Ginsenoside Rh1 Improves the Effect of Dexamethasone on Autoantibodies Production and Lymphoproliferation in MRL/lpr Mice

Ginsenoside Rh1 is able to upregulate glucocorticoid receptor (GR) level, suggesting Rh1 may improve glucocorticoid efficacy in hormone-dependent diseases. Therefore, we investigated whether Rh1 could enhance the effect of dexamethasone (Dex) in the treatment of MRL/lpr mice. MRL/lpr mice were treated with vehicle, Dex, Rh1, or Dex + Rh1 for 4 weeks. Dex significantly reduced the proteinuria and anti-dsDNA and anti-ANA autoantibodies. The levels of proteinuria and anti-dsDNA and anti-ANA autoantibodies were further decreased in Dex + Rh1 group. Dex, Rh1, or Dex + Rh1 did not alter the proportion of CD4+ splenic lymphocytes, whereas the proportion of CD8+ splenic lymphocytes was significantly increased in Dex and Dex + Rh1 groups. Dex + Rh1 significantly decreased the ratio of CD4+/CD8+ splenic lymphocytes compared with control. Con A-induced CD4+ splenic lymphocytes proliferation was increased in Dex-treated mice and was inhibited in Dex + Rh1-treated mice. Th1 cytokine IFN-γ mRNA was suppressed and Th2 cytokine IL-4 mRNA was increased by Dex. The effect of Dex on IFN-γ and IL-4 mRNA was enhanced by Rh1. In conclusion, our data suggest that Rh1 may enhance the effect of Dex in the treatment of MRL/lpr mice through regulating CD4+ T cells activation and Th1/Th2 balance

A multi-variable predictive warning model for cervical cancer using clinical and SNPs data

IntroductionCervical cancer is the fourth most common cancer among female worldwide. Early detection and intervention are essential. This study aims to construct an early predictive warning model for cervical cancer and precancerous lesions utilizing clinical data and simple nucleotide polymorphisms (SNPs).MethodsClinical data and germline SNPs were collected from 472 participants. Univariate logistic regression, least absolute shrinkage selection operator (LASSO), and stepwise regression were performed to screen variables. Logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), extreme gradient boosting(XGBoost) and neural network(NN) were applied to establish models. The receiver operating characteristic (ROC) curve was used to compare the models’ efficiencies. The performance of models was validated using decision curve analysis (DCA).ResultsThe LR model, which included 6 SNPs and 2 clinical variables as independent risk factors for cervical carcinogenesis, was ultimately chosen as the most optimal model. The DCA showed that the LR model had a good clinical application.DiscussionThe predictive model effectively foresees cervical cancer risk using clinical and SNP data, aiding in planning timely interventions. It provides a transparent tool for refining clinical decisions in cervical cancer management

GJA1 Expression and Its Prognostic Value in Cervical Cancer

Gap Junction Protein Alpha 1 (GJA1) belongs to the gap junction family and has been widely studied in cancers. We evaluated the role of GJA1 in cervical cancer (CC) using public data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. The difference of GJA1 expression level between CC and normal tissues was analyzed by the Gene Expression Profiling Interactive Analysis (GEPIA), six GEO datasets, and the Human Protein Atlas (HPA). The relationship between clinicopathological features and GJA1 expression was analyzed by the chi-squared test and the logistic regression. Kaplan–Meier survival analysis and Cox proportional hazard regression analysis were used to assessing the effect of GJA1 expression on survival. Gene set enrichment analysis (GSEA) was used to screen the signaling pathways regulated by GJA1. Immune Cell Abundance Identifier (ImmuCellAI) was chosen to analyze the immune cells affected by GJA1. The expression of GJA1 in CC was significantly lower than that in normal tissues based on the GEPIA, GEO datasets, and HPA. Both the chi-squared test and the logistic regression showed that high-GJA1 expression was significantly correlated with keratinization, hormone use, tumor size, and FIGO stage. The Kaplan–Meier curves suggested that high-GJA1 expression could indicate poor prognosis (p=0.0058). Multivariate analysis showed that high-GJA1 expression was an independent predictor of poor overall survival (HR, 4.084; 95% CI, 1.354-12.320; p=0.013). GSEA showed many cancer-related pathways, such as the p53 signaling pathway and the Wnt signaling pathway, were enriched in the high-GJA1-expression group. Immune cell abundance analysis revealed that the abundance of CD8 naive, DC, and neutrophil was significantly increased in the high-GJA1-expression group. In conclusion, GJA1 can be regarded as a potential prognostic marker of poor survival and therapeutic target in CC. Moreover, many cancer-related pathways may be the critical pathways regulated by GJA1. Furthermore, GJA1 can affect the abundance of immune cells

Small Nucleolar RNAs and Their Comprehensive Biological Functions in Hepatocellular Carcinoma

Small nucleolar RNAs (snoRNAs) are a class of highly conserved, stable non-coding RNAs involved in both post-transcriptional modification of RNA and in ribosome biogenesis. Recent research shows that the dysfunction of snoRNAs plays a pivotal role in hepatocellular carcinoma (HCC) and related etiologies, such as hepatitis B virus (HBV), hepatitis C virus (HCV), and non-alcoholic fatty liver disease (NAFLD). Growing evidence suggests that snoRNAs act as oncogenes or tumor suppressors in hepatocellular carcinoma (HCC) through multiple mechanisms. Furthermore, snoRNAs are characterized by their stability in body fluids and their clinical relevance and represent promising tools as diagnostic and prognostic biomarkers. SnoRNAs represent an emerging area of cancer research. In this review, we summarize the classification, biogenesis, activity, and functions of snoRNAs, as well as highlight the mechanism and roles of snoRNAs in HCC and related diseases. Our findings will aid in the understanding of complex processes of tumor occurrence and development, as well as suggest potential diagnostic markers and treatment targets. Furthermore, we discuss several limitations and suggest future research and application directions

A case of SLE with COVID-19 and multiple infections

The coronavirus disease 2019 (COVID-19) has become a global pandemic, which is induced by infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Patients with systemic lupus erythematosus (SLE) are susceptible to infections due to the chronic use of immunosuppressive drugs and the autoimmune disorders. Now we report a case of SLE infected with SARS-CoV-2, influenza A virus and Mycoplasma pneumoniae concurrently. The patient used hydroxychloroquine and prednisone chronically to control the SLE. After infection of SARS-CoV-2, she was given higher dose of prednisone than before and the same dosage of hydroxychloroquine. Besides, some empirical treatments such as antiviral, antibiotic and immunity regulating therapies were also given. The patient finally recovered from COVID-19. This case indicated that hydroxychloroquine may not be able to fully protect SLE patient form SARS-CoV-2. Intravenous immunoglobulin therapies and increased dose of corticosteroids might be adoptable for patient with both COVID-19 and SLE. Physicians should consider SARS-CoV-2 virus test when SLE patient presented with suspected infection or SLE flare under the epidemic of COVID-19

Pulmonary Delivery of Specialized Pro-Resolving Mediators-Based Nanotherapeutics Attenuates Pulmonary Fibrosis in Preclinical Animal Models

Pulmonary fibrosis (PF) is a chronic lung disease characterized by excess extracellular matrix deposition and prolonged inflammation that fails to resolve and is druggable. Using resolvins and their precursors for inflammation resolution, we demonstrate a nano-enabled approach for accomplishing robust antifibrotic effects in bleomycin- or engineered nanomaterial-induced mouse and rat PF models. Targeting the lipid peroxidation-triggered NLRP3 inflammasome and NF-κB pathway in macrophages and the ROS-mediated TGF-β/Smad and S1P signaling in epithelial cells results in these potent protective effects at the ng/mL dosimetry. We further develop an inhalable biocompatible nanoparticle that encapsulates fish oil, a chosen resolvin precursor, with phosphatidylcholine and polyethylene glycol to enhance drug permeability and facilitate crossing the mucosal barrier, forming "fish-oilsome" (FOS). Oropharyngeal aspiration and inhalation of FOS improved the anti-inflammatory status, histological characteristics, and pulmonary function in fibrotic lungs, which was mechanistically supported by transcriptomic and proteomic analyses. Further, scale-up engineered FOS samples with the desired physicochemical properties, anti-PF efficacy, and in vivo biocompatibility were validated in different batch sizes (up to 0.2 L/batch). This study provides a practical and translatable approach to promoting inflammation resolution and PF treatment

Data_Sheet_2_A multi-variable predictive warning model for cervical cancer using clinical and SNPs data.PDF

IntroductionCervical cancer is the fourth most common cancer among female worldwide. Early detection and intervention are essential. This study aims to construct an early predictive warning model for cervical cancer and precancerous lesions utilizing clinical data and simple nucleotide polymorphisms (SNPs).MethodsClinical data and germline SNPs were collected from 472 participants. Univariate logistic regression, least absolute shrinkage selection operator (LASSO), and stepwise regression were performed to screen variables. Logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), extreme gradient boosting(XGBoost) and neural network(NN) were applied to establish models. The receiver operating characteristic (ROC) curve was used to compare the models’ efficiencies. The performance of models was validated using decision curve analysis (DCA).ResultsThe LR model, which included 6 SNPs and 2 clinical variables as independent risk factors for cervical carcinogenesis, was ultimately chosen as the most optimal model. The DCA showed that the LR model had a good clinical application.DiscussionThe predictive model effectively foresees cervical cancer risk using clinical and SNP data, aiding in planning timely interventions. It provides a transparent tool for refining clinical decisions in cervical cancer management.</p

Data_Sheet_3_A multi-variable predictive warning model for cervical cancer using clinical and SNPs data.CSV

IntroductionCervical cancer is the fourth most common cancer among female worldwide. Early detection and intervention are essential. This study aims to construct an early predictive warning model for cervical cancer and precancerous lesions utilizing clinical data and simple nucleotide polymorphisms (SNPs).MethodsClinical data and germline SNPs were collected from 472 participants. Univariate logistic regression, least absolute shrinkage selection operator (LASSO), and stepwise regression were performed to screen variables. Logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), extreme gradient boosting(XGBoost) and neural network(NN) were applied to establish models. The receiver operating characteristic (ROC) curve was used to compare the models’ efficiencies. The performance of models was validated using decision curve analysis (DCA).ResultsThe LR model, which included 6 SNPs and 2 clinical variables as independent risk factors for cervical carcinogenesis, was ultimately chosen as the most optimal model. The DCA showed that the LR model had a good clinical application.DiscussionThe predictive model effectively foresees cervical cancer risk using clinical and SNP data, aiding in planning timely interventions. It provides a transparent tool for refining clinical decisions in cervical cancer management.</p

Data_Sheet_1_A multi-variable predictive warning model for cervical cancer using clinical and SNPs data.PDF

IntroductionCervical cancer is the fourth most common cancer among female worldwide. Early detection and intervention are essential. This study aims to construct an early predictive warning model for cervical cancer and precancerous lesions utilizing clinical data and simple nucleotide polymorphisms (SNPs).MethodsClinical data and germline SNPs were collected from 472 participants. Univariate logistic regression, least absolute shrinkage selection operator (LASSO), and stepwise regression were performed to screen variables. Logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), extreme gradient boosting(XGBoost) and neural network(NN) were applied to establish models. The receiver operating characteristic (ROC) curve was used to compare the models’ efficiencies. The performance of models was validated using decision curve analysis (DCA).ResultsThe LR model, which included 6 SNPs and 2 clinical variables as independent risk factors for cervical carcinogenesis, was ultimately chosen as the most optimal model. The DCA showed that the LR model had a good clinical application.DiscussionThe predictive model effectively foresees cervical cancer risk using clinical and SNP data, aiding in planning timely interventions. It provides a transparent tool for refining clinical decisions in cervical cancer management.</p