Objective:To explore the research status of artificial intelligence in nursing in China, so as to provide reference for conducting research domestically.Methods:The relevant literature collected from January 1, 2003 to May 1, 2023 was searched on China National Knowledge Infrastructure, WanFang Data, and VIP. CiteSpace and VOS viewer were used to visually analyze the research status, hotspots, and frontiers of artificial intelligence in nursing in China from the aspects of annual publication volume, region, institution, author, high-frequency keywords, and emerging words.Results:A total of 494 articles were included. In terms of publication volume, domestic research showed an overall upward trend, with publishing institutions mostly concentrated in economically developed and medically advanced regions. The authors of the articles often collaborated with research groups, with a co-authorship rate of 81.17% (401/494), but there was relatively little collaboration between groups. Research hotspots were focused on the application of artificial intelligence in geriatric nursing, rehabilitation nursing, Operating Rooms, and drug formulation, with a majority of research related to robots.Conclusions:The research enthusiasm in related fields in China is currently on the rise. Research on artificial intelligence in nursing management, nursing education, and psychological nursing can be strengthened, and the application of artificial intelligence can be enriched. At the same time, research institutions and personnel should strengthen mutual cooperation, and relevant national departments should improve laws and regulations to promote the vigorous development of this research field.

Objective:To analyze the current research status of ideological and political education content in domestic nursing courses and facilitate nursing education reform.Methods:Using purposeful sampling, 25 exemplary cases of ideological and political education in nursing courses from Xinhua Net were selected. NVivo 12 software was employed for content analysis, summarizing the ideological and political elements, carriers of these elements, and types of integration methods in nursing courses.Results:Nursing educators have thoroughly explored ideological and political elements, with the most attention given to professional ethics, though less focus was placed on personality development. A variety of carriers for ideological and political elements were utilized, with practical experience being the most common, but there was a lack of integration with policy and regulations. Various integration methods were applied, with interactive methods being the most frequently used, while practical approaches were less common.Conclusions:Future nursing education should emphasize fostering students' sense of national identity and focus more on personality development. It is important to promote a more balanced use of carriers for ideological and political elements, paying particular attention to policy-related content. The use of information technology in teaching should also be explored to flexibly apply various teaching methods, achieving the subtle integration of ideological and political education within nursing curricula.

Objective:To analyze the prevalence of influenza B virus and its HA and NA genes characteristics during 2017—2020 in Songjiang district, Shanghai.Methods:Samples from suspected cases of influenza were detected using real-time fluorescence reverse transcription-polymerase chain reaction. Influenza B virus positive specimens were cultured by MDCK cells and embryonated chicken eggs. The hamagglutinin (HA) and neuraminidase (NA) genes of influenza virus B strains were sequenced and analyzed for gene evolution and amino acid variation. The sensitivity of the strains to oseltamivir and zanamivir was studied by neuraminidase inhibition assay.Results:The positive rate of influenza B virus in Songjiang district from 2017 to 2020 was 14.24% (506/3554). Influenza B virus circulated mainly in winter-spring season and alternated between B/Victoria and B/Yamagata subtypes. Subclade 1A (△3) B was the predominant Victoria lineage circulating in Songjiang district. Otherwise, the remaining strains belonged to subclade 1A(△2)and clade 1A. Compared with vaccine strain B/Brisbane/60/2008, the nucleotide homology of circulating strains HA (NA) was 97.62%~98.19% (98.11%~98.78%). The mutations of HA in 1A (△3) B subclade mainly were in 120 loop and 160 loop. All the Yamagata strains circulating in Songjiang district belonged to clade 3. Compared with vaccine strain B/Phuket/3073/2013, the nucleotide homology of HA and NA were 98.71%~99.04% and 98.69%~99.27%, respectively. No amino acid change was found in the antigenic clusters. Neuraminidase inhibition assay showed that the influenza B virus strains were sensitive to both oseltamivir and zanamivir.Conclusions:Inconsistence of the antigenicity between the vaccine strains and the circulating strains during 2017—2020, influenza B virus should be persistently monitored, including the epidemic situation and the etiological characteristics, which might be helpful in vaccine screening and drug development.

Objective To analyze the seasonal characteristics of scarlet fever in Songjiang District from 2012 to 2021, and to provide references for the prevention and control of scarlet fevers. Methods The incidence data of scarlet fever in Songjiang District from 2012 to 2021 were collected through the China Disease Prevention and Control Information System. The seasonal characteristics and peak of scarlet fever incidence were analyzed using concentration and circular distribution methods. Results The average annual reported incidence rate of scarlet fever in Songjiang District from 2012 to 2021 was 20.15/100 000. The M value of the concentration analysis was 0.18. The results of the circular distribution method showed that the peak day of scarlet fever from March to August was May 12, and the epidemic peak period was from April 3 to June 20. From September to February of the next year, the peak day of scarlet fever was December 21, and the epidemic peak period was from December 2 to January 9 of the next year. The differences were all statistically significant (P values were all less than 0.05). Conclusion The peaks of scarlet fever in Songjiang District mainly occur in May and December. It is suggested that the monitoring methods and prevention strategies should be adjusted in time according to Seasonal characteristics of scarlet fever.