Is the disappearance of the cervical flexion-relaxation phenomenon associated with cervical degeneration in healthy people?

PURPOSE: This study aims to explore the differences in cervical degeneration between healthy people with and without cervical flexion-relaxation phenomenon (FRP) and to identify whether the disappearance of cervical FRP is related to cervical degeneration. METHODS: According to the flexion relaxation ratio (FRR), healthy subjects were divided into the normal FRP group and the abnormal FRP group. Besides, MRI was used to evaluate the degeneration of the passive subsystem (vertebral body, intervertebral disc, cervical sagittal balance, etc.) and the active subsystem (deep flexors [DEs], deep extensors [DFs], and superficial extensors [SEs]). In addition, the correlation of the FRR with the cervical degeneration score, C2-7Cobb, Borden method, relative total cross-sectional area (rTCSA), relative functional cross-sectional area (rFCSA), and fatty infiltration ratio (FIR) was analyzed. RESULTS: A total of 128 healthy subjects were divided into the normal FRP group (n=52, 40.63%) and the abnormal FRP group (n=76, 59.38%). There were significant differences between the normal FRP group and the abnormal FRP group in the cervical degeneration score (z=-6.819, P<0.001), C2-7Cobb (t=2.994, P=0.004), Borden method (t=2.811, P=0.006), and FIR of DEs (t=-4.322, P<0.001). The FRR was significantly correlated with the cervical degeneration score (r=-0.457, P<0.001), C2-7Cobb (r=0.228, P=0.010), Borden method (r=0.197, P=0.026), and FIR of DEs (r=-0.253, P=0.004). CONCLUSION: The disappearance of cervical FRP is related to cervical degeneration. A new hypothesis mechanism for FRP is proposed. The cervical FRP test is an effective and noninvasive examination for the differential diagnosis of healthy people, people with potential NSNP, and patients with NSNP.

Multi role ChatGPT framework for transforming medical data analysis.

The application of ChatGPTin the medical field has sparked debate regarding its accuracy. To address this issue, we present a Multi-Role ChatGPT Framework (MRCF), designed to improve ChatGPT's performance in medical data analysis by optimizing prompt words, integrating real-world data, and implementing quality control protocols. Compared to the singular ChatGPT model, MRCF significantly outperforms traditional manual analysis in interpreting medical data, exhibiting fewer random errors, higher accuracy, and better identification of incorrect information. Notably, MRCF is over 600 times more time-efficient than conventional manual annotation methods and costs only one-tenth as much. Leveraging MRCF, we have established two user-friendly databases for efficient and straightforward drug repositioning analysis. This research not only enhances the accuracy and efficiency of ChatGPT in medical data science applications but also offers valuable insights for data analysis models across various professional domains.

Recapitulation of the Powassan virus life cycle in cell culture.

Powassan virus (POWV) is a tick-borne flavivirus known for causing fatal neuroinvasive diseases in humans. Recently, there has been a noticeable increase in POWV infections, emphasizing the urgency of understanding viral replication, pathogenesis, and developing interventions. Notably, there are no approved vaccines or therapeutics for POWV, and its classification as a biosafety level-3 (BSL-3) agent hampers research. To overcome these obstacles, we developed a replicon system, a self-replicating RNA lacking structural proteins, making it safe to operate in a BSL-2 environment. We constructed a POWV replicon carrying the Gaussia luciferase (Gluc) reporter gene and blasticidin (BSD) selectable marker. Continuous BSD selection led to obtain a stable POWV replicon-carrying Huh7 cell lines. We identified cell culture adaptive mutations G4079A, G4944T and G6256A, resulting in NS2AR195K, NS3G122G, and NS3V560M, enhancing RNA replication. We demonstrated the utility of the POWV replicon system for high-throughput screening (HTS) assay to identify promising antivirals against POWV replication. We further explored the applications of the POWV replicon system, generating single-round infectious particles (SRIPs) by transfecting Huh7-POWV replicon cells with plasmids encoding viral capsid (C), premembrane (prM), and envelope (E) proteins, and revealed the distinct antigenic profiles of POWV with ZIKV. In summary, the POWV replicon and SRIP systems represent crucial platforms for genetic and functional analysis of the POWV life cycle and facilitating the discovery of antiviral drugs.IMPORTANCEIn light of the recent surge in human infections caused by POWV, a biosafety level-3 (BSL-3) classified virus, there is a pressing need to understand the viral life cycle and the development of effective countermeasures. To address this, we have pioneered the establishment of a POWV RNA replicon system and a replicon-based POWV SRIP system. Importantly, these systems are operable in BSL-2 laboratories, enabling comprehensive investigations into the viral life cycle and facilitating antiviral screening. In summary, these useful tools are poised to advance our understanding of the POWV life cycle and expedite the development of antiviral interventions.

Potential mechanisms and drug prediction of Rheumatoid Arthritis and primary Sjögren's Syndrome: A public databases-based study.

Rheumatoid arthritis (RA) and primary Sjögren's syndrome (pSS) are the most common systemic autoimmune diseases, and they are increasingly being recognized as occurring in the same patient population. These two diseases share several clinical features and laboratory parameters, but the exact mechanism of their co-pathogenesis remains unclear. The intention of this study was to investigate the common molecular mechanisms involved in RA and pSS using integrated bioinformatic analysis. RNA-seq data for RA and pSS were picked up from the Gene Expression Omnibus (GEO) database. Co-expression genes linked with RA and pSS were recognized using weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis. Then, we screened two public disease-gene interaction databases (GeneCards and Comparative Toxicogenomics Database) for common targets associated with RA and pSS. The DGIdb database was used to predict therapeutic drugs for RA and pSS. The Human microRNA Disease Database (HMDD) was used to screen out the common microRNAs associated with RA and pSS. Finally, a common miRNA-gene network was created using Cytoscape. Four hub genes (CXCL10, GZMA, ITGA4, and PSMB9) were obtained from the intersection of common genes from WGCNA, differential gene analysis and public databases. Twenty-four drugs corresponding to hub gene targets were predicted in the DGIdb database. Among the 24 drugs, five drugs had already been reported for the treatment of RA and pSS. Other drugs, such as bortezomib, carfilzomib, oprozomib, cyclosporine and zidovudine, may be ideal drugs for the future treatment of RA patients with pSS. According to the miRNA-gene network, hsa-mir-21 may play a significant role in the mechanisms shared by RA and pSS. In conclusion, we identified commom targets as potential biomarkers in RA and pSS from publicly available databases and predicted potential drugs based on the targets. A new understanding of the molecular mechanisms associated with RA and pSS is provided according to the miRNA-gene network.

Integrating scRNA-seq and bulk RNA-seq to characterize infiltrating cells in the colorectal cancer tumor microenvironment and construct molecular risk models.

Colorectal cancer (CRC) is a malignancy that is both highly lethal and heterogeneous. Although the correlation between intra-tumoral genetic and functional heterogeneity and cancer clinical prognosis is well-established, the underlying mechanism in CRC remains inadequately understood. Utilizing scRNA-seq data from GEO database, we re-isolated distinct subsets of cells, constructed a CRC tumor-related cell differentiation trajectory, and conducted cell-cell communication analysis to investigate potential interactions across cell clusters. A prognostic model was built by integrating scRNA-seq results with TCGA bulk RNA-seq data through univariate, LASSO, and multivariate Cox regression analyses. Eleven distinct cell types were identified, with Epithelial cells, Fibroblasts, and Mast cells exhibiting significant differences between CRC and healthy controls. T cells were observed to engage in extensive interactions with other cell types. Utilizing the 741 signature genes, prognostic risk score model was constructed. Patients with high-risk scores exhibited a significant correlation with unfavorable survival outcomes, high-stage tumors, metastasis, and low responsiveness to chemotherapy. The model demonstrated a strong predictive performance across five validation cohorts. Our investigation involved an analysis of the cellular composition and interactions of infiltrates within the microenvironment, and we developed a prognostic model. This model provides valuable insights into the prognosis and therapeutic evaluation of CRC.

A random forest algorithm-based prediction model for moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia.

BACKGROUND: Postoperative pain is one of the most common complications after surgery. In order to detect early and intervene in time for moderate to severe postoperative pain, it is necessary to identify risk factors and construct clinical prediction models. This study aimed to identify significant risk factors and establish a better-performing model to predict moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia. METHODS: Patients who underwent orthopedic surgery under general anesthesia were divided into patients with moderate to severe pain group (group P) and patients without moderate to severe pain group (group N) based on VAS scores. The features selected by Lasso regression were processed by the random forest and multivariate logistic regression models to predict pain outcomes. The classification performance of the two models was evaluated through the testing set. The area under the curves (AUC), the accuracy of the classifiers, and the classification error rate for both classifiers were calculated, the better-performing model was used to predict moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia. RESULTS: A total of 327 patients were enrolled in this study (228 in the training set and 99 in the testing set). The incidence of moderate to severe postoperative pain was 41.3%. The random forest model revealed a classification error rate of 25.2% and an AUC of 0.810 in the testing set. The multivariate logistic regression model revealed a classification error rate of 31.3% and an AUC of 0.764 in the testing set. The random forest model was chosen for predicting clinical outcomes in this study. The risk factors with the greatest and second contribution were immobilization and duration of surgery, respectively. CONCLUSIONS: The random forest model can be used to predict moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia, which is of potential clinical application value.

Distinct molecular subtypes of systemic sclerosis and gene signature with diagnostic capability.

Background: As Systemic Sclerosis (SSc) is a connective tissue ailment that impacts various bodily systems. The study aims to clarify the molecular subtypes of SSc, with the ultimate objective of establishing a diagnostic model that can inform clinical treatment decisions. Methods: Five microarray datasets of SSc were retrieved from the GEO database. To eliminate batch effects, the combat algorithm was applied. Immune cell infiltration was evaluated using the xCell algorithm. The ConsensusClusterPlus algorithm was utilized to identify SSc subtypes. Limma was used to determine differential expression genes (DEGs). GSEA was used to determine pathway enrichment. A support vector machine (SVM), Random Forest(RF), Boruta and LASSO algorithm have been used to select the feature gene. Diagnostic models were developed using SVM, RF, and Logistic Regression (LR). A ROC curve was used to evaluate the performance of the model. The compound-gene relationship was obtained from the Comparative Toxicogenomics Database (CTD). Results: The identification of three immune subtypes in SSc samples was based on the expression profiles of immune cells. The utilization of 19 key intersectional DEGs among subtypes facilitated the classification of SSc patients into three robust subtypes (gene_ClusterA-C). Gene_ClusterA exhibited significant enrichment of B cells, while gene_ClusterC showed significant enrichment of monocytes. Moderate activation of various immune cells was observed in gene_ClusterB. We identified 8 feature genes. The SVM model demonstrating superior diagnostic performance. Furthermore, correlation analysis revealed a robust association between the feature genes and immune cells. Eight pertinent compounds, namely methotrexate, resveratrol, paclitaxel, trichloroethylene, formaldehyde, silicon dioxide, benzene, and tetrachloroethylene, were identified from the CTD. Conclusion: The present study has effectively devised an innovative molecular subtyping methodology for patients with SSc and a diagnostic model based on machine learning to aid in clinical treatment. The study has identified potential molecular targets for therapy, thereby offering novel perspectives for the treatment and investigation of SSc.

A compendium of mitochondrial molecular characteristics provides novel perspectives on the treatment of rheumatoid arthritis patients.

Rheumatoid arthritis (RA) is an autoimmune disease that exhibits a high degree of heterogeneity, marked by unpredictable disease flares and significant variations in the response to available treatments. The lack of optimal stratification for RA patients may be a contributing factor to the poor efficacy of current treatment options. The objective of this study is to elucidate the molecular characteristics of RA through the utilization of mitochondrial genes and subsequently construct and authenticate a diagnostic framework for RA. Mitochondrial proteins were obtained from the MitoCarta database, and the R package limma was employed to filter for differentially expressed mitochondrial genes (MDEGs). Metascape was utilized to perform enrichment analysis, followed by an unsupervised clustering algorithm using the ConsensuClusterPlus package to identify distinct subtypes based on MDEGs. The immune microenvironment, biological pathways, and drug response were further explored in these subtypes. Finally, a multi-biomarker-based diagnostic model was constructed using machine learning algorithms. Utilizing 88 MDEGs present in transcript profiles, it was possible to classify RA patients into three distinct subtypes, each characterized by unique molecular and cellular signatures. Subtype A exhibited a marked activation of inflammatory cells and pathways, while subtype C was characterized by the presence of specific innate lymphocytes. Inflammatory and immune cells in subtype B displayed a more modest level of activation (Wilcoxon test P < 0.05). Notably, subtype C demonstrated a stronger correlation with a superior response to biologics such as infliximab, anti-TNF, rituximab, and methotrexate/abatacept (P = 0.001) using the fisher test. Furthermore, the mitochondrial diagnosis SVM model demonstrated a high degree of discriminatory ability in distinguishing RA in both training (AUC = 100%) and validation sets (AUC = 80.1%). This study presents a pioneering analysis of mitochondrial modifications in RA, offering a novel framework for patient stratification and potentially enhancing therapeutic decision-making.

Comparison of Flexion Relaxation Phenomenon After Single-Level and Multi-Level Anterior Cervical Discectomy and Fusion.

BACKGROUND: This prospective cohort study aimed to assess the influence of the number of fused segments in cervical paravertebral muscles by comparing the changes of the cervical flexion relaxation phenomenon (FRP) after single-level versus multilevel anterior cervical discectomy and fusion (ACDF). METHODS: A total of 115 patients who had undergone ACDF were retrospectively recruited and divided into a 1-level group (n = 44), a 2-level group (n = 40) and a 3- to 4-level group (n = 31). The flexion relaxation experiment was carried out 3 days preoperatively and 12 months postoperatively by surface electromyography (SEMG). Patients were examined using the neck visual analog scale, cervical Japanese Orthopedic Association score, Neck Disability Index, and C2-C7 range of motion (ROM). RESULTS: There was a significant difference in the time-related changes in flexion relaxation ratio (FRR) among the 3 study groups before and after surgery (F = 85.701; P < .001). Thirty-five patients (79.55%) with 1-level ACDF and 11 patients (27.5%) with 2-level ACDF had FRP were restored to normal at 12 months postoperatively; however, only 1 patient (3.33%) had normalized FRP after 3- to 4-level ACDF. There were significant differences in the time-related changes of the normalized SEMG root mean square values in each phase before and after surgery (P = .018, <.001, <.001, and <.001). A significant correlation was found between the changes in C2-C7 ROM and FRR in the 3 study groups (P = .007 for 1 level, P = .003 for 2 levels, and P = .036 for 3-4 levels). CONCLUSIONS: Single-level ACDF contributes to normalizing the FRP of cervical paravertebral muscles, which is not ideally recovered by 2-level ACDF. In contrast, 3- or 4-level ACDF could not normalize the cervical FRP. Our research supports the passive structure hypothesis.

Identification of immunity-related lncRNAs and construction of a ceRNA network of potential prognostic biomarkers in acute myeloid leukemia.

Objective: Using bioinformatics analyses, this study aimed to identify lncRNAs related to the immune status of acute myeloid leukemia (AML) patients and ascertain the potential impact in immunity-related competing endogenous RNA (ceRNA) networks on AML prognosis. Methods: AML-related RNA-seq FPKM data, AML-related miRNA expression microarray data, and gene sets associated with immunity-related pathways were, respectively, obtained from the TCGA, GEO, and ImmReg databases. An immunity-related ceRNA network was then constructed according to the predicted interactions between AML-related mRNAs, lncRNAs, and miRNAs. After performing LASSO and multivariate Cox regression analyses, lncRNAs in the ceRNA network were used to establish an AML prognostic model. According to mutual regulatory relationships and consistent trends of expression among candidate ceRNAs, two ceRNA subnetworks related to the AML prognostic model were determined. Finally, the correlation between the expression levels of mRNAs, lncRNAs, and miRNAs in each ceRNA subnetwork and immune cell infiltration (assessed by combining the ESTIMATE and CIBERSORT methods and ssGSEA) was analyzed. Results: A total of 424 immunity-related differentially expressed (IR-DE) mRNAs (IR-DEmRNAs), 191 IR-DElncRNAs, and 69 IR-DEmiRNAs were obtained, and a ceRNA network of 20 IR-DElncRNAs, 6 IR-DEmRNAs, and 3 IR-DEmiRNAs was established. Univariate Cox regression analysis was conducted on 20 IR-DElncRNAs, and 7 of these were identified to be significantly correlated with the overall survival (OS) time in AML patients. Then, two IR-DElncRNAs (MEG3 and HCP5) were screened as independent OS-related factors by LASSO and multivariable Cox regression analyses, and a prognostic model was constructed to evaluate the survival risk in AML patients. Survival analyses indicated that the OS of patients was often poor in the high-risk group. Additionally, from this model, two ceRNA regulatory pathways, namely, MEG3/miR-125a-5p/SEMA4C and HCP5/miR-125b-5p/IL6R, which were potentially involved in the immune regulation of AML prognosis were identified. Conclusion: lncRNAs HCP5 and MEG3 may act as key ceRNAs in the pathogenesis in AML by regulating immune cell representation as part of the regulatory lncRNA-miRNA-mRNA axes. The candidate mRNAs, lncRNAs, and miRNAs included in the ceRNA network identified here may serve as useful prognostic biomarkers and immunotherapeutic targets for AML.

Mendelian randomization analysis suggests no associations of herpes simplex virus infections with systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) characterized by immune dysfunction is possibly more vulnerable to herpes simplex virus (HSV) infection. The infection has been intensively considered a common onset and exacerbation of SLE. This study is aimed at elucidating the causal association between SLE and HSV. A bidirectional two-sample Mendelian Randomization (TSMR) analysis was systematically conducted to explore the causal effect of SLE and HSV on each other. The causality was estimated by inverse variance weighted (IVW), MR-Egger and weighted median methods based on the summary-level genome-wide association studies (GWAS) data from a publicly available database. Genetically proxied HSV infection exhibited no causal association with SLE in the forward MR analysis using IVW method (odds ratio [OR] = 0.987; 95% confidence interval [CI]: 0.891-1.093; p = 0.798), nor did HSV-1 IgG (OR = 1.241; 95% CI: 0.874-1.762; p = 0.227) and HSV-2 IgG (OR = 0.934; 95% CI: 0.821-1.062; p = 0.297). Similar null results with HSV infection (OR = 1.021; 95% CI: 0.986-1.057; p = 0.245), HSV-1 IgG (OR = 1.003; 95% CI: 0.982-1.024; p = 0.788) and HSV-2 IgG (OR = 1.034; 95% CI: 0.991-1.080; p = 0.121) were observed in the reverse MR where SLE served as the exposure. Our study demonstrated no causal association between the genetically predicted HSV and SLE.

Comparison of One-Level Osteotomy and Two-Level Osteotomy for Thoracolumbar Kyphotic Deformity in Ankylosing Spondylitis: A Systematic Review and Meta-Analysis.

BACKGROUND: One- and two-level osteotomies have been used to treat thoracolumbar kyphosis in patients with ankylosing spondylitis (AS). However, the effectiveness and safety of the 2 methods have not been systematically compared, and few studies have reported on which method might be more suitable for treating AS. METHODS: We performed a systematic literature search. All comparative studies of one- and two-level osteotomy for the treatment of thoracolumbar kyphosis caused by AS were included. Efficacy was determined by the radiographic outcomes, including global kyphosis, sagittal vertical axis, and lumbar lordosis and the Oswestry disability index. The complication rates were used to assess the safety. We analyzed the data using Review Manager software. RESULTS: A total of 10 studies were included and used for the meta-analysis. We pooled the studies according to the type of data they had reported to evaluate the radiographic correction and incidence of complications. We found that two-level osteotomy provided advantages compared with one-level osteotomy for spinal correction, including global kyphosis and lumbar lordosis. However, the correction was dependent on the degree of preoperative kyphosis. In addition, we found no differences between the 2 groups in the correction of the sagittal vertical axis or improvement in the Oswestry disability index. However, we did find differences in the incidence of neural complications, dural tear, and operative site infection. CONCLUSIONS: Our findings have shown that one-level osteotomy is safer than two-level osteotomy. For AS patients with nonsevere kyphosis, one-level osteotomy will be sufficient for spinal sequence correction and maintenance of balance, and two-level osteotomy will not be needed. For AS patients with severe kyphosis, two-level osteotomy can be recommended for better orthopedic effects; however, a careful surgical technique is required.

[Construction of a Prognostic Model of Multiple Myeloma Based on Metabolism-Related Genes].

OBJECTIVE: To screen the prognostic biomarkers of metabolic genes in patients with multiple myeloma (MM), and construct a prognostic model of metabolic genes. METHODS: The histological database related to MM patients was searched. Data from MM patients and healthy controls with complete clinical information were selected for analysis.The second generation sequencing data and clinical information of bone marrow tissue of MM patients and healthy controls were collected from human protein atlas (HPA) and multiple myeloma research foundation (MMRF) databases. The gene set of metabolism-related pathways was extracted from Molecular Signatures Database (MSigDB) by Perl language. The biomarkers related to MM metabolism were screened by difference analysis, univariate Cox risk regression analysis and LASSO regression analysis, and the risk prognostic model and Nomogram were constructed. Risk curve and survival curve were used to verify the grouping effect of the model. Gene set enrichment analysis (GSEA) was used to study the difference of biological pathway enrichment between high risk group and low risk group. Multivariate Cox risk regression analysis was used to verify the independent prognostic ability of risk score. RESULTS: A total of 8 mRNAs which were significantly related to the survival and prognosis of MM patients were obtained (P<0.01). As molecular markers, MM patients could be divided into high-risk group and low-risk group. Survival curve and risk curve showed that the overall survival time of patients in the low-risk group was significantly better than that in the high risk group (P<0.001). GSEA results showed that signal pathways related to basic metabolism, cell differentiation and cell cycle were significantly enriched in the high-risk group, while ribosome and N polysaccharide biosynthesis signaling pathway were more enriched in the low-risk group. Multivariate Cox regression analysis showed that the risk score composed of the eight metabolism-related genes could be used as an independent risk factor for the prognosis of MM patients, and receiver operating characteristic curve (ROC) showed that the molecular signatures of metabolism-related genes had the best predictive effect. CONCLUSION: Metabolism-related pathways play an important role in the pathogenesis and prognosis of patients with MM. The clinical significance of the risk assessment model for patients with MM constructed based on eight metabolism-related core genes needs to be confirmed by further clinical studies.

A Compendium of Mucosal Molecular Characteristics Provides Novel Perspectives on the Treatment of Ulcerative Colitis.

BACKGROUND AND AIMS: Ulcerative colitis [UC] is a complex heterogeneous disease. This study aims to reveal the underlying molecular features of UC using genome-scale transcriptomes of patients with UC, and to develop and validate a novel stratification scheme. METHODS: A normalised compendium was created using colon tissue samples (455 patients with UC and 147 healthy controls [HCs]), covering genes from 10 microarray datasets. Upregulated differentially expressed genes [DEGs] were subjected to functional network analysis, wherein samples were grouped using unsupervised clustering. Additionally, the robustness of subclustering was further assessed by two RNA sequencing datasets [100 patients with UC and 16 HCs]. Finally, the Xgboost classifier was applied to the independent datasets to evaluate the efficacy of different biologics in patients with UC. RESULTS: Based on 267 upregulated DEGs of the transcript profiles, UC patients were classified into three subtypes [subtypes A-C] with distinct molecular and cellular signatures. Epithelial activation-related pathways were significantly enriched in subtype A [named epithelial proliferation], whereas subtype C was characterised as the immune activation subtype with prominent immune cells and proinflammatory signatures. Subtype B [named mixed] was modestly activated in all the signalling pathways. Notably, subtype A showed a stronger association with the superior response of biologics such as golimumab, infliximab, vedolizumab, and ustekinumab compared with subtype C. CONCLUSIONS: We conducted a deep stratification of mucosal tissue using the most comprehensive microarray and RNA sequencing data, providing critical insights into pathophysiological features of UC, which could serve as a template for stratified treatment approaches.

Tetramisole is a new IK1 channel agonist and exerts IK1 -dependent cardioprotective effects in rats.

Cardiac ischemia, hypoxia, arrhythmias, and heart failure share the common electrophysiological changes featured by the elevation of intracellular Ca2+ (Ca2+ overload) and inhibition of the inward rectifier potassium (IK1 ) channel. IK1 channel agonists have been considered a new type of anti-arrhythmia and cardioprotective agents. We predicted using a drug repurposing strategy that tetramisole (Tet), a known anthelminthic agent, was a new IK1 channel agonist. The present study aimed to experimentally identify the above prediction and further demonstrate that Tet has cardioprotective effects. Results of the whole-cell patch clamp technique showed that Tet at 1-100 µmol/L enhanced IK1 current, hyperpolarized resting potential (RP), and shortened action potential duration (APD) in isolated rat cardiomyocytes, while without effects on other ion channels or transporters. In adult Sprague-Dawley (SD) rats in vivo, Tet showed anti-arrhythmia and anticardiac remodeling effects, respectively, in the coronary ligation-induced myocardial infarction model and isoproterenol (Iso, i.p., 3 mg/kg/day, 10 days) infusion-induced cardiac remodeling model. Tet also showed anticardiomyocyte remodeling effect in Iso (1 µmol/L) infused adult rat ventricular myocytes or cultured H9c2 (2-1) cardiomyocytes. Tet at 0.54 mg/kg in vivo or 30 µmol/L in vitro showed promising protections on acute ischemic arrhythmias, myocardial hypertrophy, and fibrosis. Molecular docking was performed and identified the selective binding of Tet with Kir2.1. The cardioprotection of Tet was associated with the facilitation of IK1 channel forward trafficking, deactivation of PKA signaling, and inhibition of intracellular calcium overload. Enhancing IK1 may play dual roles in anti-arrhythmia and antiventricular remodeling mediated by restoration of Ca2+ homeostasis.

Characteristics of the Gut Microbiome and Its Relationship With Peripheral CD4+ T Cell Subpopulations and Cytokines in Rheumatoid Arthritis.

This study investigated the association between intestinal microbiota abundance and diversity and cluster of differentiation (CD)4+ T cell subpopulations, cytokine levels, and disease activity in rheumatoid arthritis RA. A total of 108 rheumatoid arthritis (RA) patients and 99 healthy control (HC) subjects were recruited. PICRUSt2 was used for functional metagenomic predictions. Absolute counts of peripheral CD4+ T cell subpopulations and cytokine levels were detected by flow cytometry and with a cytokine bead array, respectively. Correlations were analyzed with the Spearman rank correlation test. The results showed that the diversity of intestinal microbiota was decreased in RA patients compared to HCs. At the phylum level, the abundance of Firmicutes, Fusobacteriota, and Bacteroidota was decreased while that of Actinobacteria and Proteobacteria was increased and at the genus level, the abundance of Faecalibacterium, Blautia, and Escherichia-Shigella was increased while that of Bacteroides and Coprococcus was decreased in RA patients compared to HC subjects. The linear discriminant analysis effect size indicated that Bifidobacterium was the most significant genus in RA. The most highly enriched Kyoto Encyclopedia of Genes and Genomes pathway in RA patients was amino acid metabolism. The relative abundance of Megamonas, Monoglobus, and Prevotella was positively correlated with CD4+ T cell counts and cytokine levels; and the relative numbers of regulatory T cells (Tregs) and T helper (Th17)/Treg ratio were negatively correlated with disease activity in RA. These results suggest that dysbiosis of certain bacterial lineages and alterations in gut microbiota metabolism lead to changes in the host immune profile that contribute to RA pathogenesis.

Development and validation of a GRGPI model for predicting the prognostic and treatment outcomes in head and neck squamous cell carcinoma.

Background: Head and neck squamous cell carcinoma (HNSCC) is among the most lethal and most prevalent malignant tumors. Glycolysis affects tumor growth, invasion, chemotherapy resistance, and the tumor microenvironment. Therefore, we aimed at identifying a glycolysis-related prognostic model for HNSCC and to analyze its relationship with tumor immune cell infiltrations. Methods: The mRNA and clinical data were obtained from The Cancer Genome Atlas (TCGA), while glycolysis-related genes were obtained from the Molecular Signature Database (MSigDB). Bioinformatics analysis included Univariate cox and least absolute shrinkage and selection operator (LASSO) analyses to select optimal prognosis-related genes for constructing glycolysis-related gene prognostic index(GRGPI), as well as a nomogram for overall survival (OS) evaluation. GRGPI was validated using the Gene Expression Omnibus (GEO) database. A predictive nomogram was established based on the stepwise multivariate regression model. The immune status of GRGPI-defined subgroups was analyzed, and high and low immune groups were characterized. Prognostic effects of immune checkpoint inhibitor (ICI) treatment and chemotherapy were investigated by Tumor Immune Dysfunction and Exclusion (TIDE) scores and half inhibitory concentration (IC50) value. Reverse transcription-quantitative PCR (RT-qPCR) was utilized to validate the model by analyzing the mRNA expression levels of the prognostic glycolysis-related genes in HNSCC tissues and adjacent non-tumorous tissues. Results: Five glycolysis-related genes were used to construct GRGPI. The GRGPI and the nomogram model exhibited robust validity in prognostic prediction. Clinical correlation analysis revealed positive correlations between the risk score used to construct the GRGPI model and the clinical stage. Immune checkpoint analysis revealed that the risk model was associated with immune checkpoint-related biomarkers. Immune microenvironment and immune status analysis exhibited a strong correlation between risk score and infiltrating immune cells. Gene set enrichment analysis (GSEA) pathway enrichment analysis showed typical immune pathways. Furthermore, the GRGPIdel showed excellent predictive performance in ICI treatment and drug sensitivity analysis. RT-qPCR showed that compared with adjacent non-tumorous tissues, the expressions of five genes were significantly up-regulated in HNSCC tissues. Conclusion: The model we constructed can not only be used as an important indicator for predicting the prognosis of patients but also had an important guiding role for clinical treatment.

Telmisartan Potentiates Insulin Secretion via Ion Channels, Independent of the AT1 Receptor and PPARγ.

Angiotensin II type 1 (AT1) receptor blockers (ARBs), as antihypertensive drugs, have drawn attention for their benefits to individuals with diabetes and prediabetes. However, the direct effects of ARBs on insulin secretion remain unclear. In this study, we aimed to investigate the insulinotropic effect of ARBs and the underlying electrophysiological mechanism. We found that only telmisartan among the three ARBs (telmisartan, valsartan, and irbesartan) exhibited an insulin secretagogue role in rat islets. Independent of AT1 receptor and peroxisome proliferator-activated receptor γ (PPARγ), telmisartan exerted effects on ion channels including voltage-dependent potassium (Kv) channels and L-type voltage-gated calcium channels (VGCCs) to promote extracellular Ca2+ influx, thereby potentiating insulin secretion in a glucose-dependent manner. Furthermore, we identified that telmisartan directly inhibited Kv2.1 channel on a Chinese hamster ovary cell line with Kv2.1 channel overexpression. Acute exposure of db/db mice to a telmisartan dose equivalent to therapeutic doses in humans resulted in lower blood glucose and increased plasma insulin concentration in OGTT. We further observed the telmisartan-induced insulinotropic and electrophysiological effects on pathological pancreatic islets or ß-cells isolated from db/db mice. Collectively, our results establish an important insulinotropic function of telmisartan distinct from other ARBs in the treatment of diabetes.

Sphingosine 1-phosphate Stimulates Insulin Secretion and Improves Cell Survival by Blocking Voltage-dependent K+ Channels in ß Cells.

Recent studies suggest that Sphingosine 1-phosphate (S1P) plays an important role in regulating glucose metabolism in type 2 diabetes. However, its effects and mechanisms of promoting insulin secretion remain largely unknown. Here, we found that S1P treatment decreased blood glucose level and increased insulin secretion in C57BL/6 mice. Our results further showed that S1P promoted insulin secretion in a glucose-dependent manner. This stimulatory effect of S1P appeared to be irrelevant to cyclic adenosine monophosphate signaling. Voltage-clamp recordings showed that S1P did not influence voltage-dependent Ca2+ channels, but significantly blocked voltage-dependent potassium (Kv) channels, which could be reversed by inhibition of phospholipase C (PLC) and protein kinase C (PKC). Calcium imaging revealed that S1P increased intracellular Ca2+ levels, mainly by promoting Ca2+ influx, rather than mobilizing intracellular Ca2+ stores. In addition, inhibition of PLC and PKC suppressed S1P-induced insulin secretion. Collectively, these results suggest that the effects of S1P on glucose-stimulated insulin secretion (GSIS) depend on the inhibition of Kv channels via the PLC/PKC signaling pathway in pancreatic ß cells. Further, S1P improved ß cell survival; this effect was also associated with Kv channel inhibition. This work thus provides new insights into the mechanisms whereby S1P regulates ß cell function in diabetes.

Novel Small Molecule Glucagon-Like Peptide-1 Receptor Agonist S6 Stimulates Insulin Secretion From Rat Islets.

Glucagon-like peptide-1 receptor (GLP-1R) agonist-based therapeutics for type 2 diabetes mellitus have attracted worldwide attention. However, there are challenges in the development of small molecule GLP-1R agonists owing to the complexity of ligand recognition and signal induction mechanisms. Here, we attained S6 using virtual screening and fluorescent imaging plate reader (FLIPR)-based calcium assays. The purpose of this study was to identify and characterize S6, a novel small molecule GLP-1R agonist. Data from cellular thermal shift assay (CETSA) and Bio-Layer Interferometry (BLI) indicated that S6 could bind potently with GLP-1R. Radioimmunoassay data showed that S6 potentiated insulin secretion in a glucose-dependent manner and the insulinotropic effect was mediated by GLP-1R. Calcium imaging techniques suggested that S6 elevated the intracellular calcium concentration [(Ca2+)i] by activating GLP-1R. In patch-clamp experiments, we demonstrated that S6 inhibited voltage-dependent K+ (Kv) channels in a GLP-1R-dependent fashion. Besides, S6 significantly prolonged action potential duration but had no effect on voltage-dependent Ca2+ channels. In summary, these findings indicate that S6 stimulates glucose-dependent insulin secretion mainly by acting on GLP-1R, inhibiting Kv channels, increasing (Ca2+)i. This study will provide direction for the screening and development of novel small-molecule agents targeting GLP-1R in the future.