Search

BackgroundMajor depressive disorder (MDD) is a common mental illness characterized by persistent sadness and a loss of interest in activities. Using smartphones and wearable devices to monitor the mental condition of patients with MDD has been examined in several studies. However, few studies have used passively collected data to monitor mood changes over time. ObjectiveThe aim of this study is to examine the feasibility of monitoring mood status and stability of patients with MDD using machine learning models trained by passively collected data, including phone use data, sleep data, and step count data. MethodsWe constructed 950 data samples representing time spans during three consecutive Patient Health Questionnaire-9 assessments. Each data sample was labeled as Steady or Mood Swing, with subgroups Steady-remission, Steady-depressed, Mood Swing-drastic, and Mood Swing-moderate based on patients’ Patient Health Questionnaire-9 scores from three visits. A total of 252 features were extracted, and 4 feature selection models were applied; 6 different combinations of types of data were experimented with using 6 different machine learning models. ResultsA total of 334 participants with MDD were enrolled in this study. The highest average accuracy of classification between Steady and Mood Swing was 76.67% (SD 8.47%) and that of recall was 90.44% (SD 6.93%), with features from all types of data being used. Among the 6 combinations of types of data we experimented with, the overall best combination was using call logs, sleep data, step count data, and heart rate data. The accuracies of predicting between Steady-remission and Mood Swing-drastic, Steady-remission and Mood Swing-moderate, and Steady-depressed and Mood Swing-drastic were over 80%, and the accuracy of predicting between Steady-depressed and Mood Swing-moderate and the overall Steady to Mood Swing classification accuracy were over 75%. Comparing all 6 aforementioned combinations, we found that the overall prediction accuracies between Steady-remission and Mood Swing (drastic and moderate) are better than those between Steady-depressed and Mood Swing (drastic and moderate). ConclusionsOur proposed method could be used to monitor mood changes in patients with MDD with promising accuracy by using passively collected data, which can be used as a reference by doctors for adjusting treatment plans or for warning patients and their guardians of a relapse. Trial RegistrationChinese Clinical Trial Registry ChiCTR1900021461; http://www.chictr.org.cn/showprojen.aspx?proj=36173

The mental health of students learning online is a critical task for many countries around the globe. The research purpose was to analyse the factors affecting the quality of mental health of young individuals who learnt under conditions of not total lockdowns but adaptive quarantine restrictions. The research involved 186 volunteers from Zhengzhou University of Technology, 94 were first-year students, and 92 were fourth-year students. The experimental group involved first-year students, and the control group involved fourth-year students. An average age of the participants in the experimental group was 18.3 years, and in the control group, the average age was 22.4 years. The scholars conducted the research after four months of distance learning under the adaptive quarantine. The students could be involved in their usual entertainment activities and interpersonal communication outside the home. The Behavioural Health Measure, better known as BHM-20, was the core psychometric tool. The research finds that distance learning is less effective for first-year students than for fourth-year students because the former cannot effectively adapt and communicate in a new social environment, and develop trusting interpersonal relationships with fellow students and teachers. The research results coincide with other research on this issue and demonstrate a low degree of mental resilience during and after the pandemic. Previous research is not suitable for the analysis of the mental health of students under adaptive quarantine, including the freshmen, considered the most vulnerable group. The article will be useful for professionals interested in distance education in higher educational institutions, workers of socio-psychological services at universities or individuals involved in adapting curriculum materials for distance learning.

Objective To explore the effects of ginsenoside Rg1 on blood-brain barrier, neuroinflammation and behavioral function of traumatic brain injury (TBI) mouse model. MethodsThe experiment was divided into two parts. In the first part, 27 SPF male BALB/c mice were randomly divided into blank group, sham operation group and TBI model group, with 9 mice in each group. TBI model group was made by controlled cortical impact (CCI) after craniotomy, while sham operation group was only performed craniotomy without any treatment, and the blank group was not treated at all. The effect of modeling was evaluated after operation. In the second part, 50 male BALB/c mice were randomly divided into sham operation group, three different drug dosage groups and solvent (DMSO) control group, with 8 mice in each group. The drug treatment groups were injected with ginsenoside Rg1 at the doses of 10, 20 and 40 mg/kg respectively 6 hours after TBI model had been successfully established, while the DMSO control group was given the same amount of 1% DMSO for one week, twice a day. Modified neurological severity scores (mNSS) were performed on the 1st, 3rd, 7th and 14th day after modeling, and the blood-brain barrier leakage was detected by Western blotting on the 3rd day after modeling. On the 14th and 16th day, the elevated cross maze test and water maze test were used to detect the neurobehavioral function. On the 28th day after anesthesia and perfusion, the brains were taken out, and the neuroinflammation such as activation of microglia and astrocytes was observed by immunofluorescence staining. Results The expression level of MMP-9, a marker of blood-brain barrier, decreased in ginsenoside Rg1 treatment group (P

Crops have diverse structures and complex growth environments. RGB image data can reflect the texture and color features of plants accurately, while 3D data contains information about crop volume. The combination of RGB image and 3D point cloud data can achieve the extraction of two-dimensional and three-dimensional phenotypic parameters of crops, which is of great significance for the research of phenomics methods. In this study, potatoe plants were chosen as the research subject, and RGB cameras and laser scanners were used to collect 50 potato RGB images and 3D laser point cloud data. The segmentation accuracy of four deep learning semantic segmentation methods, OCRNet, UpNet, PaNet, and DeepLab v3+, were compared and analyzed for the RGB images. OCRNet, which demonstrated higher accuracy, was used to perform semantic segmentation on top-view RGB images of potatoes. Mean shift clustering algorithm was optimized for laser point cloud data processing, and single-plant segmentation of laser point cloud data was completed. Stem and leaf segmentation of single-plant potato point cloud data were accurately performed using Euclidean clustering and K-Means clustering algorithms. In addition, a strategy was proposed to establish a one-to-one correspondence between RGB images and point clouds of single-plant potatoes using pot numbering. 8 2D phenotypic parameters and 10 3D phenotypic parameters, including maximum width, perimeter, area, plant height, volume, leaf length, and leaf width, etc., were extracted from RGB images and laser point clouds, respectively. Finally, the accuracy of three representative and easily measurable phenotypic parameters, leaf number, plant height, and maximum width were evaluated. The mean absolute percentage errors (MAPE) were 8.6%, 8.3% and 6.0%, respectively, while the root mean square errors (RMSE) were 1.371 pieces, 3.2 cm and 1.86 cm, respectively, and the determination coefficients (R2) were 0.93, 0.95 and 0.91, respectively. The research results indicated that the extracted phenotype parameters can accurately and efficiently reflect the growth status of potatoes. Combining the RGB image data of potatoes with three-dimensional laser point cloud data can fully exploit the advantages of the rich texture and color characteristics of RGB images and the volumetric information provided by three-dimensional point clouds, achieving non-destructive, efficient, and high-precision extraction of two-dimensional and three-dimensional phenotype parameters of potato plants. The achievements of this study could not only provide important technical support for the cultivation and breeding of potatoes but also provide strong support for phenotype-based research.