Benzyl glutamate NCA was graft-polymerized onto a porous
poly(tetrafluoroethylene) membrane in order
to study the effects of pH and ionic strength on permeation rate.
The membrane was first glow-discharged in the
presence of ammonia in order to produce amino groups on the surface.
Following graft polymerization the graft
chains were hydrolyzed to yield poly(glutamic acid). The rate
of water permeation through this poly(glutamic acid)-grafted polymer membrane was pH-dependent and found to be slow under
high-pH conditions and fast under low-pH conditions. Under high-pH conditions, randomly coiled graft
chains extend to close the pores. The chains form
a helix structure and open the pores under low-pH conditions. The
magnitude of the permeation rate was dependent
upon the length and density of graft chains. Ionic strength also
affected the permeation rate.
Introduction: Few biomarkers can be clinically used to diagnose and assess the severity of depression. However, a decrease in activity and sleep efficiency can be observed in depressed patients, and recent technological developments have made it possible to measure these changes. In addition, physiological changes, such as heart rate variability, can be used to distinguish depressed patients from normal persons; these parameters can be used to improve diagnostic accuracy. The proposed research will explore and construct machine learning models capable of detecting depressive episodes and assessing their severity using data collected from wristband-type wearable devices.
Methods: Patients with depressive symptoms and healthy subjects will wear a wristband-type wearable device for 7 days; data on triaxial acceleration, pulse rate, skin temperature, and ultraviolet light will be collected. On the seventh day of wearing, the severity of depressive episodes will be assessed using SCID-5, HAMD, and other scales. Data for up to five 7-day periods of device wearing will be collected from each subject. Using wearable device data associated with clinical symptoms as supervisory data, we will explore and build a machine learning model capable of identifying the presence or absence of depressive episodes and predicting the HAMD scores for an unknown data set.
Discussion: Our machine learning model could improve the clinical diagnosis and management of depression through the use of a wearable medical device.
IntroductionFew biomarkers can be used clinically to diagnose and assess the severity of depression. However, a decrease in activity and sleep efficiency can be observed in depressed patients, and recent technological developments have made it possible to measure these changes. In addition, physiological changes, such as heart rate variability, can be used to distinguish depressed patients from normal persons; these parameters can be used to improve diagnostic accuracy. The proposed research will explore and construct machine learning models capable of detecting depressive episodes and assessing their severity using data collected from wristband-type wearable devices.Methods and analysisPatients with depressive symptoms and healthy subjects will wear a wristband-type wearable device for 7 days; data on triaxial acceleration, pulse rate, skin temperature, and ultraviolet light will be collected. On the seventh day of wearing, the severity of depressive episodes will be assessed using Structured Clinical Interview for DSM-5 (SCID-5), Hamilton Depression Rating Scale (HAMD), and other scales. Data for up to five 7-day periods of device wearing will be collected from each subject. Using wearable device data associated with clinical symptoms as supervisory data, we will explore and build a machine learning model capable of identifying the presence or absence of depressive episodes and predicting the HAMD scores for an unknown data set.DiscussionOur machine learning model could improve the clinical diagnosis and management of depression through the use of a wearable medical device.Clinical trial registration[https://jrct.niph.go.jp/latest-detail/jRCT1031210478], identifier [jRCT1031210478].
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