Background: A cluster of pneumonia cases were reported by Wuhan Municipal Health Commission, China in December 2019. A novel coronavirus was eventually identified, and became the COVID-19 epidemic that affected public health and life. We investigated the psychological status and behavior changes of the general public in China from January 30 to February 3, 2020. Methods: Respondents were recruited via social media (WeChat) and completed an online questionnaire. We used the State-Trait Anxiety Inventory, Self-rating Depression Scale, and Symptom Checklist-90 to evaluate psychological status. We also investigated respondents’ behavior changes. Quantitative data were analyzed by t-tests or analysis of variance, and classified data were analyzed with chi-square tests. Results: In total, 608 valid questionnaires were obtained. More respondents had state anxiety than trait anxiety (15.8% vs 4.0%). Depression was found among 27.1% of respondents and 7.7% had psychological abnormalities. About 10.1% of respondents suffered from phobia. Our analysis of the relationship between subgroup characteristics and psychological status showed that age, gender, knowledge about COVID-19, degree of worry about epidemiological infection, and confidence about overcoming the outbreak significantly influenced psychological status. Around 93.3% of respondents avoided going to public places and almost all respondents reduced Spring Festival-related activities. At least 70.9% of respondents chose to take three or more preventive measures to avoid infection. The three most commonly used prevention measures were making fewer trips outside and avoiding contact (98.0%), wearing a mask (83.7%), and hand hygiene (82.4%). Conclusions: We need to pay more attention to public psychological stress, especially among young people, as they are likely to experience anxiety, depression, and psychological abnormalities. Different psychological interventions could be formulated according to the psychological characteristics of different gender and age groups. The majority of respondents followed specific behaviors required by the authorities, but it will take time to observe the effects of these behaviors on the epidemic.

A novel coronavirus disease 2019 (COVID-19) emerged around December 2019 in Wuhan, China and has spread rapidly worldwide ( Lu et al., 2020). Until March 27, 2020, the Chinese health authorities had reported 82082 confirmed COVID-19 cases in China with 3298 deaths and 381443 confirmed cases with 20787 deaths outside China. The World Health Organization (WHO) named the virus SARS-CoV-2, which belongs to a distinct clade from the human severe acute respiratory syndrome CoV (SARS-CoV) and Middle East respiratory syndrome CoV (MERS-CoV) ( Zhu et al., 2020). At present, there is no effective therapy against this new virus. Identifying effective antiviral agents to treat the COVID-19 is of most urgency.

Metagenomic next-generation sequencing (mNGS) is an effective diagnostic method for infectious diseases, however, its clinical utility for tuberculosis (TB) diagnosis remains to be demonstrated. A total of 322 bronchoalveolar lavage fluid (BALF) samples were collected from 311 suspected and confirmed pulmonary TB patients and tested by mNGS, acid-fast bacillus (AFB) smear by microscopy, Xpert® MTB/RIF (Xpert), mycobacterium culture and bacterial/fungal culture. Diagnostic performance of mNGS was compared with conventional methods for detection of Mycobacterium tuberculosis complex (MTBC) and other pathogens in BALF. Underlying factors associated with positive detection in pulmonary TB patients were investigated. mNGS, Xpert and culture presented a high proportion of complete matching for MTBC detection (244/322, 75.8%). In pulmonary TB patients pre-treatment the sensitivity of MTBC detection by mNGS, Xpert, culture and smear was 59.9% (85/142), 69.0% (98/142), 59.9% (85/142) and 24.6% (35/142), respectively, and 79.6% overall; MTBC was detected by mNGS in 33.2% (5/34) Xpert and culture negative samples. Positive MTBC detection by mNGS was affected by Vitamin D, erythrocyte sedimentation rate, TB initial treatment/retreatment, and cavity in chest imaging (χ2 = 37.42, P < 0.001), but not by prior anti-TB therapy within 3 months. mNGS was able to detect new potential pathogens in 8.7% (28/322) of samples. Combining mNGS with conventional detection methods could increase the detection rate for MTBC. Additionally, mNGS could identify pathogens in a non-targeted approach for better diagnosis of coinfection.

Q fever is a worldwide zoonosis caused by Coxiella burnetii (Cb). From January 2018 to November 2019, plasma samples from 2,382 patients with acute fever of unknown cause at a hospital in Zhuhai city of China were tested using metagenomic next-generation sequencing (mNGS). Of those tested, 138 patients (5.8%) were diagnosed with Q fever based on the presence of Cb genomic DNA detected by mNGS. Among these, 78 cases (56.5%) presented from Nov 2018 to Mar 2019, suggesting an outbreak of Q fever. 55 cases with detailed clinical information that occurred during the outbreak period were used for further analysis. The vast majority of plasma samples from those Cb-mNGS-positive patients were positive in a Cb-specific quantitative polymerase chain reaction (n = 38) and/or indirect immunofluorescence assay (n = 26). Mobile phone tracing data was used to define the area of infection during the outbreak. This suggested the probable infection source was Cb-infected goats and cattle at the only official authorized slaughterhouse in Zhuhai city. Phylogenic analysis based on genomic sequences indicated Cb strains identified in the patients, goat and cattle were formed a single branch, most closely related to the genomic group of Cb dominated by strains isolated from goats. Our study demonstrates Q fever was epidemic in 2018–2019 in Zhuhai city, and this is the first confirmed epidemic of Q fever in a contemporary city in China.

Background: Adolescent depression is increasing globally, and family conflict may contribute to its transmission across generations. However, longitudinal evidence on these dynamics remains sparse. This study examines the longitudinal associations between family conflict and adolescents’ and parents’ depressive symptoms from three waves of data. Methods: Data from the 2016–2020 China Family Panel Studies (CFPS) were analyzed, including 1,772 adolescents (Mean age = 12.4, SD = 1.68 in 2016) and their parents. Family conflict was measured using three questions from adolescents, while depressive symptoms were assessed using the Center for Epidemiological Studies Depression Scale (CESD). Multiplelinear regression, latent growth models (LGM), and cross-lagged panel models (CLPM) were employed to examine longitudinal associations between family conflict and depressive symptoms. Results: The results revealed that family conflict correlated with adolescents’ depressive symptoms (r = 0.580,p < 0.001). Adolescents’ depressive symptoms also exacerbated family conflict (β1 = 0.030, p < 0.05; β2 = 0.032, p < 0.01) across three waves, while family conflict had a limited contribution to parents’ depressive symptoms. Mothers’ depressive symptoms influenced adolescents’ depressive symptoms significantly (β = 0.043,p < 0.05), while adolescents’ depressive symptoms were transmitted to fathers’ depressive symptoms (β = 0.080,p < 0.01) between Wave 2 and Wave 3. Moreover, the mother’s education level negatively predicted adolescents’ depressive symptoms (β = -0.296,p < 0.05). Conclusions: Family conflict plays a critical role in adolescents’ depressive symptoms and its intergenerational transmission. The findings underscore the pivotal role of family dynamics in mental health, especially in the development of adolescents’ depressive symptoms. Interventions aimed at reducing family conflict may help mitigate depressive symptoms across generations.