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Rapid eye movement (REM) sleep behavior disorder (RBD) is associated with Parkinson’s disease (PD). In this study, a smartwatch-based sensor is utilized as a convenient tool to detect the abnormal RBD phenomenon in PD patients. Instead, a questionnaire with sleep quality assessment and sleep physiological indices, such as sleep stage, activity level, and heart rate, were measured in the smartwatch sensors. Therefore, this device can record comprehensive sleep physiological data, offering several advantages such as ubiquity, long-term monitoring, and wearable convenience. In addition, it can provide the clinical doctor with sufficient information on the patient’s sleeping patterns with individualized treatment. In this study, a three-stage sleep staging method (i.e., comprising sleep/awake detection, sleep-stage detection, and REM-stage detection) based on an accelerometer and heart-rate data is implemented using machine learning (ML) techniques. The ML-based algorithms used here for sleep/awake detection, sleep-stage detection, and REM-stage detection were a Cole–Kripke algorithm, a stepwise clustering algorithm, and a k-means clustering algorithm with predefined criteria, respectively. The sleep staging method was validated in a clinical trial. The results showed a statistically significant difference in the percentage of abnormal REM between the control group (1.6 ± 1.3; n = 18) and the PD group (3.8 ± 5.0; n = 20) (p = 0.04). The percentage of deep sleep stage in our results presented a significant difference between the control group (38.1 ± 24.3; n = 18) and PD group (22.0 ± 15.0, n = 20) (p = 0.011) as well. Further, our results suggested that the smartwatch-based sensor was able to detect the difference of an abnormal REM percentage in the control group (1.6 ± 1.3; n = 18), PD patient with clonazepam (2.0 ± 1.7; n = 10), and without clonazepam (5.7 ± 7.1; n = 10) (p = 0.007). Our results confirmed the effectiveness of our sensor in investigating the sleep stage in PD patients. The sensor also successfully determined the effect of clonazepam on reducing abnormal REM in PD patients. In conclusion, our smartwatch sensor is a convenient and effective tool for sleep quantification analysis in PD patients.

The concentrations of Zn and sulfhydryl (SH) groups in the digestive tract tissue of common carp and some aquatic animals were studied. It was found that Zn and bound SH groups could be used as indicators for detecting the Zn‐binding protein in the digestive tract tissue of common carp. The digestive tract tissue of the fish underwent subcellular fractionation, and it was found that the nuclei/cell debris fraction contained most of the DNA (85%), Na+/K+‐ATPase (82%), organic phosphate (90%) and the Zn‐binding protein (79%), but only part of the 5′‐nucletidase and alkaline phosphatase (<23%). The nuclei/cell debris fraction of the digestive tract tissue of common carp was treated with either collagenase type I or type IV, and subfractionated by sucrose density centrifugation. It was found that treatment with collagenase type IV could release more than 50% of the Zn‐binding protein, Na+/K+‐ATPase and organic phosphate from collagen. Sections of digestive tract tissue of common carp were stained for Zn. It was observed that Zn can be found mainly on the edge of the epithelial layer, and everywhere in the ‘membrane‐like’ portion of the submucosal and muscular layers. It is proposed that most of the Zn‐binding protein in the digestive tract tissue of common carp is located on the basolateral plasma membranes of the epithelial cells and on the surrounding muscle cells that are attached to the collagen type IV of basal laminae.

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