Causal relationships between Sjögren's syndrome and Parkinson's disease: A Mendelian randomization study.

BACKGROUND: Epidemiological and observational studies have indicated an association between Sjögren's syndrome (SS) and Parkinson's disease (PD). However, consistent conclusions have not been reached due to various limitations. In order to determine whether SS and PD are causally related, we conducted a Mendelian randomization study (MR) with two samples. METHODS: Data for SS derived from the FinnGen consortium's R9 release (2495 cases and 365 533 controls). Moreover, data for PD were acquired from the publicly available GWAS of European ancestry, which involved 33 674 cases and 449 056 controls. The inverse variance weighted, along with four other effective methodologies, were employed to comprehensively infer the causal relationships between SS and PD. To assess the estimation's robustness, a number of sensitivity studies were performed. To determine the probability of reverse causality, we performed a reverse MR analysis. RESULTS: There was no evidence of a significant causal effect of SS on PD risks based on the MR [odds ratio (OR) = 1.03; 95% confidence interval (CI) = 0.95-1.11; p = .45]. Similarly, no evidence supported the causal effects of PD on SS (OR = 0.92; 95% CI = 0.81-1.04; p = .20). These findings held up under rigorous sensitivity analysis. CONCLUSIONS: MR bidirectional analysis did not reveal any cause-and-effect relationship between SS and PD, or vice versa. Further study of the mechanisms that may underlie the probable causal association between SS and PD is needed.

Association between type 1 diabetes mellitus and ankylosing spondylitis: a two-sample Mendelian randomization study.

Objective: The development of ankylosing spondylitis (AS) is closely related to autoimmune system dysfunction. Type 1 diabetes mellitus (T1DM) is an autoimmune disease that is a risk factor for many diseases. This study aimed to investigate the causal relationship between T1DM mellitus and AS genetically. Methods: A genome-wide association study (GWAS) of causal relationships between exposure (T1DM) and outcome (AS) was performed using summary data from the GWAS database. We conducted a two-sample Mendelian randomization (MR) study of these two diseases. Inverse variance weighting (IVW) was used as the primary analysis method, with MR Egger, weighted median, and weighted mode used as supplementary methods. Sensitivity analyses were performed using Cochran's Q test, MR-Egger intercept, MR-Pleiotropy RESidual Sum and outlier methods, leave-one-out analysis, and funnel plots. Results: A total of 11 single nucleotide polymorphisms (SNPs)were identified for instrumental variables(IVs) for MR analysis.IVW found that T1DM was causally associated with AS ((IVW: OR = 1.0006 (95% CI 1.0001, 1.0011), p = 0.0057; MR-Egger: OR = 1.0003 (95% CI 0.9995, 1.0012), p = 0.4147; weighted median: OR = 1.0006 (95% CI 1.0003, 1.0008), p = 0.0001; weighted mode: OR = 1.0007 (95% CI 1.0005, 1.0009), p = 0.0001). No horizontal pleiotropy was found for the MR-Egger intercept, and leave -one-out analysis found that the results remained stable after the removal of individual SNPs. Conclusion: The results of the two-sample MR analysis supported a causal relationship between T1DM and AS risk.

V-Fuzz: Vulnerability Prediction-Assisted Evolutionary Fuzzing for Binary Programs.

Fuzzing is a technique of finding bugs by executing a target program recurrently with a large number of abnormal inputs. Most of the coverage-based fuzzers consider all parts of a program equally and pay too much attention to how to improve the code coverage. It is inefficient as the vulnerable code only takes a tiny fraction of the entire code. In this article, we design and implement an evolutionary fuzzing framework called V-Fuzz, which aims to find bugs efficiently and quickly in limited time for binary programs. V-Fuzz consists of two main components: 1) a vulnerability prediction model and 2) a vulnerability-oriented evolutionary fuzzer. Given a binary program to V-Fuzz, the vulnerability prediction model will give a prior estimation on which parts of a program are more likely to be vulnerable. Then, the fuzzer leverages an evolutionary algorithm to generate inputs which are more likely to arrive at the vulnerable locations, guided by the vulnerability prediction result. The experimental results demonstrate that V-Fuzz can find bugs efficiently with the assistance of vulnerability prediction. Moreover, V-Fuzz has discovered ten common vulnerabilities and exposures (CVEs), and three of them are newly discovered.