Aim: The search for prognostic biomarkers and the construction of a prognostic risk model for hepatocellular carcinoma (HCC) based on N7-methyladenosine (m7G) methylation regulators.Methods: HCC transcriptomic data and clinical data were obtained from The Cancer Genome Atlas database and Shanghai Ninth People’s Hospital, respectively. m7G methylation regulators were extracted, differential expression analysis was performed using the R software “limma” package, and one-way Cox regression analysis was used to screen for prognostic associations of m7G regulators. Using multi-factor Cox regression analysis, a prognostic risk model for HCC was constructed. Each patient’s risk score was calculated using the model, and patients were divided into high- and low-risk groups according to the median risk score. Cox regression analysis was used to verify the validity of the model in the prognostic assessment of HCC in conjunction with clinicopathological characteristics.Results: The prognostic model was built using the seven genes, namely, CYFIP1, EIF4E2, EIF4G3, GEMIN5, NCBP2, NUDT10, and WDR4. The Kaplan–Meier survival analysis showed poorer 5-years overall survival in the high-risk group compared with the low-risk group, and the receiver-operating characteristic (ROC) curve suggested good model prediction (area under the curve AUC = 0.775, 0.820, and 0.839 at 1, 3, and 5 years). The Cox regression analysis included model risk scores and clinicopathological characteristics, and the results showed that a high-risk score was the only independent risk factor for the prognosis of patients with HCC.Conclusions: The developed bioinformatics-based prognostic risk model for HCC was found to have good predictive power.

Objective: To develop and validate an admission warning strategy that incorporates the general emergency department indicators for predicting the hospital discharge outcome of patients with traumatic brain injury (TBI) in China. Methods: This admission warning strategy was developed in a primary cohort that consisted of 605 patients with TBI who were admitted within 6 h of injury. The least absolute shrinkage and selection operator and multivariable logistic regression analysis were used to develop the early warning strategy of selected indicators. Two sub-cohorts consisting of 180 and 107 patients with TBI were used for the external validation. Results: Indicators of the strategy included three categories: baseline characteristics, imaging and laboratory indicators. This strategy displayed good calibration and good discrimination. A high C-index was reached in the internal validation. The multicenter external validation cohort still showed good discrimination C-indices. Decision curve analysis (DCA) showed the actual needs of this strategy when the possibility threshold was 0.01 for the primary cohort, and at thresholds of 0.02–0.83 and 0.01–0.88 for the two sub-cohorts, respectively. In addition, this strategy exhibited a significant prognostic capacity compared to the traditional single predictors, and this optimization was also observed in two external validation cohorts. Conclusions: We developed and validated an admission warning strategy that can be quickly deployed in the emergency department. This strategy can be used as an ideal tool for predicting hospital discharge outcomes and providing objective evidence for early informed consent of the hospital discharge outcome to the family members of TBI patients.

Background: In this study, a prognostic model based on pyroptosis-related genes was established to predict overall survival (OS) in patients with glioblastoma (GBM).Methods: The gene expression data and clinical information of GBM patients were obtained from The Cancer Genome Atlas (TCGA), and bioinformatics analysis of differentially expressed genes was performed. LASSO Cox regression model was used to construct a three-pyroptosis-related gene signature, and validation was performed using an experimental cohort.Results: A total of three pyroptosis-related genes (CASP4, CASP9, and NOD2) were used to construct a survival prognostic model, and experimental validation was performed using an experimental cohort. Receiver operating characteristic (ROC) analysis was performed, and the area under the ROC curves (AUC) was 0.921, 0.840, and 0.905 at 1, 3, and 5 years, respectively. Functional analysis revealed that T-cell activation, regulation of T-cell activation, leukocyte cell-cell adhesion, and positive regulation of cell adhesion among other immune-related functions were enriched, and immune-related processes were different between the two risk groups.Conclusion: In this study, a novel prognostic model based on three pyroptosis-related genes is constructed and used to predict the prognosis of GBM patients. The model can accurately and conveniently predict the 1-, 3-, and 5-year OS of GBM patients.

Background: Volatile organic compounds (VOCs) are a large group of chemicals widely used in People’s Daily life. There is increasing evidence of the cumulative toxicity of VOCs. However, the association between VOCs and the risk of COPD has not been reported.Objective: We comprehensively evaluated the association between VOCs and COPD.Methods: Our study included a total of 1,477 subjects from the National Health and Nutrition Examination Survey, including VOCs, COPD, and other variables in the average US population. Multiple regression models and smooth-curve fitting (penalty splines) were constructed to examine potential associations, and stratified analyses were used to identify high-risk groups.Results: We found a positive association between blood benzene and blood o-xylene concentrations and COPD risk and identified a concentration relationship between the two. That is, when the blood benzene and O-xylene concentrations reached 0.28 ng/mL and 0.08 ng/mL, respectively, the risk of COPD was the highest. In addition, we found that gender, age, and MET influence the relationship, especially in women, young people, and people with low MET.Significance: This study revealed that blood benzene and blood o-xylene were independently and positively correlated with COPD risk, suggesting that long-term exposure to benzene and O-xylene may cause pulmonary diseases, and providing a new standard of related blood VOCs concentration for the prevention of COPD.

Asthma is a common chronic respiratory disease, with onset usually occurring during childhood or adolescence. The aim of this study was to estimate the dynamic changes in the burden of asthma disease among adolescents (aged 15–39 years). Between 1990 and 2019, our comprehensive analysis spanned 204 nations and territories to ascertain the global prevalence of asthma. This extensive evaluation meticulously quantified the disease's burden by examining a range of critical metrics—including mortality rates, prevalence figures, and disability-adjusted life-years (DALYs)—across diverse demographics. Our investigation systematically segmented the data by age, gender, and year, as well as by geographical regions and the Socio-demographic Index (SDI), offering an in-depth perspective on the multifaceted impact of asthma worldwide. From 1990 to 2019, there was a global increase in the incidence cases of asthma among young adults, with the number rising from 6487957.18 (95 %UI: 4578735.08–8736387.55) to 7604488.39 (95 % UI:5428024.98–10177808.25). The prevalence rate of asthma among young adults exhibited a decline from 580.09 per 1,00,000 (95 %UI:481.37–757.28) to 504.28 per 1,00,000 (95 %UI:400.64–633.26). It is also noteworthy that the incidence rates in the remaining SDI regions, although lower, also showed varying degrees of increase in 2015. The age-standardized disease burden rate for asthma among young adults has exhibited a decline over the course of the previous three decades. Nevertheless, regions characterized by lower SDI demonstrate elevated age-standardized mortality rates for asthma, thereby warranting focused attention and prioritized allocation of medical resources.

Background: Periodontal disease is very common worldwide and is one of the main causes of tooth loss in adults. Periodontal disease is characterized by chronic inflammation that can destroy adjacent alveolar bone and lead to a loss of periodontal ligaments. Although previous studies have found that a daily diet can influence the development of periodontal disease (e.g., a diet low in carbohydrates and rich in vitamins C and D and fiber can have a protective effect). Periodontal disease may present as gingivitis or periodontitis. However, studies on the role of healthy eating index in periodontitis are lacking. The purpose of this study was to assess the association between healthy eating index and periodontitis.Methods: We analyzed data collected from participants in the National Health and Nutrition Examination Survey (NHANES), a nationally representative survey conducted in 2-year cycles from 2013 to 2014. As part of our analysis, we developed multivariate logistic regression models to examine the independent association between the healthy eating index and periodontitis. We evaluated the significance of association using odds ratios (OR) with 95% confidence intervals (95%CI).Results: Individuals with a lower total healthy eating index had a higher prevalence of periodontitis. Adjusted multivariate regression models showed that a higher healthy diet index was associated with a lower prevalence of periodontitis (OR = 0.69, 95% CI: 0.55–0.86, P < 0.05).Conclusion: The results of the study showed that dietary structure was associated with the prevalence of periodontitis. Patients with a higher healthy eating index had a lower prevalence of periodontitis. These findings will need to be confirmed by longitudinal, prospective studies in the future.