. those requiring hospitalisation for COVID-19. We also found that long-lasting sleep symptoms are at the core of post-acute sequelae of COVID-19 and associate with the COVID-19 severity when COVID-19 cases are compared with COVID-negative cases. Specifically, fatigue (61.3%), insomnia symptoms (49.6%) and excessive daytime sleepiness (35.8%) were highly prevalent amongst respondents reporting long-lasting symptoms after hospitalisation for COVID-19. Understanding the importance of sleep-related symptoms in post-acute sequelae of COVID-19 has a clinical relevance when diagnosing and treating long-COVID.