Effect of serum metabolites on the risk of iridocyclitis: a bidirectional Mendelian randomization study.

Previous research has linked serum metabolite levels to iridocyclitis, yet their causal relationship remains unexplored. This study investigated this potential causality by analyzing pooled data from 7824 iridocyclitis patients in a Genome-Wide Association Study (GWAS) using Mendelian randomization (MR) and linkage disequilibrium score regression (LDSC). Employing rigorous quality control and comprehensive statistical methods, including sensitivity analyses, we examined the influence of 486 serum metabolites on iridocyclitis. Our MR analysis identified 23 metabolites with significant causal effects on iridocyclitis, comprising 17 known and 6 unidentified metabolites. Further refinement using Cochran's Q test and MR-PRESSO indicated 16 metabolites significantly associated with iridocyclitis risk. LDSC highlighted the heritability of certain metabolites, underscoring genetic influences on their levels. Notably, tryptophan, proline, theobromine, and 7-methylxanthine emerged as risk factors, while 3,4-dihydroxybutyrate appeared protective. These findings enhance our understanding of the metabolic interactions in iridocyclitis, offering insights for diagnosis, unraveling pathophysiological mechanisms, and informing potential avenues for prevention and personalized treatment.

Genetically determined metabolites in allergic conjunctivitis: A Mendelian randomization study.

Background: Allergic conjunctivitis (AC) afflicts a significant portion of the global populace. Yet, its metabolic foundations remain largely unexplored. Methods: We applied Mendelian Randomization (MR) and Linkage Disequilibrium Score Regression (LDSC) to scrutinize a cohort comprising 20 958 AC cases and 356 319 controls. Data were amalgamated from the metabolomics GWAS server and the FinnGen project, under strict quality control protocols. Results: Using two-sample MR analysis, 486 blood metabolites were investigated in relation to AC. The IVW approach highlighted 18 metabolites as closely tied to AC risk; of these, 16 retained significance post sensitivity assessments for heterogeneity and horizontal pleiotropy. LDSC analysis, adopted to bolster our findings and negate confounders from shared genetic markers, revealed 8 metabolites with marked heritability, including: palmitate (OR = 0.614), 3-methoxytyrosine (OR = 0.657), carnitine (OR = 1.368), threonate (OR = 0.828), N-[3-(2-Oxopyrrolidin-1-yl)propyl]acetamide (OR = 1.257), metoprolol acid metabolite (OR = 0.982), oleoylcarnitine (OR = 0.635), and 2-palmitoylglycerophosphocholine (OR = 1.351). Conclusion: AC is precipitated by ocular responses to environmental allergens. Our study unveils a causal link between 8 blood metabolites and AC. This insight accentuates the role of metabolites in AC onset, suggesting novel avenues for its early prediction, targeted prevention, and tailored therapeutic interventions.

Prediction of molecular-specific mutagenic alerts and related mechanisms of chemicals by a convolutional neural network (CNN) model based on SMILES split.

Structural alerts (SAs) are essential to identify chemicals for toxicity evaluation and health risk assessment. We constructed a novel SMILES split-based deep learning model (SSDL) that was trained and verified with 5850 chemicals from the ISSSTY database and 384 external test chemicals from published papers. The training accuracy was above 0.90 and the evaluation metrics (precision, recall and F1-score) all reached 0.78 or above on both internal and external test chemicals. In this model, the molecular-specific fragment importance of chemicals was first quantified independently. Then, the SA identification method based on the importance of these fragments was statistically analyzed and verified with the ISSSTY test and external test chemicals containing one of 28 typical SAs, and most of the performances were better than that of expert rules. Furthermore, a mutagenicity mechanism prediction method was developed using 237 chemicals with four known mutagenic mechanisms based on molecular similarity calibrated by the SSDL method and fragment importance, which significantly improved accuracy in three mechanisms and had comparable accuracy in the other one compared to traditional methods. Overall, the SSDL model quantifying fragment toxicity within molecules would be a novel potentially powerful tool in the determination and visualization of molecular-specific SAs and the prediction of mutagenicity mechanisms for environmental or industrial compounds and drugs.

Association of high LDL concentrations with erectile dysfunction from a Mendelian randomization study.

Lipid metabolism plays a key role in erectile dysfunction. Our purpose was to evaluate the influence of lipid-lowering drugs on erectile dysfunction employing a two-sample Mendelian randomization (MR) study. Genetic instruments were employed to represent the exposure of lipid-lowering drugs. Inverse variance-weighted MR (IVWMR) was employed to calculate the estimation of effects. IVW-MR analysis showed that the positive relationship between the expression of HMGCR and the risk of erectile dysfunction (odds ratio [OR] = 1.27, 95% confidence interval [CI] 1.03-1.57; p = 0.028). No significant relationship was detected between NPC1L1, PSK9 expression and erectile dysfunction. This MR study suggested that HMGCR inhibitors are a more desirable treatment modality for patients with ED.

Experimental Study on Lap-Spliced Performance of High-Strength Stainless Steel Wire Mesh in Engineering Cementitious Composites.

To investigate the mechanical properties of high-strength stainless steel wire mesh (HSSSWM) in Engineering Cementitious Composites (ECCs) and determine a reasonable lap length, a total of 39 specimens in 13 sets were designed and fabricated by considering the diameter of the steel strand, spacing of the transverse steel strand, and lap length. The lap-spliced performance of the specimens was tested through a pull-out test. The results revealed two failure modes in the lap connection of steel wire mesh in ECCs: pull-out failure and rupture failure. The spacing of the transverse steel strand had little effect on the ultimate pull-out force, but it restricted the slip of the longitudinal steel strand. A positive correlation was found between the spacing of the transverse steel strand and the slip amount of the longitudinal steel strand. With an increase in lap length, the slip amount and 'lap stiffness' to peak load increased, while the ultimate bond strength decreased. Based on the experimental analysis, a calculation formula for lap strength considering the correction coefficient ß was established.

HOTAIR as a diagnostic and prognostic biomarker of gastrointestinal cancers: an updated meta-analysis and bioinformatics analysis based on TCGA data.

Gastrointestinal cancers are the most common type of cancer affecting humans. High expression of HOX transcript antisense intergenic RNA (HOTAIR), a long noncoding RNA (lncRNA), in various types of different tumors may be associated with poor prognosis. In the present study, we performed a meta-analysis of the relationship between HOTAIR expression and gastrointestinal cancers. Five databases were comprehensively searched for all literature until January 2023. Moreover, the target genes of HOTAIR were predicted by coexpression analysis based on The Cancer Genome Atlas (TCGA) gene expression matrix for six gastrointestinal cancer types. Finally, the mechanism through which HOTAIR affects tumors of the digestive system was systematically reviewed. Our results showed that the high HOTAIR expression group had worse outcomes with a pooled hazard ratio (HR) of 1.56 (95% confidence interval [CI] = 1.38-1.75, P<0.001). Furthermore, HOTAIR was identified as an unfavorable prognostic factor for overall survival (OS) in the esophageal carcinoma (ESCA) and gastric cancer (GC), as the HR were 1.94 and 1.58, respectively. The high correlation between the expression of homeobox C (HOXC) family genes and HOTAIR, with correlation coefficients of 0.863 (HOXC11), 0.664 (HOXC10), 0.645 (HOXC8), and 0.581 (HOXC12). The 'cell cycle' pathway and pathways relating to infections, namely 'herpes simplex virus 1 infection' and 'complement and coagulation cascades' were significantly enriched in Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Also, we perform a systematic review to summarize the related oncogenic mechanism of HOTAIR. In conclusion, the HOTAIR has been identified as a potential prognostic factor in patients with gastrointestinal cancers.

Bibliometric and visualization analysis of global research trends on immunosenescence (1970-2021).

BACKGROUND: Immunosenescence, the aging of the immune system, leads to a decline in the body's adaptability to the environment and plays an important role in various diseases. Immunosenescence has been widely studied in recent years. However, to date, no relevant bibliometric analyses have been conducted. This study aimed to analyze the foundation and frontiers of immunosenescence research through bibliometric analysis. METHODS: Articles and reviews on immunosenescence from 1970 to 2021 were obtained from the Web of Science Core Collection. Countries, institutions, authors, journals, references, and keywords were analyzed and visualized using VOSviewer and CiteSpace. The R language and Microsoft Excel 365 were used for statistical analyses. RESULTS: In total, 3763 publications were included in the study. The global literature on immunosenescence research has increased from 1970 to 2021. The United States was the most productive country with 1409 papers and the highest H-index. Italy had the highest average number of citations per article (58.50). Among the top 10 institutions, 50 % were in the United States. The University of California was the most productive institution, with 159 articles. Kroemer G, Franceschi C, Goronzy JJ, Solana R, and Fulop T were among the top 10 most productive and co-cited authors. Experimental Gerontology (n = 170) published the most papers on immunosenescence. The analysis of keywords found that current research focuses on "inflammaging", "gut microbiota", "cellular senescence", and "COVID-19". CONCLUSIONS: Immunosenescence research has increased over the years, and future cooperation and interaction between countries and institutions must be expanded. The connection between inflammaging, gut microbiota, age-related diseases, and immunosenescence is a current research priority. Individualized treatment of immunosenescence, reducing its negative effects, and promoting healthy longevity will become an emerging research direction.

Potential disease biomarkers for diabetic retinopathy identified through Mendelian randomization analysis.

Background: Diabetic retinopathy (DR), a leading cause of vision loss, has limited options for effective prevention and treatment. This study aims to utilize genomics and proteomics data to identify potential drug targets for DR. Methods: We utilized plasma protein quantitative trait loci data from the Atherosclerosis Risk in Communities Study and the Icelandic Decoding Genetics Study for discovery and replication, respectively. Genetic associations with DR, including its subtypes, were derived from the FinnGen study. Mendelian Randomization (MR) analysis estimated associations between protein levels and DR risk, complemented by colocalization analysis to examine shared causal variants. Results: Our MR analysis identified significant associations of specific plasma proteins with DR and proliferative DR (PDR). Elevated genetically predicted levels of WARS (OR = 1.16; 95% CI = 0.095-0.208, FDR = 1.31×10-4) and SIRPG (OR = 1.15; 95% CI = 0.071-0.201, FDR = 1.46×10-2) were associated with higher DR risk, while increased levels of ALDOC (OR = 1.56; 95% CI = 0.246-0.637, FDR = 5.48×10-3) and SIRPG (OR = 1.15; 95% CI = 0.068-0.208, FDR = 4.73×10-2) were associated with higher PDR risk. These findings were corroborated by strong colocalization evidence. Conclusions: Our study highlights WARS, SIRPG, and ALDOC as significant proteins associated with DR and PDR, providing a basis for further exploration in drug development. Additional studies are needed to validate these proteins as disease biomarkers across diverse populations.

Analytical and Numerical Modeling of the Pullout Behavior between High-Strength Stainless Steel Wire Mesh and ECC.

Bond behavior is a key factor in the engineering application of composite material. This study focuses on the constitutive model of the bond behavior between high-strength stainless steel strand mesh and Engineered Cementitious Composites (ECC). In this paper, the effects of strand diameter, bond length and transverse steel strand spacing on bond behavior were studied based on 51 direct pullout tests. Experimental results showed that the high-strength stainless steel strand mesh provided specimens an excellent ductility. Based on the experimental data, the existing bond-slip model was revised using the theory of damage mechanics, which fully considered the influence of the steel strand diameter on the initial tangent stiffness of the bond-slip curve. The results of the model verification analysis show that errors are within 10% for most parameters of the bond-slip model proposed, especially in the ascending section, the errors are within 5%, indicating that the calculated results using the revised model are in good agreement with the test results. In addition, the revised model was applied to the finite element analysis by using the software ABAQUS to simulate the pullout test, in which the spring-2 nonlinear spring element was used to stimulate the bond behavior between steel strand meshes and ECC. The simulation results show that the numerical analysis fits the experimental result well, which further verifies the accuracy of the model and the feasibility and applicability of the numerical analysis method.