Zhenyu Li

The relationship between different autoimmune diseases (ADs) and pancreatitis has been reported in epidemiological studies. This study systematically investigated the causal effects of ADs factors on pancreatitis using Mendelian randomization (MR). We selected 17 common ADs as exposures from the largest genome-wide association study dataset in Europe. The outcome variables included acute pancreatitis (AP), chronic pancreatitis (CP), alcohol-induced acute pancreatitis, and alcohol-induced chronic pancreatitis. A variety of analytical methods were used to comprehensively evaluate the causal relationship between ADs and various types of pancreatitis, with sensitivity analyses, including Cochran’s Q test and MR-Egger intercept test performed to verify the reliability of the results. Finally, a reverse MR analysis was performed to evaluate the possibility of reverse causation. Genetically predicted type 1 diabetes (OR = 1.032, 95% CI = 1.008–1.056, P = .0086) and ulcerative colitis (OR = 1.065, 95% CI = 1.012–1.121, P = .0151) were associated with an increased risk of AP. primary sclerosing cholangitis (OR = 1.080, 95% CI = 1.013–1.153, P = .0194) increased the risk of CP. Reverse MR analysis suggested a potential protective causal association between alcohol-induced acute pancreatitis and hashimoto’s thyroiditis (OR = 0.962, 95% CI = 0.936–0.989, P = .0056), although this finding should be regarded as suggestive because it did not remain statistically significant after Bonferroni correction. Sensitivity analysis did not detect horizontal pleiotropy or heterogeneity. Genetic evidence supports a causal link between type 1 diabetes, ulcerative colitis , and AP, as well as between primary sclerosing cholangitis and CP. This analysis provides new insights into ADs as risk factors for pancreatitis, but further experimental research is needed to explore the potential causal relationships between these factors.

Liquid biopsy is moving beyond mutation-centric assays to multimodal frameworks that integrate cell-free DNA (cfDNA) signals with additional analytes such as circulating tumor cells (CTCs) and extracellular vesicles (EVs). In this review, we summarize emerging technologies across analytes for early cancer detection, emphasizing sequencing and error-suppression strategies and the growing evidence for multi-cancer early detection (MCED), tissue-of-origin (TOO) inference, diagnostic triage, and longitudinal surveillance. At low tumor fractions, fragmentomic and methylation features preserve tissue and chromatin context; when combined with radiomics using deep learning, they support blood-first, high-specificity risk stratification, increase positive predictive value (PPV), reduce unnecessary procedures, and enhance early prediction of treatment response and relapse. Building on these findings, we propose a pathway-aware workflow: initial blood-based risk scoring, followed by organ-directed imaging, and targeted secondary testing when indicated. We further recommend that model reports include not only discrimination metrics but also calibration, decision-curve analysis, PPV/negative predictive value (NPV) at fixed specificity, and TOO accuracy, alongside multi-site external validation and blinded dataset splits to improve generalizability. Overall, liquid biopsy is transitioning from signal discovery to deployable multimodal decision systems; standardized pre-analytical and analytical workflows, robust error suppression, and prospective real-world evaluations will be pivotal for clinical implementation.

The incidence and mortality from colorectal cancer are gradually increasing, while the incidence of transverse colon cancer is relatively low, accounting for 10% of all colon cancers^[1]. Hence, the surgeons have limited experience in the surgical treatment of transverse colon cancer. Radical resection of transverse colon cancer follows the principle of CME^[2].