To develop an automatic sleep stage analysis model for children and evaluate the effect of the model on the diagnosis of sleep-disordered breathing (SDB). Patients and Methods: Three hundred and forty-four SDB patients aged between 2 to 18 years who completed polysomnography (PSG) to assess the severity of the disease were enrolled in this study. We developed deep neural networks to stage sleep from electroencephalography (EEG), electrooculography (EOG) and electromyogram (EMG). The model performance was estimated by accuracy, precision, recall, F1-score, and Cohen's Kappa coefficient (ĸ). And we compared the difference in calculation of sleep parameters among the technicians, the model ensemble, and the single-channel EEG model. Results:The numbers of raw data divided into training, validation, and testing were 240, 36, and 68, respectively. The best performance appeared in the model ensemble of which the accuracy was 83.36% (ĸ=0.7817) in 5-stages, and the accuracy was 96.76% (ĸ=0.8236) in 2-stages. The single-channel EEG model showed the classification satisfyingly as well. There was no significant difference in TST, SE, SOL, time in W, time in N1+N2, time in N3, and OAHI between technician and the model (P>0.05). On the datasets from sleep-EDF-13 and sleep-EDF-18, the average classification accuracies achieved were 92.76% and 91.94% in 5-stages by using the proposed method, respectively. Conclusion: This research established the model for pediatric automatic sleep stage classification with satisfying reliability and generalizability. In addition, it could be applied for calculating quantitative sleep parameters and evaluating the severity of SDB.

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Background Two outbreaks of acute conjunctivitis occurred successively with an interval of five days in two boarding primary schools in Weixi Lisu autonomous county, Diqing Tibetan autonomous prefecture, Yunnan. The aims of this study were to determine the intensity of and characteristics of outbreaks, as well as the clinical manifestation of patients and risk factors infected, and the pathogen causing two outbreaks. Methods An outbreak investigation and a case-control study were conducted in two primary schools. The relevant specimens were collected by case definition, Next generation sequencing was adopted to identify the pathogen, and the epidemiological investigation method was used to analyze the related epidemiological characteristics such as risk factors. The phylogenetic tree was constructed by MEGA 7.0. Results A total of 331 acute conjunctivitis cases, as acute hemorrhagic conjunctivitis probable cases, were reported in two schools and the attack rates were 30.59% (171/559, 95%CI: 26.76-34.42) and 20.41% (160/784, 95%CI: 17.58-23.24), respectively. Cases occurred in all grades and classes in both schools, and only one staff was ill in each school. Epidemic situations lasted for 54 days and 45 days, respectively. Epidemic curve of two breaks appearing two peaks indicated the mode of person-to-person transmission for two outbreaks. The patients had typical manifestations of epidemic keratoconjunctivitis (EKC) such as acute onset, follicular hyperplasia, pseudomembrane formation, preauricular lymphadenopathy, corneal involvement and blurred vision, and the longer course of the disease (average 9.40 days, longest 23 days and shortest 7 days). The risk factor in infection was close contact with the patient or personal items contaminated by the patient. The pathogen caused the outbreaks is HAdV-8. The virus was highly homologous to the 2016 HAdV-8 strain in Tibet, China. Conclusions This study strongly suggests that HAdV-8 could lead to serious consequences. This is the second report of a HAdV-8 associated EKC outbreak in mainland of China. Tibetan HAdV-8 might be circulating in southwest China, it is necessary to monitor the pathogen of acute conjunctivitis in this area.

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