Novel radiopaque ethanol injection: physicochemical properties, animal experiments, and clinical application in vascular malformations.

BACKGROUND: Despite the efficacy of absolute ethanol (EtOH), its radiolucency introduces several risks in interventional therapy for treating vascular malformations. This study aims to develop a novel radiopaque ethanol injection (REI) to address this issue. METHODS: Iopromide is mixed with ethanol to achieve radiopacity and improve the physicochemical properties of the solution. Overall, 82 male New Zealand white rabbits are selected for in vivo radiopacity testing, peripheral vein sclerosis [animals were divided into the following 5 groups (n = 6): negative control (NC, saline, 0.250 ml/kg), positive control (EtOH, 0.250 ml/kg), low-dose REI (L-D REI, 0.125 ml/kg), moderate-dose REI (M-D REI, 0.250 ml/kg), and high-dose REI (H-D REI 0.375 ml/kg)], pharmacokinetic analyses (the blood sample was harvested before injection, 5 min, 10 min, 20 min, 40 min, 1 h, 2 h, 4 h, and 8 h after injection in peripheral vein sclerosis experiment), peripheral artery embolization [animals were divided into the following 5 groups (n = 3): NC (saline, 0.250 ml/kg), positive control (EtOH, 0.250 ml/kg), L-D REI (0.125 ml/kg), M-D REI (0.250 ml/kg), and H-D REI (0.375 ml/kg)], kidney transcatheter arterial embolization [animals were divided into the following 4 groups (n = 3): positive control (EtOH, 0.250 ml/kg), L-D REI (0.125 ml/kg), M-D REI (0.250 ml/kg), and H-D REI (0.375 ml/kg); each healthy kidney was injected with saline as negative control], and biosafety evaluations [animals were divided into the following 5 groups (n = 3): NC (0.250 ml/kg), high-dose EtOH (0.375 ml/kg), L-D REI (0.125 ml/kg), M-D REI (0.250 ml/kg), and H-D REI (0.375 ml/kg)]. Then, a prospective cohort study involving 6 patients with peripheral venous malformations (VMs) is performed to explore the clinical safety and effectiveness of REI. From Jun 1, 2023 to August 31, 2023, 6 patients [age: (33.3 ± 17.2) years] with lingual VMs received sclerotherapy of REI and 2-month follow-up. Adverse events and serious adverse events were evaluated, whereas the efficacy of REI was determined by both the traceability of the REI under DSA throughout the entire injection and the therapeutic effect 2 months after a single injection. RESULTS: The REI contains 81.4% ethanol (v/v) and 111.3 mg/ml iodine, which can be traced throughout the injection in the animals and patients. The REI also exerts a similar effect as EtOH on peripheral venous sclerosis, peripheral arterial embolization, and renal embolization. Furthermore, the REI can be metabolized at a similar rate compared to EtOH and Ultravist® and did not cause injury to the animals' heart, liver, spleen, lungs, kidneys and brain. No REI-related adverse effects have occurred during sclerotherapy of VMs, and 4/6 patients (66.7%) have achieved complete response at follow-up. CONCLUSION: In conclusion, REI is safe, exerts therapeutic effects, and compensates for the radiolucency of EtOH in treating VMs. TRIAL REGISTRATION: The clinical trial was registered as No. ChiCTR2300071751 on May 24 2023.

Assessing volatile organic compounds exposure and chronic obstructive pulmonary diseases in US adults.

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.

Effect of SARS-CoV-2 infection on asthma patients.

Background: SARS-CoV-2 causes coronavirus disease 2019 (COVID-19), a new coronavirus pneumonia, and containing such an international pandemic catastrophe remains exceedingly difficult. Asthma is a severe chronic inflammatory airway disease that is becoming more common around the world. However, the link between asthma and COVID-19 remains unknown. Through bioinformatics analysis, this study attempted to understand the molecular pathways and discover potential medicines for treating COVID-19 and asthma. Methods: To investigate the relationship between SARS-CoV-2 and asthma patients, a transcriptome analysis was used to discover shared pathways and molecular signatures in asthma and COVID-19. Here, two RNA-seq data (GSE147507 and GSE74986) from the Gene Expression Omnibus were used to detect differentially expressed genes (DEGs) in asthma and COVID-19 patients to find the shared pathways and the potential drug candidates. Results: There were 66 DEGs in all that were classified as common DEGs. Using a protein-protein interaction (PPI) network created using various bioinformatics techniques, five hub genes were found. We found that asthma has some shared links with the progression of COVID-19. Additionally, protein-drug interactions with common DEGs were also identified in the datasets. Conclusion: We investigated possible links between COVID-19 and asthma using bioinformatics databases, which might be useful in treating COVID-19 patients. More studies on populations affected by these diseases are needed to elucidate the molecular mechanism behind their association.

Clinical Implications of Necroptosis Genes Expression for Cancer Immunity and Prognosis: A Pan-Cancer Analysis.

Background: Necroptosis, a form of programmed cell death, is increasingly being investigated for its controversial role in tumorigenesis and progression. Necroptosis suppresses tumor formation and tumor development by killing tumor cells; however, the necrotic cells also promote tumor formation and tumor development via the immunosuppressive effect of necroptosis and inflammatory response caused by cytokine release. Thus, the exact mechanism of necroptosis in pan-cancer remains unknown. Methods: The data of 11,057 cancer samples were downloaded from the TCGA database, along with clinical information, tumor mutation burden, and microsatellite instability information of the corresponding patients. We used the TCGA data in a pan-cancer analysis to identify differences in mRNA level as well as single nucleotide variants, copy number variants, methylation profiles, and genomic signatures of miRNA-mRNA interactions. Two drug datasets (from GDSC, CTRP) were used to evaluate drug sensitivity and resistance against necroptosis genes. Results: Necroptosis genes were aberrantly expressed in various cancers. The frequency of necroptosis gene mutations was highest in lung squamous cell carcinoma. Furthermore, the correlation between necroptosis gene expression in the tumor microenvironment and immune cell infiltration varied for different cancers. High necroptosis gene expression was found to correlate with NK, Tfh, Th1, CD8_T, and DC cells. These can therefore be used as biomarkers to predict prognosis. By matching gene targets with drugs, we identified potential candidate drugs. Conclusion: Our study showed the genomic alterations and clinical features of necroptosis genes in 33 cancers. This may help clarify the link between necroptosis and tumorigenesis. Our findings may also provide new approaches for the clinical treatment of cancer.

Construction and Validation of a m7G-Related Gene-Based Prognostic Model for Gastric Cancer.

Background: Gastric cancer (GC) is one of the most common malignant tumors of the digestive system. Chinese cases of GC account for about 40% of the global rate, with approximately 1.66 million people succumbing to the disease each year. Despite the progress made in the treatment of GC, most patients are diagnosed at an advanced stage due to the lack of obvious clinical symptoms in the early stages of GC, and their prognosis is still very poor. The m7G modification is one of the most common forms of base modification in post-transcriptional regulation, and it is widely distributed in the 5' cap region of tRNA, rRNA, and eukaryotic mRNA. Methods: RNA sequencing data of GC were downloaded from The Cancer Genome Atlas. The differentially expressed m7G-related genes in normal and tumour tissues were determined, and the expression and prognostic value of m7G-related genes were systematically analysed. We then built models using the selected m7G-related genes with the help of machine learning methods.The model was then validated for prognostic value by combining the receiver operating characteristic curve (ROC) and forest plots. The model was then validated on an external dataset. Finally, quantitative real-time PCR (qPCR) was performed to detect gene expression levels in clinical gastric cancer and paraneoplastic tissue. Results: The model is able to determine the prognosis of GC samples quantitatively and accurately. The ROC analysis of model has an AUC of 0.761 and 0.714 for the 3-year overall survival (OS) in the training and validation sets, respectively. We determined a correlation between risk scores and immune cell infiltration and concluded that immune cell infiltration affects the prognosis of GC patients. NUDT10, METTL1, NUDT4, GEMIN5, EIF4E1B, and DCPS were identified as prognostic hub genes and potential therapeutic agents were identified based on these genes. Conclusion: The m7G-related gene-based prognostic model showed good prognostic discrimination. Understanding how m7G modification affect the infiltration of the tumor microenvironment (TME) cells will enable us to better understand the TME's anti-tumor immune response, and hopefully guide more effective immunotherapy methods.

m7G Methylation-Related Genes as Biomarkers for Predicting Overall Survival Outcomes for Hepatocellular Carcinoma.

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.

A Novel Model Based on Necroptosis-Related Genes for Predicting Prognosis of Patients With Prostate Adenocarcinoma.

Background: Necroptosis is a newly recognized form of cell death. Here, we applied bioinformatics tools to identify necroptosis-related genes using a dataset from The Cancer Genome Atlas (TCGA) database, then constructed a model for prognosis of patients with prostate cancer. Methods: RNA sequence (RNA-seq) data and clinical information for Prostate adenocarcinoma (PRAD) patients were obtained from the TCGA portal (http://tcga-data.nci.nih.gov/tcga/). We performed comprehensive bioinformatics analyses to identify hub genes as potential prognostic biomarkers in PRAD u followed by establishment and validation of a prognostic model. Next, we assessed the overall prediction performance of the model using receiver operating characteristic (ROC) curves and the area under curve (AUC) of the ROC. Results: A total of 5 necroptosis-related genes, namely ALOX15, BCL2, IFNA1, PYGL and TLR3, were used to construct a survival prognostic model. The model exhibited excellent performance in the TCGA cohort and validation group and had good prediction accuracy in screening out high-risk prostate cancer patients. Conclusion: We successfully identified necroptosis-related genes and constructed a prognostic model that can accurately predict 1- 3-and 5-years overall survival (OS) rates of PRAD patients. Our riskscore model has provided novel strategy for the prediction of PRAD patients' prognosis.