Current evidence in the field of pediatric sleep medicine indicates that PSG has clinical utility in the diagnosis and management of sleep related breathing disorders. The accurate diagnosis of SRBD in the pediatric population is best accomplished by integration of polysomnographic findings with clinical evaluation.

While there has been significant progress made in surgical techniques for the treatment of OSA, there is a lack of rigorous data evaluating surgical modifications of the upper airway. Systematic and methodical investigations are needed to improve the quality of evidence, assess additional outcome measures, determine which populations are most likely to benefit from a particular procedure or procedures, and optimize perioperative care.

Alzheimer’s disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity. Here we report an interpretable deep learning strategy that delineates unique Alzheimer’s disease signatures from multimodal inputs of MRI, age, gender, and Mini-Mental State Examination score. Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a multilayer perceptron and generates precise, intuitive visualization of individual Alzheimer’s disease risk en route to accurate diagnosis. The model was trained using clinically diagnosed Alzheimer’s disease and cognitively normal subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (n = 417) and validated on three independent cohorts: the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) (n = 382), the Framingham Heart Study (n = 102), and the National Alzheimer’s Coordinating Center (NACC) (n = 582). Performance of the model that used the multimodal inputs was consistent across datasets, with mean area under curve values of 0.996, 0.974, 0.876 and 0.954 for the ADNI study, AIBL, Framingham Heart Study and NACC datasets, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n = 11), and high-risk cerebral regions predicted by the model closely tracked post-mortem histopathological findings. This framework provides a clinically adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for Alzheimer’s disease diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.

Importance The association between peripheral inflammatory biomarkers and Alzheimer disease (AD) is not consistent in the literature. It is possible that chronic inflammation, rather than 1 episode of inflammation, interacts with genetic vulnerability to increase the risk for AD. Objective To study the interaction between the apolipoprotein E ( ApoE ) genotype and chronic low-grade inflammation and its association with the incidence of AD. Design, Setting, and Participants In this cohort study, data from 2656 members of the Framingham Heart Study offspring cohort (Generation 2; August 13, 1971-November 27, 2017) were evaluated, including longitudinal measures of serum C-reactive protein (CRP), diagnoses of incident dementia including AD, and brain volume. Chronic low-grade inflammation was defined as having CRP at a high cutoff level at a minimum of 2 time points. Statistical analysis was performed from December 1, 1979, to December 31, 2015. Main Outcomes and Measures Development of AD and brain volumes. Results Of the 3130 eligible participants, 2656 (84.9%; 1227 men and 1429 women; mean [SD] age at last CRP measurement, 61.6 [9.5] years) with both ApoE status and longitudinal CRP measurements were included in this study analysis. Median (interquartile range) CRP levels increased with mean (SD) age (43.3 [9.6] years, 0.95 mg/L [0.40-2.35 mg/L] vs 59.1 [9.6] years, 2.04 mg/L [0.93-4.75 mg/L] vs 61.6 [9.5] years, 2.21 mg/L [1.05-5.12 mg/L]; P < .001), but less so among those with ApoE4 alleles, followed by ApoE3 then ApoE2 genotypes. During the 17 years of follow-up, 194 individuals (7.3%) developed dementia, 152 (78.4%) of whom had AD. ApoE4 coupled with chronic low-grade inflammation, defined as a CRP level of 8 mg/L or higher, was associated with an increased risk of AD, especially in the absence of cardiovascular diseases (hazard ratio, 6.63; 95% CI, 1.80-24.50; P = .005), as well as an increased risk of earlier disease onset compared with ApoE4 carriers without chronic inflammation (hazard ratio, 3.52; 95% CI, 1.27-9.75; P = .009). This phenomenon was not observed among ApoE3 and ApoE2 carriers with chronic low-grade inflammation. Finally, a subset of 1761 individuals (66.3%) underwent brain magnetic resonance imaging, and the interaction between ApoE4 and chronic low-grade inflammation was associated with brain atrophy in the temporal lobe (β = –0.88, SE = 0.22; P < .001) and hippocampus (β = –0.04, SE = 0.01; P = .005), after adjusting for confounders. Conclusions...