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Study objectivesTo assess the accuracy of WatchPAT (WP—Itamar-Medical, Caesarea, Israel) enhanced with a novel systolic upstroke analysis coupled with respiratory movement analysis derived from a dedicated snoring and body position (SBP) sensor, to enable automated algorithmic differentiation between central sleep apnea (CSA) and obstructive sleep apnea (OSA) compared with simultaneous in-lab sleep studies with polysomnography (PSG).MethodsEighty-four patients with suspected sleep-disordered breathing (SDB) underwent simultaneous WP and PSG studies in 11 sleep centers. PSG scoring was blinded to the automatically analyzed WP data.ResultsOverall WP apnea-hypopnea index (AHI; mean ± SD) was 25.2 ± 21.3 (range 0.2–101) versus PSG AHI 24.4 ± 21.2 (range 0–110) (p = 0.514), and correlation was 0.87 (p < 0.001). Using a threshold of AHI ≥ 15, the sensitivity and specificity of WP versus PSG for diagnosing sleep apnea were 85% and 70% respectively and agreement was 79% (kappa = 0.867). WP central AHI (AHIc) was 4.2 ± 7.7 (range 0–38) versus PSG AHIc 5.9 ± 11.8 (range 0–63) (p = 0.034), while correlation was 0.90 (p < 0.001). Using a threshold of AHI ≥ 15, the sensitivity and specificity of WP versus PSG for diagnosing CSA were 67% and 100% respectively with agreement of 95% (kappa = 0.774), and receiver operator characteristic (ROC) area under the curve of 0.866, (p < 0.01). Using a threshold of AHI ≥ 10 showed comparable overall sleep apnea and CSA diagnostic accuracies.ConclusionsThese findings show that WP can accurately detect overall AHI and effectively differentiate between CSA and OSA.

PurposeThis study aimed to determine the prevalence of post-stroke depression (PSD) during the first year and its associated factors, especially focusing on sleep quality and fatigue severity.MethodsA cross-sectional study was conducted among stroke patients in Vietnam’s National Geriatric Hospital. Data were collected by using standardized questionnaires for interviewing and evaluating patients at the research site. Several covariables were presented including demographics, stroke-related characteristics, activities of daily living, post-stroke fatigue, and sleep quality (Pittsburgh Sleep Quality Index [PSQI] scale). PSD was assessed as an outcome variable through the Patient Health Questionnaire-9 scale. To summarize sociodemographic and clinical variables, descriptive statistics were performed. A logistic regression model was used to explore the factors related to PSD.ResultsOf 157 patients with stroke, mean age 73.1 (± 9.6), PSD was present in 60 patients (38%). The global score and all PSQI components of participants with PSD showed worse levels than those without depression. Furthermore, the prevalence of PSD was higher in patients with low IADL scores and functional disability at high levels. In the multivariate logistic regression analysis, the patients with PSD showed higher Fatigue Severity Scale (FSS) scores (OR = 4.11; 95% CI = 1.39; 12.19) and higher scores in two domains of the PSQI scale including subjective sleep quality (OR = 3.03; 95% CI = 1.21; 7.58) and sleep disturbance (OR = 5.22; 95% CI = 1.33; 20.47).ConclusionThere is a significant prevalence of depression following stroke. Furthermore, post-stroke fatigue and two PSQI scale components (subjective sleep quality and sleep disturbance) were shown to be associated with PSD. This finding may guide early screening and intervention strategies to address depression following stroke.

Background The Epworth Sleepiness Scale (ESS) is a brief instrument to identify sleep propensity. However, little is known about the psychometric performance of the Spanish version in university students. The study aimed to study the validity and reliability of the ESS in Colombian university students. Methods A psychometric study was designed with 465 students of health-related careers between 18 and 29 years old ( M  = 20.48, SD  = 2.27); 66.67% of the students were women. A confirmatory factor analysis was conducted, comparisons of scores between men and women, correlations with insomnia (Athens Insomnia Scale, AIS), anxiety (GAD-7), depression (PHQ-9), and sleep hygiene (SHI-10) and sex differential item functioning as indicators of validity and Cronbach’s alpha and McDonald’s omega were calculated as estimators of internal consistency. Results The ESS showed a unidimensional structure, similar scores in men and women, statistically significant correlations with AIS, GAD-7, PHQ-9, and SHI-10, without sex differential item functioning, and high internal consistency (Cronabch’s alpha and McDonald’s omega of 0.82). Conclusion The Spanish version of the ESS presents acceptable validity and reliability indicators in Colombian university students. However, these findings must be corroborated in other samples of Spanish-speaking participants.

BackgroundPeople with serious mental illnesses (SMIs) have three-fold higher rates of comorbid insomnia than the general population, which has downstream effects on cognitive, mental, and physical health. Cognitive Behavioral Therapy for Insomnia (CBT-i) is a safe and effective first-line treatment for insomnia, though the therapy’s effectiveness relies on completing nightly sleep diaries which can be challenging for some people with SMI and comorbid cognitive deficits. Supportive technologies such as mobile applications and sleep sensors may aid with completing sleep diaries. However, commercially available CBT-i apps are not designed for individuals with cognitive deficits. To aid with this challenge, we have developed an integrated mobile application, named “Sleep Catcher,” that will automatically incorporate data from a wearable fitness tracker and a bed sensor to track nightly sleep duration, overnight awakenings, bed-times, and wake-times to generate nightly sleep diaries for CBT-i.MethodsThe application development process will be described—writing algorithms to generating useful data, creating a clinician web portal to oversee patients and the mobile application, and integrating sleep data from device platforms and user input.ResultsThe mobile and web applications were developed using Flutter, IBM Code Engine, and IBM Cloudant database. The mobile application was developed with a user-centered approach and incremental changes informed by a series of beta tests. Special user-interface features were considered to address the challenges of developing a simple and effective mobile application targeting people with SMI.ConclusionThere is strong potential for synergy between engineering and mental health expertise to develop technologies for specific clinical populations. Digital health technologies allow for the development of multi-disciplinary solutions to existing health disparities in vulnerable populations, particularly in people with SMI.

PurposeThis study estimated thresholds for clinically important responses and minimally important differences for two indicators of improvement for the 10-item version of the functional outcomes of sleep questionnaire (FOSQ-10).MethodsParticipants with excessive daytime sleepiness with narcolepsy or obstructive sleep apnea received 12 weeks of solriamfetol treatment. Participants completed the FOSQ-10 and other patient-reported outcome measures, including the single-item patient global impression of change (PGI-C) assessment. Clinicians completed the single-item clinician global impression of change (CGI-C) for each participant. Data from the two studies were analyzed separately, both without regard to treatment assignment. In total, 690 participants (47% female, mean age 48 years, 77% Caucasian, 91% from North America) were enrolled. Two clinically important changes, defined as a minimally important difference and a clinically important response, were determined using distribution and anchor-based analyses. A receiver operating characteristic analysis was used to determine the optimal FOSQ-10 change threshold.ResultsSpearman correlations between change in FOSQ-10 scores and PGI-C and CGI-C were − 0.57 and − 0.49 for participants with narcolepsy and − 0.42 and − 0.37 for participants with obstructive sleep apnea. Receiver operating characteristic analysis suggested minimally important difference and clinically important response estimates of 1.7 and 2.5 and 1.8 and 2.2 points in narcolepsy and obstructive sleep apnea, respectively.ConclusionsMinimally important difference and clinically important response estimates for the FOSQ-10 for adults with excessive daytime sleepiness in narcolepsy or obstructive sleep apnea will be helpful for interpreting changes over time and defining a clinical responder.ClinicalTrials.gov identifiersNCT02348593 (first submitted January 15, 2015) and NCT02348606 (first submitted January 15, 2015)

BackgroundThere is the highest estimated number of patients with obstructive sleep apnea (OSA) in China. Early treatment could lead to fewer complications associated with OSA. This study aimed to analyze the factors influencing help-seeking from the first symptom discovery to treatment in OSA.MethodsSemi-structured interview outline was designed to conduct face-to-face interview based on the analyses of a great number of related literatures on the delay in seeking medical attention of patients with OSA. 15 patients diagnosed were interviewed between June 2021 to September 2022 in general hospital of Shenyang, Northeastern of China. Qualitative data was analyzed by content analysis using the Model of Pathways to Treatment.ResultsAnalyses identified factors contributing to elapsed time from first symptom discovery to received treatment that are linked to disease characteristic, patients, health system organization. Appraisal interval is most obvious for patients with OSA, but it is difficult to pinpoint precisely because the patients didn’t remember exactly when the first symptom was detected.ConclusionsPatients diagnosed with OSA didn’t initially interpret the snore as a warning sign and even thought it was a blessing. The findings provided guidance or avenues for reducing elapsed time between the first symptom and received treatment.

BackgroundDiagnostics of obstructive sleep apnea (OSA) is based on apnea-hypopnea index (AHI) determined as full-night average of occurred events. We investigate our hypothesis that intra-night variation in the frequency of obstructive events affects diagnostics and prognostics of OSA and should therefore be considered in clinical practice.MethodsPolygraphic recordings of 1989 patients (mean follow-up 18.3 years) with suspected OSA were analyzed. Number and severity of individual obstructive events were calculated hourly for the first 6 h of sleep. OSA severity was determined based on the full-night AHI and AHI for the 2 h when the obstructive event frequency was highest (AHI2h). Hazard ratios for all-cause, cardiovascular, and non-cardiovascular mortalities were calculated for different OSA severity categories based on the full-night AHI and AHI2h.ResultsFrequency and duration of obstructive events varied hour-by-hour increasing towards morning. Using AHI2h led to a statistically significant rearrangement of patients between the OSA severity categories. The use of AHI2h for severity classification showed clearer relationship between the OSA severity and mortality than the full-night AHI.ConclusionsCurrently, the intra-night variation in frequency and severity of obstructive events is completely ignored by conventional, full-night AHI and considering this information could improve the diagnostics of OSA.

Development of a safe and effective method for gene delivery to hepatocytes is a critical step toward gene therapy for liver diseases. Here, we assessed the parameters for gene delivery to the livers of large animals (pigs, 40–65 kg) using an image-guided hydrodynamics-based procedure that involves image-guided catheter insertion into the lobular hepatic vein and hydrodynamic injection of reporter plasmids using a computer-controlled injector. We demonstrated that injection parameters (relative position of the catheter in the hepatic vasculature, intravascular pressure upon injection, and injection volume) are directly related to the safety and efficiency of the procedure. By optimizing these parameters, we explored for the first time, the advantage of the procedure for sequential injections to multiple lobes in human-sized pigs. The optimized procedure resulted in sustained expression of the human α-1 antitrypsin gene in livers for more than 2 months after gene delivery. In addition, repeated hydrodynamic gene delivery was safely conducted and no adverse events were seen in the entire period of the study. Our results support the clinical applicability of the image-guided hydrodynamic gene delivery method for the treatment of liver diseases.

Abstract The biomanufacturing industry has now the opportunity to upgrade its production processes to be in harmony with the latest industrial revolution. Technology creates capabilities that enable smart manufacturing while still complying with unfolding regulations. However, many biomanufacturing companies, especially in the biopharma sector, still have a long way to go to fully benefit from smart manufacturing as they first need to transition their current operations to an information-driven future. One of the most significant obstacles towards the implementation of smart biomanufacturing is the collection of large sets of relevant data. Therefore, in this work, we both summarize the advances that have been made to date with regards to the monitoring and control of bioprocesses, and highlight some of the key technologies that have the potential to contribute to gathering big data. Empowering the current biomanufacturing industry to transition to Industry 4.0 operations allows for improved productivity through information-driven automation, not only by developing infrastructure, but also by introducing more advanced monitoring and control strategies.

Development of biofluid-based molecular diagnostic tests for cancer is an important step towards tumor characterization and real-time monitoring in a minimally invasive fashion. Extracellular vesicles (EVs) are released from tumor cells into body fluids and can provide a powerful platform for tumor biomarkers because they carry tumor proteins and nucleic acids. Detecting rare point mutations in the background of wild-type sequences in biofluids such as blood and cerebrospinal fluid (CSF) remains a major challenge. Techniques such as BEAMing (beads, emulsion, amplification, magnetics) PCR and droplet digital PCR (ddPCR) are substantially more sensitive than many other assays for mutant sequence detection. Here, we describe a novel approach that combines biofluid EV RNA and BEAMing RT-PCR (EV-BEAMing), as well droplet digital PCR to interrogate mutations from glioma tumors. EVs from CSF of patients with glioma were shown to contain mutant IDH1 transcripts, and we were able to reliably detect and quantify mutant and wild-type IDH1 RNA transcripts in CSF of patients with gliomas. EV-BEAMing and EV-ddPCR represent a valuable new strategy for cancer diagnostics, which can be applied to a variety of biofluids and neoplasms.