Background Patients with COVID-19 have increased sleep disturbances and decreased sleep quality during and after the infection. The current published literature focuses mainly on qualitative analyses based on surveys and subjective measurements rather than quantitative data. Objective In this paper, we assessed the long-term effects of COVID-19 through sleep patterns from continuous signals collected via wearable wristbands. Methods Patients with a history of COVID-19 were compared to a control arm of individuals who never had COVID-19. Baseline demographics were collected for each subject. Linear correlations among the mean duration of each sleep phase and the mean daily biometrics were performed. The average duration for each subject’s total sleep time and sleep phases per night was calculated and compared between the 2 groups. Results This study includes 122 patients with COVID-19 and 588 controls (N=710). Total sleep time was positively correlated with respiratory rate (RR) and oxygen saturation (SpO 2 ). Increased awake sleep phase was correlated with increased heart rate, decreased RR, heart rate variability (HRV), and SpO 2 . Increased light sleep time was correlated with increased RR and SpO 2 in the group with COVID-19. Deep sleep duration was correlated with decreased heart rate as well as increased RR and SpO 2 . When comparing different sleep phases, patients with long COVID-19 had decreased light sleep (244, SD 67 vs 258, SD 67; P =.003) and decreased deep sleep time (123, SD 66 vs 128, SD 58; P =.02). Conclusions Regardless of the demographic background and symptom levels, patients with a history of COVID-19 infection demonstrated altered sleep architecture when compared to matched controls. The sleep of patients with COVID-19 was characterized by decreased total sleep and deep sleep.

The development of cancer is a complex multistage process. Over the past few decades, the model organism Drosophila melanogaster has been crucial in identifying cancer‐related genes and pathways and elucidating mechanisms underlying growth regulation in development. Investigations using Drosophila has yielded new insights into the molecular mechanisms involved in tumor initiation and progression. In this review, we describe various tumor models that have been developed in recent years using different Drosophila tissues, such as the imaginal tissue, the neural tissue, the gut, the ovary, and hematopoietic cells. We discuss underlying genetic alterations, cancer‐like characteristics, as well as similarities and key differences among these models. We also discuss how disruptions in stem cell division and differentiation result in tumor formation in diverse tissues, and highlight new concepts developed using the fly model to understand context‐dependent tumorigenesis. We further discuss the progress made in Drosophila to explore tumor–host interactions that involve the innate immune response to tumor growth and the cachexia wasting phenotype. This article is categorized under: Cancer > Genetics/Genomics/Epigenetics Cancer > Stem Cells and Development Cancer > Molecular and Cellular Physiology

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BACKGROUND Patients with COVID-19 have increased sleep disturbances and decreased sleep quality during and after the infection. The current published literature focuses mainly on qualitative analyses based on surveys and subjective measurements rather than quantitative data. OBJECTIVE In this paper, we assessed the long-term effects of COVID-19 through sleep patterns from continuous signals collected via wearable wristbands. METHODS Patients with a history of COVID-19 were compared to a control arm of individuals who never had COVID-19. Baseline demographics were collected for each subject. Linear correlations among the mean duration of each sleep phase and the mean daily biometrics were performed. The average duration for each subject’s total sleep time and sleep phases per night was calculated and compared between the 2 groups. RESULTS This study includes 122 patients with COVID-19 and 588 controls (N=710). Total sleep time was positively correlated with respiratory rate (RR) and oxygen saturation (SpO₂). Increased awake sleep phase was correlated with increased heart rate, decreased RR, heart rate variability (HRV), and SpO₂. Increased light sleep time was correlated with increased RR and SpO₂ in the group with COVID-19. Deep sleep duration was correlated with decreased heart rate as well as increased RR and SpO₂. When comparing different sleep phases, patients with long COVID-19 had decreased light sleep (244, SD 67 vs 258, SD 67; <i>P</i>=.003) and decreased deep sleep time (123, SD 66 vs 128, SD 58; <i>P</i>=.02). CONCLUSIONS Regardless of the demographic background and symptom levels, patients with a history of COVID-19 infection demonstrated altered sleep architecture when compared to matched controls. The sleep of patients with COVID-19 was characterized by decreased total sleep and deep sleep.

Introduction: Larger left atrial appendage (LAA) ostium area and greater left atrial (LA) volume have been associated with an increased risk of ischemic stroke. Catheter ablation (CA) of atrial fibrillation (AF) leads to morphological and functional changes within the LA and LAA, some of which are not well studied. Here, we present findings regarding post-ablation changes of the LAA ostia and correlate them with various LA, LAA and left ventricular (LV) functional and morphological metrics. Methods: This retrospective analysis included patients scheduled to undergo first-time radiofrequency CA for AF. Catheter ablation techniques included PVI with or without additional ablations. Cardiac magnetic resonance imaging (CMR) was used to assess LA, LAA and LV morphology and function, including LAA ostium area, LA/LAA volume and volume index, LA ejection fraction, LA strain, and LV ejection fraction. A Kruskal-Wallis test was used for correlating LAA ostial dimensions with other LA morphological and functional metrics. The t-test or two-sample Wilcoxon test were used to compare LA and LAA morphological parameters. Results: A total of 101 patients with AF were included in this study. The mean age was 60.1 ± 11.1 years, 69% were male, the average BMI was 29.22 ± 5.08. The LAA ostial area reduced significantly from 3.84 ± 1.15 cm before ablation to 3.42 ± 0.96 cm after ablation (p=0.0004). This reduction was asymmetrical, as the minor axis length decreased from 1.92 cm to 1.77 cm without significant changes in the major axis. LVEF increased from a pre-ablation average of 48.26% to a post-ablation average of 53.62% (p=0.015). Correlation of pre-ablation LVEF and LAA ostium area showed a near-significant negative trend (r=-0.21, p=0.083). LAEF correlated negatively with LAA ostial area (r=-0.289, p=0.0057), total LA strain (r=-0.248, p=0.0185), and passive LA strain (r=-0.208, p=0.049). Conclusion: There is a significant asymmetrical reduction of the LAA ostial area after AF ablation that is independent of LVEF changes. Larger LAA ostial area was associated with lower LAEF and LA strain. Remodeling of the LAA after AF ablation may help account for reduced risk of stroke and increased cardiac function.

BACKGROUND The WEAICOR (Wearables to Investigate the Long Term Cardiovascular and Behavioral Impacts of COVID-19) study was a prospective observational study that used continuous monitoring to detect and analyze biometrics. Compliance to wearables was a major challenge when conducting the study and was crucial for the results. OBJECTIVE The aim of this study was to evaluate patients’ compliance to wearable wristbands and determinants of compliance in a prospective COVID-19 cohort. METHODS The Biostrap (Biostrap USA LLC) wearable device was used to monitor participants’ biometric data. Compliance was calculated by dividing the total number of days in which transmissions were sent by the total number of days spent in the WEAICOR study. Univariate correlation analyses were performed, with compliance and days spent in the study as dependent variables and age, BMI, sex, symptom severity, and the number of complications or comorbidities as independent variables. Multivariate linear regression was then performed, with days spent in the study as a dependent variable, to assess the power of different parameters in determining the number of days patients spent in the study. RESULTS A total of 122 patients were included in this study. Patients were on average aged 41.32 years, and 46 (38%) were female. Age was found to correlate with compliance (<i>r</i>=0.23; <i>P</i>=.01). In addition, age (<i>r</i>=0.30; <i>P</i>=.001), BMI (<i>r</i>=0.19; <i>P</i>=.03), and the severity of symptoms (<i>r</i>=0.19; <i>P</i>=.03) were found to correlate with days spent in the WEAICOR study. Per our multivariate analysis, in which days spent in the study was a dependent variable, only increased age was a significant determinant of compliance with wearables (adjusted <i>R</i>²=0.1; <i>β</i>=1.6; <i>P</i>=.01). CONCLUSIONS Compliance is a major obstacle in remote monitoring studies, and the reasons for a lack of compliance are multifactorial. Patient factors such as age, in addition to environmental factors, can affect compliance to wearables.