Parkinson disease(PD)is the second most common neurodegenerative disease,and its motor and non-motor symptoms seriously affect the patients' quality of life.The existing methods of evaluation of PD contain a lot of shortages,such as inter-rater variability and recall bias,which promote patients,clinicians and researchers to have a strong demand for more objective and long-term assessment and monitoring methods.The wearable device-based quantitative technology,which is easy to operate and have the features of objective quantification,daily permanence and meticulous accuracy,makes it have an extensive series of perspective and advantage for application in the management of PD.This article describes,and illustrates with several examples,the applications of wearable devices in the diagnosis and treatment of various motor symptoms such as bradykinesia,resting tremor,gait disorder,motor fluctuation,dyskinesia and non-motor symptoms.We also discuss its current limitations and future directions.

Objective To obtain the monitoring measurement range of quality indicators of red blood cells,plasma and derivatives and leukocyte-reduced apheresis platelets provided by blood stations in Hebei province,explore the distribution of monitoring values and the change of monitoring level,so as to further strengthen the homogenization construction of quality control laboratories in blood stations in Hebei.Methods In 2023,the sampling data of 12 blood stations in Hebei from 2015 to 2022 were collected,scatter plots were made and the range markers were set,and the"mean±SD"line was taken as the upper limit and lower limit of the measurement range.In 2024,the monitoring values in 2023 were added,and the changes of two measurement ranges were compared to analyze the stability and overall level.Results Comparison of the measurement range from 2015 to 2022 and the measurement range from 2015 to 2023 showed that the standard deviation of the content of deleukocyte suspension of red blood cells-hemoglobin,washed erythrocyte-hemoglobin,washed erythrocyte-su-pernatant protein,cryoprecipitate coagulation factor-FⅧ,fresh frozen plasma-FⅧ,leukocyte-reduced apheresis platelets-leukocyte residue and leukocyte-reduced apheresis platelet-red blood cell concentration decreased from 8.132 to 7.993,6.252 to 6.104,0.273 to 0.267,57.506 to 56.276,0.920 to 0.892,0.653 to 0.644 and 2.653 to 2.603,respectively.The narrowing of the standard deviation range of the above items led to more concentrated monitoring values and reduced disper-sion.Comparison of the measurement range from 2015 to 2022 and the measurement range from 2015 to 2023 showed that the mean value of leukocyte residue of the deleukocyte suspension of red blood cells,hemoglobin content of the wash eryth-rocyte,protein content of supernatant of the wash erythrocyte,hemolysis rate of the wash erythrocyte,FⅧ content of the cryoprecipitate coagulation factor,plasma protein content of the fresh frozen plasma,FⅧ content of the fresh frozen plasma,platelet content of the leukocyte-reduced apheresis platelets changed from 0.362 to 0.476,44.915 to 44.861,0.280 to 0.283,0.137 to 0.142,133.989 to 133.271,60.262 to 60.208,1.301 to 1.277 and 3.036 to 3.033,respectively,and was closer to the national standard line,which reflects an increase in the number of unqualified monitoring values or values close to the national standard line in 2023.The long-term qualified rate of coagulation items was low,and no improvement has been ob-served.The stability of biochemical items has been enhanced but overall deviation has occurred,with the average value close to the national standard line.The possibility of subsequent testing failure has increased.The counting items showed no obvi-ous common characteristics.Conclusion The use of"mean±SD"in the analysis can visually display the distribution of mo-nitoring values of different items in Hebei,forming an indicator measurement range covering the past nine years.It shows the characteristics of each item,and provides reference for subsequent quality control laboratory data analysis of each blood sta-tions to takes active measures to improve the monitoring level.

Humans ; Parkinson Disease/diagnosis* ; Machine Learning