Objective To investigate the effects of long non-coding RNA 00894(LINC00894)gene on prolifera-tion and metastasis of human gastric cancer cells,and to verify the regulatory relationship of LINC00894,miR-205-5p and ZEB1 in gastric cancer.Methods The expression level of LINC00894 in gastric cancer cell lines,normal gastric lines,clinical gastric cancer and normal gastric tissue samples were determined by RT-qPCR.Through fol-low-up,the relationship between the expression level of LINC00894 and the prognosis of gastric cancer patients was explored.LINC00894 knockdown cell lines and overexpression cell lines were constructed,and the knockdown and overexpression efficiency was detected by RT-qPCR.Cell proliferation and metastatic capacity were determined by CCK 8,clone formation and Transwell assays.Dual-luciferase reporter assays,RT-qPCR assays and Western blot assays were used to examine the targeted regulatory relationships of LINC00894,miR-205-5p and ZEB1.Results The expression of LINC00894 gene in gastric cancer tissues or cells was significantly higher than that in normal gas-tric tissues or cells,moreover,gastric cancer patients with high LINC00894 gene expression had a worse prognosis.The knockdown of LINC00894 inhibited the viability,clonogenesis,migration and invasion ability of gastric cancer cells,and conversely,the overexpression of LINC00894 obtained the opposite results.LINC00894 promoted ZEB1 expression by targeted downregulation of miR-205-5p expression.LINC00894 promoted the expression of ZEB1 by targeting miR-205-5p and down-regulating its expression.Conclusion LINC00894 serves as an oncogene in gastric cancer and may promote proliferation and metastasis of gastric cancer cells via regulating miR-205-5p/ZEB1 axis.

Dengue fever is an acute mosquito-borne infectious disease caused by dengue virus and widely spread worldwide. Many factors, such as pathogens, vector organisms, climate, and social environment, affect its transmission and prevalence. The local dengue fever epidemic caused by imported cases in China shows a trend of increasing epidemic latitude and more widespread epidemic areas. However, the traditional monitoring and early warning models of dengue fever mainly focus on researching a single factor and a single area. Establishing a multi-factor forecast and early warning system is urgent to strengthen the early warning capability for the dengue fever epidemic. This paper mainly discusses the epidemic characteristics, the influencing factors, and the surveillance and early warning models of dengue fever in China to provide a reference for the effective prevention and control of dengue fever in China.

Objective:To analyze the epidemiological and clinical characteristics of respiratory syncytial virus (RSV) infection in children in Jiangsu Province from 2014 to 2023.Methods:The acute respiratory infection cases in children aged 0-14 years were selected from outpatient/emergency or inpatient departments in 2 surveillance sentinel hospitals, respectively, in Nanjing, Suzhou and Taizhou of Jiangsu from 1 July 2014 to 31 December 2023, and RSV nucleic acid test was conducted and the intensity of the RSV infection was accessed by WHO influenza epidemiological threshold method, and case information and clinical data were collected. χ2 test was used to compare the differences between groups, and the Bonferroni method was used for pairwise comparisons between groups. Results:In 4 946 cases of acute respiratory infections, the RSV positive rate was 8.21% (406/4 946), and the age M( Q1, Q3) of the cases was 1 (0, 3) years. The RSV positive rate was 10.92% (258/2 362) during 2014-2019 and 6.06% (118/1 948) during 2019-2023, the difference was significant ( χ2=31.74, P<0.001). RSV infection mainly occurred from October to March during 2014-2019, with the incidence peak in December and moderate or higher intensity. The seasonality of RSV infection was not obvious during 2019-2023, with low intensity. The RSV positive rate was highest in children in age group 0- years (17.85%, 151/846), and the positive rate declined gradually with age ( χ2=184.51, P<0.001). The RSV positive rate was higher in inpatient cases (9.84%, 244/2 480) than in outpatient/emergency cases (6.57%, 162/2 466) ( χ2=17.54, P<0.001). In the 155 RSV infection cases with complete clinical data, the clinical symptoms mainly included cough (99.35%, 154/155), fever (55.48%, 86/155), and shortness of breath (45.16%, 70/155). In the cases aged <6 months, the proportion of those with fever was low, but the proportion of those with shortness of breath, transferred to intensive care units, and receiving oxygen therapy were higher (all P<0.05). Children aged <6 months and those with underlying diseases were more likely to have severe RSV infection (all P<0.05). Conclusions:RSV infection in children in Jiangsu Province showed seasonal prevalence in winter from 2014 to 2019. Since 2020, the seasonal characteristics of the epidemic have changed, the epidemic period has been dispersed and the epidemic intensity has decreased. Infants <1 year old were at high risk for RSV infection, and those <6 months old and with underlying diseases might have severe infection.

Objective Based on a novel type of cell death induced by disulfide stress,known as disulfidptosis,this study explores the role of long non-coding RNA(LncRNA)in gastric cancer and establishes a prognosis model re-lated to disulfidptosis,providing a new method for assessing the prognosis of gastric cancer treatment.Methods Transcriptomic data from gastric cancer and normal tissue samples were obtained from the public database TCGA,and disulfidptosis-related LncRNAs were selected through Pearson analysis and LASSO-Cox regression analysis.A relevant prognostic model for gastric cancer was constructed based on the above LncRNAs and validated by function-al enrichment analysis,tumour microenvironment and immune cell infiltration analysis,drug sensitivity analysis and quantitative reverse transcription PCR(RT-qPCR).Results In this study,400 disulfide death-associated LncR-NAs were identified and five of them were screened to construct a prognostic model for assessing the prognosis of gastric cancer patients.The models showed in validation that the survival of the high-risk score group was shorter than that of the low-risk score group(P＜0.05).In addition,the predictive ability of the prognostic model(AUC=0.725)was better than that based only on basic characteristics such as age and gender.The expression levels of disulfide death-associated LncRNAs differed between normal and gastric cancer tissues(P＜0.001).Conclusion The disulfidptosis-related LncRNA prognosis model developed in this study can effectively assess the prognosis of gastric cancer patients and the tumor microenvironment,providing potential targets and a theoretical basis for new immunotherapeutic strategies for gastric cancer.

In recent years, global outbreaks of infectious diseases, such as COVID-19, have triggered great concern about emerging infectious diseases. With the rapid development of next-generation sequencing technology and bioinformatic tools for viral metagenomics, there is now a widespread capability to detect and identify various known and unknown pathogenic microorganisms within both environmental and biological contexts. Furthermore, the continuous evolution of machine learning methods has led to the development and application of multiple rapid and highly accurate approaches for virus identification. Concurrently, owing to the continual progress in machine learning methods, several rapid and accurate virus identification techniques have been widely developed and applied. Therefore, this review aims to systematically summarize the key methodologies, frameworks, and the scope of applicability within the field of viral metagenomics, with a specific focus on virus identification and prediction. It could facilitate a deeper understanding of viral characteristics, identify potential novel pathogens, and provide technical support for the early prevention and control of infectious diseases.

Objective:To explore the application of COVID-19-specific IgG antibody in identifying epidemic Omicron BA.5 strain infection.Method:Omicron BF.7/BA.5 naturally infected population, healthy population vaccinated with the COVID-19 vaccine, and Omicron BF.7/BA.5 breakthrough cases were enrolled into this study. The serum WT-S-IgG and BA.5-S-IgG were detected by indirect ELISA, and the serum-specific IgG antibody levels of different populations were compared. The application value of the two antibody titers and the ratio of the two antibodies in identifying Omicron BA.5 epidemic strain infection were explored by the ROC curve, aiming to provide technical support for pathogen diagnosis.Results:The antibody titers of WT-S-IgG and BA.5-S-IgG in the breakthrough cases were higher than those in the naturally infected population and the healthy population ( P<0.05). The area under the curve (AUC) of WT-S-IgG and BA.5-S-IgG in identifying epidemic Omicron BA.5 strain infection was 0.947 and 0.961, respectively. The AUC of BA.5-S-IgG and WT-S-IgG antibody titer ratio was 0.873. When the antibody titer ratio was 0.855, the sensitivity and specificity were 80.00% and 90.00%, respectively. According to the interval since the last infection, the AUC of the ratio of BA.5-S-IgG to WT-S-IgG antibody titer to identify the infection of epidemic strains less than 30 days and more than 30 days was 0.887 and 0.863, respectively, and the sensitivity and specificity were both above 80%. Conclusion:Both BA.5-S-IgG and WT-S-IgG, as well as the combination of these two antibodies, are of high value in the identification of epidemic strains.

Clinicians need to focus on various points in the diagnosis and treatment of insomnia.This article prescribed the treatment protocol based on the unique features,such as insomnia in the elderly,women experiencing specific physiologi-cal periods,children insomnia,insomnia in sleep-breathing disorder patients,insomnia in patients with chronic liver and kidney dysfunction.It pro-vides some reference for clinicians while they make decision on diagnosis,differentiation and treat-ment methods.

In recent years, global outbreaks of infectious diseases, such as COVID-19, have triggered great concern about emerging infectious diseases. With the rapid development of next-generation sequencing technology and bioinformatic tools for viral metagenomics, there is now a widespread capability to detect and identify various known and unknown pathogenic microorganisms within both environmental and biological contexts. Furthermore, the continuous evolution of machine learning methods has led to the development and application of multiple rapid and highly accurate approaches for virus identification. Concurrently, owing to the continual progress in machine learning methods, several rapid and accurate virus identification techniques have been widely developed and applied. Therefore, this review aims to systematically summarize the key methodologies, frameworks, and the scope of applicability within the field of viral metagenomics, with a specific focus on virus identification and prediction. It could facilitate a deeper understanding of viral characteristics, identify potential novel pathogens, and provide technical support for the early prevention and control of infectious diseases.

Objective:To explore the application of COVID-19-specific IgG antibody in identifying epidemic Omicron BA.5 strain infection.Method:Omicron BF.7/BA.5 naturally infected population, healthy population vaccinated with the COVID-19 vaccine, and Omicron BF.7/BA.5 breakthrough cases were enrolled into this study. The serum WT-S-IgG and BA.5-S-IgG were detected by indirect ELISA, and the serum-specific IgG antibody levels of different populations were compared. The application value of the two antibody titers and the ratio of the two antibodies in identifying Omicron BA.5 epidemic strain infection were explored by the ROC curve, aiming to provide technical support for pathogen diagnosis.Results:The antibody titers of WT-S-IgG and BA.5-S-IgG in the breakthrough cases were higher than those in the naturally infected population and the healthy population ( P<0.05). The area under the curve (AUC) of WT-S-IgG and BA.5-S-IgG in identifying epidemic Omicron BA.5 strain infection was 0.947 and 0.961, respectively. The AUC of BA.5-S-IgG and WT-S-IgG antibody titer ratio was 0.873. When the antibody titer ratio was 0.855, the sensitivity and specificity were 80.00% and 90.00%, respectively. According to the interval since the last infection, the AUC of the ratio of BA.5-S-IgG to WT-S-IgG antibody titer to identify the infection of epidemic strains less than 30 days and more than 30 days was 0.887 and 0.863, respectively, and the sensitivity and specificity were both above 80%. Conclusion:Both BA.5-S-IgG and WT-S-IgG, as well as the combination of these two antibodies, are of high value in the identification of epidemic strains.

Objective:To investigate the value of quantitative parameters of amide proton transfer-weighted (APTw) imaging and dynamic contrast-enhanced (DCE)-MRI for preoperative assessment of human epidermal growth factor receptor 2 (Her-2) gene expression in endometrial cancer (EC).Methods:This research conducted a diagnostic pilot study involving 68 patients with pathologically confirmed EC at the First Hospital of Dalian Medical University from August 2019 to August 2023. Patients were categorized into Her-2-positive group (33 cases) and Her-2-negative group (35 cases) based on postoperative Her-2 gene expression results. Utilizing the APTw and DCE-MRI sequences, quantitative parameters including the asymmetric magnetization transfer ratio (MTR asym) for APTw and the volumetric transfer constant (K trans), plasma volume fraction (V p), extracellular mesenchymal space (V e), and rate constant (K ep) for DCE-MRI were acquired for the lesion site. Statistical differences in the values of each quantitative parameter between the two groups were evaluated using two independent sample t test or Mann-Whitney U test. The study incorporated quantitative parameters and clinicopathological data of patients to identify independent predictors of EC Her-2 gene expression through logistic regression analysis. A diagnostic model was developed using binary logistic regression analysis. The effectiveness of the parameters and diagnostic model was evaluated using receiver operating characteristic curves. DeLong test was used to compare the differences between the areas under the curves (AUC). Results:The study found statistically significant differences in MTR asym, K trans, and V e between the Her-2-positive group and the Her-2-negative group ( Z=2.55, P=0.011; t=-2.03, P=0.047; t=-2.13, P=0.037). However, the differences in V p and K ep were not statistically significant ( Z=0.58, P=0.560; Z=0.19, P=0.849). MTR asym emerged as a significant independent predictor of Her-2 gene expression in EC ( OR=1.016, 95% CI 1.003-1.030, P=0.014). Incorporating MTR asym, K trans, and V e, the diagnostic model yielded an AUC (95% CI) of 0.745 (0.625-0.864). The AUC (95% CI) for MTR asym, K trans, and V e alone were 0.680 (0.551-0.808), 0.623 (0.485-0.760), and 0.656 (0.523-0.789) respectively. The differences in AUC between the diagnostic model and individual predictors MTR asym, K trans, and V e were not found to be statistically significant ( Z=1.40, 1.92, 1.37, P=0.163, 0.055, 0.171). Conclusion:The quantitative parameters of APTw and DCE-MRI sequences can preoperatively assess EC Her-2 gene expression from a different perspective, with MTR asym potentially serving as a valuable independent predictor.