Knee osteoarthritis (KOA) is one of the most common degenerative diseases, and its core feature is the degeneration and damage of articular cartilage. The cartilage degeneration of KOA is due to the destruction of dynamic balance caused by the activation of chondrocytes by various factors, with oxidative stress playing an important role in the pathogenesis of KOA. The overproduction of reactive oxygen species (ROS) is a result of oxidative stress, which is caused by a redox process that goes awry in the inherent antioxidant defence system of the human body. Superoxide dismutase (SOD) inside and outside chondrocytes plays a key role in regulating ROS in cartilage. Additionally, synovitis is a key factor in the development of KOA. In an inflammatory environment, hypoxia in synovial cells leads to mitochondrial damage, which leads to an increase in ROS levels, which further aggravates synovitis. In addition, oxidative stress significantly accelerates the telomere shortening and ageing of chondrocytes, while ageing promotes the development of KOA, damages the regulation of redox of mitochondria in cartilage, and stimulates ROS production to further aggravate KOA. At present, there are many drugs to regulate the level of ROS, but these drugs still need to be developed and verified in animal models of KOA. We discuss mainly how oxidative stress plays a part in the development of KOA. Although the current research has achieved some results, more research is needed.

Objective: Ankylosing spondylitis (AS) is associated with a variety of gut microbiotas. We aim to analyze the causal relationship between the two at the genetic level. Methods: Mendelian randomization (MR) is a type of instrumental variables (IVs) analysis; MR follows the Mendelian genetic rule of “parental alleles are randomly assigned to offspring” and takes genetic variation as IVs to infer the causal association between exposure factors and study outcome in observational studies. Genome-wide association study (GWAS) summary data of AS were from the FinnGen consortium, and the gut microbiota (Bacteroides, Streptococcus, Proteobacteria, Lachnospiraceae) were from the MiBioGen consortium. The TwoSampleMR and MRPRESSO packages of the R were used to perform a two-sample MR study. Random-effects inverse variance weighted (IVW) was the main analysis method, and MR Egger, weighted median, simple mode, and weighted mode were used as supplementary methods. We examined heterogeneity and horizontal pleiotropy, and examined whether the analysis results were influenced by a single SNP. We applied radial variants of the IVW and MR-Egger model for the improved visualization of the causal estimate. We further examined the causal relationship between AS and gut microbiota, and the robustness of the analysis results. Finally, we performed maximum likelihood, penalized weighted median, and IVW (fixed effects) to further identify the potential causal association. Results: The random-effects IVW results showed that Bacteroides (p = 0.965, OR 95% confidence interval [CI] = 0.990 [0.621–1.579]), Streptococcus (p = 0.591, OR 95% CI = 1.120 [0.741–1.692]), Proteobacteria (p = 0.522, OR 95% CI = 1.160 [0.737–1.826]), and Lachnospiraceae (p = 0.717, OR 95% CI = 1.073 [0.732–1.574]) have no genetic causal relationship with AS. There was no heterogeneity, horizontal pleiotropy or outliers, and results were normally distributed. The MR analysis results were not driven by a single SNP. Conclusions: This study showed that Bacteroides, Streptococcus, Proteobacteria and Lachnospiraceae, four common gut microbiotas associated with AS, had no causal relationship with AS at the genetic level. This study makes a positive contribution to the genetics of AS, but the insufficient number of gut microbiota included is a limitation.

Background: The incidence of pulmonary embolism complications in the literature ranges from 10 to 50%, with a 0.5–10% risk of fatal pulmonary embolism. However, the biological cause of pulmonary embolism is unknown. Methods: This study used data from the Genome-Wide Association Study (GWAS) of Pulmonary Embolism and Human Blood Metabolites from the UK Biobank, and the data from subjects of European ancestry were analyzed. We explored the relationship between pulmonary embolism and blood metabolites in three ways. We first analyzed the genetic correlation between pulmonary embolism and human blood metabolites using the linkage disequilibrium score regression (LDSC) and then analyzed the causal relationship between pulmonary embolism and meaningful blood metabolites obtained from the LDSC, a procedure for which we used Mendelian randomization analysis. Finally, we obtained transcriptome sequencing data for patients with a pulmonary embolism from the GEO database, analyzed differentially expressed genes (DEGs) in patients with pulmonary embolism versus healthy populations, and compared the DEGs with the resulting blood metabolite genes to further validate the relationship between pulmonary embolism and blood metabolites. Result: We found six human blood metabolites genetically associated with pulmonary embolism, stearic acid glycerol phosphate ethanolamine (correlation coefficient = 0.2582, P = 0.0493), hydroxytryptophan (correlation coefficient = 0.2894, P = 0.0435), and N1-methyladenosine (correlation coefficient = 0.0439, P = 0.3728), and a significant causal relationship was discovered between hydroxytryptophan and pulmonary embolism. After screening microarray data from the GEO database, we performed differential gene analysis on the GSE19151 dataset and screened a total of 22,216 genes with P values less than 0.05, including 17,361 upregulated genes and 4854 downregulated genes. By comparing the resulting differentially expressed genes with six genes encoding blood metabolites, LIPC and NAT2 were found to be differentially expressed in association with pulmonary embolism.

Rheumatoid arthritis is a chronic autoimmune disease characterized by persistent hyperplasia of the synovial membrane and progressive erosion of articular cartilage. Disequilibrium between the proliferation and death of RA fibroblast-like synoviocytes (RA-FLSs) is the critical factor in progression of RA. Naringin has been reported to exert anti-inflammatory and antioxidant effect in acute and chronic animal models of RA. However, the therapeutic effect and underlying mechanisms of naringin in human RA-FLS remain unclear. Based on network pharmacology, the corresponding targets of naringin were identified using SwissTargetPrediction database, STITCH database, and Comparative Toxicogenomics Database. Deferentially expressed genes (DEGs) in RA were obtained from the GEO database. The protein–protein interaction (PPI) networks of intersected targets were constructed using the STRING database and visualized using Cytoscape. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, and the pathways directly related to pathogenesis of RA were integrated manually. Further, in vitro studies were carried out based on network pharmacology. 99 target genes were intersected between targets of naringin and DEGs. The PPI network and topological analysis indicated that IL-6, MAPK8, MMP-9, TNF, and MAPK1 shared the highest centrality among all. GO analysis and KEGG analysis indicated that target genes were mostly enriched in (hsa05200) pathways in cancer, (hsa05161) hepatitis B, (hsa04380) osteoclast differentiation, (hsa04151) PI3K-Akt signaling pathway, and (hsa05142) Chagas disease (American trypanosomiasis). In vitro studies revealed that naringin exposure was found to promote apoptosis of RA-FLS, increased the activation of caspase-3, and increased the ratio of Bax/Bcl-2 in a dose-dependent manner. Furthermore, treatment of naringin attenuated the production of inflammatory cytokines and matrix metalloproteinases (MMPs) in TNF-ɑ–induced RA-FLS. Moreover, treatment of naringin inhibited the phosphorylation of Akt and ERK in RA-FLS. Network pharmacology provides a predicative strategy to investigate the therapeutic effects and mechanisms of herbs and compounds. Naringin inhibits inflammation and MMPs production and promotes apoptosis in RA-FLS via PI3K/Akt and MAPK/ERK signaling pathways.

Several studies have linked gut microbiota to human brain activity. This study used Mendelian randomization (MR) to investigate the causal relationship between gut microbes and delirium. MR was used to select SNPs from large-scale GWAS summary data on 211 gut microbiota taxa and delirium. Inverse variance weighting (IVW), weighted median, and MR-Egger methods were used for statistical analyses. Outliers were assessed using the leave-one-out method. To avoid horizontal pleiotropy, we performed the MR-PRESSO and MR-Egger intercept tests. Cochran's Q and I₂ values for IVW and MR-Egger were used to assess heterogeneity. IVW suggested that genetic prediction of the family Desulfovibrionaceae (1.784 (1.267-2.512), P = 0.001), order Desulfovibrionales (1.501 (1.058-2.128), P = 0.023), and genus Candidatus Soleaferrea (1.322 (1.052-1.659), P = 0.016) increased the risk of delirium, but the family Oxalobacteraceae (0.841 (0.722-0.981), P = 0.027), and genera Holdemania (0.766 (0.620-0.946), P = 0.013), Ruminococcus gnavus (0.806 (0.661-0.982), P = 0.033), and Eggerthella (0.815 (0.667-0.997), P = 0.047) reduced the risk of delirium. (1) Limited sample size, (2) inability to assess gut microbiota interactions, and (3) limited to European populations. Our results suggest that presence of the microbial family Desulfovibrionaceae, order Desulfovibrionales, and genus Candidatus Soleaferrea increased the risk of delirium, whereas the Oxalobacteraceae family, and the genera Holdemania, Ruminococcus gnavus, and Eggerthella decreased the risk of delirium. However, the potential of gut probiotic interventions in the prevention of perioperative delirium should be emphasized.