Because colorectal cancer is likely to develop in many people at some point during their lives, prevention has become a high priority. Diet and nutrition play an important role during the multistep colon carcinogenic process. Garlic has been traditionally used as a spice and is well known for its medicinal properties; several studies have indicated its pharmacologic functions, including its anticarcinogenic properties. However, the mechanisms by which garlic can prevent colorectal cancer remain to be elucidated. This study investigated the effect of aged garlic extract (AGE) on the growth of colorectal cancer cells and their angiogenesis, which are important microenvironmental factors in carcinogenesis. AGE suppressed the proliferation of 3 different colorectal cancer cell lines-HT29, SW480, and SW620-in the same way, but its effects on the invasive activities of these 3 cell lines were different. the invasive activities of SW480 and SW620 cells were inhibited by AGE, whereas AGE had no effect on the invasive activity of Ht29 cells. The action of AGE appears to be dependent on the type of cancer cell. On the other hand, AGE enhanced the adhesion of endothelial cells to collagen and fibronectin and suppressed cell motility and invasion. AGE also inhibited the proliferation and tube formation of endothelial cells potently. These results suggest that AGE could prevent tumor formation by inhibiting angiogenesis through the suppression of endothelial cell motility, proliferation, and tube formation. AGE would be a good chemopreventive agent for colorectal cancer because of its antiproliferative action on colorectal carcinoma cells and inhibitory activity on angiogenesis.

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].

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.