Background: Delayed gastric emptying (DGE) is a common complication after pancreaticoduodenectomy (PD) or pylorus preserving pancreaticoduodenectomy (PPPD). However, its risk factors are still unclear. This meta-analysis aimed to identify the potential risk factors of DGE among patients undergoing PD or PPPD. Methods: We searched PubMed, EMBASE, Web of Science, Cochrane Library, Google Scholar and ClinicalTrial.gov for studies that examined the clinical risk factors of DGE after PD or PPPD from inception through July 31, 2022. We pooled odds ratios (ORs) with 95% confidence intervals (CIs) using random-effects or fixed-effects models. We also performed heterogeneity, sensitivity, and publication bias analyses. Results: The study included a total of 31 research studies, which involved 9205 patients. The pooled analysis indicated that out of 16 non-surgical related risk factors, three risk factors were found to be associated with an increased incidence of DGE. These risk factors were older age (OR 1.37, P=0.005), pre-operative biliary drainage (OR 1.34, P=0.006), and soft pancreas texture (OR 1.23, P=0.04). On the other hand, patients with dilated pancreatic duct (OR 0.59, P=0.005) had a decreased risk of DGE. Among 12 operation related risk factors，more blood loss (OR 1.33, P=0.01), post-operative pancreatic fistula (POPF) (OR 2.09, P<0.001), intra-abdominal collection (OR 3.58 , P=0.001), and intra-abdominal abscess (OR 3.06, P<0.0001) were more likely to cause DGE. However, our data also revealed 20 factors did not support stimulative factors influencing DGE. Conclusion: Age, pre-operative biliary drainage, pancreas texture, pancreatic duct size, blood loss, POPF, intra-abdominal collection and intra-abdominal abscess are significantly associated with DGE. This meta-analysis may have utility in guiding clinical practice for improvements for screening patients with high risk of DGE and selecting appropriate treatment measure.

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.

Hyperinsulinemia, a hallmark of obesity and type 2 diabetes, is an emerging risk factor for pancreatic ductal adenocarcinoma (PDAC), yet its contribution to tumor progression and stromal remodeling remains unclear. Here, we identify an insulin–exosome–TNFAIP8–STAT1 signaling axis that is associated with fibroblast phenotypic remodeling and desmoplastic progression. Insulin activates PI3K/AKT‐RAB3A signaling to enhance secretion of TNFAIP8‐enriched exosomes from PDAC cells. Internalized TNFAIP8 recruits the E3 ligase TRIM21 to facilitate STAT1 ubiquitination and degradation, leading to the induction of myofibroblastic CAF–associated features, accompanied by enhanced extracellular matrix deposition and tumor growth. High TNFAIP8 expression in patient tumors correlates with fibrosis and poor prognosis. In orthotopic models, TNFAIP8 silencing or lipid nanoparticle‐mediated shTNFAIP8 delivery reduced fibrosis, suppressed tumor progression, and enhanced gemcitabine efficacy without evident toxicity, suggesting the feasibility of a therapeutic approach. These findings uncover a mechanistic framework linking metabolic dysregulation to fibroinflammatory remodeling in PDAC, and nominate TNFAIP8 as a promising stromal‐targeted therapeutic candidate.

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.

Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant tumor with a five-year survival rate of 13%, the lowest among all malignant tumors. The work aims to use bioinformatics methods to mine Nerve-cancer crosstalk-related genes (NCCGs) in pancreatic cancer and evaluate their correlation with tumor stage and prognosis, thereby providing a new direction of development and experimental basis for pancreatic cancer treatment. This study included 185 individuals with PDAC from the TCGA database, together with clinical and RNA sequencing data. A review of prior studies revealed the mechanism of neural-cancer crosstalk and identified 42 neural-cancer crosstalk-related genes (NCCGs). Multivariate logistic regression analysis showed that NGFR (OR=39.076, 95% CI; P<0.05), CHRNB2 (OR=41.076, 95% CI; P<0.05), and CHRNA10 (OR=39.038, 95% CI; P<0.05) were identified as independent risk factors for PNI development. Pearson correlation analysis revealed that CHRNA10 was negatively connected with PDAC microsatellite instability, whereas CHRNA10, CHRNB2, and NGFR were negatively correlated with PDAC tumor mutation burden. The GEPIA database revealed that CHRNB2 expression was higher in stage I PDAC. The pancreatic cancer single-cell dataset PAAD_CRA001160 revealed that malignant tumor cells, ductal cells, endothelial cells and fibroblasts accounted for a large proportion in the tumor microenvironment of pancreatic cancer. Furthermore, the NGFR gene was shown to be more significantly expressed in various pancreatic cancer cells. Bioinformatics analysis was used to create a validated prognostic model of pancreatic cancer, which explored the critical mechanisms of neural-tumor interactions and revealed the potential of cancer-neural crosstalk-related genes as prognostic biomarkers and anti-tumor therapy targets.