Search

Human experts scoring sleep according to the American Academy of Sleep Medicine (AASM) rules are forced to select, for every 30-second epoch, one out of five stages, even if the characteristics of the neurological signals are ambiguous, a very common occurrence in clinical studies. Moreover, experts cannot score sleep in studies where these signals have not been recorded, such as in home sleep apnea testing (HSAT). In this topic review we describe how artificial intelligence can provide consistent and reliable scoring of sleep stages based on neurological signals recorded in polysomnography (PSG) and on cardiorespiratory signals recorded in HSAT. We also show how estimates of sleep stage probabilities, usually displayed as hypnodensity graph, can be used to quantify sleep stage ambiguity and stability. As an example of the application of hypnodensity in the characterization of sleep disordered breathing (SDB), we compared 49 patients with sleep apnea to healthy controls and revealed a severity-depending increase in ambiguity and decrease in stability during non-rapid eye movement (NREM) sleep. Moreover, using autoscoring of cardiorespiratory signals, we show how HSAT-derived apnea-hypopnea index and hypoxic burden are well correlated with the PSG indices in 80 patients, showing how using this technology can truly enable HSATs as alternatives to PSG to diagnose SDB.

Introduction In a recent analysis of PSG studies with six, nine and twelve human scorers, we showed that sleep stage ambiguity is the rule rather than the exception, and that sleep stage probabilities (SSPs) calculated with artificial intelligence provide an excellent estimate of this ambiguity. Here we investigate clinical and demographic factors driving this ambiguity and evaluate potential benefits of using hypnodensity in addition to the traditional hypnogram for sleep assessment. Methods SSPs were determined in 195 healthy subjects aged 20–95 years and in 49 apnea patients aged 29–73 years (AHI: 5.8–105.5 events/hour). In addition to the recommended sleep parameters, we derived the percentage of ambiguous sleep stages (SSP ≤ 0.95), the mean amount of ambiguity per sleep stage (1-SSP), sleep stage continuity (absolute difference in SSP between two epochs) and sleep stage stability (two adjacent epochs belonging to the same sleep stage with SSP > 0.95 each) as well as NREM sleep depth (weighted average of NREM SSPs). Results The percentages of ambiguous NREM epochs (±SD) increased significantly with age (Pearson’s r=0.72; p< 0.01) from 40±9% in young healthy subjects (20-39 years, n=61) to 50±9% in middle-aged healthy subjects (40-59 years, n=59) and to 62±11% in older healthy subjects (≥ 60 years, n=75). The percentages of ambiguous REM epochs did not change significantly across age (r=0.1). Apnea patients showed an increased NREM ambiguity (77±15% compared to 53±10% in age- and sex-matched healthy controls; p< 0.01), while REM ambiguity was only slightly increased (p< 0.05). Furthermore, sleep stage continuity, stability, and NREM sleep depth decrease significantly with age and are significantly lower in apnea patients than in healthy controls (p< 0.01). Conclusion Artificial intelligence-based autoscoring shows ambiguity in 40% of NREM epochs in young healthy subjects, increasing to 77% in apnea patients. Assigning a single sleep stage to each epoch and presenting sleep architecture as a traditional hypnogram may be misleading, especially for older subjects and patients with sleep-disordered breathing. A hypnodensity chart representing sleep stage probabilities reflects this sleep staging ambiguity, provides all the information contained in a hypnogram, and offers insights into sleep ambiguity, continuity, stability, and sleep depth. Support (if any)

JHEP reports 5(1), 100605 (2023). doi:10.1016/j.jhepr.2022.100605 special issue: "EASL COVID-19 papers", Published by Elsevier, Amsterdam

Conventionally, sleep and associated events are scored visually by trained technologists according to the rules summarized in the American Academy of Sleep Medicine Manual. Since its first publication in 2007, the manual was continuously updated; the most recent version as of this writing was published in 2020. Human expert scoring is considered as gold standard, even though there is increasing evidence of limited interrater reliability between human scorers. Significant advances in machine learning have resulted in powerful methods for addressing complex classification problems such as automated scoring of sleep and associated events. Evidence is increasing that these autoscoring systems deliver performance comparable to manual scoring and offer several advantages to visual scoring: (1) avoidance of the rather expensive, time-consuming, and difficult visual scoring task that can be performed only by well-trained and experienced human scorers, (2) attainment of consistent scoring results, and (3) proposition of added value such as scoring in real time, sleep stage probabilities per epoch (hypnodensity), estimates of signal quality and sleep/wake-related features, identifications of periods with clinically relevant ambiguities (confidence trends), configurable sensitivity and rule settings, as well as cardiorespiratory sleep staging for home sleep apnea testing. This chapter describes the development of autoscoring systems since the first attempts in the 1970s up to the most recent solutions based on deep neural network approaches which achieve an accuracy that allows to use the autoscoring results directly for review and interpretation by a physician.

Bernice M Wulterkens,1,2,&ast; Pedro Fonseca,1,2,&ast; Lieke WA Hermans,1 Marco Ross,3 Andreas Cerny,3 Peter Anderer,3 Xi Long,1,2 Johannes P van Dijk,1,4 Nele Vandenbussche,4 Sigrid Pillen,1,4 Merel M van Gilst,1,4 Sebastiaan Overeem1,4 1Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; 2Philips Research, Eindhoven, the Netherlands; 3Sleep and Respiratory Care, Home Healthcare Solutions, Philips Austria GmbH, Vienna, Austria; 4Sleep Medicine Center Kempenhaeghe, Heeze, the Netherlands&ast;These authors contributed equally to this workCorrespondence: Bernice M WulterkensEindhoven University of Technology, Department of Electrical Engineering, Room Flux 7.104, PO BOX 513, Eindhoven, 5600 MB, the NetherlandsEmail b.m.wulterkens@tue.nlPurpose: There is great interest in unobtrusive long-term sleep measurements using wearable devices based on reflective photoplethysmography (PPG). Unfortunately, consumer devices are not validated in patient populations and therefore not suitable for clinical use. Several sleep staging algorithms have been developed and validated based on ECG-signals. However, translation from these techniques to data derived by wearable PPG is not trivial, and requires the differences between sensing modalities to be integrated in the algorithm, or having the model trained directly with data obtained with the target sensor. Either way, validation of PPG-based sleep staging algorithms requires a large dataset containing both gold standard measurements and PPG-sensor in the applicable clinical population. Here, we take these important steps towards unobtrusive, long-term sleep monitoring.Methods: We developed and trained an algorithm based on wrist-worn PPG and accelerometry. The method was validated against reference polysomnography in an independent clinical population comprising 244 adults and 48 children (age: 3 to 82 years) with a wide variety of sleep disorders.Results: The classifier achieved substantial agreement on four-class sleep staging with an average Cohen’s kappa of 0.62 and accuracy of 76.4%. For children/adolescents, it achieved even higher agreement with an average kappa of 0.66 and accuracy of 77.9%. Performance was significantly higher in non-REM parasomnias (kappa = 0.69, accuracy = 80.1%) and significantly lower in REM parasomnias (kappa = 0.55, accuracy = 72.3%). A weak correlation was found between age and kappa (ρ = − 0.30, p< 0.001) and age and accuracy (ρ = − 0.22, p< 0.001).Conclusion: This study shows the feasibility of automatic wearable sleep staging in patients with a broad variety of sleep disorders and a wide age range. Results demonstrate the potential for ambulatory long-term monitoring of clinical populations, which may improve diagnosis, estimation of severity and follow up in both sleep medicine and research.Keywords: hypnogram, sleep staging, polysomnography, heart rate variability, wearable, pediatrics

Study Objectives To quantify the amount of sleep stage ambiguity across expert scorers and to validate a new auto-scoring platform against sleep staging performed by multiple scorers. Methods We applied a new auto-scoring system to three datasets containing 95 PSGs scored by 6–12 scorers, to compare sleep stage probabilities (hypnodensity; i.e. the probability of each sleep stage being assigned to a given epoch) as the primary output, as well as a single sleep stage per epoch assigned by hierarchical majority rule. Results The percentage of epochs with 100% agreement across scorers was 46 ± 9%, 38 ± 10% and 32 ± 9% for the datasets with 6, 9, and 12 scorers, respectively. The mean intra-class correlation coefficient between sleep stage probabilities from auto- and manual-scoring was 0.91, representing excellent reliability. Within each dataset, agreement between auto-scoring and consensus manual-scoring was significantly higher than agreement between manual-scoring and consensus manual-scoring (0.78 vs. 0.69; 0.74 vs. 0.67; and 0.75 vs. 0.67; all p < 0.01). Conclusions Analysis of scoring performed by multiple scorers reveals that sleep stage ambiguity is the rule rather than the exception. Probabilities of the sleep stages determined by artificial intelligence auto-scoring provide an excellent estimate of this ambiguity. Compared to consensus manual-scoring, sleep staging derived from auto-scoring is for each individual PSG noninferior to manual-scoring meaning that auto-scoring output is ready for interpretation without the need for manual adjustment.

Protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and associated clinical sequelae requires well-coordinated metabolic and immune responses that limit viral spread and promote recovery of damaged systems. In order to understand potential mechanisms and interactions that influence coronavirus disease 2019 (COVID-19) outcomes, we performed a multi-omics analysis on hospitalised COVID-19 patients and compared those with the most severe outcome (i.e. death) to those with severe non-fatal disease, or mild/moderate disease, that recovered. A distinct subset of 8 cytokines and 140 metabolites in sera identified those with a fatal outcome to infection. In addition, elevated levels of multiple pathobionts and lower levels of protective or anti-inflammatory microbes were observed in the faecal microbiome of those with the poorest clinical outcomes. Weighted gene correlation network analysis (WGCNA) identified modules that associated severity-associated cytokines with tryptophan metabolism, coagulation-linked fibrinopeptides, and bile acids with multiple pathobionts. In contrast, less severe clinical outcomes associated with clusters of anti-inflammatory microbes such as Bifidobacterium or Ruminococcus, short chain fatty acids (SCFAs) and IL-17A. Our study uncovered distinct mechanistic modules that link host and microbiome processes with fatal outcomes to SARS-CoV-2 infection. These features may be useful to identify at risk individuals, but also highlight a role for the microbiome in modifying hyperinflammatory responses to SARS-CoV-2 and other infectious agents.

Automated sleep stage classification using heart rate variability (HRV) may provide an ergonomic and low-cost alternative to gold standard polysomnography, creating possibilities for unobtrusive home-based sleep monitoring. Current methods however are limited in their ability to take into account long-term sleep architectural patterns. A long short-term memory (LSTM) network is proposed as a solution to model long-term cardiac sleep architecture information and validated on a comprehensive data set (292 participants, 584 nights, 541.214 annotated 30 s sleep segments) comprising a wide range of ages and pathological profiles, annotated according to the Rechtschaffen and Kales (R&K) annotation standard. It is shown that the model outperforms state-of-the-art approaches which were often limited to non-temporal or short-term recurrent classifiers. The model achieves a Cohen’s k of 0.61 ± 0.15 and accuracy of 77.00 ± 8.90% across the entire database. Further analysis revealed that the performance for individuals aged 50 years and older may decline. These results demonstrate the merit of deep temporal modelling using a diverse data set and advance the state-of-the-art for HRV-based sleep stage classification. Further research is warranted into individuals over the age of 50 as performance tends to worsen in this sub-population.

Background and aims Chronic hepatitis C virus (HCV) infection is associated with a wide range of immunopathological manifestations, which are significantly improved by successful interferon-based treatment. There is paucity of data on the impact of interferon-free HCV clearance on immunopathological manifestations, which might be expected to disappear more frequently as compared to what reported in interferon-induced HCV-clearance. We have investigated liver autoimmune serology before and after interferon-free clearance of HCV by treatment with direct acting antiviral agents (DAA). Method Patients within the Swiss Hepatitis C Cohort Study who underwent successful (SVR 12) HCV treatment with DAA were tested for autoimmune liver serology according to dedicated guidelines before and at least 6 months after end of treatment. Results A total of 235 patients were included; 62% males; median age 56 years; 27% with cirrhosis. Median time between end of DAA treatment and post-treatment serum sampling was 17 months. At least one autoantibody before treatment was found in 175 (74%) patients ; 32 (14%) were positive for 2 autoantibodies; no patient was positive for anti-SLA, anti-LC1 or typical AMA before or after DAA. ANA disappeared in 34%, SMA in 52% and anti-LKM1 in one of two patients after successful treatment, but, unexpectedly, one or more autoantibodies appeared in 27% of pre-treatment negative subjects. Conclusion HCV clearance by DAA is associated with autoantibody disappearance in more than one third of the patients who were positive before treatment. However, the majority of the patients remain autoantibody-positive and 27% of those who were negative before treatment developed autoantibodies after DAA-induced HCV clearance. These data confirm that HCV infection is associated with autoimmunity and show that the autoimmune imprint persists after viral clearance by DAA, suggesting that long-term follow-up may be warranted.

Unobtrusive home sleep monitoring using wrist-worn wearable photoplethysmography (PPG) could open the way for better sleep disorder screening and health monitoring. However, PPG is rarely included in large sleep studies with gold-standard sleep annotation from polysomnography. Therefore, training data-intensive state-of-the-art deep neural networks is challenging. In this work a deep recurrent neural network is first trained using a large sleep data set with electrocardiogram (ECG) data (292 participants, 584 recordings) to perform 4-class sleep stage classification (wake, rapid-eye-movement, N1/N2, and N3). A small part of its weights is adapted to a smaller, newer PPG data set (60 healthy participants, 101 recordings) through three variations of transfer learning. Best results (Cohen’s kappa of 0.65 ± 0.11, accuracy of 76.36 ± 7.57%) were achieved with the domain and decision combined transfer learning strategy, significantly outperforming the PPG-trained and ECG-trained baselines. This performance for PPG-based 4-class sleep stage classification is unprecedented in literature, bringing home sleep stage monitoring closer to clinical use. The work demonstrates the merit of transfer learning in developing reliable methods for new sensor technologies by reusing similar, older non-wearable data sets. Further study should evaluate our approach in patients with sleep disorders such as insomnia and sleep apnoea.

Ces recommandations ont ete elaborees par diverses societes professionnelles et organisations de patients pour assurer un programme de suivi pour les patients traites avec succes.

Introduction There have been significant advances in machine learning in recent years. This means that powerful methods are now available for classification problems, such as scoring sleep stages from neurological or cardiorespiratory signals. In the present work, validation studies for both applications are presented. Methods To determine the 5 sleep stages from the neurological signals, 54 sleep-wake-related features were calculated and classified by a bidirectional long short-term memory (LSTM) network which had been trained on 1956 manual scorings of 588 PSGs from 294 subjects (supervised deep learning). To determine the 4 stages (wake, light sleep, deep sleep, REM) from cardiorespiratory signals, a convolutional neural network combined with LSTM layers was used for feature extraction and classification. This network had been trained on 685 PSGs from 391 subjects (Bakker et al. JCSM 2021). The networks obtained were validated in 428 PSGs with one and 10 PSGs with 12 manual scorings (neurological staging) as well as in 2 two datasets, each containing 296 ambulatory recordings (cardiorespiratory staging). Results Cohen’s kappa between autoscoring based on neurological signals and manual scoring was 0.74 (95%-confidence interval: 0.74-0.74) for the 428 PSGs. The intraclass correlation coefficient (ICC) for absolute agreement between autoscoring and manual scoring was for the AHI 0.97 (0.96-0.98), for the arousal index 0.79 (0.67-0.86) and for the PLMSI 0.91 (0.88-0.93). The ICC between the sleep stage probabilities derived from the 12 manual scorings and the artificial intelligence (AI) derived hypnodensity was 0.91 (0.91-0.91). Cohen’s kappa values for the cardiorespiratory sleep staging were 0.68 (0.68-0.68) and 0.64 (0.63-0.64) for the 2 datasets with 296 ambulatory recordings each. Conclusion All metrics from the PSG validation studies show substantial (Cohen’s kappa > 0.6) as well as good to excellent agreement (ICC > 0.75 or > 0.90) compared to manual scorings. As an added value of the AI-supported PSG evaluation, the probabilities of the sleep stages per epoch are determined (hypnodensity graph). The valid estimation of the sleep stages from cardiorespiratory signals by means of AI may result in improved clinical interpretation of home sleep apnea tests, which are increasingly used in the sleep-disordered breathing diagnostic pathway. Support (If Any)

Background Heterozygous ABCB4 variants are not routinely tested in adults with cholestasis because of their supposed rarity and high costs. Methods Nineteen adult patients presenting with unexplained cholestasis, and/or recurrent gallstones were included; genotyping was not done in five due to lack of health insurance approval. Results heterozygous ABCB4 variants were identified in seven patients, followed by cascade testing of 12 family members: one patient underwent liver transplantation at age 40 for end-stage liver disease; one had compensated cirrhosis; all symptomatic adults had gallstones, including four with low phospholipid-associated cholelithiasis; four had intrahepatic cholestasis of pregnancy; all children and one 54-year old female were asymptomatic. Genotype: Families A and C: c.2211G>A (p.Ala737=) combined with c.959C>T (p.Ser320Phe) in one subject; Family B: c.1130T>C (p.Ile377Thr); Family D: large deletion removing ABCB4 exons 1-4 plus ABCB1, RUNDC3B, SLC25A40, DBF4, ADAM22 exons 1-3; Family E: c.1565T>C (p.Phe522Ser) ; Family F: c.1356+2T>C combined with c.217C>G (p.Leu73Val). All patients responded to ursodeoxycholic acid. Conclusions We found ABCB4 variants in half of the adults with unexplained cholestasis and/or recurrent gallstones presenting at our center, suggesting that this condition is underdiagnosed and undertreated, with serious consequences not only for the patients and their families, but also in terms of healthcare costs.

Methylene blue is an FDA (Food and Drug Administration) and EMA (European Medicines Agency) approved drug with an excellent safety profile. It displays broad-spectrum virucidal activity in the presence of UV light and has been shown to be effective in inactivating various viruses in blood products prior to transfusions. In addition, its use has been validated for methemoglobinemia and malaria treatment. In this study, we first evaluated the virucidal activity of methylene blue against influenza virus H1N1 upon different incubation times and in the presence or absence of light activation, and then against SARS-CoV-2. We further assessed the therapeutic activity of methylene blue by administering it to cells previously infected with SARS-CoV-2. Finally, we examined the effect of co-administration of the drug together with immune serum. Our findings reveal that methylene blue displays virucidal preventive or therapeutic activity against influenza virus H1N1 and SARS-CoV-2 at low micromolar concentrations and in the absence of UV-activation. We also confirm that MB antiviral activity is based on several mechanisms of action as the extent of genomic RNA degradation is higher in presence of light and after long exposure. Our work supports the interest of testing methylene blue in clinical studies to confirm a preventive and/or therapeutic efficacy against both influenza virus H1N1 and SARS-CoV-2 infections.

Methylene blue is an FDA and EMA approved drug with an excellent safety profile. It displays broad-spectrum virucidal activity in the presence of UV light and has been shown to be effective in inactivating various viruses in blood products prior to transfusions. In addition, its use has been validated for methemoglobinemia and malaria treatment. Here we show the virucidal activity of methylene blue at low micromolar concentrations and in the absence of UV activation against SARS-CoV2.

Objective The maturation of neural network-based techniques in combination with the availability of large sleep datasets has increased the interest in alternative methods of sleep monitoring. For unobtrusive sleep staging, the most promising algorithms are based on heart rate variability computed from inter-beat intervals (IBIs) derived from ECG-data. The practical application of these algorithms is even more promising when alternative ways of obtaining IBIs, such as wrist-worn photoplethysmography (PPG) can be used. However, studies validating sleep staging algorithms directly on PPG-based data are limited. Results We applied an automatic sleep staging algorithm trained and validated on ECG-data directly on inter-beat intervals derived from a wrist-worn PPG sensor, in 389 polysomnographic recordings of patients with a variety of sleep disorders. While the algorithm reached moderate agreement with gold standard polysomnography, the performance was significantly lower when applied on PPG- versus ECG-derived heart rate variability data (kappa 0.56 versus 0.60, p

SUMMARY We present here the case of a 62-year-old man, who was referred to the emergency department with fever and cough for 3 days. He underwent liver transplantation 4 years earlier due to HCV and NASH-related cirrhosis with hepatocellular carcinoma. At admission he was in reduced general conditions. Nasopharyngeal smear specimen resulted positive for SARS-CoV-2 infection. Pulmonary low-dose CT-scan revealed bilateral subpleural ground-glass infiltrates. O2 saturation was 93%. A treatment with lopinavir/ritonavir and hydroxychloroquine twice daily was started. The patient received also cefepime and remained in isolation. Seven days later imaging showed a progression of the pulmonary infiltrates. Cefepime was replaced by meropenem. During the following 3 days the fever resolved, and the general conditions of the patient significantly improved. Consequently, treatment with lopinavir/ritonavir and hydroxychloroquine was stopped. The evolution of SARS-CoV-2 interstitial pneumonia in this immunosuppressed patient was moderate to severe and liver injury was not clinically significant. Despite its limitations, this case report confirm that the liver may be only mildly affected during SARS-CoV-2 infection, also in liver transplanted patients. Further studies are needed to assess whether the outcome of SARS-CoV-2 infection is worse in immunosuppressed patients than in the general population.

Background & AimsWith the introduction of direct-acting antiviral agents (DAA), mortality rates and causes of death among persons with hepatitis C virus (HCV) infection are likely to change over time. However, the emergence of such trends may be delayed by the relatively slow progression of chronic hepatitis C. To date, detailed analyses of cause-specific mortality among HCV-infected persons over time remain limited.MethodsWe evaluated changes in causes of death among the Swiss Hepatitis C Cohort Study (SCCS) participants, from 2008 to 2016. We analysed risk factors for all-cause and cause-specific mortality, accounting for changes in treatment, fibrosis stage and use of injectable drugs over time. Mortality ascertainment was completed by linking lost-to-follow-up participants to the Swiss Federal Statistical Office (SFSO) death registry.ResultsWe included 4,700 SCCS participants, of whom 478 died between 2008 and 2016. Linkage to the SFSO death registry substantially improved the information on causes of death (from 42% of deaths with unknown cause to 10% after linkage). Leading causes of death were liver failure (crude death rate 4.4/1000 person-years), liver cancer (3.4/1000 p-yrs) and non-liver cancer (2.8/1000 p-yrs), with an increasing proportion of cancer-related deaths over time. Cause-specific analysis showed that persons with sustained virologic response (SVR) were less at risk for liver-related mortality than those never treated or treated unsuccessfully.ConclusionsAlthough the expected decrease in mortality is not yet observable, causes of death among HCV-infected persons evolved over time. With the progressive widening of guidelines for DAA use, liver-related mortality is expected to decline in the future. Continued monitoring of cause-specific mortality will remain important to assess the long-term effect of DAA and to design effective interventions.Lay summaryLeading causes of death among persons with hepatitis C virus (HCV) infection in the Swiss Hepatitis C Cohort study evolved over the past years, with an increasing proportion of cancer-related deaths. The positive impact of new potent anti-HCV drugs on mortality among HCV-infected persons is not yet observable, due to both the slow progression of chronic hepatitis C and the progressive relaxation of guidelines for the use of those new drugs.

Study ObjectivesTo validate a previously developed sleep staging algorithm using heart rate variability (HRV) and body movements in an independent broad cohort of unselected sleep disordered patients.MethodsWe applied a previously designed algorithm for automatic sleep staging using long short-term memory recurrent neural networks to model sleep architecture. The classifier uses 132 HRV features computed from electrocardiography and activity counts from accelerometry. We retrained our algorithm using two public datasets containing both healthy sleepers and sleep disordered patients. We then tested the performance of the algorithm on an independent hold-out validation set of sleep recordings from a wide range of sleep disorders collected in a tertiary sleep medicine center.ResultsThe classifier achieved substantial agreement on four-class sleep staging (wake/N1–N2/N3/rapid eye movement [REM]), with an average κ of 0.60 and accuracy of 75.9%. The performance of the sleep staging algorithm was significantly higher in insomnia patients (κ = 0.62, accuracy = 77.3%). Only in REM parasomnias, the performance was significantly lower (κ = 0.47, accuracy = 70.5%). For two-class wake/sleep classification, the classifier achieved a κ of 0.65, with a sensitivity (to wake) of 72.9% and specificity of 94.0%.ConclusionsThis study shows that the combination of HRV, body movements, and a state-of-the-art deep neural network can reach substantial agreement in automatic sleep staging compared with polysomnography, even in patients suffering from a multitude of sleep disorders. The physiological signals required can be obtained in various ways, including non-obtrusive wrist-worn sensors, opening up new avenues for clinical diagnostics.

La Strategie suisse contre l’hepatite a – tout comme l’Organisation mondiale de la Sante (OMS) – pour objectif d’eliminer l’hepatite B et C d’ici 2030. Cela signifie que les taux de nouvelles infections et des sequelles doivent etre ramenes a proche de zero. Tous les instruments d’elimination, c’est-a-dire les mesures preventives, les therapies et les vaccinations, sont disponibles. Il s’agit maintenant de combler les lacunes en matiere d’education, de depistage et d’acces a la therapie a bas seuil.

Autoimmune phenomena and clinically apparent autoimmune diseases, including autoimmune hepatitis, are increasingly been reported not only after natural infection with the SARS-CoV-2 virus, but also after vaccination against it. We report the case of a 63-year old man without a history of autoimmunity or SARS-CoV-2 natural infection who experienced acute severe autoimmune-like hepatitis seven days after the first dose of the mRNA-1273 SARS-CoV-2 vaccine. Liver histology showed inflammatory portal infiltrate with interface hepatitis, lobular and centrilobular inflammation with centrilobular necrosis, in absence of fibrosis and steatosis. Serum immunoglobulin G was slightly elevated. Autoimmune liver serology showed an indirect immunofluorescence pattern on triple rodent tissue compatible with anti-mitochondrial antibody (AMA), but, unexpectedly, this pattern was not mirrored by positivity for primary biliary cholangitis (PBC)-specific molecular tests, indicating that this antibody is different from classical AMA. Anti-nuclear antibody (ANA) was also positive with a rim-like indirect immunofluorescence pattern on liver and HEp2 cell substrates, similar to PBC-specific ANA; however, anti-gp210 and a large panel of molecular-based assays for nuclear antigens were negative, suggesting a unique ANA in our patient. He carries the HLA DRB1*11:01 allele, which is protective against PBC. Response to prednisone treatment was satisfactory. The clinical significance of these novel specificities needs to be further evaluated in this emerging condition.

Introduction Scoring algorithms have the potential to increase polysomnography (PSG) scoring efficiency while also ensuring consistency and reproducibility. We sought to validate an updated event detection algorithm (Somnolyzer; Philips, Monroeville PA USA) against manual scoring, by analyzing a dataset we have previously used to report scoring variability across nine center-members of the Sleep Apnea Global Interdisciplinary Consortium (SAGIC). Methods Fifteen PSGs collected at a single sleep clinic were scored independently by technologists at nine SAGIC centers located in six countries, and auto-scored with the algorithm. Arousals, apneas, and hypopneas were identified according to the American Academy of Sleep Medicine recommended criteria. We calculated the intraclass correlation coefficient (ICC) and performed a Bland-Altman analysis comparing the average manual- and auto-scored apnea-hypopnea index (AHI), arousal index (ArI), apneas, obstructive apneas, central apneas, mixed apneas, and hypopneas. We hypothesized that the values from auto-scoring would show good agreement and reliability when compared to the average across manual scorers. Results Participants contributing to the original dataset had a mean (SD) age of 47 (12) years, AHI of 24.7 (18.2) events/hour, and 80% were male. The ICCs (95% confidence interval) between average manual- and auto-scoring were almost perfect (ICC=0.80–1.00) for AHI [0.989 (0.968, 0.996)], ArI [0.897 (0.729, 0.964)], hypopneas [0.992 (0.978, 0.997)], total apneas [0.973 (0.924, 0.991)], and obstructive apneas [0.919 (0.781, 0.972)], and moderately reliable (ICC=0.40–0.60] for central [0.537 (0.069, 0.815)] and mixed [0.502 (0.021, 0.798)] apneas. Similarly, Bland-Altman analyses supported good agreement for event detection between techniques, with a mean difference (limits of agreement) of only 1.45 (-3.22, 6.12) events/hour for AHI, total apneas 5.2 (-23.9, 34.3), obstructive apneas 1.8 (-45.9, 49.5), central apneas 1.8 (-9.7, 13.4), mixed apneas 1.6 (-14.8, 17.9), and hypopneas 4.3 (-12.4, 20.9). Conclusion Results support almost perfect reliability between auto-scoring and manual scoring of AHI, ArI, hypopneas, total apneas, and obstructive apneas, as well as moderate reliability for central and mixed apneas. There was good agreement between methods, with small mean differences; wider limits of agreement for specific type of apneas did not affect accuracy of the overall AHI. Thus, the auto-scoring algorithm appears reliable for event detection. Support (if any) Philips