Aims and objectives We aimed to investigate the anxiety of nurses who are supporting Wuhan in fighting against coronavirus disease 2019 (COVID‐19) infection and explore relevant influencing factors. Background The COVID‐19 outbreak poses a major threat to public health worldwide. Nurses play an important role in this epidemic. However, available data on the mental health among these nurses are limited. Design A descriptive, cross‐sectional survey was performed. Methods An online questionnaire was completed by 200 nurses who went to Wuhan to help to fight against COVID‐19 from another province. Data collection tools include the Chinese version of the Stress Overload Scale (SOS), the Self‐Rating Anxiety Scale (SAS) and General Self‐Efficacy Scale (GSES). Descriptive, single‐factor correlation and multiple regression analyses were used in exploring related influencing factors. Reporting followed the STROBE guidelines. Results The scores of SAS, SOS and GSES range from 20 to 80, 22 to 110 and 10 to 40, respectively, and the SAS (31.79 ± 7.32) and SOS (40.19 ± 12.92) and GSES scores (24.83 ± 6.60) were obtained. Anxiety was positively correlated with stress (r = .679, p < .001) but negatively correlated with self‐efficacy (r = −.326, p < .001). Multiple regression analysis showed that professional qualification, sleep, stress and self‐efficacy were the main factors affecting nurse anxiety (p = .006, <.001, <.001, .039, respectively). Conclusions Nurses who are supporting Wuhan in fighting against COVID‐19 were under a low level of anxiety. Relevance to clinical practice The current study suggests work stress reduction might be a key factor in reducing anxiety and maintaining mental health to support nurses who are fighting against COVID‐19 infection.

Schizophrenia is a common neuropsychiatric disorder with a lifetime risk of 1%. Accumulation of common polygenic variations has been found to be an important risk factor. Recent studies showed a role for the enrichment of minor alleles (MAs) of SNPs in complex diseases such as Parkinson’s disease. Here we similarly studied the role of genome wide MAs in schizophrenia using public datasets. Relative to matched controls, schizophrenia cases showed higher average values in minor allele content (MAC) or the average amount of MAs per subject. By risk prediction analysis based on weighted genetic risk score (wGRS) of MAs, we identified an optimal MA set consisting of 23 238 variants that could be used to predict 3.14% of schizophrenia cases, which is comparable to using 22q11 deletion to detect schizophrenia cases. Pathway enrichment analysis of these SNPs identified 30 pathways with false discovery rate (FDR) <0.02 and of significant P-value, most of which are known to be linked with schizophrenia and other neurological disorders. These results suggest that MAs accumulation may be a risk factor to schizophrenia and provide a method to genetically screen for this disease.

A digital image watermarking algorithm based on fast curvelet transform is proposed. Firstly, the carrier image is decomposed by fast curvelet transform, and, the watermarking image is scrambled by Arnold transform. Secondly, the binary watermarking image is embedded into the medium frequency coefficients according to the human visual characteristics and curvelet coefficients. Experiment results show that the proposed algorithm has good performance in both invisibility and security and also has good robustness against the noise, cropping, filtering, JPEG compression and other attacks

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Insulin secretion by pancreatic islet β-cells is regulated by glucose levels and is accompanied by proton generation. The voltage-gated proton channel Hv1 is present in pancreatic β-cells and extremely selective for protons. However, whether Hv1 is involved in insulin secretion is unclear. Here we demonstrate that Hv1 promotes insulin secretion of pancreatic β-cells and glucose homeostasis. Hv1-deficient mice displayed hyperglycemia and glucose intolerance because of reduced insulin secretion but retained normal peripheral insulin sensitivity. Moreover, Hv1 loss contributed much more to severe glucose intolerance as the mice got older. Islets of Hv1-deficient and heterozygous mice were markedly deficient in glucose- and K+-induced insulin secretion. In perifusion assays, Hv1 deletion dramatically reduced the first and second phase of glucose-stimulated insulin secretion. Islet insulin and proinsulin content was reduced, and histological analysis of pancreas slices revealed an accompanying modest reduction of β-cell mass in Hv1 knockout mice. EM observations also indicated a reduction in insulin granule size, but not granule number or granule docking, in Hv1-deficient mice. Mechanistically, Hv1 loss limited the capacity for glucose-induced membrane depolarization, accompanied by a reduced ability of glucose to raise Ca2+ levels in islets, as evidenced by decreased durations of individual calcium oscillations. Moreover, Hv1 expression was significantly reduced in pancreatic β-cells from streptozotocin-induced diabetic mice, indicating that Hv1 deficiency is associated with β-cell dysfunction and diabetes. We conclude that Hv1 regulates insulin secretion and glucose homeostasis through a mechanism that depends on intracellular Ca2+ levels and membrane depolarization.