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Crohn's disease (CD) is a type of heterogeneous, dysfunctional immune-mediated intestinal chronic and recurrent inflammation caused by a variety of etiologies. Cuproptosis is a newly discovered form of programmed cell death that seems to contribute to the advancement of a variety of illnesses. Consequently, the major purpose of our research was to examine the role of cuproptosis-related genes in CD. We obtained two CD datasets from the gene expression omnibus (GEO) database, and immune cell infiltration was created to investigate immune cell dysregulation in CD. Based on differentially expressed genes (DEGs) and the cuproptosis gene set, differentially expressed genes of cuproptosis (CuDEGs) were found. Then, candidate hub cuproptosis-associated genes were found using machine learning methods. Subsequently, using 437 CD samples, we explored two distinct subclusters based on hub cuproptosis-related genes. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Gene set variation analysis (GSVA) and immune infiltration analysis studies were also used to assess the distinct roles of the subclusters. Overall, 25 CuDEGs were identified, including ABCB6, BACE1, FDX1, GLS, LIAS, MT1M, PDHA1, etc. And most CuDEGs were expressed at lower levels in CD samples and were negatively related to immune cell infiltration. Through the machine learning algorithms, a seven gene cuproptosis-signature was identified and two cuproptosis-related subclusters were defined. Cluster-specific differentially expressed genes were found only in one cluster, and functional analysis revealed that they were involved in several immune response processes. And the results of GSVA showed positive significant enrichment in immune-related pathways in cluster A, while positive significant enrichment in metabolic pathways in cluster B. In addition, an immune infiltration study indicated substantial variation in immunity across different groups. Immunological scores were higher and immune infiltration was more prevalent in Cluster A. According to the current research, the cuproptosis phenomenon occurs in CD and is correlated with immune cell infiltration and metabolic activity. This information indicates that cuproptosis may promote CD progression by inducing immunological response and metabolic dysfunction. This research has opened new avenues for investigating the causes of CD and developing potential therapeutic targets for the disease.

Background This meta-analysis was to assess the incidence and risk factors of sarcopenia in gastric cancer (GC) patients and to provide clinical implications for the prevention and improvement of sarcopenia in GC patients. Methods PubMed, Embase, Cochrane, Web of Science, CNKI, Wanfang, and VIP databases (language was limited to Chinese and English) were searched for observational studies. The random-effects model was used to analyze the incidence of GC combined with sarcopenia and the odd ratio (OR) and 95% confidence interval (CI) of risk factors. Results 1244 studies were retrieved and 20 eligible studies were included. The meta-analysis revealed that the incidence of sarcopenia in GC patients was 26.6% (95% CI: 21%~32%). Age (OR = 1.128, 95% CI: 1.056 ~ 1.204, P  < 0.001), male (OR = 1.054, 95% CI: 0.620 ~ 1.791, P  < 0.005), body mass index (OR = 1.117, 95% CI: 0.881 ~ 1.414, P  < 0.001), nutritional risk screening 2002 (OR = 3.953, 95% CI: 2.038 ~ 7.668, P  < 0.05), and tumor diameter > 3 cm (OR = 1.515, 95% CI: 1.021 ~ 2.248, P  < 0.05) may be risk factors for sarcopenia in GC patients. In contrast, tumor stage (OR = 1.907, 95% CI: 0.967 ~ 3.763, P  > 0.05), gastrectomy approach (OR = 1.837, 95% CI: 1.237 ~ 2.727, P  > 0.05), differentiation type (OR = 0.586, 95%CI: 0.325 ~ 1.059, P  > 0.05), and severe adverse reactions (NLR, HB, ALB) after chemotherapy (OR = 0.926, 95%CI: 0.793 ~ 1.082, P  > 0.05) had no significant correlation with sarcopenia in GC patients. Conclusions This meta-analysis shows an increased prevalence of sarcopenia in GC patients. This analysis, which focused on Asian populations, suggested that high nutritional risk was a risk factor for sarcopenia in GC patients. Age over 65 years and tumor diameter over 3 cm may be risk factors for sarcopenia. Men may be prone to sarcopenia. Targeting these risk factors may be beneficial in the prevention of sarcopenia in GC patients.

Ginkgo biloba L. is an ancient relict plant with rich pharmacological activity and nutritional value, and its main physiologically active components are flavonoids and terpene lactones. The bZIP gene family is one of the largest gene families in plants and regulates many processes including pathogen defense, secondary metabolism, stress response, seed maturation, and flower development. In this study, genome-wide distribution of the bZIP transcription factors was screened from G. biloba  database in silico analysis. A total of 40 bZIP genes were identified in G. biloba and were divided into 10 subclasses. GbbZIP members in the same group share a similar gene structure, number of introns and exons, and motif distribution. Analysis of tissue expression pattern based on transcriptome indicated that GbbZIP08 and GbbZIP15 were most highly expressed in mature leaf. And the expression level of GbbZIP13 was high in all eight tissues. Correlation analysis and phylogenetic tree analysis suggested that GbbZIP08 and GbbZIP15 might be involved in the flavonoid biosynthesis. The transcriptional levels of 20 GbbZIP genes after SA, MeJA, and low temperature treatment were analyzed by qRT-PCR. The expression level of GbbZIP08 was significantly upregulated under 4°C. Protein–protein interaction network analysis indicated that GbbZIP09 might participate in seed germination by interacting with GbbZIP32. Based on transcriptome and degradome data, we found that 32 out of 117 miRNAs were annotated to 17 miRNA families. The results of this study may provide a theoretical foundation for the functional validation of GbbZIP genes in the future.

Camellia japonica petals are colorful, rich in anthocyanins, and possess important ornamental, edible, and medicinal value. However, the regulatory mechanism of anthocyanin accumulation in C. japonica is still unclear. In this study, an integrative analysis of the metabolome and transcriptome was conducted in five C. japonica cultivars with different petal colors. Overall, a total of 187 flavonoids were identified (including 25 anthocyanins), and 11 anthocyanins were markedly differentially accumulated among these petals, contributing to the different petal colors in C. japonica . Moreover, cyanidin-3- O- (6 ″ - O- malonyl) glucoside was confirmed as the main contributor to the red petal phenotype, while cyanidin-3- O- rutinoside, peonidin-3- O- glucoside, cyanidin-3- O- glucoside, and pelargonidin-3- O- glucoside were responsible for the deep coloration of the C. japonica petals. Furthermore, a total of 12,531 differentially expressed genes (DEGs) and overlapping DEGs (634 DEGs) were identified by RNA sequencing, and the correlation between the expression level of the DEGs and the anthocyanin content was explored. The candidate genes regulating anthocyanin accumulation in the C. japonica petals were identified and included 37 structural genes (especially CjANS and Cj4CL ), 18 keys differentially expressed transcription factors (such as GATA , MYB , bHLH , WRKY , and NAC ), and 16 other regulators (mainly including transporter proteins, zinc-finger proteins, and others). Our results provide new insights for elucidating the function of anthocyanins in C. japonica petal color expression.

Our research endeavors are directed towards unraveling the stem cell characteristics of lower-grade glioma patients, with the ultimate goal of formulating personalized treatment strategies. We computed enrichment stemness scores and performed consensus clustering to categorize phenotypes. Subsequently, we constructed a prognostic risk model using weighted gene correlation network analysis (WGCNA), random survival forest regression analysis as well as full subset regression analysis. To validate the expression differences of key genes, we employed experimental methods such as quantitative Polymerase Chain Reaction (qPCR) and assessed cell line proliferation, migration, and invasion. Three subtypes were assigned to patients diagnosed with LGG. Notably, Cluster 2 (C2), exhibiting the poorest survival outcomes, manifested characteristics indicative of the subtype characterized by immunosuppression. This was marked by elevated levels of M1 macrophages, activated mast cells, along with higher immune and stromal scores. Four hub genes—CDCA8, ORC1, DLGAP5, and SMC4—were identified and validated through cell experiments and qPCR. Subsequently, these validated genes were utilized to construct a stemness risk signature. Which revealed that Lower-Grade Glioma (LGG) patients with lower scores were more inclined to demonstrate favorable responses to immune therapy. Our study illuminates the stemness characteristics of gliomas, which lays the foundation for developing therapeutic approaches targeting CSCs and enhancing the efficacy of current immunotherapies. By identifying the stemness subtype and its correlation with prognosis and TME patterns in glioma patients, we aim to advance the development of personalized treatments, enhancing the ability to predict and improve overall patient prognosis.

Hot-start polymerase chain reaction (hot-start PCR) effectively inhibits non-specific product amplification during PCR, with hot-start Taq DNA polymerase (HS Taq) serving as the critical component. Current HS Taq preparation methods, including chemical modification and aptamer-based approaches, exhibit limitations such as prolonged activation time, reduced enzyme activity, and inadequate blocking specificity. In contrast, antibody-mediated hot-start Taq offers distinct advantages: rapid activation, stable enzymatic performance, and high blocking specificity through targeted binding to functional domains. While antibody-based hot-start Taq DNA polymerases are widely utilized for their high efficiency and specificity, their development often relies on commercial sources or complex genetic engineering. This dependency constrains the independent and customizable development of core PCR components. To address this, our study established a streamlined platform for generating highly effective monoclonal antibodies that inhibit Taq enzyme activity. The key innovation lies in our structure-guided, domain-specific immunization strategy: instead of using the full-length enzyme, we targeted the polymerase domain (Taq-P) to generate a specific monoclonal antibody that acts as a reversible inhibitor. This approach overcomes the major challenge of generating functional antibodies against cryptic epitopes, which are poorly immunogenic in the full-length protein. This antibody sterically blocks the enzyme’s active site at room temperature, preventing non-specific priming during PCR setup, and dissociates upon thermal activation to restore full activity. The resulting hot-start Taq enzyme significantly reduced non-specific amplification in quantitative PCR assays, and its practical utility was successfully demonstrated in detecting fungal pathogens and respiratory viruses using clinical samples. This study provides a feasible and effective strategy for the autonomous development of critical reagents for molecular diagnostics.

Post-traumatic stress disorder (PTSD) is a debilitating psychiatric disorder triggered by exposure to traumatic events. Emerging evidence suggests that mitochondrial dysfunction may contribute to PTSD pathogenesis by disrupting cellular energy metabolism, increasing oxidative stress, and impairing neuroplasticity. This study investigates mitochondrial dysfunction-associated biomarkers, potentially opening new avenues for targeted therapeutic approaches. Gene expression matrices from datasets GSE199841 and GSE81761 were derived from peripheral blood samples, used to identify differentially expressed genes (DEGs) between PTSD patients and healthy controls. Functional annotation and enrichment analysis were carried out using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Next, least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest were employed to screen and prioritize potential biomarkers, followed by validation through receiver operating characteristic (ROC) analysis and independent cohort from human verification. We also examined the expression of mitoDEGs in peripheral blood from PTSD-related mouse models by RT-qPCR. Given the close interplay between mitochondrial metabolism and immune function, we investigated the relationship between key MitoDEGs and immune infiltration proportions. DEGs in PTSD were enriched in mitochondrial pathways, including mitochondrial ribosome function and nitric oxide synthase regulation. Machine learning identified and as key MitoDEGs. Then we assessed the diagnostic performance of four key genes through ROC curve analysis. We evaluated four key genes using ROC analysis, showing good diagnostic performance in the discovery cohort (AUC=0.871). Results were replicated in validation cohorts (GSE81761 AUC=0.745; GSE97356 AUC=0.638). These genes correlated with immune cell proportions (regulatory T cells, naïve B cells, CD4+/CD8+ T cells) and showed conserved dysregulation in murine blood, aligning with human data. Mitochondrial-related genes and may serve as cross-species diagnostic biomarkers for PTSD, with potential links to neuroimmune mechanisms.

AbstractThe artificial fish swarm algorithm can achieve good convergence effects in the early stage, but in the late stage, the algorithm has the problems of slow convergence speed and low optimization accuracy, and it is easy to fall into local extremes, making the algorithm’s convergence effect poor. Therefore, the characteristics of fireworks algorithm are used to improve the deficiency of fish swarm algorithm that is easy to fall into local extreme value in the late stage, and FWA-artificial fish swarm algorithm is put forward. When the effect of artificial fish swarm algorithm is poor, the explosion, mutation, mapping, and selection operations of fireworks algorithm are introduced to increase the variability of artificial fish, so as to enhance the optimization speed and ability of the algorithm. Finally, the improved algorithm is tested by four typical complex functions which are difficult to find the optimal solution by traditional method. Simulation results prove that the algorithm has the advantages of faster optimization speed, higher precision, and stronger stability.

Ulcerative colitis (UC) is a chronic inflammatory disease of the colon and rectum that has no exact cause and is characterized by relapsing and remitting episodes. We aimed to find biomarkers of UC and its causes. We got GSE73661 from the GEO database and used WGCNA to find DEGs that were expressed in the same way in both normal and UC samples. To identify the co-expression modules, we used Weighted Gene Co-Expression Network Analysis. Next, we selected genes that were both DEGs and parts of main modules. Later, three datasets were used to find the hub genes, and qRT-PCR was utilized to confirm the in-silico findings. Additionally, we analyzed the connection between the hub genes and the filtration of immune cells in UC. Using the databases, we made predictions about the miRNAs and lncRNAs that regulate the hub genes and predicted possible therapeutic drugs. We found 822 DEGs and three main modules related to immunity, endoplasmic reticulum, and metabolism. Using another three datasets and human samples to confirm the mRNA expression of these genes in UC patients, XBP1 and PLPP1 were selected as hub genes, and had excellent diagnostic potential. According to the findings of the immune infiltration, patients with UC exhibited a larger proportion of immune cells. And hub genes, particularly XBP1, were closely linked to a number of immune cell infiltrations. Based on the databases and hub genes, a lncRNA-miRNA-mRNA network, including two miRNAs (miR-214-3p and miR-93-5p), two hub genes, and 124 lncRNAs, and potential therapeutic medicine were identified. We found two new genes, XBP1 and PLPP1, that are involved in UC and can help diagnose and measure the disease. XBP1 also relates to clinical scores and immune cells. We suggested a gene network and possible drugs based on them.

As a common mutation trait in plants, leaf colour mutation is related to the degree of chlorophyll and anthocyanin changes and the destruction of chloroplast structure. This study summarizes the latest research progress in leaf colour mutation mechanism, including the metabolic basis of plant leaf colour mutation, leaf colour mutation caused by gene mutation in the chlorophyll metabolism pathway, leaf colour mutation caused by blocked chloroplast development, leaf colour mutation controlled by key transcription factors and non-coding RNAs, leaf colour mutation caused by environmental factors, and leaf colour mutation due to the involvement of the mevalonate pathway. These results will lay a theoretical foundation for leaf colour development, leaf colour improvement, and molecular breeding for leaf colour among tree species.