: Insomnia is one of the most often complaints in the stroke patients and its incidence rate in the stroke patients is more higher than in general population.Insomnia can adversely affect the stroke patients' rehabilitation.Sedative hypnotics are still the most often used for insomnia after stroke currently.Furthermore,several studies have compared different pharmachotherapies and nonpharmachological treatments for insomnia after stroke.

Objective:To explore the regional brain activities in healthy adolescent subjects after sleep deprivation (SD) using amplitude of low-frequency fluctuation (ALFF) method.Methods:Total of 16 healthy adolescent subjects (8 males,8 females;aged 13-20 years) were recruited in the community and the campus through the internet and posters.Each of the 16 healthy adolescent subject underwent the attention network test and magnetic resonance imaging (MRI) session twice:once was after rested wakefulness (RW condition),and the other was after SD condition.Amplitude of low frequency fluctuation (ALFF) method was used to assess the local brain features.The mean ALFF signal values of the different brain areas were performed to investigate their relationships with the accuracy rate,reaction time and lapse rate in the attention network test,and were analyzed with a receiver operating characteristic (ROC) curve to investigate their sensitivities and specificities to distinguish the SD condition from the RW condition.Results:Subjects showed a lower response accuracy rate [(83 ± 12) ％ vs.(97 ± 4) ％,P ＜ 0.05],a longer response time [(832 ± 134) ms vs.(715 ± 97) ms,P ＜ 0.05] and a higher lapse rate [(15 ± 11)％ vs.(2.4 ±7.3)％,P ＜0.05] under SD condition than under RW condition.They showed higher ALFF area in the right cuneus (BA 17,BA 18),and lower ALFF areas in the right lentiform nucleus,right claustrum,left dorsolateral prefrontal cortex (BA 46) and left inferior parietal cortex (BA 39) under SD condition than under RW condition.Under SD condition,the mean ALFF signal value of the right claustrum showed a significant positive correlation with the accuracy rate (r =0.69,P ＜0.05),and a negative correlation with the lapse rate (r =-0.71,P ＜0.05).The mean ALFF signal value of the dorsolateral prefrontal cortex showed a significant positive correlation with the reaction time (r =0.68,P ＜ 0.05).The values of area under the curve of the right cuneus,right lentiform nucleus,right claustrum,left dorsolateral prefrontal cortex and left inferior parietal cortice were 0.9,0.8,0.9,0.8 and 0.9,respectively.These different ALFF areas also showed high degree of sensitivities and specificities.Conclusion:Sleep deprivation leads to the dysfunction in the default mode network,anticorrelatedtask-positive network,and advanced cognitive function brain areas,and the functional compensation in the visual network.

Objective:To screen the incidence of proteinuria in rural areas of Shanxi province and construct a risk prediction model of proteinuria based on machine learning algorithm.Methods:It was a cross-sectional investigation study. The residents ≥30 years old in rural areas of Shanxi province from April to November 2019 were screened by multi-stage stratified sampling method, and data from questionnaire surveys, physical examinations, and laboratory examinations were collected. Urine albumin/creatinine ratio ≥30 mg/g was defined as proteinuria, and the incidence of proteinuria was calculated. Subjects were divided into proteinuria group and non-proteinuria group. The machine learning binary classification model of proteinuria and non-proteinuria was constructed based on the stackable integrated logistic regression algorithm (SE-LR), logistic regression, support vector machine, decision tree, random forest and extreme gradient lift algorithms, respectively. The area under the receiver operating characteristic curve, accuracy, recall, and F1 weights were used to evaluate the predictive efficiency of the comparison models. Finally, the importance of the predictive features of the model with the best overall performance was ranked.Results:There were 8 869 rural residents included in the study, aged (58.59±9.49) years old, with 3 872 males (43.66%) and 4 997 females (56.34%). The prevalence of proteinuria in rural areas of Shanxi province was 13.49% (1 196/8 869). Blood pressure, pulse, body mass index, waist circumference, proportion of obesity or overweight, proportion of hypertension, proportion of moderate to severe salt intake, glycosylated hemoglobin, uric pH value, urinary specific gravity, proportion of positive urinary occult blood, proportion of positive urinary glucose, proportion of positive urinary ketone body, proportion of urinary red blood cell count ≥5/μl, proportion of urinary white blood cell count ≥10/μl and urinary α1 microglobulin in the proteinuria group were all higher than those in the non-proteinuria group (all P<0.05). The proportions of lack of exercise and drinking history in the proteinuria group were lower than those in non-proteinuria group (both P<0.05). The overall performance of SE-LR model was the best, with the area under the curve (0.736, 95% CI 0.719-0.746) slightly lower than that of the logistic regression model (0.745, 95% CI 0.680-0.762), and the highest accuracy (0.844), recall rate (0.621) and F1 weighting value (0.801). In the SE-LR model, the orders of importance of the top 10 features were urinary α1- microglobulin, urinary occult blood, urinary sugar, uric acid basicity, smoking history,overweight or obesity, body mass index, total cholesterol, glycosylated hemoglobin and hypertension. Conclusions:The prevalence of proteinuria is high in rural areas of Shanxi province. The risk prediction model of proteinuria established by machine learning algorithm can predict the risk of proteinuria and identify its risk factors, which can provide a scientific basis for disease prevention, intervention, and treatment in the community and clinic to a certain extent.