Browsing by Subject "AMERICAN ACADEMY"

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  • Tan, Xiao; van Egmond, Lieve; Partinen, Markku; Lange, Tanja; Benedict, Christian (2019)
    Sleep and circadian disruptions are frequently observed in patients across hospital wards. This is alarming, since impaired nocturnal sleep and disruption of a normal circadian rhythm can compromise health and disturb processes involved in recovery from illness (eg, immune functions). With this in mind, the present narrative review discusses how patient characteristics (sleep disorders, anxiety, stress, chronotype, and disease), hospital routines (pain management, timing of medication, nocturnal vital sign monitoring, and physical inactivity), and hospital environment (light and noise) may all contribute to sleep disturbances and circadian misalignment in patients. We also propose hospital-based strategies that may help reduce sleep and circadian disruptions in patients admitted to the hospital. (C) 2018 The Authors. Published by Elsevier B.V.
  • Ranta, Jukka; Airaksinen, Manu; Kirjavainen, Turkka; Vanhatalo, Sampsa; Stevenson, Nathan J. (2021)
    Objective To develop a non-invasive and clinically practical method for a long-term monitoring of infant sleep cycling in the intensive care unit. Methods Forty three infant polysomnography recordings were performed at 1-18 weeks of age, including a piezo element bed mattress sensor to record respiratory and gross-body movements. The hypnogram scored from polysomnography signals was used as the ground truth in training sleep classifiers based on 20,022 epochs of movement and/or electrocardiography signals. Three classifier designs were evaluated in the detection of deep sleep (N3 state): support vector machine (SVM), Long Short-Term Memory neural network, and convolutional neural network (CNN). Results Deep sleep was accurately identified from other states with all classifier variants. The SVM classifier based on a combination of movement and electrocardiography features had the highest performance (AUC 97.6%). A SVM classifier based on only movement features had comparable accuracy (AUC 95.0%). The feature-independent CNN resulted in roughly comparable accuracy (AUC 93.3%). Conclusion Automated non-invasive tracking of sleep state cycling is technically feasible using measurements from a piezo element situated under a bed mattress. Significance An open source infant deep sleep detector of this kind allows quantitative, continuous bedside assessment of infant's sleep cycling.
  • Kaasalainen, Touko; Ekholm, Marja; Siiskonen, Teemu; Kortesniemi, Mika (2021)
    Cone beam computed tomography (CBCT) is a diverse 3D x-ray imaging technique that has gained significant popularity in dental radiology in the last two decades. CBCT overcomes the limitations of traditional twodimensional dental imaging and enables accurate depiction of multiplanar details of maxillofacial bony structures and surrounding soft tissues. In this review article, we provide an updated status on dental CBCT imaging and summarise the technical features of currently used CBCT scanner models, extending to recent developments in scanner technology, clinical aspects, and regulatory perspectives on dose optimisation, dosimetry, and diagnostic reference levels. We also consider the outlook of potential techniques along with issues that should be resolved in providing clinically more effective CBCT examinations that are optimised for the benefit of the patient.
  • Arponen, Heidi; Bachour, Adel; Bäck, Leif; Valta, Helena; Mäkitie, Antti; Waltimo-Siren, Janna; Mäkitie, Outi (2018)
    BackgroundPatients with Osteogenesis imperfecta (OI) suffer from increased bone fracture tendency generally caused by a mutation in genes coding for type I collagen. OI is also characterized by numerous co-morbidities, and recent data from questionnaire studies suggest that these may include increased risk for sleep apnea, a finding that lacks clinical evidence from cohort studies. In this cross-sectional study, 25 adults with OI underwent clinical otorhinolaryngology examination as well as overnight polysomnography to address the question. The participants were aged between 19 and 77years, and ten of them had mild clinical OI phenotype, seven had a moderately severe phenotype, and eight had a severe phenotype.ResultsWe found obstructive sleep apnea (apnea hypopnea index 5/h) in as many as 52% of the OI patients in the cohort. Unexpectedly, however, no correlation was present between sleep apnea and daytime sleepiness, experienced bodily pain, severity of OI, Mallampati score, or neck circumference.ConclusionsSeeing that the usual predictors showed no association with occurrence of sleep apnea, we conclude that obstructive sleep apnea may easily be left as an undetected disorder in individuals with OI. Recurrent nocturnal hypoxia due to episodes of apneas can even affect bone metabolism, thereby further aggravating bone fragility in patients with OI.