A network analysis of internet gaming disorder symptoms.

We examined the symptoms network of Internet Gaming Disorder (IGD). We recruited 793 heavy game players online and 184 of them were diagnosed with IGD. We measured IGD via the 27-item IGD Scale and analyzed symptoms to examine how they interacted with each other to drive the addictive behaviors. Furthermore, we added motives and psychological well-being variables into the network. We found that the most central symptoms were conflict, withdrawal, and tolerance. Overall, two distinct groups of symptoms were discovered: one group for addictive symptoms and the other for psychological consequences.

Risk factors associated with mental illness in hospital discharged patients infected with COVID-19 in Wuhan, China.

The coronavirus disease 2019 (COVID-19) pandemic can have a profound impact on the mental health of patients who survived the illness. However, little is known about the prevalence rate of mental health disorders among hospital discharged COVID-19 patients and its associated factors. A cross-sectional survey of hospital discharged patients was conducted April 11-22, 2020 in Wuhan, China (where the pandemic began). 675 participants completed the survey, including 90 (13.3%) medical staff (physicians and nurses who had been ill). We used Fisher's exact test and multivariable logistic regression methods to explore the risk factors associated with mental health problems (anxiety, depression, and PTSD symptoms associated with COVID-19 hospitalization). Adverse mental health effects of COVID-19 are evident after discharge from the hospital, with sleep difficulties highlighted as a central issue. As we found that perceived discrimination was a central predictor of mental illness, preventing and addressing social stigma associated with COVID-19 may be crucial for improving mental health for recovered patients.

Characteristics and Outcomes of a Sample of Patients With COVID-19 Identified Through Social Media in Wuhan, China: Observational Study.

BACKGROUND: The number of deaths worldwide caused by coronavirus disease (COVID-19) is increasing rapidly. Information about the clinical characteristics of patients with COVID-19 who were not admitted to hospital is limited. Some risk factors of mortality associated with COVID-19 are controversial (eg, smoking). Moreover, the impact of city closure on mortality and admission rates is unknown. OBJECTIVE: The aim of this study was to explore the risk factors of mortality associated with COVID-19 infection among a sample of patients in Wuhan whose conditions were reported on social media. METHODS: We enrolled 599 patients with COVID-19 from 67 hospitals in Wuhan in the study; 117 of the participants (19.5%) were not admitted to hospital. The demographic, epidemiological, clinical, and radiological features of the patients were extracted from their social media posts and coded. Telephone follow-up was conducted 1 month later (between March 15 and 23, 2020) to check the clinical outcomes of the patients and acquire other relevant information. RESULTS: The median age of patients with COVID-19 who died (72 years, IQR 66.5-82.0) was significantly higher than that of patients who recovered (61 years, IQR 53-69, P<.001). We found that lack of admission to hospital (odds ratio [OR] 5.82, 95% CI 3.36-10.1; P<.001), older age (OR 1.08, 95% CI 1.06-1.1; P<.001), diffuse distribution (OR 11.09, 95% CI 0.93-132.9; P=.058), and hypoxemia (odds ratio 2.94, 95% CI 1.32-6.6; P=.009) were associated with increasing odds of death. Smoking was not significantly associated with mortality risk (OR 0.9, 95% CI 0.44-1.85; P=.78). CONCLUSIONS: Older age, diffuse distribution, and hypoxemia are factors that can help clinicians identify patients with COVID-19 who have poor prognosis. Our study suggests that aggregated data from social media can also be comprehensive, immediate, and informative in disease prognosis.

[Spatial structure of Quercus mongolica forest in Beijing Songshan Mountain Nature Reserve].

Based on the survey data of a 0.6 hm2 plot in Songshan Mountain Nature Reserve, and by using the uniform angle index, neighborhood comparison and mingling degree, this paper analyzed the spatial structural characteristics of Quercus mongolica forest in the Reserve. There were 10 populations in the tree layer of the forest, among which, Q. mongolica had the obvious advantage in population density and basal area, being the dominant and constructive species of the tree layer. The average mingling degree of the forest was 0.299, suggesting the low mingling degree of stands. The dominant species was mainly none- or less-mixed, while the accompanying species were generally well-mixed. In the spatial structural units, Q. mongolica and Pinus tabulaeformis populations were mainly of dominant, sub-dominant, and intermediate trees, accounting for 68% and 58% of the total plants of the populations, respectively, while the advantages of other populations were not obvious, being mostly of suppressed trees. The spatial pattern of the stands was of aggregative distribution, but the aggregation degree was not high and the aggregation scale was not large.

[Spatial patterns of different age-class individuals in Pinus tabulaeformis forest in Songshan Nature Reserve of Beijing, China].

By the method of point pattern analysis, this paper studied the spatial patterns of different age-class individuals in the Pinus tabulaeformis forest in Songshan Nature Reserve of Beijing, and the spatial association between the individuals. The results showed that there existed greater differences in the densities of different age-class P. tabulaeformis individuals, e.g., age-class I had the density of 15 plants per hectare, while age-class III had the density of 924 plants per hectare. The spatial patterns of different age-class individuals and the spatial association between the individuals had a close relationship with the spatial scale. In the scale of < 40 m, the individuals of all age-classes tended to clump, and there was a stronger spatial association between the individuals of different age-classes. When the scale was larger than 30 m or 40 m, the individuals of all age-classes would disperse randomly, and their spatial association would be weakened greatly.