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Background: Bibliometric analysis is a mature method for quantitative evaluation of academic productivity. In view of the rapid development of research in the field of systemic lupus erythematosus (SLE) in the past decade, we used bibliometric methods to comprehensively analyze the literature in the field of SLE from 2013 to 2022. Methods: The relevant literature in the field of SLE from 2013 to 2022 was screened in the Web of Science Core Collection database. After obtaining and sorting out the data, CiteSpace and VOSviewer software were used to visualize the relevant data, and SPSS software was used for scientific statistics. Results: A total of 18,450 publications were included in this study. The number of articles published over the past 10 years has generally shown an upward trend, while Altmetric attention scores have also shown a clear upward trend in general and in most countries. Citation analysis and Altmetric analysis can mutually prove and supplement the influence of papers. The USA, China, Japan, Italy, and the UK are the most productive countries, but China and Japan are significantly inferior to other countries in terms of research influence. Four of the top ten authors are at the center of the collaboration network. LUPUS is the most contributing journal. The theme of systemic lupus erythematosus research mainly focuses on the pathogenesis, treatment, and management of SLE, and the emerging trend is related research on machine learning and immune cells. Conclusion: This study shows the research status of SLE, clarifies the main contributors in this field, discusses and analyzes the research hotspots and trends in this field, and provides reference for further research in this field to promote the development of SLE research. Key Points • Through bibliometric analysis, Altmetric analysis, and visual analysis, we reveal the global productivity characteristics of SLE-related papers in the past 10 years. • The number of global SLE-related studies has shown a significant increase, indicating that SLE is still a hot topic and deserves further study. • Citation analysis and Altmetric analysis can mutually prove and supplement the influence of papers, and the attention of related literature among non-professional researchers is increasing. • The theme of SLE research mainly focuses on the pathogenesis, treatment, and management of SLE. The emerging trend is machine learning and immune cells, which may provide new strategies for the diagnosis and treatment of SLE in the future. [ABSTRACT FROM AUTHOR]

Objectives: To characterize the abundance and relative composition of seawater bacterioplankton communities in Changle city using Illumina MiSeq sequencing and bacterial culture techniques. Methods: Seawater samples and physicochemical factors were collected from the coastal zone of Changle city on 8 September 2019. Nineteen filter membranes were obtained after using a suction filtration system. We randomly selected eight samples for total seawater bacteria (SWDNA group) sequencing and three samples for active seawater bacteria (SWRNA group) sequencing by Illumina MiSeq. The remaining eight samples were used for bacterial culture and identification. Alpha diversity including species coverage (Coverage), species diversity (Shannon–Wiener and Simpson index), richness estimators (Chao1), and abundance‐based richness estimation (ACE) were calculated to assess biodiversity of seawater bacterioplankton. Beta diversity was used to evaluate the differences between samples. The species abundance differences were determined using the Wilcoxon rank‐sum test. Statistical analyses were performed in R environment. Results: The Alpha diversity in the SWDNA group in each index was ACE 3206.99, Chao1 2615.12, Shannon 4.64, Simpson 0.05, and coverage 0.97; the corresponding index was ACE 1199.55, Chao1 934.75, Shannon 3.49, Simpson 0.09, and coverage 0.99. The sequencing results of seawater bacterial genes in the coastal waters of Changle city showed that the phyla of high‐abundance bacteria of both the SWDNA and SWRNA groups included Cyanobacteria, Proteobacteria, and Bacteroidetes. The main classes included Oxyphotobacteria, Alphaproteobacteria, and Gammaproteobacteria. The main genera included Synechococcus CC9902, Chloroplast, and Cyanobium_PCC‐6307. Beta diversity analysis showed a significant difference between the SWDNA and SWRNA groups (P < 0.05). The species abundance differences between SWDNA and SWRNA groups after Wilcoxon rank‐sum test showed that, at the phylum level, Actinomycetes was more abundant in SWDNA group (9.17 vs 1.02%, P < 0.05); at the class level, Actinomycetes (δ‐ Proteus) was more abundant in SWDNA group (9.47% vs 1.01%, P < 0.05); and at the genus level, Chloroplast was more abundant in SWRNA group (13.07% vs 44.57%, P < 0.05). Nine species and 53 colonies were found by bacterial culture: 20 strains of Vibrio (37.74%), 22 strains of Enterobacter (41.51%), and 11 strains of non‐fermentative bacteria (20.75%). Conclusion: Illumi MiSeq sequencing of seawater bacteria revealed that the total bacterial community groups and the active bacterial community groups mainly comprised Cyanobacteria, Proteobacteria, and Bacteroides at the phylum level; Oxyphotobacteria, α‐Proteobacteria, and γ‐Proteobacteria at the class level; with Synechococcus_CC9902, Chloroplast, and Cyanobium_PCC‐6307 comprising the predominant genera. Exploring the composition and differences of seawater bacteria assists understanding regarding the biodiversity and the infections related to seawater bacteria along the coast of the Changle, provides information that will aid in the diagnosis and treatment of such infections. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to explore the bacterial community composition following ocean bacterial infection using an animal model. Methods: This animal-based experiment was conducted from September 2019 to November 2019. Eighteen seawater filter membranes were collected from Changle City, Fujiian Province, China, on September 8, 2019. Ten filter membranes were used for implantation. Eight filter membranes that were used in the bacterial culture for the exploration of seawater bacteria were assigned to the seawater group (SG). Fourteen healthy adult New Zealand rabbits were randomly divided into the experimental group (EG) and control group (CG). Seawater filter membranes and asepsis membranes were implanted into the tibia in the EG and CG, respectively. One week after surgery, tibial bone pathology tissues were collected and assessed using light microscopy and scanning electron microscopy (SEM). Medullary cavity tissues were collected for the performance of Illumina MiSeq sequencing and bacterial culture. The differences between EG and CG were assessed by pathological observation under light microscopy and SEM, high-throughput bacterial sequencing, and bacterial culture. Results: Compared with the CG, the infection rate was 100%, and the mortality value was 20% after the implantation of the filter membranes in the EG. Both light microscopy and SEM showed that a large number of bacteria were distributed in the bone marrow cavity after ocean bacterial infection. No bacterial growth was found in the CG. Illumina MiSeq sequencing found that Firmicutes, Proteobacteria, Thermotogae, Fusobacteria, Bacteroidetes, and Actinobacteria were the dominant bacteria at the phylum level and Clostridium_sensu_stricto_7, Haloimpatiens, Clostridium_sensu_stricto_15, Clostridiaceae_1, Clostridium_sensu_stricto_18, and Oceanotoga were the dominant bacteria in genus level among the EG. In the bacterial culture of the medullary cavity tissues, Klebsiella pneumoniae, Shewanella algae, Staphylococcus aureus, Escherichia coli, Enterobacter cloacae, and Vibrio vulnificus were the predominant infective species. Moreover, compared with the SG, the EG showed a higher detection rate of E. coli and S. aureus (P = 0.008 and P = 0.001, respectively). The detection rates of V. alginolyticus, V. parahaemolyticus, and V. fluvialis were higher in the SG than the EG (P = 0.007, P = 0.03, and P = 0.03, respectively). Conclusions: Our model, which was comprehensively evaluated using four techniques: histopathology and SEM observation, gene detection, and bacteria culture, provides a scientific basis for the clinical diagnosis and treatment of patients in such settings. [ABSTRACT FROM AUTHOR]

• Different harvest times of corn stover for bio-butanol production were evaluated. • The cultivar differences on sugars yield and bio-butanol production were compared. • Juice and hydrolysate of the pretreated bagasse were used for ABE fermentation. • Corn stover harvest after 150 days of cultivation is the optimized harvest time. • The cultivar Zhongxing 718 provides the highest ABE yield of 4.6 t/ha. In this study, the fresh corn stover that contained fermentable juice and lignocellulosic fiber was used as the raw material for biobutanol production. After utilizing the sugar-contain juice for first generation bio-butanol production, the remaining bagasse was alkaline pretreated. The enzymatic hydrolysate of the pretreated bagaase was used for second generation bio-butanol production. During the process, the impacts of harvest time and cultivars on acetone-butanol-ethanol (ABE) production were evaluated. Results indicated that different harvest time and cultivars sharply influenced on fermentable sugars yields from juice and bagasse, and further affected ABE yield. Compared with the conventional harvest time after 170 days of cultivation (obtained withered and yellow stover), corn stover obtained after 150 days of cultivation showed higher ABE yield. Besides, among three normal corn cultivars growing in Northeast of China, the cultivar Zhongxing 718, with higher planting density and sugars yield per unit weight, offered the highest ABE yield of 4.6 t/ha. [ABSTRACT FROM AUTHOR]