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Gastric cancer (GC) is one of the most common malignancies in digestive system. Accumulating evidence reveals the critical role of long noncoding RNAs (lncRNAs) in GC development. The study aimed to explore the functions and mechanism of lncRNA actin alpha 2, smooth muscle antisense RNA 1 (ACTA2-AS1) in GC. Reverse transcription-quantitative polymerase chain reaction analyses and subcellular fractionation assays showed that ACTA2-AS1 was lowly expressed in GC cells and was mainly distributed in the cytoplasm. Overexpressed ACTA2-AS1 inhibited GC cell viability, proliferation, migration, invasion, and epithelial-mesenchymal transition process, as suggested by cell counting kit-8 assays, colony formation assays, wound healing assays, Transwell assays and Western blot analyses. Mechanistically, ACTA2-AS1 served as a competing endogenous RNA (ceRNA) to bind with miR-378a-3p and thereby, antagonized the inhibitory effect of miR-378a-3p on the expression of messenger RNA phosphatidylinositol specific phospholipase C X domain containing 2 (PLCXD2). The binding capacity between miR-378a-3p and ACTA2-AS1 (or PLCXD2) was detected by RNA pulldown assays, luciferase reporter assays and RNA immunoprecipitation assays. Moreover, PLCXD2 knockdown rescued the inhibitory effect of ACTA2-AS1 overexpression on malignant behaviors of GC cells. Overall, ACTA2-AS1 inhibits malignant phenotypes of GC cells by acting as a ceRNA to target miR-378a-3p/PLCXD2 axis.

The article discusses the relationship between lipid metabolism disorders and psoriasis, highlighting the importance of understanding the causal links between cholesterol levels and autoimmune diseases. The study critiques a previous publication by Zhou et al. for methodological flaws and outdated dataset choices, suggesting a re-analysis using more current data. The re-analysis found significant associations between triglycerides and specific lipid components with psoriasis risk, while no causal link was found between HDL-C, LDL-C, and total cholesterol with psoriasis. The study emphasizes the need for precise data and adherence to Mendelian randomization principles in confirming causal relationships. [Extracted from the article]

Background: Previous studies have emphasized the association between the intake of artificial sweeteners (AS) and type 2 diabetes mellitus (T2DM), but the causative relationship remains ambiguous. Methods: This study employed univariate Mendelian randomization (MR) analysis to assess the causal link between AS intake from various sources and T2DM. Linkage disequilibrium score (LDSC) regression was used to evaluate the correlation between phenotypes. Multivariate and mediation MR were applied to investigate confounding factors and mediating effects. Data on AS intake from different sources (N = 64,949) were sourced from the UK Biobank, while T2DM data were derived from the DIAbetes Genetics Replication And Meta-analysis.The primary method adopted was inverse variance weighted (IVW), complemented by three validation techniques. Additionally, a series of sensitivity analyses were performed to evaluate pleiotropy and heterogeneity. Results: LDSC analysis unveiled a significant genetic correlation between AS intake from different sources and T2DM (rg range: -0.006 to 0.15, all P < 0.05). After correction by the false discovery rate (FDR), the primary IVW method indicated that AS intake in coffee was a risk factor for T2DM (OR = 1.265, 95% CI: 1.035–1.545, P = 0.021, PFDR = 0.042). Further multivariable and mediation MR analyses pinpointed high density lipoprotein-cholesterol (HDL-C) as mediating a portion of this causal relationship. In reverse MR analysis, significant evidence suggested a positive correlation between T2DM and AS intake in coffee (β = 0.013, 95% CI: 0.004–0.022, P = 0.004, PFDR = 0.012), cereal (β = 0.007, 95% CI: 0.002–0.012, P = 0.004, PFDR = 0.012), and tea (β = 0.009, 95% CI: 0.001–0.017, P = 0.036, PFDR = 0.049). No other causal associations were identified (P > 0.05, PFDR > 0.05). Conclusion: The MR analysis has established a causal relationship between AS intake in coffee and T2DM. The mediation by HDL-C emphasizes potential metabolic pathways underpinning these relationships [ABSTRACT FROM AUTHOR]

A commentary published in the journal Lung discusses a study by Huang et al. that explores the relationship between adipokines and idiopathic pulmonary fibrosis (IPF) using Mendelian randomization (MR) analysis. The commentary raises concerns about the study's methodology, particularly regarding the choice of datasets and the selection of outcome phenotypes. The authors of the commentary conducted a reanalysis using different datasets and found no causal relationship between adipokines and IPF. They emphasize the importance of precise dataset identification and adherence to MR principles in establishing potential causal connections. [Extracted from the article]

Previous studies have highlighted the association between cannabis use and diabetes and its complications; however, the causality remains ambiguous. Univariate Mendelian randomization (MR), multivariate MR, mediation MR, and linkage disequilibrium score (LDSC) analysis to assess the causal relationship between cannabis use and 12 diabetic phenotypes. Summary statistics for lifetime cannabis use (N = 184,765) and cannabis use disorder (CUD) (N = 374,287) from genome-wide association studies. The primary method used was inverse-variance-weighted (IVW). A range of sensitivity analyses ensured the robustness of the results. LDSC analysis revealed a significant genetic correlation between CUD and T2DM, as well as between lifetime cannabis use and four diabetic phenotypes (P < 0.05). After correction by false discovery rate (FDR), the primary IVW analysis indicates that the genetically predicted CUD is positively associated with the risk of diabetic hypoglycemia (OR = 1.11, 95% CI 1.04–1.20, P = 0.003, P FDR = 0.04) and proliferative diabetic retinopathy (PDR) (OR = 1.12, 95% CI 1.04–1.19, P = 4.89×10−4, P FDR =0.01). Additionally, suggestive evidence links CUD with increased risks of diabetic nephropathy, type 1 diabetes mellitus (T1DM), diabetic retinopathy, and T1DM associated with diabetic ketoacidosis (P < 0.05 & P FDR > 0.05). No causal relationship was detected between lifetime cannabis use and diabetic phenotypes (P > 0.05 & P FDR > 0.05). Multivariable and mediation MR analyses revealed that glycated hemoglobin A1c partially mediates the causal effect of CUD on PDR and diabetic hypoglycemia. This MR study suggests that CUD may have a causal role in several diabetic disease phenotypes. • LDSC analysis shows significant genetic correlation between cannabis use disorder (CUD) and T2DM phenotypes. • Genetically predicted CUD associated with increased risk of diabetic hypoglycemia and proliferative diabetic retinopathy. • No detected causal link between lifetime cannabis use and diabetic phenotypes after correction. • Mendelian randomization provides insights into the potential causal roles of CUD in diabetic disease phenotypes. • HbA1c identified as a partial mediator for the causal effect between cannabis and diabetic outcomes. [ABSTRACT FROM AUTHOR]

Established an impact-unbalanced suspended rotor system mechanics models, and the dimensionless motion equation of system is deduced, using the fourth order Runge-Kutta method for the numerical calculation of this model, the integrated use of bifurcation diagram, orbit diagram, Poincare section diagram, the axis displacement time history diagram, maximum moment diagram and duty ratio diagram study the dynamic behavior of the system, identify the impact of the rotor, the maximum torque and impact by the proportion of time, and analyzed the speed ratio and the change of the stiffness coefficient of the influence of the nonlinear dynamic characteristics of the system.The analysis results show that the system presents chaotic motion in a certain parameter domain at the medium velocity stage, while the system tends to stable periodic motion in other parameter domains.With the increase of the stiffness coefficient, the chaotic motion region and the maximum moment of the system increase, and the stability of the system decreases.

In the "worst-case" selection of hip prosthesis wear, it is necessary to calculate the contact stress of the acetabular liner. However, there are various combinations of acetabular prostheses. If calculated one by one, it will cause a large workload, a repeated and tedious calculation problem. To solve this problem, a machine learning prediction method by combining principal component analysis and support vector regressions (PCA-SVR) was established. First, the finite element method is used to analyze and calculate the contact stress of the acetabular liner in a typical combination to form a basic data set with the key size of the acetabular prosthesis as input and the contact stress as output; then, based on this data set, the PCA reduces the dimension of the input to obtain a new data set. Finally, based on this data set, SVR is used to establish the mapping model, and the optimal value of the model parameter C and is obtained by combining K-fold cross-validation and grid search method. The maximum absolute error of the prediction on the test data set is only 0.1986, the root mean square error RMSE is only 0.09309, and the R² value is 0.9426, which verifies the effectiveness of the prediction model. At the same time, the prediction performance is compared with the Ridge regression and Lasso models, which further verifies the superiority of the proposed method.

Objective: The objective of this study was to summarize the accuracy of preoperative vascular invasion with endoscopic ultrasound (EUS) and computed tomography (CT) test performance in pancreatic cancer with meta-analysis Method: Two reviewers searched MEDLINE database to identify relevant studies. The reference lists of the trials were manually searched. Included studies used surgical and/or histological findings as the 'gold standard,' and provided sufficient data to construct a diagnostic 2 × 2 table. A statistical program of Meta-Disc was used to calculate the pooled sensitivity, specificity, positive LR, negative LR, DOR, and the SROC curve. Publication bias was assessed by Deeks' asymmetry test. Sensitivity analysis and subgroup analysis were calculated to down the heterogeneity. Meta-regression was calculated to evaluate potential sources of heterogeneity Result: A total of 30 studies with 1,554 patients were included for the analysis, nine of these studies compared EUS with CT to assess the diagnostic efficiency The pooled sensitivity of EUS and CT was 72 % (95 % CI 67-77 %) and 63 % (95 % CI 58-67 %), and the pooled specificity of EUS and CT was 89 % (95 % CI 86-92 %) and 92 % (95 % CI 90-94 %), respectively. The positive LR of EUS and CT was 5.14 (95 % CI 3.14-8.40) and 6.21 (95 % CI 3.96-9.71), and the negative LR was 0.36 (95 % CI 0.25-0.52) and 0.41 (95 % CI 0.31-0.55), respectively. The AUCs of EUS and CT were 0.9037 and 0.8948. The subgroup analysis of nine studies performed both EUS and CT showed CT scan with a lower sensitivity of 48 % (95 % CI 0.40-0.56), when compared to EUS of 69 % (95 % CI 0.61-0.77). The overall AUCs of CT scan appear to be lower (AUCs = 0.8589), compared with EUS (AUCs = 0.9379) Conclusion: EUS performed better than CT in differentiating vascular invasion preoperative on pancreatic cancer. EUS could provide other additional information when compared with CT. [ABSTRACT FROM AUTHOR]