Syphilis epidemic among men who have sex with men: A global systematic review and meta-analysis of prevalence, incidence, and associated factors.

Background: We aimed to estimate the prevalence and incidence of syphilis at global, regional and national levels for human immunodeficiency virus (HIV)-positive and HIV-negative men who have sex with men (MSM) and explore the association between demographic and social behavioural factors and syphilis infection. Methods: We searched PubMed, Embase, and the Cochrane Library from 1 January 2012 to 31 December 2022 for studies of reported crude syphilis prevalence or incidence in MSM or with sufficient data to calculate prevalence or incidence rate in MSM. Results: We included 376 articles reporting on 409 records from 62 countries to calculate syphilis prevalence and incidence in MSM. The pooled prevalence of syphilis in MSM was 10.4%, with substantial differences between countries and regions. Syphilis prevalence was substantially higher in HIV-positive than in HIV-negative MSM. The pooled incidence of syphilis in MSM was 76.4 per 1000 person-years. Older age, lower education, nitrite or recreational drug use, group sex, and multiple sexual partners were identified as risk factors for syphilis infection. Conclusions: A disproportionate geographic pattern of syphilis infection in MSM and significant threats of syphilis infection were revealed. The 'hidden risk' in specific regions and the inadequately elucidated drivers of high-risk behaviours, need to be fully acknowledged and addressed. Registration: PROSPERO: CRD42023422218.

Heart failure-related genes associated with oxidative stress and the immune landscape in lung cancer.

Background: Lung cancer is a common comorbidity of heart failure (HF). The early identification of the risk factors for lung cancer in patients with HF is crucial to early diagnosis and prognosis. Furthermore, oxidative stress and immune responses are the two critical biological processes shared by HF and lung cancer. Therefore, our study aimed to select the core genes in HF and then investigate the potential mechanisms underlying HF and lung cancer, including oxidative stress and immune responses through the selected genes. Methods: Differentially expressed genes (DEGs) were analyzed for HF using datasets extracted from the Gene Expression Omnibus database. Functional enrichment analysis was subsequently performed. Next, weighted gene co-expression network analysis was performed to select the core gene modules. Support vector machine models, the random forest method, and the least absolute shrinkage and selection operator (LASSO) algorithm were applied to construct a multigene signature. The diagnostic values of the signature genes were measured using receiver operating characteristic curves. Functional analysis of the signature genes and immune landscape was performed using single-sample gene set enrichment analysis. Finally, the oxidative stress-related genes in these signature genes were identified and validated in vitro in lung cancer cell lines. Results: The DEGs in the GSE57338 dataset were screened, and this dataset was then clustered into six modules using weighted gene co-expression network analysis; MEblue was significantly associated with HF (cor = -0.72, p < 0.001). Signature genes including extracellular matrix protein 2 (ECM2), methyltransferase-like 7B (METTL7B), meiosis-specific nuclear structural 1 (MNS1), and secreted frizzled-related protein 4 (SFRP4) were selected using support vector machine models, the LASSO algorithm, and the random forest method. The respective areas under the curve of the receiver operating characteristic curves of ECM2, METTL7B, MNS1, and SFRP4 were 0.939, 0.854, 0.941, and 0.926, respectively. Single-sample gene set enrichment analysis revealed significant differences in the immune landscape of the patients with HF and healthy subjects. Functional analysis also suggested that these signature genes may be involved in oxidative stress. In particular, METTL7B was highly expressed in lung cancer cell lines. Meanwhile, the correlation between METTL7B and oxidative stress was further verified using flow cytometry. Conclusion: We identified that ECM2, METTL7B, MNS1, and SFRP4 exhibit remarkable diagnostic performance in patients with HF. Of note, METTL7B may be involved in the co-occurrence of HF and lung cancer by affecting the oxidative stress immune responses.

The association between GLIM criteria-defined malnutrition and 2-year unplanned hospital admission in outpatients with unintentional weight loss: A retrospective cohort study.

BACKGROUND: This study aimed to assess malnutrition using the Global Leadership Initiative on Malnutrition (GLIM) criteria and Subjective Global Assessment (SGA) at baseline and determine the GLIM criteria that best predicted unplanned hospitalization in outpatients with unintentional weight loss (UWL). METHODS: We performed a retrospective cohort study of 257 adult outpatients with UWL. The GLIM criteria and SGA agreement were reported using the Cohen kappa coefficient. Kaplan-Meier survival curves and adjusted Cox regression analyses were used for survival data. Logistic regression was used for the other correlation analysis. RESULTS: This study collected data from 257 patients for 2 years. Based on the GLIM criteria and SGA, malnutrition prevalence was 79.0% and 72.0%, respectively (κ = 0.728, P < 0.001). Using the SGA as a standard, GLIM had a sensitivity of 97.8%, a specificity of 69.4%, a positive predictive value of 89.2%, and a negative predictive value of 92.6%. Malnutrition was associated with higher rates of unplanned hospital admission independent of other prognostic factors (GLIM: hazard ratio [HR]=2.85, 95% CI=1.22-6.68; SGA: HR=2.07, 95% CI=1.13-3.79). Of the five GLIM criteria-related diagnostic combinations, disease burden or inflammation was the most important to predict unplanned hospital admission in multivariable analysis (HR=3.27, 95% CI=2.03-5.28). CONCLUSION: There was good agreement between the GLIM criteria and the SGA. GLIM-defined malnutrition, as well as all five GLIM criteria-related diagnosis combinations, had the potential to predict unplanned hospital admissions in outpatients with UWL within 2 years.

Effects of hepatitis B vaccine boosters in different levels of epidemic areas on anti-HBs-negative children after primary vaccination.

Introduction. The widespread of hepatitis B virus is a severe global public problem, and the infant hepatitis B vaccine has been proved effective. But the failure of the immune response was reported in studies, and boosters were recommended. There were few studies about the effect of hepatitis B vaccine boosters in different levels of the epidemic area.Hypothesis. Booster immunization is recommended because there may be a lack of immunization in infants vaccinated with the hepatitis B vaccine. In order to verify the effectiveness of booster immunization, this study hypothesized that it worked well in different levels of endemic areas.Aim. To evaluate the effects of hepatitis B vaccine boosters on children from the areas with different prevalence of hepatitis B whose hepatitis B surface antibody (anti-HBs) were negative (<10 mIU ml-1).Methodology. A total of 940 children were initially enrolled in screening; however, 421 were excluded. The participants were divided into three groups according to the different areas they come from: group I, low epidemic area; group II, middle epidemic area; and group III, high epidemic area. In total, 519 subjects were administered three doses of booster hepatitis B vaccine (0-1-6 months, 10 µg). The antibody titre changes were examined at four time points: 1 month after dose 1, 1 month, 1 year and 5 years after dose 3.Results. The protective seroconversion rates in three groups were 96.30, 97.16, 96.63% at 1 month after dose 1, and 100.00, 100.00, 100.00% at 1 month after dose 3, and 97.79, 100.00, 98.50% at 1 year after dose 3, and 90.77, 93.67, 93.59% at 5 years after dose 3 (P>0.05).Conclusions. This study demonstrates that three doses of booster vaccination have a longtime effect, no matter whether it is in low, middle or high prevalence areas in which subjects live.

A COVID-19 Risk Assessment Decision Support System for General Practitioners: Design and Development Study.

BACKGROUND: The coronavirus disease (COVID-19) has become an urgent and serious global public health crisis. Community engagement is the first line of defense in the fight against infectious diseases, and general practitioners (GPs) play an important role in it. GPs are facing unique challenges from disasters and pandemics in delivering health care. However, there is still no suitable mobile management system that can help GPs collect data, dynamically assess risks, and effectively triage or follow-up with patients with COVID-19. OBJECTIVE: The aim of this study is to design, develop, and deploy a mobile-based decision support system for COVID-19 (DDC19) to assist GPs in collecting data, assessing risk, triaging, managing, and following up with patients during the COVID-19 outbreak. METHODS: Based on the actual scenarios and the process of patients using health care, we analyzed the key issues that need to be solved and designed the main business flowchart of DDC19. We then constructed a COVID-19 dynamic risk stratification model with high recall and clinical interpretability, which was based on a multiclass logistic regression algorithm. Finally, through a 10-fold cross-validation to quantitatively evaluate the risk stratification ability of the model, a total of 2243 clinical data consisting of 36 dimension clinical features from fever clinics were used for training and evaluation of the model. RESULTS: DDC19 is composed of three parts: mobile terminal apps for the patient-end and GP-end, and the database system. All mobile terminal devices were wirelessly connected to the back end data center to implement request sending and data transmission. We used low risk, moderate risk, and high risk as labels, and adopted a 10-fold cross-validation method to evaluate and test the COVID-19 dynamic risk stratification model in different scenarios (different dimensions of personal clinical data accessible at an earlier stage). The data set dimensions were (2243, 15) when only using the data of patients' demographic information, clinical symptoms, and contact history; (2243, 35) when the results of blood tests were added; and (2243, 36) after obtaining the computed tomography imaging results of the patient. The average value of the three classification results of the macro-area under the curve were all above 0.71 in each scenario. CONCLUSIONS: DCC19 is a mobile decision support system designed and developed to assist GPs in providing dynamic risk assessments for patients with suspected COVID-19 during the outbreak, and the model had a good ability to predict risk levels in any scenario it covered.