Effects of brief exposure to campus environment on students' physiological and psychological health

Experiences in natural environments can enhance human wellbeing and promote the recovery of physiological and psychological health. During the COVID-19 pandemic, university students' activity off-campus was limited, thus, the campus environment was particularly important for the recovery of students' physiological and psychological health. Although the benefits of sustained natural exposure are obvious for people's physiological and psychological health, the effects of brief exposure on physiological and psychological are unclear. In the present study, four types of campus environments, including square space, dense forest space, sparse forest space, and waterfront space, were selected to explore the transient recovery effects of different types of campus environments. Sixty university students were recruited, measuring their systolic blood pressure, diastolic blood pressure, and heart rate as physical parameters to assess stress recovery. Respondents also reported scores about their personal feelings in questionnaires to evaluate their psychological states. Both physiological and psychological indicators responded to the brief natural exposure (5 min), and physiological and psychological health was restored. However, only the recovery amounts of psychological indicators were significantly different in waterfront space, dense forest space, and sparse forest space. These results indicate that being compare with other spaces, the brief exposure in the waterfront space was the most beneficial to students' psychological health recovery. This recovery was attributed to the great role played by the sense of escape, but after the brief exposure, the attraction and compatibility of the environment would hinder the psychological health recovery. In conclusion, according to tests on both physiological and psychological aspects, the waterfront environment on campus is the best choice for students' transient health recovery.

Image Segmentation and Quantification of Droplet dPCR Based on Thermal Bubble Printing Technology.

Thermal inkjet printing can generate more than 300,000 droplets of picoliter scale within one second stably, and the image analysis workflow is used to quantify the positive and negative values of the droplets. In this paper, the SimpleBlobDetector detection algorithm is used to identify and localize droplets with a volume of 24 pL in bright field images and suppress bright spots and scratches when performing droplet location identification. The polynomial surface fitting of the pixel grayscale value of the fluorescence channel image can effectively compensate and correct the image vignetting caused by the optical path, and the compensated fluorescence image can accurately classify positive and negative droplets by the k-means clustering algorithm. 20 µL of the sample solution in the result reading chip can produce more than 100,000 effective droplets. The effective droplet identification correct rate of 20 images of random statistical samples can reach more than 99% and the classification accuracy of positive and negative droplets can reach more than 98% on average. This paper overcomes the problem of effectively classifying positive and negative droplets caused by the poor image quality of photographed picolitre ddPCR droplets caused by optical hardware limitations.

The Severity of COVID-19 and Its Determinants: A Systematic Review and Meta-Analysis in China

To analyse the prevalence of severe and critical COVID-19 cases and its determinants, a systematic review and meta-analysis were conducted using Review Manager. Four English and two Chinese databases were used to identify and explore the relationships between the severity of COVID-19 and its determinants, with no restrictions on publication date. The odds ratio and 95% CI were combined to assess the influencing level of all factors. Twenty-three articles containing a total of 15,828 cases of COVID-19 were included in this systematic review. The prevalence of severe and critical COVID-19 cases was 17.84% and 4.9%, respectively. A total of 148 factors were identified, which included behavioural, symptom, comorbidity, laboratory, radiographic, exposure, and other factors. Among them, 35 factors could be included in the meta-analysis. Specifically, for example, the male (OR 1.55, 95% CI 1.42–1.69) and elderly (OR 1.06, 95% CI 1.03–1.10) populations tended to experience severe and critical illness. Patients with cough, dyspnea, fatigue, fever, and gastrointestinal symptoms could have severe and critical diseases. Regarding laboratory results, albumin, aspartate aminotransferase, creatinine, D-dimer, fibrinogen, neutrophils, procalcitonin, platelets, and respiratory rate were potential factors that could be used to predict the severity of COVID.