Explore the vast range of titles available.

BackgroundImmune system dysregulation plays a critical role in aortic valve calcification (AVC) and metabolic syndrome (MS) pathogenesis. The study aimed to identify pivotal diagnostic candidate genes for AVC patients with MS.MethodsWe obtained three AVC and one MS dataset from the gene expression omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and module gene via Limma and weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, protein–protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression and random forest) were used to identify candidate immune-associated hub genes for diagnosing AVC with MS. To assess the diagnostic value, the nomogram and receiver operating characteristic (ROC) curve were developed. Finally, immune cell infiltration was created to investigate immune cell dysregulation in AVC.ResultsThe merged AVC dataset included 587 DEGs, and 1,438 module genes were screened out in MS. MS DEGs were primarily enriched in immune regulation. The intersection of DEGs for AVC and module genes for MS was 50, which were mainly enriched in the immune system as well. Following the development of the PPI network, 26 node genes were filtered, and five candidate hub genes were chosen for nomogram building and diagnostic value evaluation after machine learning. The nomogram and all five candidate hub genes had high diagnostic values (area under the curve from 0.732 to 0.982). Various dysregulated immune cells were observed as well.ConclusionFive immune-associated candidate hub genes ( BEX2 , SPRY2 , CXCL16 , ITGAL , and MORF4L2 ) were identified, and the nomogram was constructed for AVC with MS diagnosis. Our study could provide potential peripheral blood diagnostic candidate genes for AVC in MS patients.

Both tumour-infiltrating immune cells and inflammation-related genes that can mediate immune infiltration contribute to the initiation and prognosis of patients with colon cancer. In this study, we developed a method to predict the survival outcomes among colon cancer patients and direct immunotherapy and chemotherapy. We obtained patient data from The Cancer Genome Atlas (TCGA) and captured inflammation-related genes from the GeneCards database. The package “ConsensusClusterPlus” was used to generate molecular subtypes based on inflammation-related genes obtained by differential expression analysis and univariate Cox analysis. A prognostic signature including four genes (PLCG2, TIMP1, BDNF and IL13) was also constructed and was an independent prognostic factor. Cluster 2 and higher risk scores meant worse overall survival and higher expression of human leukocyte antigen and immune checkpoints. Immune cell infiltration calculated by the estimate, CIBERSORT, TIMER, ssGSEA algorithms, tumour immune dysfunction and exclusion (TIDE), and tumour stemness indices (TSIs) were also compared on the basis of inflammation-related molecular subtypes and the risk signature. In addition, analyses of stratification, somatic mutation, nomogram construction, chemotherapeutic response prediction and small-molecule drug prediction were performed based on the risk signature. We finally used qRT–PCR to detect the expression levels of four genes in colon cancer cell lines and obtained results consistent with the prediction. Our findings demonstrated a four-gene prognostic signature that could be useful for prognostication in colon cancer patients and designing personalized treatments, which could provide new versions of personalized management for these patients.

To predict the residual stress distribution of the Ti-4Al-1.5Mn (TC2) alloy in the manufacturing process, quickly and accurately, a precise dynamic constitutive model for rheological behavior and a new simplified approach for numerical simulation were proposed. The dynamic stress-strain curves indicate that the enhancement effect and plasticizing of the TC2 alloy are sensitive to high strain rates. The dispersed β particles play an important role in the formation of the adiabatic shear band and not widened significantly. The average relative error of 1.04% and the correlation coefficient of 0.9949 indicate that the modified Johnson-Cook constitutive model well describes the rheological behavior. Then, with the help of the Almen test, an efficient but simplified approach was proposed to achieving coverage and uniform loading in simulation. At last, the residual stress contribution of the TC2 alloy in the shot peening test is in a good agreement with the simulation results by random multipellet model.

Background Diabetes-associated cognitive dysfunction (DACD), a prevalent complication of diabetes with learning, memory, and executive function impairments, lacks targeted therapeutic options. While trace elements, oxidative stress, and inflammation are linked to DACD, the role of serum copper and its interaction with inflammatory/oxidative biomarkers in cognitive regulation remains unclear in diabetic populations. Methods This cross-sectional study analyzed data from the National Health and Nutrition Examination Survey (NHANES) 2011–2014, including 1,149 participants (861 non-diabetic, 288 diabetic). Cognitive function was assessed via the Animal Fluency Test (AFT) and Digit Symbol Substitution Test (DSST). Serum copper levels were measured, alongside inflammatory indices: neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, neutrophil-to-monocyte-lymphocyte ratio, systemic immune-inflammation index and systemic inflammation response index; and oxidative stress markers: γ-glutamyl transferase, uric acid, and high-density lipoprotein cholesterol (HDL-C). Associations were analyzed using multivariable linear regression, causal mediation analysis, restricted cubic spline models and sex/age subgroup stratification. Results Diabetic participants had lower DSST scores than non-diabetic individuals ( P  < 0.001). In diabetic participants, serum copper was negatively associated with AFT scores (β = − 0.132, P  = 0.034) and positively correlated with HDL-C (β = 0.559, P  = 2.11e-06). HDL-C was the sole factor that statistically mediated the association between serum copper and DSST scores (average causal mediation effect = 0.095, 95% CI: 0.046–0.153, P  < 0.001). A non-linear relationship emerged: HDL-C remained stable at serum copper < 20 µg/dL but increased significantly when copper exceeded 25 µg/dL ( P  < 0.001). Stratified analyses revealed threshold heterogeneity (all P  < 0.05): males had a lower serum copper threshold (24.5 µg/dL, 95% CI: 21.8–27.2) than females (26.1 µg/dL, 95% CI: 23.4–28.8), and adults ≥ 65 years had a higher threshold (27.3 µg/dL, 95% CI: 24.5–30.1) than those < 65 years (23.8 µg/dL, 95% CI: 21.1–26.5). Conclusions This study identifies a diabetes-specific statistical association between serum copper, HDL, and cognitive function in DACD. The 25 µg/dL copper threshold (exploratory inflection point) marks where HDL-C-mediated effects become prominent, while sex- and age-specific threshold differences highlight population heterogeneity. This threshold offers a reference for trace element-lipid interaction research but requires validation in independent cohorts before potential use in DACD risk stratification.

As the Internet of Things devices are deployed on a large scale, location-based services are being increasingly utilized. Among these services, kNN (k-nearest neighbor) queries based on road network constraints have gained importance. This study focuses on the CkNN (continuous k-nearest neighbor) queries for non-uniformly distributed moving objects with large-scale dynamic road network constraints, where CkNN objects are continuously and periodically queried based on their motion evolution. The present CkNN high-concurrency query under the constraints of a super-large road network faces problems, such as high computational cost and low query efficiency. The aim of this study is to ensure high concurrency nearest neighbor query requests while shortening the query response time and reducing global computation costs. To address this issue, we propose the DVTG-Index (Dynamic V-Tree Double-Layer Grid Index), which intelligently adjusts the index granularity by continuously merging and splitting subgraphs as the objects move, thereby filtering unnecessary vertices. Based on DVTG-Index, we further propose the DVTG-CkNN algorithm to calculate the initial kNN query and utilize the existing results to speed up the CkNN query. Finally, extensive experiments on real road networks confirm the superior performance of our proposed method, which has significant practical applications in large-scale dynamic road network constraints with non-uniformly distributed moving objects.

Liquid lenses are the simplest and cheapest optical lenses, and various studies have been conducted to develop tunable-focus liquid lenses. In this study, a simple and easily implemented method for achieving tunable-focus liquid lenses was proposed and experimentally validated. In this method, charges induced by a corona discharge in the air were injected into dielectric liquid, resulting in “electropressure” at the interface between the air and the liquid. Through a 3D-printed U-tube structure, a tunable-focus liquid lens was fabricated and tested. Depending on the voltage, the focus of the liquid lens can be adjusted in large ranges (−∞ to −9 mm and 13.11 mm to ∞). The results will inspire various new liquid-lens applications.

Research on pre-deformation influences on material properties in multistep hot forming is of important scientific interest. In this paper, hot tensile tests at 850 °C and a strain rate of 0.001 s−1 were performed to study the microstructural evolution and mechanical properties of Ti-6Al-4V with pre-strains at 0.05, 0.1 and 0.15. The tensile test results showed that the specimen with 0.05 pre-strain exhibited higher flow stress and larger elongation. Additionally, increasing the pre-strain resulted in a decrease in ultimate tensile strength (UTS) and elongation (EL). The EBSD results showed that the main deformation mechanism of Ti-6Al-4V was high-angle grain boundary sliding. Pre-strain promoted dynamic recrystallization (DRX) by increasing the deformation substructure. The refinement of grains and the eradication of dislocations enhanced the deformability, resulting in an increase in flow stress.

Background Cancer stem cells (CSCs) were linked to cancer aggressiveness and poor prognosis in patients with hepatocellular carcinoma (HCC). Methods We integrated two external HCC cohorts to develop the stem cell subtypes according to unsupervised clustering with 26 stem cell gene sets. Between the subtypes, differences in prognosis, clinical characteristics, recognized HCC subtypes, metabolic profile, immune-related features, somatic mutation, and drug sensitivity were examined. The prognostic signature was created, and validated by numerous cohorts, and used to assess the efficacy of immunotherapy and transcatheter arterial chemoembolization (TACE) treatment. The nomogram was developed based on the signature and clinical features. We further examined the function of KIF20A in HCC and proved that KIF20A had the potential to regulate the stemness of HCC cells through western blot. Results Low stem cell patterns, a good prognosis, positive clinical features, specific molecular subtypes, low metastatic characteristics, and an abundance of metabolic and immunological aspects were associated with Cluster 1, whereas Cluster 2 was the reverse. Chemotherapy and immunotherapy were more effective in Cluster 1. Cluster 1 and CTNNB1 and ALB mutation were more closely. Additionally, the prognosis, immunotherapeutic, and TACE therapy responses were all worse in the high-risk group. The nomogram could predict the survival probability of HCC patients. KIF20A was discovered to be overexpressed in HCC and was revealed to be connected to the stemness of the HepG2 cell line. Conclusions Two stem cell subgroups with different prognoses, metabolic, and immunological characteristics in HCC patients were identified. We also created a 7-gene prognostic signature and a nomogram to estimate the survival probability. The function of KIF20A in HCC stemness was initially examined.

CCDC58, a member of the CCDC protein family, has been primarily associated with the malignant progression of hepatocellular carcinoma (HCC) and breast cancer, with limited research conducted on its involvement in other tumor types. We aimed to assess the significance of CCDC58 in pan-cancer. We utilized the TCGA, GTEx, and UALCAN databases to perform the differential expression of CCDC58 at both mRNA and protein levels. Prognostic value was evaluated through univariate Cox regression and Kaplan–Meier methods. Mutation and methylation analyses were conducted using the cBioPortal and SMART databases. We identified genes interacting with and correlated to CCDC58 through STRING and GEPIA2, respectively. Subsequently, we performed GO and KEGG enrichment analyses. To gain insights into the functional status of CCDC58 at the single-cell level, we utilized CancerSEA. We explored the correlation between CCDC58 and immune infiltration as well as immunotherapy using the ESTIMATE package, TIMER2.0, TISIDB, TIDE, TIMSO, and TCIA. We examined the relationship between CCDC58 and tumor heterogeneity, stemness, DNA methyltransferases, and MMR genes. Lastly, we constructed a nomogram based on CCDC58 in HCC and investigated its association with drug sensitivity. CCDC58 expression was significantly upregulated and correlated with poor prognosis across various tumor types. The mutation frequency of CCDC58 was found to be increased in 25 tumors. We observed a negative correlation between CCDC58 expression and the methylation sites in the majority of tumors. CCDC58 showed negative correlations with immune and stromal scores, as well as with NK T cells, Tregs, CAFs, endothelial cells, and immunomodulators. Its value in immunotherapy was comparable to that of tumor mutational burden. CCDC58 exhibited positive correlations with tumor heterogeneity, stemness, DNA methyltransferase genes, and MMR genes. In HCC, CCDC58 was identified as an independent risk factor and demonstrated potential associations with multiple drugs. CCDC58 demonstrates significant clinical value as a prognostic marker and indicator of immune response across various tumor types. Its comprehensive analysis provides insights into its potential implications in pan-cancer research.

As the Internet of Things devices are deployed on a large scale, location-based services are being increasingly utilized. Among these services, kNN (k-nearest neighbor) queries based on road network constraints have gained importance. This study focuses on the CkNN (continuous k-nearest neighbor) queries for non-uniformly distributed moving objects with large-scale dynamic road network constraints, where CkNN objects are continuously and periodically queried based on their motion evolution. The present CkNN high-concurrency query under the constraints of a super-large road network faces problems, such as high computational cost and low query efficiency. The aim of this study is to ensure high concurrency nearest neighbor query requests while shortening the query response time and reducing global computation costs. To address this issue, we propose the DVTG-Index (Dynamic V-Tree Double-Layer Grid Index), which intelligently adjusts the index granularity by continuously merging and splitting subgraphs as the objects move, thereby filtering unnecessary vertices. Based on DVTG-Index, we further propose the DVTG-CkNN algorithm to calculate the initial kNN query and utilize the existing results to speed up the CkNN query. Finally, extensive experiments on real road networks confirm the superior performance of our proposed method, which has significant practical applications in large-scale dynamic road network constraints with non-uniformly distributed moving objects.