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BackgroundThis study aimed to explore the association between a new inflammatory marker, systemic immune-inflammation index (SII), and the risk of Parkinson’s disease (PD) in adult population.MethodsA cross-sectional design was used, participants were recruited from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2020. Three logistic regression models were used to explore the association between SII and the risk of PD, and subgroup analysis and sensitivity analysis were used. In addition, the restricted cubic spline (RCS) was used to explore the dose-response relationship between SII and PD. Receiver operating characteristic (ROC) curves was used to explore the diagnostic value of SII for PD.ResultsA total of 54,027 adults (mean age 35 years) were included in this study. The results of logistic regression showed that after adjusted for all covariates, compared with the Q1 group (lowest quartile in SII), the risk of PD in the Q3 group (OR = 1.82, 95%CI = 1.20–2.82, p < 0.001) and the Q4 group increased (OR = 2.49, 95%CI = 1.69–3.77, p < 0.001), with p-trend < 0.001. After excluding individuals with any missing values, sensitivity analysis also found a positive association between SII and PD. Subgroup analysis showed that this association was more significant in women, younger than 60 years old, non-smokers, alcohol drinkers, non-obese, and without a history of stroke, diabetes, or coronary heart disease. In addition, there was a positive dose-response relationship between SII and PD, and SII had an acceptable diagnostic value for PD (AUC = 0.72).ConclusionSII is positively correlated with the prevalence of PD in the adult population, and SII can help differentiate between PD and non-PD cases.

ObjectiveTo investigate the diagnostic value of metagenomic next-generation sequencing (mNGS) in detecting pathogens from joint infection (JI) synovial fluid (SF) samples with previous antibiotic exposure.MethodsFrom January 2019 to January 2022, 59 cases with suspected JI were enrolled. All cases had antibiotic exposure within 2 weeks before sample collection. mNGS and conventional culture were performed on SF samples. JI was diagnosed based on history and clinical symptoms in conjunction with MSIS criteria. The diagnostic values, including sensitivity, specificity, positive/negative predictive values (PPV/NPV), and accuracy, were in comparison with mNGS and culture.ResultsThere were 47 of the 59 cases diagnosed with JI, while the remaining 12 were diagnosed with non-infectious diseases. The sensitivity of mNGS was 68.1%, which was significantly higher than that of culture (25.5%, p

IntroductionDuring public health emergencies, online rumors spread widely on social media, causing public information anxiety and emotional fluctuations. Analyzing the co-evolution patterns of online rumor themes and emotions is essential for implementing proactive and precise governance of online rumors during such events.MethodsRumor texts from mainstream fact-checking platforms during the COVID-19 pandemic were collected and analyzed in phases based on the crisis lifecycle theory. The LDA topic model was applied to analyze the distribution of rumor themes at different stages. The Baidu AI Sentiment Analysis API was used to study the emotional tendencies of rumors at different stages. Line graphs were utilized to analyze the co-evolution characteristics of rumor themes and emotions.ResultsDuring the COVID-19 pandemic, the themes of online rumors can be categorized into five types: epidemic prevention and control, panic-inducing, production and livelihood, virus dissemination, and social figures. These themes exhibited repetition and fluctuation at different stages of the pandemic. The emotions embedded in pandemic-related online rumors evolved with the progression of the pandemic. Panic-inducing rumors co-evolved with negative emotions, while epidemic prevention and control rumors co-evolved with positive emotions.ConclusionThe study results help to understand the public’s focus and emotional tendencies at different stages of the COVID-19 pandemic, thereby enabling targeted public opinion guidance and crisis management.

IntroductionAutoimmune thyroid diseases (AITDs) are prevalent disorders, primarily encompassing Graves’ disease (GD) and Hashimoto’s thyroiditis (HT). Despite their common occurrence, the etiology of AITDs remains elusive. Th9 cells, a new subset of CD4+T cells with immunomodulatory properties, have been linked to the development of various autoimmune diseases. However, research on the role of Th9 cells in AITDs is limited. MethodsWe investigated the expression of Th9 cells,their functional cytokine IL-9, and transcription factor IRF4 in peripheral blood mononuclear cells (PBMCs) and plasma of AITD patients and healthy controls. Additionally, we explored the genetic association between four loci polymorphisms (rs31564, rs2069879, rs1859430, and rs2069868) of the IL-9 gene and AITDs.ResultsWe reported, for the first time, that refractory GD patients exhibited elevated mRNA levels of IL-9 and IRF4 in PBMCs, increased IL-9 protein levels in plasma, and a higher proportion of Th9 cells in peripheral blood when compared to normal controls. Furthermore, human recombinant IL-9 protein was found to enhance IFN-g secretion in PBMCs from both GD patients and normal controls. At the genetic association level, after adjusting for age and sex, the rs2069879 polymorphism exhibited a significant association with AITDs under an additive model (P

With the rapid development of 3D printing technology, porous titanium scaffolds have provided a new restoration method to repair bone defects. Compared with the traditional body-centered cubic (bcc) dot matrix structure with a simple arrangement and repetitive structure, the topology-driven properties of triply periodic minimal surfaces (TPMS) can offer a continuous surface and smooth curvature, an excellent platform for cell proliferation. In this study, we used reverse engineering techniques to model the mandible. Sheet and solid networks of gyroid structure, the most common type of TPMS, were selected for porous design and then molded using metal 3D printing technology. At the same time, the surface treatment parameters of sandblasted, large-grit, and acid-etched (SLA) were optimized by orthogonal experimental design. Then, the optimized SLA parameter was used to treat the gyroid with 70% porosity. The result showed that reverse engineering reconstructed the TPMS-based mandibular model had good formability. Furthermore, the best surface morphology, wettability, and roughness were obtained for 3D printed Ti6Al4V under the treatment of 80 mesh Al2O3, blasting distances of 4 cm, and a 1:1:2 acid ratio. Moreover, the mechanical properties of Sheet-Gyroid and Solid-Gyroid were significantly different at 70% porosity. The porosity of the scaffolds was close to the design porosity after SLA treatment. However, no significant changes were found in its mechanical properties, all matching the mandible’s mechanical properties to meet the implantation conditions.

ObjectivePost-operative biochemical relapse (BCR) continues to occur in a significant percentage of patients with localized prostate cancer (PCa). Current stratification methods are not adequate to identify high-risk patients. The present study exploits the ability of deep learning (DL) algorithms using the H2O package to combine multi-omics data to resolve this problem.MethodsFive-omics data from 417 PCa patients from The Cancer Genome Atlas (TCGA) were used to construct the DL-based, relapse-sensitive model. Among them, 265 (63.5%) individuals experienced BCR. Five additional independent validation sets were applied to assess its predictive robustness. Bioinformatics analyses of two relapse-associated subgroups were then performed for identification of differentially expressed genes (DEGs), enriched pathway analysis, copy number analysis and immune cell infiltration analysis.ResultsThe DL-based model, with a significant difference (P = 6e-9) between two subgroups and good concordance index (C-index = 0.767), were proven to be robust by external validation. 1530 DEGs including 678 up- and 852 down-regulated genes were identified in the high-risk subgroup S2 compared with the low-risk subgroup S1. Enrichment analyses found five hallmark gene sets were up-regulated while 13 were down-regulated. Then, we found that DNA damage repair pathways were significantly enriched in the S2 subgroup. CNV analysis showed that 30.18% of genes were significantly up-regulated and gene amplification on chromosomes 7 and 8 was significantly elevated in the S2 subgroup. Moreover, enrichment analysis revealed that some DEGs and pathways were associated with immunity. Three tumor-infiltrating immune cell (TIIC) groups with a higher proportion in the S2 subgroup (p = 1e-05, p = 8.7e-06, p = 0.00014) and one TIIC group with a higher proportion in the S1 subgroup (P = 1.3e-06) were identified.ConclusionWe developed a novel, robust classification for understanding PCa relapse. This study validated the effectiveness of deep learning technique in prognosis prediction, and the method may benefit patients and prevent relapse by improving early detection and advancing early intervention.

Background: Graves’ disease (GD) is a common autoimmune disease, and its pathogenesis is unclear. Studies have found that the occurrence of GD is related to the immune disorder caused by the interaction of genetic susceptibility and environmental factors. The CD4+ T cell subset is closely related to the immune disorder of GD. LncRNAs are RNA molecules with a length of more than 200 nt and are involved in a variety of autoimmune diseases. However, the roles of lncRNAs in recurrent GD are still elusive. The purpose of this study is to identify lncRNA and mRNA expression profile in relapsed Graves’ disease.Method: CD4+ T cells from 12 recurrent GD and 8 healthy controls were collected for high-throughput sequencing. The gene-weighted co-expression network analysis (WGCNA) was used to construct the co-expression module relevant to recurrent GD, and the key genes in the module were verified by RT-PCR.Results: There are 602 upregulated lncRNAs and 734 downregulated lncRNAs in CD4+ T cells in recurrent GD patients compared with the healthy controls. The module most relevant to GD recurrence was constructed using WGCNA, and the key genes in the module were verified by RT-PCR. We found that the expression of RPL8, OAS2, NFAT5, DROSHA, NONHSAT093153.2, NONHSAT118924.2, and NONHSAT209004.1 was significantly decreased in GD group (p < 0.001, p < 0.001, p < 0.01, p < 0.05, p < 0.001, p < 0.05, and p < 0.01, respectively).Conclusion: LncRNAs are closely related to the recurrence of GD. For the first time, we constructed the expression profile of lncRNAs and mRNAs in CD4+ T cells in recurrent GD patients.

As a malignant tumor of the central nervous system, glioma exhibits high incidence and poor prognosis. Circular RNA HIPK3 (circHIPK3) is a circular RNA (circRNA) related to cancer progression. However, the role of circHIPK3 in gliomas remains unclear. The purpose of this study was to investigate the role of circHIPK3 in gliomas and its mechanism. The qRT-PCR method was used to determine the expression pattern of circHIPK3 in glioma cell lines. CCK-8 assay was used to detect cell proliferation. Cell migration and invasion were evaluated using the Transwell assay. Our results showed that circHIPK3 expression was significantly up-regulated in glioma tissues and cell lines. In vitro, the down-regulation of circHIPK3 significantly inhibited the proliferation, migration and invasion of glioma cells. Besides, we demonstrated that circHIPK3 acted as a sponge to absorb miR-124 and promoted CCND2 expression. In summary, our results indicated that circHIPK3 had carcinogenic effects by regulating the expression of CCND2 in glioma by sponging miR-124. These findings provided favorable evidence to reveal the role of circHIPK3 in the development of gliomas.

Purpose: Sjögren’s syndrome (SS) is an autoimmune disease characterized by dry mouth and eyes. To date, the exact molecular mechanisms of its etiology are still largely unknown. The aim of this study was to identify SS related key genes and functionally enriched pathways using the weighted gene co-expression network analysis (WGCNA).Materials and Methods: We downloaded the microarray data of 190 SS patients and 32 controls from Gene Expression Omnibus (GEO). Gene network was constructed and genes were classified into different modules using WGCNA. In addition, for the hub genes in the most related module to SS, gene ontology analysis was applied. The expression profile and diagnostic capacity (ROC curve) of interested hub genes were verified using a dataset from the GEO. Moreover, gene set enrichment analysis (GSEA) was also performed.Results: A total of 1483 differentially expressed genes were filtered. Weighted gene coexpression network was constructed and genes were classified into 17 modules. Among them, the turquoise module was most closely associated with SS, which contained 278 genes. These genes were significantly enriched in 10 Gene Ontology terms, such as response to virus, immune response, defense response, response to cytokine stimulus, and the inflammatory response. A total of 19 hub genes (GBP1, PARP9, EPSTI1, LOC400759, STAT1, STAT2, IFIH1, EIF2AK2, TDRD7, IFI44, PARP12, FLJ20035, PARP14, ISGF3G, XAF1, RSAD2,LY6E, IFI44L, and DDX58) were identified. The expression levels of the five interested genes including EIF2AK2, GBP1, PARP12, PARP14, and TDRD7 were also confirmed. ROC curve analysis determined that the above five genes’ expression can distinguish SS from controls (the area under the curve is all greater than 0.7). GSEA suggests that the SS samples with highly expressed EIF2AK2 or TDRD7 genes are correlated with inflammatory response, interferon α response, and interferon γ response.Conclusion: The present study applied WGCNA to generate a holistic view of SS and provide a basis for the identification of potential pathways and hub genes that may be involved in the development of SS.