Search

The orchid Calanthe discolor, which has high ornamental and medicinal value, is mainly distributed in Zhejiang, Jiangsu, and southeast Hubei Provinces of China, as well as in Japan and the southern Korean peninsula. In this study, the whole chloroplast genome sequence of C. discolor was first assembled using high-throughput Illumina paired-end technology, providing data to evaluate the evolution of this species. The C. discolor chloroplast genome was158,286 bp long, including a large single-copy region of 87,095 bp, a small single-copy region of 18,407 bp, and two copies of a repeat region (26,392-bp each). The overall G + C content was 41.2%. A total of 133 genes were predicted from the genome, including 87 protein-coding genes, eight ribosomal RNAs, 38 transfer RNAs. Phylogenetic analysis indicated a close relationship between C. discolor and C. bicolor.

Abstract C2′-halogenation has been recognized as an essential modification to enhance the drug-like properties of nucleotide analogs. The direct C2ʹ-halogenation of the nucleotide 2′-deoxyadenosine-5′-monophosphate (dAMP) has recently been achieved using the Fe(II)/α-ketoglutarate-dependent nucleotide halogenase AdaV. However, the limited substrate scope of this enzyme hampers its broader applications. In this study, we report two halogenases capable of halogenating 2ʹ-deoxyguanosine monophosphate (dGMP), thereby expanding the family of nucleotide halogenases. Computational studies reveal that nucleotide specificity is regulated by the binding pose of the phosphate group. Based on these findings, we successfully engineered the substrate specificity of these halogenases by mutating second-sphere residues. This work expands the toolbox of nucleotide halogenases and provides insights into the regulation mechanism of nucleotide specificity.

The complex nature of microbiome data has made the differential abundance analysis challenging. Microbiome abundance counts are often skewed to the right and heteroscedastic (also known as overdispersion), potentially leading to incorrect inferences if not properly addressed. In this paper, we propose a simple yet effective framework to tackle the challenges by integrating Poisson (log-linear) regression with standard error estimation through the Bootstrap method and Sandwich robust estimation. Such standard error estimates are accurate and yield satisfactory inference even if the distributional assumption or the variance structure is incorrect. Our approach is validated through extensive simulation studies, demonstrating its effectiveness in addressing overdispersion and improving inference accuracy. Additionally, we apply our approach to two real datasets collected from the human gut and vagina, respectively, demonstrating the wide applicability of our methods. The results highlight the efficacy of our covariance estimators in addressing the challenges of microbiome data analysis. The corresponding software implementation is publicly available at https://github.com/yimshi/robustestimates.

In recent years, there has been significant research interest in the field of immunotherapy for non-small cell lung cancer (NSCLC) within the academic community. Given the observed variations in individual responses, despite similarities in histopathologic type, immunohistochemical index, TNM stage, or mutation status, the identification of a reliable biomarker for early prediction of therapeutic responses is of utmost importance. Conventional medical imaging techniques primarily focus on macroscopic tumor monitoring, which may no longer adequately fulfill the requirements of clinical diagnosis and treatment. CT (computerized tomography) or PEF/CT-based radiomics has the potential to investigate the molecular-level biological attributes of tumors, such as PD-1/PD-L1 expression and tumor mutation burden, which offers a novel approach to assess the effectiveness of immunotherapy and forecast patient prognosis. The utilization of cutting-edge radiological imaging techniques, including radiomics, PET/CT, machine learning, and artificial intelligence, demonstrates significant potential in predicting diagnosis, treatment response, immunosuppressive characteristics, and immune-related adverse events. The current review highlights that CT scan-based radiomics is a reliable and feasible way to predict the benefits of immunotherapy in patients with advanced NSCLC.

Objective This study aims to explore the effect of NONHSAT042241 on the function of rheumatoid arthritis -fibroblast-like synoviocyte (RA-FLS) and the underlying mechanisms.Methods RA-FLS was treated with NONHSAT042241 overexpression and NONHSAT042241 knockdown lentiviruses. Cell counting kit-8 (CCK-8) assay, colony formation assay, flow cytometry, Transwell assay, western-blot, ELISA, and qRT-PCR were used to measure the changes of cell proliferation, apoptosis, invasion, secretion of inflammatory cytokines and matrix metalloproteinases (MMPs). Fluorescent in situ hybridization (FISH) assay, RNA pull-down assay, mass spectrometry (MS) and RNA immunoprecipitation (RIP) were used to find the target proteins that bond to NONHSAT042241, and western-blot was used to detect the expression of related proteins of Wnt/β-catenin signaling pathway.Results Overexpression of NONHSAT042241 inhibited the proliferation of RA-FLS (p

Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management by identifying patterns within multidimensional biomedical data to improve classification, diagnosis, and treatment predictions. In this review, we summarize the applications of ML for RA management. Emerging studies or applications have developed diagnostic and predictive models for RA that utilize a variety of data modalities, including electronic health records, imaging, and multi-omics data. High-performance supervised learning models have demonstrated an Area Under the Curve (AUC) exceeding 0.85, which is used for identifying RA patients and predicting treatment responses. Unsupervised learning has revealed potential RA subtypes. Ongoing research is integrating multimodal data with deep learning to further improve performance. However, key challenges remain regarding model overfitting, generalizability, validation in clinical settings, and interpretability. Small sample sizes and lack of diverse population testing risks overestimating model performance. Prospective studies evaluating real-world clinical utility are lacking. Enhancing model interpretability is critical for clinician acceptance. In summary, while ML shows promise for transforming RA management through earlier diagnosis and optimized treatment, larger scale multisite data, prospective clinical validation of interpretable models, and testing across diverse populations is still needed. As these gaps are addressed, ML may pave the way towards precision medicine in RA.

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent synovial inflammation and progressive joint destruction. Macrophages are key effector cells that play a central role in RA pathogenesis through their ability to polarize into distinct functional phenotypes. An imbalance favoring pro-inflammatory M1 macrophages over anti-inflammatory M2 macrophages disrupts immune homeostasis and exacerbates joint inflammation. Multiple signaling pathways, including Notch, JAK/STAT, NF-κb, and MAPK, regulate macrophage polarization towards the M1 phenotype in RA. Metabolic reprogramming also contributes to this process, with M1 macrophages prioritizing glycolysis while M2 macrophages utilize oxidative phosphorylation. Redressing this imbalance by modulating macrophage polarization and metabolic state represents a promising therapeutic strategy. Furthermore, complex bidirectional interactions exist between synovial macrophages and fibroblast-like synoviocytes (FLS), forming a self-perpetuating inflammatory loop. Macrophage-derived factors promote aggressive phenotypes in FLS, while FLS-secreted mediators contribute to aberrant macrophage activation. Elucidating the signaling networks governing macrophage polarization, metabolic adaptations, and crosstalk with FLS is crucial to developing targeted therapies that can restore immune homeostasis and mitigate joint pathology in RA.

In rice cultivation, the traits of semi-dwarfism and glutinous texture are pivotal for optimizing yield potential and grain quality, respectively. Xiangdaowan (XDW) rice, renowned for its exceptional aromatic properties, has faced challenges due to its tall stature and high amylose content, resulting in poor lodging resistance and suboptimal culinary attributes. To address these issues, we employed CRISPR/Cas9 technology to precisely edit the SD1 and Wx genes in XDW rice, leading to the development of stable genetically homozygous lines with desired semi-dwarf and glutinous characteristics. The sd1-wx mutant lines exhibited reduced gibberellin content, plant height, and amylose content, while maintaining hardly changed germination rate and other key agronomic traits. Importantly, our study demonstrated that exogenous GA3 application effectively promoted growth by compensating for the deficiency of endogenous gibberellin. Based on this, a semi-dwarf glutinous elite rice (Oryza sativa L.) Lines was developed without too much effect on most agronomic traits. Furthermore, a comparative transcriptome analysis unveiled that differentially expressed genes (DEGs) were primarily associated with the anchored component of the membrane, hydrogen peroxide catabolic process, peroxidase activity, terpene synthase activity, and apoplast. Additionally, terpene synthase genes involved in catalyzing the biosynthesis of diterpenoids to gibberellins were enriched and significantly down-regulated. This comprehensive study provides an efficient method for simultaneously enhancing rice plant height and quality, paving the way for the development of lodging-resistant and high-quality rice varieties.

High-technology industries have gained substantial recognition as pivotal drivers of economic growth and technological advancement in modern society. The imperative of sustainable development in high-tech industries cannot be overemphasized, as it plays a crucial role in enabling long-term growth, fostering innovation, and assuming environmental responsibility. This article presents a study on sustainable development in high-tech industries using Boundary Shell theory. The study investigates the role of the stable and sustainable entropy criterion for the Boundary Shell system of high-tech industries from an entropy balance perspective. It analyzes the upper and lower limits of the Boundary Shell support force. Additionally, it improves the traditional boundary system ratio model to comprehensively and objectively evaluate the sustainable development of high-tech industries. The results illustrate that the Boundary Shell of industrial innovation is stronger than that of external dependency, with a reversed ranking of internal evaluation factor strengths compared to the traditional model. This research integrates reaction-diffusion equations theory with entropy balance equations theory to address sustainability issues in the high-tech industry. We further analyze the sustainable development of the high-tech industry through a Boundary Shell theory perspective to advance sustainability in high-tech industries. Moreover, it provides useful insights into the sustainable development of high-tech industries.

Objective: To investigate the potential association between Anoikis-related genes, which are responsible for preventing abnormal cellular proliferation, and rheumatoid arthritis (RA).Methods: Datasets GSE89408, GSE198520, and GSE97165 were obtained from the GEO with 282 RA patients and 28 healthy controls. We performed differential analysis of all genes and HLA genes. We performed a protein-protein interaction network analysis and identified hub genes based on STRING and cytoscape. Consistent clustering was performed with subgrouping of the disease. SsGSEA were used to calculate immune cell infiltration. Spearman’s correlation analysis was employed to identify correlations. Enrichment scores of the GO and KEGG were calculated with the ssGSEA algorithm. The WGCNA and the DGIdb database were used to mine hub genes’ interactions with drugs.Results: There were 26 differentially expressed Anoikis-related genes (FDR = 0.05, log2FC = 1) and HLA genes exhibited differential expression (P < 0.05) between the disease and control groups. Protein-protein interaction was observed among differentially expressed genes, and the correlation between PIM2 and RAC2 was found to be the highest; There were significant differences in the degree of immune cell infiltration between most of the immune cell types in the disease group and normal controls (P < 0.05). Anoikis-related genes were highly correlated with HLA genes. Based on the expression of Anoikis-related genes, RA patients were divided into two disease subtypes (cluster1 and cluster2). There were 59 differentially expressed Anoikis-related genes found, which exhibited significant differences in functional enrichment, immune cell infiltration degree, and HLA gene expression (P < 0.05). Cluster2 had significantly higher levels in all aspects than cluster1 did. The co-expression network analysis showed that cluster1 had 51 hub differentially expressed genes and cluster2 had 72 hub differentially expressed genes. Among them, three hub genes of cluster1 were interconnected with 187 drugs, and five hub genes of cluster2 were interconnected with 57 drugs.Conclusion: Our study identified a link between Anoikis-related genes and RA, and two distinct subtypes of RA were determined based on Anoikis-related gene expression. Notably, cluster2 may represent a more severe state of RA.

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation and joint damage. The signaling lymphocytic activation molecule (SLAMF) family of receptors are expressed on various hematopoietic and non-hematopoietic cells and can regulate both immune cell activation and cytokine production. Altered expression of certain SLAMF receptors contributes to aberrant immune responses in RA. In RA, SLAMF1 is upregulated on T cells and may promote inflammation by participating in immune cell-mediated responses. SLAMF2 and SLAMF4 are involved in regulating monocyte tumor necrosis factor production and promoting inflammation. SLAMF7 activates multiple inflammatory pathways in macrophages to drive inflammatory gene expression. SLAMF8 inhibition can reduce inflammation in RA by blocking ERK/MMPs signaling. Of note, there are differences in SLAMF receptor (SFR) expression between normal and arthritic joint tissues, suggesting a role as potential diagnostic biomarkers. This review summarizes recent advances on the roles of SLAMF receptors 1, 2, 4, 7, and 8 in RA pathogenesis. However, further research is needed to elucidate the mechanisms of SLAMF regulation of immune cells in RA. Understanding interactions between SLAMF receptors and immune cells will help identify selective strategies for targeting SLAMF signaling without compromising normal immunity. Overall, the SLAMF gene family holds promise as a target for precision medicine in RA, but additional investigation of the underlying immunological mechanisms is needed. Targeting SLAMF receptors presents opportunities for new diagnostic and therapeutic approaches to dampen damaging immune-mediated inflammation in RA.

Rheumatic and autoimmune diseases are a group of immune system-related disorders wherein the immune system mistakenly attacks and damages the body’s tissues and organs. This excessive immune response leads to inflammation, tissue damage, and functional impairment. Therapeutic approaches typically involve medications that regulate immune responses, reduce inflammation, alleviate symptoms, and target specific damaged organs. Tripterygium wilfordii Hook. f., a traditional Chinese medicinal plant, has been widely studied in recent years for its application in the treatment of autoimmune diseases, including rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis. Numerous studies have shown that preparations of Tripterygium wilfordii have anti-inflammatory, immunomodulatory, and immunosuppressive effects, which effectively improve the symptoms and quality of life of patients with autoimmune diseases, whereas the active metabolites of T. wilfordii have been demonstrated to inhibit immune cell activation, regulate the production of inflammatory factors, and modulate the immune system. However, although these effects contribute to reductions in inflammatory responses and the suppression of autoimmune reactions, as well as minimize tissue and organ damage, the underlying mechanisms of action require further investigation. Moreover, despite the efficacy of T. wilfordii in the treatment of autoimmune diseases, its toxicity and side effects, including its potential hepatotoxicity and nephrotoxicity, warrant a thorough assessment. Furthermore, to maximize the therapeutic benefits of this plant in the treatment of autoimmune diseases and enable more patients to utilize these benefits, efforts should be made to strengthen the regulation and standardized use of T. wilfordii.

Abstract Objectives To assess the differences in circulating DNA methylation levels of CXCR5 between rheumatoid arthritis (RA) and osteoarthritis (OA) and healthy controls (HC), and the correlation of methylation changes with clinical characteristics of RA patients. Methods Peripheral blood samples were collected from 239 RA patients, 30 patients with OA, and 29 HC. Target region methylation sequencing to the promoter region of CXCR5 was achieved using MethylTarget. The methylation level of cg04537602 and methylation haplotype were compared among the three groups, and the correlation between methylation levels and clinical characteristics of RA patients was performed by Spearman's rank correlation analysis. Results The methylation level of cg04537602 was significantly higher in the peripheral blood of RA patients compared with OA patients (p = 1.3 × 10−3) and in the HC group (p = 5.5 × 10−4). The sensitivity was enhanced when CXCR5 methylation level combined with rheumatoid factor and anti–cyclic citrullinated peptide with area under curve (AUC) of 0.982 (95% confidence interval 0.970–0.995). The methylation level of cg04537602 in RA was positively correlated with C‐reactive protein (CRP) (r = .16, p = .01), and in RA patients aged 60 years and above, cg04537602 methylation levels were positively correlated with CRP (r = .31, p = 4.7 × 10−4), tender joint count (r = .21, p = .02), visual analog scales score (r = .21, p = .02), Disease Activity Score in 28 joints (DAS28) using the CRP level DAS28‐CRP (r = .27, p = 2.1 × 10−3), and DAS28‐ESR (r = .22, p = .01). We also observed significant differences of DNA methylation haplotypes in RA patients compared with OA patients and HC, which was consistent with single‐loci‐based CpG methylation measurement. Conclusion The methylation level of CXCR5 was significantly higher in RA patients than in OA and HC, and correlated with the level of inflammation in RA patients, our study establishes a link between CXCR5 DNA methylation and clinical features that may help in the diagnosis and disease management of RA patients.

IntroductionHomeodomain-interacting protein kinase 3 (HIPK3) plays an important role in cell proliferation, apoptosis, and inflammation. Over-expression of HIPK3 in immune cells in rheumatoid arthritis (RA) has been reported. In this study, we investigated blood methylation levels and clinical characteristics of RA in a Chinese population.MethodsA total of 235 patients with RA, 30 with osteoarthritis (OA), and 30 matched healthy controls were recruited. The methylation status of seven CpGs in the differentially methylated region of HIPK3 (cg05501357) was measured using targeted methylation-sequencing technology. The association between methylation haplotypes and the overall methylation status of HIPK3 with clinical characteristics was assessed using generalized linear regression.ResultsAll seven CpGs showed hypomethylation status in RA blood compared with OA and normal individuals (overall p= 1.143×10-8 and FDR= 2.799×10-7), which is consistent with the previously reported high expression of HIPK3 in RA immune cells. Among all seven CpGs, 33286785 showed the highest predictive power with an area under the curve (AUC) of 0.829; we received a higher AUC=0.864 when we combined HIPK3 with anti-citrullinated protein antibodies (ACPA -) and rheumatoid factor (RF +) in the prediction model, indicating that when a patient’s ACPA is negative, HIPK3 can assist RF as a new clinical index for the diagnosis of RA. We also found that HIPK3 methylation levels were negatively correlated with C-reactive protein (CRP; r= -0.16, p= 0.01). Methylation haplotypes were analyzed, and the full methylation haplotype (FMH; r= 0.16, p= 0.01) and full non-methylation haplotype (FNH; r= 0.18, p= 0.0061) were negatively correlated with CRP.ConclusionCirculating blood methylation levels in the protein region of HIPK3 can be utilized as a supportive diagnostic biomarker and CRP level indicator for RA.

ObjectivesHTR2A is previously identified as a susceptibility gene for rheumatoid arthritis (RA). In this study, we performed the association analysis between DNA methylation of HTR2A with RA within peripheral blood samples.MethodsWe enrolled peripheral blood samples from 235 patients with RA, 30 osteoarthritis (OA) patients, and 30 healthy controls. The DNA methylation levels of about 218 bp from chr13: 46898190 to chr13: 46897973 (GRCh38/hg38) around HTR2A cg15692052 from patients were analyzed by targeted methylation sequencing.ResultsWe measured methylation status for 7 CpGs in the promoter region of HTR2A and obseved overall methylation status are signficantly increased in RA compared with normal inviduals (FDR= 9.05 x 10-5). The average cg15692052 methylation levels (methylation score) showed a positive correlation with CRP (r=0.15, P=0.023). Compared with the OA group or HC group, the proportion of haplotypes CCCCCCC (FDR=0.02 and 2.81 x 10-6) is signficantly increased while TTTTTCC (FDR =0.01) and TTTTTTT(FDR =6.92 x 10-3) are significantly decreased in RA. We find methylation haplotypes combining with RF and CCP could signficantly enhance the performance of the diagnosing RA and its comorbidities (hypertension, interstitial lung disease, and osteoporosis), especially in interstitial lung disease.ConclusionsIn our study, we found signficant hypermethylation of promoter region of HTR2A which indicates the potential clinical diagnostic role in rheumatoid arthritis.

Background: Among critically ill patients in the intensive care unit (ICU), sepsis is an urgent global public health problem due to its high incidence, high mortality rate and complex pathogenesis. Objective: This study was to evaluate the predictive value of neutrophil-to-lymphocyte ratio (NLR), and neutrophil-to-lymphocyte and platelet ratio (NLPR) in-hospital mortality in septic patients on days 1, 3 and 5 in ICU. Methods: The data of septic patients admitted to the ICU of the Sixth Affiliated Hospital of Sun Yat-sen University from March, 2018 to July, 2019 were collected. NLR and NLPR were calculated and multivariate logistic regression analysis was performed to identify the relationship between them and in-hospital mortality, respectively. Receiver operating characteristic curve (ROC) was used to determine the efficacy and optimal cutoff value of diagnostic tests. Results: A total of 173 septic patients were included in this analysis, including 108 cases in the survival group and 65 in the death group, with a total mortality rate of 37.6%. A multivariate logistic regression analysis showed that NLR on day 5 was independently correlated with in-hospital mortality rate (OR 1.041, 95% CI: 1.008–1.074), and Day 5 NLPR was also independently associated with in-hospital mortality rate (OR 1.020, 95% CI: 1.001–1.040). The areas under the receiver operating characteristic curve (AUC) of the NLR on days 1 and 3 was 0.513 and 0.542 respectively, and the optimal cutoff value were 23.16 and 15.48, and the AUC of the NLR on day 5 was 0.589, and the best cutoff value was 15.85. The AUC of NLPR on day 1 and 3 was 0.517 and 0.547, respectively, and the optimal cutoff value was 10.25 and 18.47. The AUC of NLPR on day 5 was the largest, 0.654, and the optimal cutoff value was 8.22. After combined NLPR on day 5 with age and sequential organ failure assessment (SOFA) scores, the AUC increase to 0.718. Among the joint predictors, the optimal cutoff value for NLPR on day 5 was 9.31. Conclusion: We found that Day 5 NLPR and NLR were independently correlated with in-hospital mortality. Day 5 NLPR Combined with age and SOFA scores may be help predict mortality in ICU septic hospitalized patients.

Rheumatoid arthritis (RA) is an autoimmune disease accompanied by metabolic alterations. The metabolic profiles of patients with RA can be determined using targeted and non-targeted metabolomics technology. Metabolic changes in glucose, lipid, and amino acid levels are involved in glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, the arachidonic acid metabolic pathway, and amino acid metabolism. These alterations in metabolic pathways and metabolites can fulfill bio-energetic requirements, promote cell proliferation, drive inflammatory mediator secretion, mediate leukocyte infiltration, induce joint destruction and muscle atrophy, and regulate cell proliferation, which may reflect the etiologies of RA. Differential metabolites can be used as biomarkers for the diagnosis, prognosis, and risk prediction, improving the specificity and accuracy of diagnostics and prognosis prediction. Additionally, metabolic changes associated with therapeutic responses can improve the understanding of drug mechanism. Metabolic homeostasis and regulation are new therapeutic strategies for RA. In this review, we provide a comprehensive overview of advances in metabolomics for RA.

Rheumatoid arthritis (RA) is a chronic inflammatory disease that leads to joint damage and even disability. Although there are various clinical therapies for RA, some patients still have poor or no response. Thus, the development of new drug targets remains a high priority. In this review, we discuss the role of G-protein-coupled receptors (GPCRs), including chemokine receptors, melanocortin receptors, lipid metabolism-related receptors, adenosine receptors, and other inflammation-related receptors, on mechanisms of RA, such as inflammation, lipid metabolism, angiogenesis, and bone destruction. Additionally, we summarize the latest clinical trials on GPCR targeting to provide a theoretical basis and guidance for the development of innovative GPCR-based clinical drugs for RA.

Secreted frizzled-related protein 1 (SFRP1) is a member of secretory glycoprotein SFRP family. As a primitive gene regulating cell growth, development and transformation, SFRP1 is widely expressed in human cells, including various cancer cells and fibroblast-like synoviocytes (FLS) of rheumatoid arthritis (RA). Deletion or silencing of SFRP1 involves epigenetic and other mechanisms, and participates in biological behaviors such as cell proliferation, migration and cell pyroptosis, which leads to disease progression and poor prognosis. In this review, we discuss the role of SFRP1 in the pathogenesis of RA-FLS and summarize different experimental platforms and recent research results. These are helpful for understanding the biological characteristics of SFRP1 in RA, especially the mechanism by which SFRP1 regulates RA-FLS pyroptosis through Wnt/β-catenin and Notch signaling pathways. In addition, the epigenetic regulation of SFRP1 in RA-FLS is emphasized, which may be considered as a promising biomarker and therapeutic target of RA.

Gout, a chronic inflammatory arthritis disease, is characterized by hyperuricemia and caused by interactions between genetic, epigenetic, and metabolic factors. Acute gout symptoms are triggered by the inflammatory response to monosodium urate crystals, which is mediated by the innate immune system and immune cells (e.g., macrophages and neutrophils), the NACHT, LRR, and PYD domains-containing protein 3 (NLRP3) inflammasome activation, and pro-inflammatory cytokine (e.g., IL-1β) release. Recent studies have indicated that the multiple programmed cell death pathways involved in the inflammatory response include pyroptosis, NETosis, necroptosis, and apoptosis, which initiate inflammatory reactions. In this review, we explore the correlation and interactions among these factors and their roles in the pathogenesis of gout to provide future research directions and possibilities for identifying potential novel therapeutic targets and enhancing our understanding of gout pathogenesis.

MicroRNAs (miRNAs) play crucial roles in regulating the transcriptome and development of rheumatoid arthritis (RA). Currently, a comprehensive map illustrating how miRNAs regulate transcripts, pathways, immune system differentiation, and their interactions with terminal cells such as fibroblast-like synoviocytes (FLS), immune-cells, osteoblasts, and osteoclasts are still laking. In this review, we summarize the roles of miRNAs in the susceptibility, pathogenesis, diagnosis, therapeutic intervention, and prognosis of RA. Numerous miRNAs are abnormally expressed in cells involved in RA and regulate target genes and pathways, including NF-κB, Fas-FasL, JAK-STAT, and mTOR pathways. We outline how functional genetic variants of miR-499 and miR-146a partly explain susceptibility to RA. By regulating gene expression, miRNAs affect T cell differentiation into diverse cell types, including Th17 and Treg cells, thus constituting promising gene therapy targets to modulate the immune system in RA. We summarize the diagnostic and prognostic potential of blood-circulating and cell-free miRNAs, highlighting the opportunity to combine these miRNAs with antibodies to cyclic citrullinated peptide (ACCP) to allow accurate diagnosis and prognosis, particularly for seronegative patients. Furthermore, we review the evidence implicating miRNAs as promising biomarkers of efficiency and response of, and resistance to, disease-modifying anti-rheumatic drugs and immunotherapy. Finally, we discuss the autotherapeutic effect of miRNA intervention as a step toward the development of miRNA-based anti-RA drugs. Collectively, the current evidence supports miRNAs as interesting targets to better understand the pathogenetic mechanisms of RA and design more efficient therapeutic interventions.

Rheumatoid arthritis (RA), one of the most common immune system diseases, mainly affects middle-aged and elderly individuals and has a serious impact on the quality of life of patients. Pain and disability caused by RA are significant symptoms negatively affecting patients, and they are especially seen when inappropriate treatment is administered. Effective therapeutic strategies have evolved over the past few decades, with many new disease-modifying antirheumatic drugs (DMARDs) being used in the clinic. Owing to the breakthrough in the treatment of RA, the symptoms of patients who could not be treated effectively in the past few years have been relieved. However, some patients complain about symptoms that have not been reported, implying that there are still some limitations in the RA treatment and evaluation system. In recent years, biomarkers, an effective means of diagnosing and evaluating the condition of patients with RA, have gradually been used in clinical practice to evaluate the therapeutic effect of RA, which is constantly being improved for accurate application of treatment in patients with RA. In this article, we summarize a series of biomarkers that may be helpful in evaluating the therapeutic effect and improving the efficiency of clinical treatment for RA. These efforts may also encourage researchers to devote more time and resources to the study and application of biomarkers, resulting in a new evaluation system that will reduce the inappropriate use of DMARDs, as well as patients’ physical pain and financial burden.

Activated fibroblast-like synoviocytes (FLSs) play a central role in the formation of synovial pannus and joint destruction in rheumatoid arthritis (RA). Targeting FLSs could be a potential therapeutic strategy. The objective of this study is to explore the role of c-Jun N-terminal kinase (JNK) in proliferation, migration and invasion of FLSs promoted by the sonic hedeghog (SHH) signaling pathway in patients with RA. Activation of SHH signaling was evaluated by real-time PCR and Western Blot. Levels of phosphorylation of JNK and c-Jun were detected by Western Blot. FLSs proliferation was quantified by Cell Counting Kit-8 (CCK-8) assay and flow cytometry. Cell migration and invasion were assessed by wound healing assay and Transwell chamber assay. Invasiveness of FLSs in vivo was evaluated using a humanized synovitis animal model. We observed that treatment of SHH agonist (SAG) significantly increased the levels of phosphorylation of JNK and c-Jun, while SHH antagonist (cyclopamine) significantly decreased the expression of phospho-JNK and phospho-c-Jun in FLSs. The elevated level of phospho-c-Jun stimulated by SAG was decreased in the presence of JNK inhibitor (SP600125) (P < 0.001). FLSs proliferation, migration and invasion were promoted by SHH agonist (P < 0.05). However, the enhanced aggressiveness of FLSs was abolished in the presence of JNK inhibitor (P < 0.05). In vivo study showed that the invasion of FLSs into cartilage was increased by SHH overexpression and the excessive invasiveness was inhibited by blockade of JNK signaling (P < 0.01). These results suggest that JNK is one of the downstream molecules mediating the effect of SHH signaling in FLSs. These findings indicate that SHH-JNK signaling could be a potential therapeutic target to suppress the aggressiveness of FLSs and prevent articular damage of RA.

Group IIA secreted/synovial phospholipase A(2) (GIIAPLA(2)) is an enzyme involved in the synthesis of eicosanoids such as prostaglandin E(2) (PGE(2)), the main eicosanoid contributing to pain and inflammation in rheumatic diseases. We designed, by molecular modeling, 7 novel analogs of 3-{4-[5(indol-1-yl)pentoxy]benzyl}-4H-1,2,4-oxadiazol-5-one, denoted C1, an inhibitor of the GIIAPLA(2) enzyme. We report the results of molecular dynamics studies of the complexes between these derivatives and GIIAPLA(2), along with their chemical synthesis and results from PLA(2) inhibition tests. Modeling predicted some derivatives to display greater GIIAPLA(2) affinities than did C1, and such predictions were confirmed by in vitro PLA(2) enzymatic tests. Compound C8, endowed with the most favorable energy balance, was shown experimentally to be the strongest GIIAPLA(2) inhibitor. Moreover, it displayed an anti-inflammatory activity on rabbit articular chondrocytes, as shown by its capacity to inhibit IL-1beta-stimulated PGE(2) secretion in these cells. Interestingly, it did not modify the COX-1 to COX-2 ratio. C8 is therefore a potential candidate for anti-inflammatory therapy in joints.

Multifunctional buried interface modification is key to boost the performance of perovskite solar cells (PSCs). Herein, a multifunctional passivator, N-(2-hydroxyethyl) ethylene diamine triacetic acid trisodium salt (HEDTA-3Na), is employed to modify an SnO2 electron transport layer (ETL) for fabricating efficient and stable PSCs. It is found that the addition of HEDTA-3Na not only reduces defect state density by passivating uncoordinated Sn4+ and Pb2+ defects in an ETL/perovskite interface, but also forms appropriate energy band alignment, facilitating electron extraction, and improves the quality of the perovskite film with enlarged grain sizes. In addition, the Na+ ions can diffuse into the perovskite layer to passivate iodine vacancy defects and inhibit ion migration. As a result, the PSC with HEDTA-3Na modification achieves a champion PCE of 23.11% with enhanced VOC and FF compared to the control device (20.82%). Moreover, the unencapsulated HEDTA-3Na-modified PSC retains 90% of its initial efficiency after 1600 hours at 25 °C and 20–25% relative humidity under ambient conditions. This work offers a simple and effective approach to synergistically improve the performance of PSCs with a single multifunctional molecule. [ABSTRACT FROM AUTHOR]

BACKGROUND: The timing of tracheostomy in ventilated patients remains controversial. This study aimed to compare the effect of early tracheostomy (≤7 d) with late tracheostomy (>7 d) on the prognosis of patients requiring prolonged mechanical ventilation. METHODS: This was a retrospective observational cohort study. The data of 175 patients who received tracheostomy at the ICU between January 1, 2015--July 31, 2022, were collected. Patients were excluded from the study if medical records were incomplete or they underwent tracheostomy as part of a planned operation procedure. One-to-one propensity score matching was used to correct the baseline characteristics between the early and late tracheostomy groups. The treatment process and outcomes were compared between the two groups. The primary outcome was the incidence of ventilatorassociated pneumonia (VAP) between groups. RESULTS: After propensity score matching, 88 subjects were included in the analysis. Compared with the late tracheostomy group, the incidence of VAP, hospital length of stay, sedation-free days, ventilator-free days, and ICU-free days were longer in the early tracheostomy group. There were no significant differences in the 90-d mortality between the two groups. CONCLUSIONS; Early tracheostomy can reduce the occurrence of complications for ICU patients. [ABSTRACT FROM AUTHOR]

Green manures (GM) combined with fertilizer reduction is an efficient measure to alleviate the environmental issues caused by the overapplication of chemical fertilizer. However, both the environmental and economic benefits remain unclear in coastal regions that are affected by both soil salinization and climate change. A field experiment was conducted in low-yield cropland in the Yellow River Delta to explore the effects of green manure rotation combined with fertilizer reduction on the soil environment and economic sustainability. Two GM species, Vicia villosa (HV) and Orychophragmus violaceus (OV), a traditional winter wheat (Wheat), were grown in winter, with a fallow control (Fallow). In the subsequent maize season, three fertilizer rate treatments, a full rate of 600 kg ha-1 compound fertilizer (F100), 85% of the full rate (F85), and 70% of the full rate (F70), were applied in each former treatment. The results indicated that GM return markedly increased soil total N (TN) and P (TP) before the V6 stage of maize (when maize had 6 leaves). The average TN contents of HV and OV increased by 56.9% and 38.5%, respectively, compared with that of Wheat, while the values for TP were 13.6% and 16.9%. Compared with Fallow, the maize yields of HV and OV increased by 25.6% and 13.8%, respectively, while that of Wheat decreased by 9.1%. The average partial fertilizer productivities (PFPs) for HV and OV increased by 25.3% and 14.0% compared with Fallow, while Wheat decreased by 8.9%. The PFPs for F85 and F70 increased by 19.4% and 37.7%, respectively, compared with F100. Reducing the fertilizer rate to 70% in the HV-maize rotation pattern did not reduce but increased the total net profit and rate of return. Thus, HV-maize rotation combined with 30% fertilizer reduction is suggested for sustainable agriculture in this region. [ABSTRACT FROM AUTHOR]

Green building has been deemed an important endeavor to promote sustainable building development. However, knowledge from different standards, different companies, and different software in the green building domain is difficult to share and reuse since different terminologies, measurement indicators, and criteria are adopted. Therefore, there is a need to create a consistent knowledge representation model in the green building domain. This study proposes a green building ontology (GBOnto) which is an abstract conceptualization of the knowledge in the green building domain. To build the ontology more effectively, this study adopts the ontology learning method which is based on NLP and machine learning techniques. An improved TF-IDF method is introduced to extract concepts in the green building domain. Concept inclusion and semantic networks method are integrated to extract taxonomic relations. The associate rule method is used for extracting non-taxonomic relations. Finally, all these methods are implemented by adopting software and Python programming. The GB-Onto is evaluated through consistency checking and criteriabased evaluation. The GB-Onto fills the knowledge gap by providing a formal and shared vocabulary for the green building domain which promotes knowledge reuse and sharing among different stakeholders. [ABSTRACT FROM AUTHOR]