Purpose To investigate the anxiety and depression levels of frontline clinical nurses working in 14 hospitals in Gansu Province, China, during this period. Design A cross‐sectional survey was conducted online between February 7 and 10, 2020, with a convenience sample of 22,034 nurses working in 14 prefecture and city hospitals in Gansu Province, located in northwest China. Methods A self‐reported questionnaire with four parts (demographic characteristics, general questions related to novel coronavirus‐infected pneumonia, self‐rating anxiety scale, and self‐rating depression scale) was administered. Descriptive statistics including frequencies, means, and SDs were computed. The associations between anxiety and depression with sociodemographic characteristics, work‐related concerns, and impacts were analyzed, followed by multiple stepwise linear regression to identify factors that best predicted the nurses’ anxiety and depression levels. Findings A total of 21,199 questionnaires were checked to be valid, with an effective recovery rate of 96.21%. The mean ± SD age of the respondents was 31.89 ± 7.084 years, and the mean ± SD length of service was 9.40 ± 7.638 years. The majority of the respondents were female (98.6%) and married (73.1%). Some demographic characteristics, related concerns, and impacts of COVID‐19 were found to be significantly associated with both anxiety (p < .001) and depression (p < .001). Nurses who needed to take care of children or elderly relatives, took leave from work because they were worried about COVID‐19, avoided contact with family and friends, and wanted to obtain more COVID‐19‐related knowledge had higher levels of both anxiety and depression. Conclusions Results show that nurses faced with the COVID‐19 outbreak are at risk for experiencing anxiety and depression. Demographic background, psychosocial factors, and work‐related factors predicted the psychological responses. The family responsibilities and burdens of women may explain the higher levels of anxiety and depression among nurses with these obligations as compared to those without. On the other hand, nurses who chose not to take leave from work or who did not avoid going to work during this period were less anxious and depressed. Clinical Relevance Professional commitment might be a protective factor for adverse psychological responses. It is pertinent to provide emotional support for nurses and recognize their professional commitment in providing service to people in need.

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Background and PurposeAlzheimer’s disease (AD) is a devastating neurodegenerative disorder with no cure, and available treatments are only able to postpone the progression of the disease. Mild cognitive impairment (MCI) is considered to be a transitional stage preceding AD. Therefore, prediction models for conversion from MCI to AD are desperately required. These will allow early treatment of patients with MCI before they develop AD. This study performed a systematic review and meta-analysis to summarize the reported risk prediction models and identify the most prevalent factors for conversion from MCI to AD.MethodsWe systematically reviewed the studies from the databases of PubMed, CINAHL Plus, Web of Science, Embase, and Cochrane Library, which were searched through September 2021. Two reviewers independently identified eligible articles and extracted the data. We used the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) checklist for the risk of bias assessment.ResultsIn total, 18 articles describing the prediction models for conversion from MCI to AD were identified. The dementia conversion rate of elderly patients with MCI ranged from 14.49 to 87%. Models in 12 studies were developed using the data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). C-index/area under the receiver operating characteristic curve (AUC) of development models were 0.67–0.98, and the validation models were 0.62–0.96. MRI, apolipoprotein E genotype 4 (APOE4), older age, Mini-Mental State Examination (MMSE) score, and Alzheimer’s Disease Assessment Scale cognitive (ADAS-cog) score were the most common and strongest predictors included in the models.ConclusionIn this systematic review, many prediction models have been developed and have good predictive performance, but the lack of external validation of models limited the extensive application in the general population. In clinical practice, it is recommended that medical professionals adopt a comprehensive forecasting method rather than a single predictive factor to screen patients with a high risk of MCI. Future research should pay attention to the improvement, calibration, and validation of existing models while considering new variables, new methods, and differences in risk profiles across populations.

Recent data have shown that the purinergic receptor P2X4 plays key roles in inflammatory responses. We evaluated whether P2X4 inhibition could affect the development of arthritis and autoimmunity in collagen-induced arthritis (CIA) model. P2X4 antisense oligonucleotide (asODN) was injected intravenously via tail vein into the CIA mice to selectively inhibit P2X4 expression daily for 14 days. P2X4 asODN treatment reduced the clinical score of CIA in mice. P2X4 asODN also decreased the levels of serum IL-1β, TNF-α, IL-6, and IL-17. P2X4 asODN treatment significantly inhibited synovial inflammation and joint destruction. P2X4 asODN treatment also suppressed the NLR family, pyrin domain containing 1 (NLRP1) inflammasome activation in CIA mice and synovial cells of human rheumatoid arthritis. These data show that P2X4 asODN confers a therapeutic benefit on CIA. Inhibition of the NLRP1 inflammasome signaling pathway is the underlying mechanism of action.