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OBJECTIVE Parkinson's disease(PD)is a progressive neurodegenerative disease clinically char-acterized by dyskinesia,tremor,rigidity,abnormal gait,whereas 90%of patients with PD suffer from defects of the sense of smell before the appearance of the motor dysfunctions.However,the mechanism of olfactory disor-der is still not clear.METHODS We utilized olfaction based delayed paired association task in head-fixed mice.We focused on functional role of neural circuit using opto-genetic techniques.In addition,we viewed the synaptic transmission by slice physiological recording and count-ed the cell number of targeted circuits.RESULTS AND CONCLUSION In our experiments,olfactory working memory impairments were found in the PD mice,and the working memory impairment appeared before motor dys-functions.Furthermore,we also investigated the functional role of neural circuit for olfactory working memory in PD mice.Meanwhile,the excitatory post synaptic currents were decreased as a result of presynaptic release proba-bility suppression in PD mice.However cell loss wasn't found in working memory related circuit recently.These will provide a new idea of clinic diagnosis for PD.
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Objective:To compare the prediction efficiency of traditional linear regression model and four machine learning models on the learning behavior of clinical medical postgraduates, and to explore the pros and cons and applicability of different prediction models.Methods:A total of 6,922 clinical medical postgraduates were surveyed, their comprehensive learning behavior scores were obtained through the learning behavior scale. In the training set, Lasso linear regression and artificial neural network, decision tree, Bootstrap random forest, and lifting tree were used to build prediction models respectively. The above models were used to predict the validation set data and compare the prediction efficiency.Results:The comprehensive learning behavior score of clinical medical postgraduates was (3.31±0.54) points, and the overall compliance rate was 74.02%. In the linear regression model, the influence of age, school level, degree type, learning interest, pressure and satisfaction on learning behavior were statistically significant. In the prediction of validation set, the sensitivity, specificity, and accuracy of the linear regression model were 0.484, 0.914, and 0.801, respectively. The indexes of the four machine learning models were higher than those of the traditional linear regression model, and the Bootstrap random forest had the highest elevation.Conclusion:The linear regression model has a good prediction effect on learning behavior, and machine learning is superior to linear regression model in terms of accuracy of prediction. However, traditional linear regression models are superior to machine learning models in computational efficiency and interpretability.
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Objective @#To understand the epidemiological characteristics of injury deaths among residents in Taizhou,and to provide evidence for prevention and control of injury death. @*Methods @#The monitoring data of injury deaths in Taizhou residents from 2010 to 2016 were derived from the Chronic Disease Surveillance Information Management System of Zhejiang Province. Descriptive epidemiological methods were used to analyze injury mortality,cause of death,population characteristics and life lost due to injury. @*Results @#From 2010 to 2016,a total of 26 313 injury death cases were reported in Taizhou,with an average annual injury mortality rate of 63.61/100 000 and a standardized rate of 56.64/100 000; the mortality rate of injury from 2010 to 2016 showed a downward trend year by year（P<0.05）,and the annual change percentage（APC）was -7.1%. The mortality rates of 0-14 years old,15-44 years old,45-64 years old,65 years old and above group were 15.37/100 000,22.45/100 000, 69.64/100 000 and 315.69/100 000. There were statistically significant differences in the mortality rates of residents between different age groups （P<0.05）. Except for there were no statistically significance differences between the mortality rates of 15-44 years old and 0-14 years old in 2013 and 2014（both P>0.008 3）. The mortality rate in each year from 2010 to 2016 were decreased by 0-14 years old,15-44 years old,45-64 years old,65 years old and above group （all P<0.008 3）. The mortality rate of all age groups showed a downward trend year by year（P<0.05）. The top five injury death causes were accidental falls（17.97/100 000）,motor vehicle traffic accidents（13.97/100 000）,drowning（5.59/100 000）,suicide （5.25/100 000）,other accidents and harmful effects（4.50/100 000）,accounting for 84.35% of the total number of deaths. The injury death causes of the 0-14 years old group were mainly drowning,which was 407 cases,accounting for 1.55% of the total number of deaths; for 15-44 years old group and the 45-64 years old group,the main causes were motor vehicle traffic accidents,which were 1 373 and 2 354 cases,accounting for 5.22% and 8.95%,respectively; for 65 years old and above group,the main cause was mainly accidental falls,which was 6 777 cases,accounting for 25.76%. The years of potential life lost （PYLL） due to injury was 328 678 person-years and the years of potential life lost rate （PYLLR） was 7.95‰.@*Conclusion @#The injury mortality rates of Taizhou residents were declined from 2010 to 2016. The mortality rate of elderly residents due to injury were high and accidental falls was the main cause of injury deaths.