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In this trial evaluating mepolizumab, an anti–interleukin-5 antibody, the rate of COPD exacerbations among patients whose COPD was characterized by an increased number of eosinophils in the circulating blood was lower with mepolizumab than with placebo.

Background: The capacity to diagnose obstructive sleep apnoea (OSA) must be expanded to meet an estimated disease burden of nearly one billion people worldwide. Validated alternatives to the gold standard polysomnography (PSG) will improve access to testing and treatment. This study aimed to evaluate the diagnosis of OSA, using measurements of mandibular movement (MM) combined with automated machine learning analysis, compared to in-home PSG. Methods: 40 suspected OSA underwent single overnight in-home sleep testing with PSG (Nox A1, ResMed, Australia) and simultaneous MM monitoring (Sunrise, Sunrise SA, Belgium). PSG recordings were manually analysed by two expert sleep centres (Grenoble and London); MM analysis was automated. The Obstructive Respiratory Disturbance Index calculated from the MM monitoring (MM-ORDI) was compared to the PSG (PSG-ORDI) using intraclass correlation coefficient and Bland-Altman analysis. Receiver operating characteristic curves (ROC) were constructed to optimise the diagnostic performance of the MM monitor at different PSG-ORDI thresholds (5, 15 and 30 events/hour). Results: 31 patients were included in the analysis (58% men; mean (SD) age: 48 (15) years; BMI: 30.4 (7.6) kg/m2). Good agreement was observed between MM-ORDI and PSG-ORDI (median bias 0.00; 95% CI -23.25 to +9.73 events/hour). However, for patients with no or mild OSA, MM monitoring overestimated disease severity (PSG-ORDI 5-15: MM-ORDI overestimation +3.70 (95% CI -0.53 to +18.32) events/hour). In patients with moderate-severe OSA, there was an underestimation (PSG-ORDI >15: MM-ORDI underestimation -8.70 (95% CI -28.46 to +4.01) events/hour). ROC optimal cut-off values for PSG-ORDI thresholds of 5, 15, 30 events/hour were: 9.53, 12.65 and 24.81 events/hour, respectively. These cut-off values yielded a sensitivity of 88, 100 and 79%, and a specificity of 100, 75, 96%. The positive predictive values were: 100, 80, 95% and the negative predictive values 89, 100, 82%, respectively. Conclusion: The diagnosis of OSA, using MM with machine learning analysis, is comparable to manually scored in-home PSG. Therefore, this novel monitor could be a convenient diagnostic tool that can easily be used in the patients’ own home.

Given the high prevalence of obstructive sleep apnea (OSA), there is a need for simpler and automated diagnostic approaches. To evaluate whether mandibular movement (MM) monitoring during sleep coupled with an automated analysis by machine learning is appropriate for OSA diagnosis. Diagnostic study of adults undergoing overnight in-laboratory polysomnography (PSG) as the reference method compared with simultaneous MM monitoring at a sleep clinic in an academic institution (Sleep Laboratory, Centre Hospitalier Universitaire Université Catholique de Louvain Namur Site Sainte-Elisabeth, Namur, Belgium). Patients with suspected OSA were enrolled from July 5, 2017, to October 31, 2018. Obstructive sleep apnea diagnosis required either evoking signs or symptoms or related medical or psychiatric comorbidities coupled with a PSG-derived respiratory disturbance index (PSG-RDI) of at least 5 events/h. A PSG-RDI of at least 15 events/h satisfied the diagnosis criteria even in the absence of associated symptoms or comorbidities. Patients who did not meet these criteria were classified as not having OSA. Agreement analysis and diagnostic performance were assessed by Bland-Altman plot comparing PSG-RDI and the Sunrise system RDI (Sr-RDI) with diagnosis threshold optimization via receiver operating characteristic curves, allowing for evaluation of the device sensitivity and specificity in detecting OSA at 5 events/h and 15 events/h. Among 376 consecutive adults with suspected OSA, the mean (SD) age was 49.7 (13.2) years, the mean (SD) body mass index was 31.0 (7.1), and 207 (55.1%) were men. Reliable agreement was found between PSG-RDI and Sr-RDI in patients without OSA (n = 46; mean difference, 1.31; 95% CI, -1.05 to 3.66 events/h) and in patients with OSA with a PSG-RDI of at least 5 events/h with symptoms (n = 107; mean difference, -0.69; 95% CI, -3.77 to 2.38 events/h). An Sr-RDI underestimation of -11.74 (95% CI, -20.83 to -2.67) events/h in patients with OSA with a PSG-RDI of at least 15 events/h was detected and corrected by optimization of the Sunrise system diagnostic threshold. The Sr-RDI showed diagnostic capability, with areas under the receiver operating characteristic curve of 0.95 (95% CI, 0.92-0.96) and 0.93 (95% CI, 0.90-0.93) for corresponding PSG-RDIs of 5 events/h and 15 events/h, respectively. At the 2 optimal cutoffs of 7.63 events/h and 12.65 events/h, Sr-RDI had accuracy of 0.92 (95% CI, 0.90-0.94) and 0.88 (95% CI, 0.86-0.90) as well as posttest probabilities of 0.99 (95% CI, 0.99-0.99) and 0.89 (95% CI, 0.88-0.91) at PSG-RDIs of at least 5 events/h and at least 15 events/h, respectively, corresponding to positive likelihood ratios of 14.86 (95% CI, 9.86-30.12) and 5.63 (95% CI, 4.92-7.27), respectively. Automatic analysis of MM patterns provided reliable performance in RDI calculation. The use of this index in OSA diagnosis appears to be promising.

Preventing the cytokine storm observed in COVID-19 is a crucial goal for reducing the occurrence of severe acute respiratory failure and improving outcomes. Here, we identify Aldo-Keto Reductase 1B10 (AKR1B10) as a key enzyme involved in the expression of pro-inflammatory cytokines. The analysis of transcriptomic data from lung samples of patients who died from COVID-19 demonstrates an increased expression of the gene encoding AKR1B10. Measurements of the AKR1B10 protein in sera from hospitalised COVID-19 patients suggests a significant link between AKR1B10 levels and the severity of the disease. In macrophages and lung cells, the over-expression of AKR1B10 induces the expression of the pro-inflammatory cytokines Interleukin-6 (IL-6), Interleukin-1β (IL-1β) and Tumor Necrosis Factor a (TNFα), supporting the biological plausibility of an AKR1B10 involvement in the COVID-19-related cytokine storm. When macrophages were stressed by lipopolysaccharides (LPS) exposure and treated by Zopolrestat, an AKR1B10 inhibitor, the LPS-induced production of IL-6, IL-1β, and TNFα is significantly reduced, reinforcing the hypothesis that the pro-inflammatory expression of cytokines is AKR1B10-dependant. Finally, we also show that AKR1B10 can be secreted and transferred via extracellular vesicles between different cell types, suggesting that this protein may also contribute to the multi-organ systemic impact of COVID-19. These experiments highlight a relationship between AKR1B10 production and severe forms of COVID-19. Our data indicate that AKR1B10 participates in the activation of cytokines production and suggest that modulation of AKR1B10 activity might be an actionable pharmacological target in COVID-19 management.

Monitoring unintentional air leaks in continuous positive airway pressure (CPAP) treatment for obstructive sleep apnea (OSA) is essential for therapy success. While leaks are often attributed to improperly sealed masks, mouth openings may also cause them, requiring interventions. Recent studies demonstrated distinctive mandibular jaw movement (MJM) signal patterns during sleep related to respiratory events and sleep stages. Analysing MJM during CPAP treatment reveals air leak peaks coinciding with maximal MJM amplitude during obstructive events, and air leak decreases corresponding to arousals. Examining leaks with MJM offers valuable insights into their origins and might open new avenues for CPAP management. Monitoring unintentional air leaks in continuous positive airway pressure (CPAP) treatment for obstructive sleep apnea (OSA) is essential for therapy success. Mandibular jaw movemement signals could provide valuable insights into the origins of air leaks and might open new avenues for CPAP management.

Background The rapid emergence and the high disease burden of the novel coronavirus SARS-CoV-2 have created a medical need for readily available drugs that can decrease viral replication or blunt the hyperinflammatory state leading to severe COVID-19 disease. Azithromycin is a macrolide antibiotic, known for its immunomodulatory properties. It has shown antiviral effect specifically against SARS-CoV-2 in vitro and acts on cytokine signaling pathways that have been implicated in COVID-19. Methods DAWn-AZITHRO is a randomized, open-label, phase 2 proof-of-concept, multicenter clinical trial, evaluating the safety and efficacy of azithromycin for treating hospitalized patients with COVID-19. It is part of a series of trials testing promising interventions for COVID-19, running in parallel and grouped under the name DAWn-studies. Patients hospitalized on dedicated COVID wards are eligible for study inclusion when they are symptomatic (i.e., clinical or radiological signs) and have been diagnosed with COVID-19 within the last 72 h through PCR (nasopharyngeal swab or bronchoalveolar lavage) or chest CT scan showing typical features of COVID-19 and without alternate diagnosis. Patients are block-randomized (9 patients) with a 2:1 allocation to receive azithromycin plus standard of care versus standard of care alone. Standard of care is mostly supportive, but may comprise hydroxychloroquine, up to the treating physician’s discretion and depending on local policy and national health regulations. The treatment group receives azithromycin qd 500 mg during the first 5 consecutive days after inclusion. The trial will include 284 patients and recruits from 15 centers across Belgium. The primary outcome is time from admission (day 0) to life discharge or to sustained clinical improvement, defined as an improvement of two points on the WHO 7-category ordinal scale sustained for at least 3 days. Discussion The trial investigates the urgent and still unmet global need for drugs that may impact the disease course of COVID-19. It will either provide support or else justify the discouragement of the current widespread, uncontrolled use of azithromycin in patients with COVID-19. The analogous design of other parallel trials of the DAWN consortium will amplify the chance of identifying successful treatment strategies and allow comparison of treatment effects within an identical clinical context. Trial registration EU Clinical trials register EudraCT Nb 2020-001614-38 . Registered on 22 April 2020

Sleep bruxism (SBx) activity is classically identified by capturing masseter and/or temporalis masticatory muscles electromyographic activity (EMG-MMA) during in-laboratory polysomnography (PSG). We aimed to identify stereotypical mandibular jaw movements (MJM) in patients with SBx and to develop rhythmic masticatory muscles activities (RMMA) automatic detection using an artificial intelligence (AI) based approach. This was a prospective, observational study of 67 suspected obstructive sleep apnea (OSA) patients in whom PSG with masseter EMG was performed with simultaneous MJM recordings. The system used to collect MJM consisted of a small hardware device attached on the chin that communicates to a cloud-based infrastructure. An extreme gradient boosting (XGB) multiclass classifier was trained on 79,650 10-second epochs of MJM data from the 39 subjects with a history of SBx targeting 3 labels: RMMA episodes (n=1072), micro-arousals (n=1311), and MJM occurring at the breathing frequency (n=77,267). Validated on unseen data from 28 patients, the model showed a very good epoch-by-epoch agreement (Kappa = 0.799) and balanced accuracy of 86.6% was found for the MJM events when using RMMA standards. The RMMA episodes were detected with a sensitivity of 84.3%. Class-wise receiver operating characteristic (ROC) curve analysis confirmed the well-balanced performance of the classifier for RMMA (ROC area under the curve: 0.98, 95% confidence interval [CI] 0.97-0.99). There was good agreement between the MJM analytic model and manual EMG signal scoring of RMMA (median bias -0.80 events/h, 95% CI -9.77 to 2.85). SBx can be reliably identified, quantified, and characterized with MJM when subjected to automated analysis supported by AI technology.

Background The patterns of mandibular movements (MM) during sleep can be used to identify increased respiratory effort periodic large-amplitude MM (LPM), and cortical arousals associated with “sharp” large-amplitude MM (SPM). We hypothesized that Cheyne Stokes breathing (CSB) may be identified by periodic abnormal MM patterns. The present study aims to evaluate prospectively the concordance between CSB detected by periodic MM and polysomnography (PSG) as gold-standard. The present study aims to evaluate prospectively the concordance between CSB detected by periodic MM and polysomnography (PSG) as gold-standard. Methods In 573 consecutive patients attending an in-laboratory PSG for suspected sleep disordered breathing (SDB), MM signals were acquired using magnetometry and scored manually while blinded from the PSG signal. Data analysis aimed to verify the concordance between the CSB identified by PSG and the presence of LPM or SPM. The data were randomly divided into training and validation sets (985 5-min segments/set) and concordance was evaluated using 2 classification models. Results In PSG, 22 patients (mean age ± SD: 65.9 ± 15.0 with a sex ratio M/F of 17/5) had CSB (mean central apnea hourly indice ± SD: 17.5 ± 6.2) from a total of 573 patients with suspected SDB. When tested on independent subset, the classification of CSB based on LPM and SPM is highly accurate (Balanced-accuracy = 0.922, sensitivity = 0.922, specificity = 0.921 and error-rate = 0.078). Logistic models based odds-ratios for CSB in presence of SPM or LPM were 172.43 (95% CI: 88.23–365.04; p  < 0.001) and 186.79 (95% CI: 100.48–379.93; p  < 0.001), respectively. Conclusion CSB in patients with sleep disordered breathing could be accurately identified by a simple magnetometer device recording mandibular movements.