The causal relationship between immune cells mediating FIT3L, CCL4, OSM, and skin-derived deteriorated tumors.

OBJECTIVE: Observational studies have identified a dual effect of circulating inflammatory proteins and immune cells on cancer progression. However, the specific mechanisms of action have not been clarified in the exacerbation of cutaneous-origin tumors. Therefore, this study aims to investigate whether the causal relationship between circulating inflammatory factors and basal cell carcinoma (BCC), cutaneous malignant melanoma (SKCM), and cutaneous squamous cell carcinoma (cSCC) is regulated by immune cells. METHODS: This study employed the Two-Sample Mendelian Randomization (TSMR) approach to investigate the causal relationships between 91 circulating inflammatory factors and three prevalent types of skin cancer from a genetic perspective. Bayesian Weighted Mendelian Randomization (BWMR) was also used to validate correlation and reverse MR to assess inverse relationships. Subsequent sensitivity analyses were conducted to limit the impact of heterogeneity and pleiotropy. Finally, the two-step Mendelian Randomization (two-step MR) method was utilized to ascertain the mediating effects of specific immune cell traits in the causal pathways linking circulating inflammatory factors with BCC, SKCM, and cSCC. RESULTS: The Inverse Variance Weighted (IVW) method and the Bayesian Weighted Algorithm collectively identified nine inflammatory factors causally associated with BCC, SKCM, and cSCC. The results from Cochran's Q test, mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO), and MR-Egger intercept were not statistically significant (p < 0.05). Additionally, the proportions mediated by CD4+ CD8dim T cell %leukocyte, CD4-CD8-Natural Killer T %T cell, and CD20 on IgD-CD38-B cell for FIt3L, CCL4, and OSM were 9.26%, 8.96%, and 10.16%, respectively. CONCLUSION: Immune cell levels potentially play a role in the modulation process between circulating inflammatory proteins and cutaneous-origin exacerbated tumors. This finding offers a new perspective for the in-depth exploration of cutaneous malignancies.

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.

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.

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.

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.

A comparison of therapeutic dosages of decitabine in treating myelodysplastic syndrome: a meta-analysis.

Decitabine is used to treat myelodysplastic syndrome (MDS). This meta-analysis evaluated the efficacy and safety of different dosing regimens of decitabine in treating intermediate and/or high-risk MDS. Medline, Cochrane, EMBASE, and Google Scholar databases were searched up to October 23, 2015. Randomized controlled trials, prospective, cohort, and case series studies were included. Fifteen studies were included with a total of 1378 patients. The decitabine 100 mg/m2/course dosing regimen had a greater overall response rate than the 60-75 mg/m2/course (51 vs. 25%; P = 0.003). It also had higher complete response rate compared with the 135 mg/m2/course regimen (24.2 vs.13.7%; P = 0.016). The three dosing regimens were similar with respect to bone marrow complete response and partial response and hematologic improvement (P values > 0.05). Decitabine 135 mg/m2/course regimen had similar hematologic improvement as best supportive care (P = 0.066). The incidence of neutropenia, thrombocytopenia, infections, and anemia was similar across treatment groups (range, 31 to 38%; P values ≥ 0.899). The 100 mg/m2/course decitabine regimen showed benefit with respect to overall response rate compared with the 60-75 mg/m2/course regimen, as well as greater improvement in complete response rate compared with the 135 mg/m2/course regimen. All three dosing regimens had similar frequency of adverse events.

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.

Association of thrombocytopenia with immune checkpoint inhibitors: a large-scale pharmacovigilance analysis based on the data from FDA adverse event reporting system database.

Introduction: An increasing number of immune-related adverse events (irAEs) induced by immune checkpoint inhibitors (ICIs) have been reported during clinical treatment. We aimed to explore the clinical characteristics of patients with ICIs-induced ITP under different therapeutic strategies based on the FAERS database and explore the potential biological mechanisms in combination with TCGA pan-cancer data. Methods: Data from FAERS were collected for ICIs adverse reactions between January 2012 and December 2022. Disproportionality analysis identified ICIs-induced ITP in the FAERS database using the reporting odds ratio (ROR), proportional reporting ratio (PRP), Bayesian confidence propagation neural network (BCPNN), and multi-item gamma Poisson shrinker algorithms (MGPS). The potential biological mechanisms underlying ITP induced by ICIs were examined using TCGA transcriptome data on cancers. Results: In the FAERS, 345 ICIs-induced ITP reports were retrieved, wherein 290 (84.06%) and 55 (15.94%) were reported as monotherapy and combination therapy, respectively. The median age of the reported patients with ICIs-induced ITP was 69 years (IQR 60-76), of which 62 (18%) died and 47 (13.6%) had a life-threatening outcome. The majority of reported indications were lung, skin, and bladder cancers, and the median time to ITP after dosing was 42 days (IQR 17-135), with 64 patients (43.5%) experiencing ITP within 30 days of dosing and 88 patients experiencing ITP in less than 2 months (59.9%). The occurrence of ICIs-induced ITP may be associated with ICIs-induced dysregulation of the mTORC1 signaling pathway and megakaryocyte dysfunction. Conclusion: There were significant reporting signals for ITP with nivolumab, pembrolizumab, cemiplimab, atezolizumab, avelumab, durvalumab, ipilimumab, nivolumab/ipilimumab, and pembrolizumab/ipilimumab. Patients treated with anti-PD-1 in combination with anti-CTLA-4 are more likely to have an increased risk of ICIs-induced ITP. Patients with melanoma are at a higher risk of developing ITP when treated with ICI and should be closely monitored for this risk within 60 days of treatment. The potential biological mechanism of ICIs-induced ITP may be related to the dysfunction of megakaryocyte autophagy through the overactivation of the mTOR-related signaling pathway. This study provides a comprehensive understanding of ICIs-induced ITP. Clinicians should pay attention to this potentially fatal adverse reaction.

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.

Deciphering the molecular landscape of rheumatoid arthritis offers new insights into the stratified treatment for the condition.

Background: For Rheumatoid Arthritis (RA), a long-term chronic illness, it is essential to identify and describe patient subtypes with comparable goal status and molecular biomarkers. This study aims to develop and validate a new subtyping scheme that integrates genome-scale transcriptomic profiles of RA peripheral blood genes, providing a fresh perspective for stratified treatments. Methods: We utilized independent microarray datasets of RA peripheral blood mononuclear cells (PBMCs). Up-regulated differentially expressed genes (DEGs) were subjected to functional enrichment analysis. Unsupervised cluster analysis was then employed to identify RA peripheral blood gene expression-driven subtypes. We defined three distinct clustering subtypes based on the identified 404 up-regulated DEGs. Results: Subtype A, named NE-driving, was enriched in pathways related to neutrophil activation and responses to bacteria. Subtype B, termed interferon-driving (IFN-driving), exhibited abundant B cells and showed increased expression of transcripts involved in IFN signaling and defense responses to viruses. In Subtype C, an enrichment of CD8+ T-cells was found, ultimately defining it as CD8+ T-cells-driving. The RA subtyping scheme was validated using the XGBoost machine learning algorithm. We also evaluated the therapeutic outcomes of biological disease-modifying anti-rheumatic drugs. Conclusions: The findings provide valuable insights for deep stratification, enabling the design of molecular diagnosis and serving as a reference for stratified therapy in RA patients in the future.

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.

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.

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.

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.

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.

[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.

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.

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.