Pancreatic solid tumors encompass diverse pathological subtypes. Objective, accurate, and comprehensive imaging examinations and diagnostic reports are essential for preoperative staging, treatment planning, and prognostic evaluation. Currently, China lacks corresponding guidelines or consensus documents, leading to prominent issues including subjective diagnostic reports, incomplete descriptions, and inconsistent terminology. The present guideline was developed to standardize diagnostic imaging reporting of pancreatic solid tumors in China. Relevant domestic and international evidence on imaging examination techniques, key reporting elements, and diagnostic criteria was systematically reviewed and synthesized. This guideline was developed by a multidisciplinary expert panel through systematic evidence retrieval and appraisal, GRADE-based recommendation grading, modified Delphi consensus, and external review. A total of 20 evidence-based recommendations, 13 strong and 7 weak, were formulated, in aspects of imaging examination and diagnostic reporting standards, including the measurement of the tumor size of pancreatic solid tumors, assessment of the obstruction of the main pancreatic duct and common bile duct, definition, assessment, and clinical significance of pancreatic parenchymal atrophy, the assessment of obstructive acute pancreatitis, pseudocysts/retention cysts, and peripancreatic vessels, criteria for resectability, regional lymph node assessment, criteria for suspicious lymph nodes and descriptions of their specific location, and detection of hepatic and peritoneal metastases. Implementation of this guideline in clinical practice will help standardize the accuracy and consistency of diagnostic imaging reports for pancreatic solid tumors in China, thereby advancing standardized imaging diagnosis and informing clinical treatment decisions.

Pancreatic cystic neoplasms (PCNs) are increasingly detected in clinical practice, yet substantial variability exists in imaging interpretation and reporting, which may affect clinical decision-making. This guideline was developed to standardize diagnostic imaging evaluation and reporting for PCNs. A multidisciplinary expert panel conducted literature search and critical appraisal of domestic and international evidence, identified key clinical questions, and formulated recommendations using the Grading of Recommendations Assessment, Development and Evaluation framework. A modified Delphi consensus process and external review were performed to ensure the robustness of the recommendations. A total of 21 key questions were addressed, covering essential aspects of imaging evaluation and reporting for PCNs, including the preferred imaging modality for suspected lesions; standardized measurement of cyst size and mural nodules and their clinical significance; definitions of cyst wall and septal thickening; optimal imaging approaches for assessing the relationship between cystic lesions and the main pancreatic duct; measurement and evaluation of main pancreatic duct diameter and dilation; imaging-based classification of intraductal papillary mucinous neoplasms and serous cystic neoplasms; assessment of ductal obstruction, calcification, hemorrhage, and pancreatitis-related changes; criteria for suspicious lymph nodes; differentiation of PCNs from pancreatic pseudocysts or retention cysts; and recommended imaging modalities and follow-up intervals. This guideline provides a structured and evidence-based framework for imaging evaluation and reporting of PCNs, which may improve the consistency and clarity of imaging reports and support clinical decision-making.

Alpha-1 antitrypsin deficiency (AATD) is an autosomal codominant genetic disorder caused by mutations in the SERPINA1 gene. It results in reduced circulating levels of alpha-1 antitrypsin (AAT), a serine proteinase inhibitor (PI) primarily produced by hepatocytes. The most common deficient alleles are PI*S and PI*Z, with PI*ZZ homozygotes having the most severe deficiency and highest risk for lung and liver disease. While AATD is well established as a cause of early-onset emphysema and liver cirrhosis, emerging evidence suggests a potential association with the formation of arterial aneurysms. The pathophysiological rationale for this association centers on protease-antiprotease imbalance and potential extracellular matrix degradation of elastin in arterial vessel walls. Several studies have reported increased frequencies of AATD alleles in patients with abdominal aortic aneurysms and intracranial aneurysms compared to the general population, with some demonstrating statistically significant associations. Additionally, patients with the PI*ZZ genotype have been shown to have larger aortic diameters, greater aortic stiffness, and reduced distensibility compared to controls. However, the evidence is inconsistent, as several large studies have failed to demonstrate significant associations between AATD and aneurysm formation. Overall, current evidence suggests an association of AATD with the development of arterial aneurysms. However, it is also clear that the presence of AATD alone is not sufficient to increase the risk of developing new-onset arterial aneurysms.

Primary sclerosing cholangitis (PSC) is an immune-mediated cholestatic liver disease. Its molecular etiology remains poorly defined, hindering the development of mechanism-based diagnostics and therapies. Therefore, this study aimed to identify key molecular drivers and causal biomarkers of PSC by integrating transcriptomics, machine learning, and genetic causal inference.

We deployed an integrated computational framework combining transcriptomics, network biology, machine learning, and genetic causal inference. Peripheral blood transcriptomes from PSC patients and controls were analyzed to identify disease-associated modules. Candidate genes were refined via protein-protein interaction networks and a multi-algorithm machine learning screen. Causal inference was performed using two-sample Mendelian randomization, integrating plasma protein quantitative trait loci with PSC genome-wide association study summary statistics.

Transcriptomic analysis revealed a PSC-associated module enriched in ribosome biogenesis and protein homeostasis pathways. A machine learning-optimized nine-gene signature (including PTMA, SUMO1, Shwachman-Bodian-Diamond syndrome (SBDS), RPL7, EIF1AX, ANP32A, PCNA, FAM98A, and MPHOSPH6) achieved high diagnostic accuracy (mean AUC = 0.908) and was consistently downregulated in PSC. This signature was linked to a remodeled immune microenvironment characterized by myeloid skewing and specific transcriptional-immune covariation patterns. Mendelian randomization identified SBDS as a putatively causal protective factor, where genetically instrumented higher plasma SBDS protein levels were robustly associated with a lower PSC risk (IVW OR = 0.525, 95% CI: 0.356–0.773, P = 0.001). Sensitivity analyses supported the validity of the Mendelian randomization assumptions.

Our study establishes disrupted ribosome homeostasis as a causal pathway in PSC and nominates plasma SBDS as a high-confidence diagnostic biomarker and therapeutic target. The integrative framework provides a generalizable strategy for discovering causal biomarkers in complex diseases.

Acute-on-chronic liver failure (ACLF) lacks a universally accepted definition, and recent efforts have proposed consensus organ failure criteria. In this study, we aimed to compare the clinical validity of a recently proposed consensus ACLF framework with the outcome-calibrated A-TANGO classification.

We performed a multinational cohort study including 2,398 patients from the TIH cohort (India) and 2,568 from the CATCH-LIFE cohort (China) who were hospitalized with acute decompensation of cirrhosis. ACLF was defined using A-TANGO and an operationalized version of the 2025 consensus framework. Outcomes were 28- and 90-day mortality. Analyses assessed case capture, overlap, mortality risk, sensitivity, specificity, and net reclassification improvement (NRI).

ACLF prevalence differed substantially by definition. In TIH, A-TANGO classified 79.2% as ACLF versus 42.3% by the consensus definition; in CATCH-LIFE, the corresponding values were 31.4% versus 5.8%, respectively. Most consensus ACLF cases were captured by A-TANGO, which additionally classified 26%–37% of patients as having ACLF. These patients had substantial mortality (28-day: 18.1%–26.9%; 90-day: 33.2%–37.9%), significantly higher than those negative by both frameworks and comparable to established ACLF risk thresholds. A-TANGO showed higher sensitivity for 28-day mortality (TIH: 94.1% vs. 67.8%; CATCH-LIFE: 76.1% vs. 25.6%), whereas consensus criteria were more specific. Reclassification analyses showed improved discrimination with A-TANGO (NRI: 17.1% in TIH; 27.4% in CATCH-LIFE). Within the consensus non-ACLF group, A-TANGO further stratified patients into distinct risk groups with stepwise increase in mortality.

In conclusion, the two frameworks identify fundamentally different populations. The consensus definition significantly reduces sensitivity and under-recognizes high-risk patients. Compared with consensus definitions, the outcome-calibrated framework better supports diagnosis, clinical decision-making, risk stratification, and trial design in ACLF.

Digital pathology (DP) is transitioning from an adjunct technology to an enterprise diagnostic platform in the United States. Despite accelerating clinical adoption, many laboratories face persistent barriers, including high capital and operating costs, workflow disruption, interoperability challenges, and a complex regulatory and reimbursement environment. This narrative review proposes a practical lifecycle framework for implementing and sustaining DP programs, with an emphasis on defining and operationalizing institutional artificial intelligence (AI) readiness for safe and sustainable adoption.

We performed a targeted narrative review informed by searches of PubMed/MEDLINE and Google Scholar for English-language publications from January 1, 2014 through December 31, 2025. Core search concepts included DP, whole slide imaging, image management/viewing systems, laboratory information system integration, validation, reimbursement, U.S. Food and Drug Administration clearance, Clinical Laboratory Improvement Amendments oversight, College of American Pathologists accreditation, interoperability standards, cybersecurity, and AI. We supplemented database searches with reference screening and review of primary guidance and public databases from regulatory and professional organizations in the United States. We prioritized peer-reviewed literature and used web-based regulatory sources when they represented the authoritative primary reference. We also incorporated our professional experience and knowledge in DP and AI.

Key implementation domains span foundational infrastructure (scanners, storage/networking, and integrated image management platforms), workflow redesign across pre-analytic, analytic, and post-analytic phases, validation and quality management, regulatory compliance and accreditation, cost capture, interoperability strategy, cybersecurity and access control, education and change management, and long-term governance. We also describe an institution-level AI readiness model that can be assessed across data quality, integration, validation, monitoring, governance, and workforce capabilities to support safe clinical AI deployment.

Successful DP implementation requires a lifecycle approach that couples technical build-out with workflow redesign and institutional governance. Early planning for compliance, interoperability, reimbursement strategy, and AI readiness can reduce implementation risk and position laboratories for sustained clinical and computational innovation.

Nonalcoholic fatty liver disease (NAFLD) is a prevalent metabolic disorder with a complex pathogenesis. Although epitranscriptomic modifications such as N6-methyladenosine (m6A) have been implicated in NAFLD, the role of N1-methyladenosine (m1A) and its regulators is largely unexplored. Recently, YTHDF1, a well-characterized m6A reader, was also shown to recognize m1A; however, the functional consequences of this dual specificity are unknown. This study aimed to investigate the role of YTHDF1 in NAFLD pathogenesis and to explore whether its function is mediated through recognition of RNA methylation modification on specific target mRNAs.

Expression of YTHDF1 in NAFLD was analyzed in the GEO database. Loss-of-function studies for YTHDF1 were conducted in vivo (high-fat diet-fed mice) and in vitro (free fatty acid-treated HepG2 cells) in models of NAFLD. We employed RNA-seq and m1A-MeRIP-seq to identify key targets, followed by mechanistic validation of the YTHDF1–m1A–NUPR1 axis using biochemical, histological, and mRNA stability assays.

We identified a critical role for YTHDF1 in promoting hepatic steatosis. NUPR1, a stress-induced transcriptional regulator, undergoes m1A modification. YTHDF1 directly binds to m1A-modified NUPR1 mRNA, enhancing its stability, thereby leading to elevated NUPR1 protein levels. Functionally, upregulated NUPR1 acts as a core driver of NAFLD pathogenesis by activating lipogenic and suppressing fatty acid β-oxidation genes, thereby exacerbating hepatic lipid accumulation.

Our study unveils a novel epitranscriptomic mechanism in which YTHDF1, functioning as a dual-specificity reader, governs NAFLD progression through the m1A-NUPR1 axis. This not only expands the understanding of RNA modification recognition but also establishes the YTHDF1–m1A–NUPR1 pathway as a promising therapeutic target for metabolic liver disease.