Objective To analyze the factors affecting the prevalence of hyperuricemia(HUA)in an island troop.Methods A total of 1 113 soldiers stationed on an island from December 2021 to December 2022 were selected as research objects by cluster sampling.Their lifestyle and health information were collected.Physical examination and laboratory detection were conducted.Multivariate logistic regression was used to analyze the influencing factors of HUA.Results The prevalence rate of HUA was 21.02%(234/1 113).There were significant differences in the body mass index(BMI),waist-to-hip ratio,triglyceride,alanine aminotransferase,and creatinine between the soldiers with hyperuricemia and the soldiers with normal blood uric acid(P<0.05).Multivariate logistic regression analysis showed that BMI≥24(OR=1.49,95%CI:1.09-2.05),abnormal liver function(OR=2.26,95%CI:1.31-3.92),and dyslipidemia(OR=1.46,95%CI:1.01-2.12)were positively correlated with hyperuricemia;age>30 years old(OR=0.59,95%CI:0.37-0.93)and exercise time>1 h per week(OR=0.46,95%CI:0.22-0.97)were negatively correlated with HUA.Conclusion The prevalence rate of hyperuricemia is at a high level in an island troop.BMI≥24,age≤30 years old,exercise time≤1 h per week,abnormal liver function,and dyslipidemia are the risk factors for HUA.Prevention and control measures should be taken as early as possible for the soldiers with these risk factors.

Levetiracetam （LEV） is the second generation of broad-spectrum anti-epileptic drug. LEV has the advantages of rapid absorption， short half-life， precise efficacy， good tolerance and few drug interactions. In order to improve the clinical efficacy of LEV， and reduce the occurrence of adverse reactions， children， pregnant women， the elderly， and patients with renal insufficiency should receive therapeutic drug monitoring （TDM）. Clinically， the samples are usually plasma or serum， and the TDM methods are mostly immunoassay or chromatography. There is currently no consensus on the effective concentration range of LEV， and the correlation between plasma concentration and adverse reactions is also unclear. The main factors affecting LEV plasma concentration include age， pregnancy， and patient compliance. How to interpret TDM results and adjust dosage based on the results will be the focus of future work.

BACKGROUND:Hypoxia is strongly associated with the development and progression of osteoarthritic chondrocyte injury,but the specific targets and regulatory mechanisms are unclear. OBJECTIVE:A machine learning approach was used to identify KDEL(Lys-Asp-Glu-Leu)receptor 3(KDELR3)as a characteristic gene for osteoarthritis hypoxia and immune infiltration analysis,to provide new ideas and methods for the treatment of osteoarthritis. METHODS:The osteoarthritis-related datasets were downloaded from the GEO database and the GSEA website to obtain hypoxia-related genes.The osteoarthritis datasets were batch-corrected and immune infiltration analyzed using R language,and osteoarthritis hypoxia genes were extracted for differential analysis.Differentially expressed genes were analyzed for GO function and KEGG signaling pathway.Weighted correlation network analysis(WGCNA)and machine learning were also used to screen osteoarthritis hypoxia signature genes,and in vitro cellular experiments were performed to validate expression and correlate immune infiltration analysis using the datasets and qPCR. RESULTS AND CONCLUSION:(1)8492 osteoarthritis genes were obtained by batch correction and principal component analysis,mainly strongly associated with immune cells such as Macrophages M2 and Mast cells resting;200 hypoxia genes were also obtained,resulting in 41 osteoarthritis hypoxia differentially expressed genes.(2)GO analysis involved mainly biological processes such as response to nutrient levels and glucocorticoids;cellular components such as lysosomal lumen and Golgi lumen;and molecular functions such as 14-3-3 protein binding and DNA-binding transcriptional activator activity.(3)KEGG analysis of osteoarthritis hypoxia differentially expressed genes was associated with signaling pathways such as PI3K-Akt,FoxO,and microRNAs in cancer.(4)The characteristic gene KDELR3 was obtained after using WGCNA analysis and machine learning screening.(5)The gene expression of KDELR3 was found to be higher in the test group than in the control group in the synovium(P=0.014)but lower in the meniscus(P=0.024)after validation by gene microarray.(6)In vitro chondrocyte assay showed that the expression of KDELR3 was higher in cartilage than in the control group(P=0.005),while KDELR3 was closely associated with Macrophages M0(P=0.014)and T cells follicular helper(P=0.014).Using a machine learning approach,we confirmed that KDELR3 can be used as a hypoxic signature gene for osteoarthritis and may intervene in osteoarthritis pathogenesis by improving hypoxia,expecting to provide a new direction for better treatment of osteoarthritis.

BACKGROUND:Ferroptosis is strongly associated with the occurrence and progression of osteoarthritis,but the specific characteristic genes and regulatory mechanisms are not known. OBJECTIVE:To identify osteoarthritis ferroptosis signature genes and immune infiltration analysis using the WGCNA and various machine learning methods. METHODS:The osteoarthritis dataset was downloaded from the GEO database and ferroptosis-related genes were obtained from the FerrDb website.R language was used to batch correct the osteoarthritis dataset,extract osteoarthritis ferroptosis genes and perform differential analysis,analyze differentially expressed genes for GO function and KEGG signaling pathway.WGCNA analysis and machine learning(random forest,LASSO regression,and SVM-RFE analysis)were also used to screen osteoarthritis ferroptosis signature genes.The in vitro cell experiments were performed to divide chondrocytes into normal and osteoarthritis model groups.The dataset and qPCR were used to verify expression and correlate immune infiltration analysis. RESULTS AND CONCLUSION:(1)12 548 osteoarthritis genes were obtained by batch correction and PCA analysis,while 484 ferroptosis genes were obtained,resulting in 24 differentially expressed genes of osteoarthritis ferroptosis.(2)GO analysis mainly involved biological processes such as response to oxidative stress and response to organophosphorus,cellular components such as apical and apical plasma membranes,and molecular functions such as heme binding and tetrapyrrole binding.(3)KEGG analysis exhibited that differentially expressed genes of osteoarthritis ferroptosis were related to signaling pathways such as the interleukin 17 signaling pathway and tumor necrosis factor signaling pathway.(4)After using WGCNA analysis and machine learning screening,we obtained the characteristic gene KLF2.After validation by gene microarray,we found that the gene expression of KLF2 was higher in the test group than in the control group in the meniscus(P=0.000 14).(5)In vitro chondrocyte assay showed that type Ⅱ collagen and KLF2 expression was lower in the osteoarthritis group than in the control group in chondrocytes(P<0.05),while in osteoarthritis ferroptosis,mast cells activated was closely correlated with dendritic cells(r=0.99);KLF2 was closely correlated with natural killer cells(r=-1,P=0.017)and T cells follicular helper(r=-1,P=0.017).(6)The findings indicate that using WGCNA analysis and machine learning methods confirmed that KLF2 can be a characteristic gene for osteoarthritis ferroptosis and may improve osteoarthritis ferroptosis by interfering with KLF2.

Objective To analyze the research status and trend of scarlet fever literature in China, and to provide reference for subsequent research. Methods Three major Chinese databases, CNKI, Wanfang, and VIP, as well as Web of Science English database, were used to search for literature related to scarlet fever from 2000 to 2023. Citespace6.2.R2 software was used to statistically analyze the number of publications, authors, institutions and journals, co-cited literature, keyword clustering, and other literature characteristics of the literature. Results From 2000 to 2023, a total of 1 011 Chinese literature were included in the three major Chinese databases. Since 2011, the number of publications had gradually increased, but in recent years, the number of publications had decreased. The organization with the most publications was the Shenyang Center for Disease Control and Prevention. The cluster analysis of key words mainly formed 9 cluster tags, and the high-frequency keywords mainly included epidemic characteristics, epidemiology, incidence rate, etc. A total of 84 English literature were included in the WOS database, with an overall upward trend in publication volume. The institution with the most publications was the China Center for Disease Control and Prevention, and the most frequently cited journal was ^“LANCET INFECT DIS^”.《Resurgence of scarlet fever in China: a 13-year population-based surveillance study》 was the most cited journal. After keyword cluster analysis, 9 cluster labels were mainly formed, and the keywords were mainly outbreak，Hong Kong, and Group A streptococcus. Conclusion Compared with the English literature, which mainly focuses on spatiotemporal aggregation, etiology and strain resistance, Chinese literature focuses more on epidemic surveillance, clinical features and quality nursing.

Ferroptosis is a non-apoptotic mode of cell death characterized by iron-dependent lipid-peroxide-accumulation-induced membrane lipid peroxidation and mitochondrial atrophy.It differs from other programmed cell deaths in its morphological and biochemical properties.Ferroptosis is regulated by a variety of metabolic pathways that are involved in the acute lung injury induced by hemorrhagic shock,ischemia-reperfusion,sepsis,and radiation.This article reviews the main regulatory mechanisms of ferroptosis and its role in the pathogenesis of acute lung injury in various animal models with the aim of providing new strategies for the prevention and treatment of acute lung injury.

Myocardial dysfunction is the most serious complication of sepsis. Sepsis-induced myocardial dysfunction (SMD) is often associated with gastrointestinal dysfunction, but its pathophysiological significance remains unclear. The present study found that patients with SMD had higher plasma gastrin concentrations than those without SMD. In mice, knockdown of the gastrin receptor, cholecystokinin B receptor (Cckbr), aggravated lipopolysaccharide (LPS)-induced cardiac dysfunction and increased inflammation in the heart, whereas the intravenous administration of gastrin ameliorated SMD and cardiac injury. Macrophage infiltration plays a significant role in SMD because depletion of macrophages by the intravenous injection of clodronate liposomes, 48 h prior to LPS administration, alleviated LPS-induced cardiac injury in Cckbr-deficient mice. The intravenous injection of bone marrow macrophages (BMMs) overexpressing Cckbr reduced LPS-induced myocardial dysfunction. Furthermore, gastrin treatment inhibited toll-like receptor 4 (TLR4) expression through the peroxisome proliferator-activated receptor α (PPAR-α) signaling pathway in BMMs. Thus, our findings provide insights into the mechanism of the protective role of gastrin/CCKBR in SMD, which could be used to develop new treatment modalities for SMD.

Objective:To investigate the efficacy of subarachnoid anesthesia with low-dose ropivacaine combined with sufentanil in cesarean delivery.Methods:A total of 120 women who were subjected to cesarean delivery in Tongxiang Second People's Hospital from May 2017 to April 2018 were included in this study. They were randomly assigned to undergo subarachnoid anesthesia either with low-dose ropivacaine plus sufentanil (observation group, n = 60) or low-dose ropivacaine alone (control group, n = 60) in cesarean delivery. The anesthesia effects, heart rate, mean arterial pressure, sensory block, 1-minute Apgar score, and the incidence of adverse reactions were compared between the two groups. Results:Time to anesthesia onset and time to pain recovery in the observation group were (2.07 ± 1.24) minutes and (51.29 ± 6.24) minutes, respectively, which were significantly shorter than those in the control group [(4.58 ± 1.69) minutes, (56.70 ± 7.91) minutes, t = 9.28, 4.16, both P < 0.05]. There was no significant difference in the duration of anesthesia between the two groups ( t = 0.76, P > 0.05). The heart rate, mean arterial pressure, and 1-minute Apgar score were (77.64 ± 6.20) beats/minute, (92.23 ± 1.38) mmHg, and (9.68 ± 0.70) points respectively in the observation group, which were significantly lower than those in the control group [(83.40 ± 5.93) beats/minute, (96.54 ± 4.06) mmHg, (7.59 ± 0.64) points, t = 5.20, 7.79, 17.07, all P < 0.05]. Time to onset of sensory block was significantly shorter in the observation group than in the control group [(52.07 ± 8.68) seconds vs. (64.30 ± 9.62) seconds, t = 7.31, P < 0.05]. Duration of sensory and motor block in the observation group were (161.75 ± 28.63) minutes and (86.26 ± 20.03) minutes, respectively, which were significantly longer than those in the control group [(130.29 ± 31.84) minutes, (78.60 ± 16.95) minutes, t = 5.69, 2.26, both P < 0.05]. The incidence of adverse reactions was significantly lower in the observation group than in the control group (5.00% vs. 16.67%, χ2 = 4.23, P < 0.05). Conclusion:Subarachnoid anesthesia with low-dose ropivacaine combined with sufentanil has satisfactory anesthetic effects in cesarean delivery. The combined therapy can stabilize hemodynamics, has little impact on newborns, and is highly safe.

As an important tool to evaluate cardiac autonomic function, heart rate variability (HRV) is closely associated with ischemic stroke. The change of HRV can not only increase the incidence and recurrence risk of ischemic stroke, but also affect the severity of the disease and the risk of complications, resulting in poor outcomes. Effective intervention for patients with stroke to restore their autonomic nerve function is expected to become a new target for the treatment of ischemic stroke. This article reviews the research progress of the correlation between HRV changes and ischemic stroke.