ABSTRACT
Introduction: Few 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 analysis: 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 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. Discussion: Our 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].
ABSTRACT
Effects of polyamidoamine (PAMAM) dendrimers on the intestinal absorption of poorly absorbable drugs were examined by an in situ closed loop method in rats. 5(6)-Carboxyfluorescein (CF), fluorescein isothiocyanate-dextrans (FDs) with various molecular weights, calcitonin and insulin were used as model drugs of poorly absorbable drugs. The absorption of CF, FD4 and calcitonin from the rat small intestine was significantly enhanced in the presence of PAMAM dendrimers. The absorption-enhancing effects of PAMAM dendrimers for improving the small intestinal absorption of CF were concentration and generation dependent and a maximal absorption-enhancing effect was observed in the presence of 0.5% (w/v) G2 PAMAM dendrimer. However, G2 PAMAM dendrimer had almost no absorption-enhancing effect on the small intestinal absorption of macromolecular drugs including FD10 and insulin. Overall, the absorption-enhancing effects of G2 PAMAM dendrimer in the small intestine decreased as the molecular weights of drug increased. However, G2 PAMAM dendrimer did not enhance the intestinal absorption of these drugs with different molecular weights in the large intestine. Furthermore, we evaluated the intestinal membrane damage with or without G2 PAMAM dendrimer. G2 PAMAM dendrimer (0.5% (w/v)) significantly increased the activities of lactate dehydrogenase (LDH) and the amounts of protein released from the intestinal membranes, but the activities and amounts of these toxic markers were less than those in the presence of 3% Triton X-100 used as a positive control. Moreover, G2 PAMAM dendrimer at concentrations of 0.05% (w/v) and 0.1% (w/v) did not increase the activities and amounts of these toxic markers. These findings suggested that PAMAM dendrimers at lower concentrations might be potential and safe absorption enhancers for improving absorption of poorly absorbable drugs from the small intestine.
