Autoimmune pancreatitis (AIP) is a rare immune-mediated form of chronic pancreatitis. It may affect multiple organs, and its heterogeneous clinical manifestations complicate diagnosis and management. Based on the Chinese Guidelines for the Diagnosis and Management of Autoimmune Pancreatitis (Shanghai 2012 Draft), together with the latest domestic and international guidelines and research advances, the present guideline provides 20 recommendations covering four aspects: diagnosis, treatment, follow-up, and prognosis. The aim is to improve the diagnosis and management of AIP in China and ultimately improve patient outcomes.

Colorectal cancer histopathological grading relies on the accurate segmentation of glandular structures. Current deep learning–based methods depend heavily on large-scale pixel-level annotations that are labor-intensive and not amenable to clinical practice. Weakly supervised semantic segmentation offers a promising alternative; yet, existing class activation map–based weakly supervised semantic segmentation approaches often produce incomplete, low-quality pseudo-masks that overemphasize discriminative regions and fail to provide reliable supervision for unannotated glandular structures, limiting their suitability for dense histopathology segmentation under sparse supervision. We propose a novel weakly supervised teacher–student framework that leverages sparse pathologists’ annotations and an Exponential Moving Average–stabilized teacher network to generate refined pseudo-masks.

Our framework integrates confidence-based filtering, adaptive fusion of teacher predictions with limited ground truth, and curriculum-guided refinement, enabling the student network to progressively delineate and accurately segment unannotated glandular regions. We validated our framework on an institutional colorectal cancer cohort from The Ohio State University Wexner Medical Center, consisting of 60 hematoxylin and eosin-stained whole-slide images from independent patients with varying degrees of gland differentiation, as well as on public benchmarks including the Gland Segmentation dataset (derived from stage T3–T4 colorectal adenocarcinomas), TCGA-COAD, TCGA-READ, and SPIDER.

The proposed framework achieved strong performance on the institutional dataset despite limited annotations. On the Gland Segmentation dataset, it demonstrated competitive performance compared to both weakly and fully supervised approaches, achieving a mean Intersection over Union of 80.10% ± 1.52 and a mean Dice coefficient of 89.10% ± 2.10. Moreover, cross-cohort evaluations showed robust generalization on TCGA-COAD and TCGA-READ without requiring additional annotations, while reduced performance on SPIDER reflected pronounced domain shift.

Our framework provides an annotation-efficient and generalizable paradigm for accurate gland segmentation in colorectal histopathology, offering a practical pathway toward significantly reducing annotation burdens while preserving high segmentation fidelity.

Perioperative anaphylaxis is a rare, life-threatening, iatrogenic condition that predominantly arises following anesthesia. The unique context of this condition, characterized by the concurrent administration of multiple drugs, patient draping, and altered physiological states, presents significant diagnostic and therapeutic challenges, contributing to a higher mortality rate compared to anaphylaxis in other settings. This narrative review synthesizes the evidence to delineate the evolving etiology, pathophysiology, atypical clinical presentation, evidence-based immediate management, and strategic prevention of perioperative anaphylactic reactions. The primary causative agents include neuromuscular blocking agents, antibiotics, and latex, with emerging culprits such as chlorhexidine, dyes, and novel agents like remimazolam. Diagnosis is complicated by the paucity of cutaneous signs; thus, cardiovascular collapse combined with a low end-tidal carbon dioxide level has emerged as a useful supportive diagnostic indicator that requires integration with the clinical context. Immediate management prioritizes the prompt administration of epinephrine and aggressive fluid resuscitation. Subsequent allergological investigations, primarily via skin testing and serum tryptase/histamine measurement, are paramount for identifying the causative agent and preventing its recurrence. Prevention strategies emphasize meticulous history-taking, risk stratification, and the creation of latex-free environments. Future directions must focus on establishing global surveillance networks, exploring novel biomarkers and risk factors such as the circulating microbiome—a preliminary but promising area of research—and enhancing team preparedness through simulation training to improve patient safety outcomes.

This review presents the Oncodarwinian Hypothesis, which proposes a new medical paradigm: that of cancer as a potential macro-immunoadaptive response (susceptible to fine-tuning or reprogramming/management via artificial intelligence-based 3D printed p53 superproteins). A traditional hypothesis-generation method was adopted; it entails observing a biophenomenon longitudinally (tumor-precursor out-of-control cell division), formulating and refining targeted research questions, and then, rooted in a prior interdisciplinary theoretical framework, outlining (per deductive reasoning) a testable answer or statement apt to predict outcomes. Two main theoretical findings emerge from this review: the plausibility of a wireless p53 superprotein molecular biochip (3D printed) and cancer cells’ dual-focus immunological nature. It will be necessary to approach the key issue and prognosis of (supposedly meaningless) uncontrolled cell division in a different light. Basically, the same diseasing cancer also constitutes a self-replicating immunoadaptive algorithm that needs to be deciphered. An interdisciplinary quest to unravel its “source code” involves genomic palaeontology and learning the natural selection programming language — for developing (personalized) artificial intelligence-assisted p53 superproteins.

Primary hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related mortality worldwide, with curative options still limited for patients with advanced disease. As an emerging modality of cancer immunotherapy, tumor vaccines represent a promising approach that activates the host immune system to recognize and eliminate malignant cells. Multiple vaccine platforms, including peptide vaccines, dendritic-cell vaccines, nucleic-acid vaccines, and viral-vector vaccines, have been explored for HCC. Among these, peptide- and dendritic-cell-based vaccines are supported by the most extensive clinical data, demonstrating favorable safety and immunogenicity profiles. The advent of personalized therapeutic cancer vaccines based on tumor-specific antigens has further refined the precision of vaccine design. Nevertheless, several major challenges persist, including immune suppression within the tumor immune microenvironment, marked tumor heterogeneity, immune-escape mechanisms, and limited vaccine immunogenicity, all of which hinder clinical efficacy. In addition, issues related to standardization, large-scale production, and regulatory oversight remain unresolved. Recent advances in sequencing technology, nanotechnology, and artificial intelligence have opened new avenues for optimizing vaccine platforms and delivery strategies. Combination therapies that integrate cancer vaccines with immune checkpoint inhibitors, chemotherapy, or locoregional treatments are also being actively investigated to improve patient outcomes. In summary, although vaccine-based immunotherapy for HCC is still at an early stage, its integration with personalized medicine and multimodal therapeutic strategies holds great potential for improving the long-term prognosis of patients with HCC. Therefore, this review aims to systematically summarize current advances in tumor vaccine–based immunotherapy for hepatocellular carcinoma, with a particular focus on vaccine platforms, target antigens, clinical trial outcomes, and future challenges for clinical translation.

Uterine fibroids (UFs) are common hormone-dependent tumors with a complex etiology involving both genetic and environmental factors. This study aimed to investigate, for the first time, the associations between loci from genome-wide association studies (GWAS) and environmental risk factors in UF development, with a particular focus on gene–environment interactions.

DNA samples from 737 women with UF and 451 healthy controls were genotyped for ten UF-associated GWAS single nucleotide polymorphisms (SNPs) using probe-based polymerase chain reaction in this case-control study.

SNP rs66998222 (LOC102723323, G/A) was associated with decreased UF risk in the total sample (odds ratio (OR) = 0.81, p = 0.038) and in patients with a history of induced abortion (OR = 0.70, p = 0.009). SNP rs11031731 (THEM7P, WT1, G/A) increased UF risk overall (OR = 1.39, p = 0.01), and in women with abortion history (OR = 1.60, p = 0.008) or without pelvic inflammatory disease (OR = 1.43, p = 0.02). SNPs rs641760 (PITPNM2, C/T) and rs2553772 (LOC105376626, G/T) showed protective effects depending on abortion history. SNP rs1986649 (FOXO1, C/T) was associated with later UF onset (p = 0.049) and slower growth (p = 0.017). GWAS loci influence UF-related genes involved in proliferation, inflammation, and hormone metabolism, underscoring their pathogenic role.

Induced abortions and inflammation modify the effects of GWAS-identified UF risk loci, with allele-specific impacts on hormonal, inflammatory, and repair pathways. Replication in diverse cohorts is needed to validate these population-specific effects.

This review aims to advocate for a paradigm shift in herbal safety by proposing a cohesive molecular framework that integrates advanced “omics” technologies with artificial intelligence (AI) to address the clinical challenges of herb-induced liver injury (HILI). Traditional herbal medicine constitutes a substantial, yet often unregulated, component of global healthcare, driving high patient exposure alongside a significant and escalating clinical burden of HILI. Current pharmacovigilance systems are critically undermined by fundamental deficits, including severe underreporting, unknown population denominators, and pervasive product quality failures. Furthermore, the complexity of multi-ingredient formulations and the frequency of herb-drug interactions complicate causality assessment, particularly for high-risk drugs. To bridge the gap between empirical practice and contemporary safety standards, this integrated “omics”-AI paradigm transforms herbal safety from a reactive, population-level assessment into an evidence-based, personalized system. By enabling precise risk mitigation, this approach establishes a scientifically rigorous foundation for the future of integrative liver health. In conclusion, the synergy of molecular profiling and computational intelligence provides the necessary tools to modernize herbal pharmacovigilance, ensuring that traditional wisdom is effectively harmonized with modern technological standards for enhanced patient safety.

The gut microbiota engages in a complex, bidirectional dialogue with the liver via the gut–liver axis, and its dysbiosis plays a central role in the initiation and progression of various liver diseases. This review comprehensively integrates recent advances in the common features and etiology-specific patterns of gut microbial dysbiosis in liver diseases, signal decoding of key microbial metabolite axes, gut–liver immune crosstalk mechanisms, the accelerating role of gut barrier disruption, and recent progress in the use of the microbiome as diagnostic and prognostic biomarkers. We focus on analyzing the common patterns of reduced diversity, depletion of beneficial bacteria, and enrichment of pathogenic bacteria associated with gut flora dysbiosis across different liver diseases, ranging from nonalcoholic fatty liver disease and alcoholic liver disease to cirrhosis and hepatocellular carcinoma, as well as their unique etiology-related characteristics. Core findings reveal that microbial metabolites act as key chemical messengers that precisely drive liver disease progression by modulating host metabolic, immune, and inflammatory pathways. Meanwhile, the translocation of microbes and their products resulting from disruption of gut barrier integrity serves as a key accelerator, exacerbating liver injury and related complications. Based on these mechanisms, this review further explores ecological niche remodeling strategies targeting the gut microbiota, including the existing evidence and limitations of fecal microbiota transplantation and probiotics/prebiotics, as well as the prospects of emerging precision interventions such as phage therapy, microbial enzyme inhibitors, and engineered bacteria. Finally, we emphasize the potential and personalized implementation pathways of synergistically integrating microbiota modulation with existing therapies such as antivirals, antifibrotics, immunotherapy, and metabolic surgery. Future research must focus on promoting the translation of microbiome research from association studies to clinical applications through multi-omics integration and prospective clinical trials, ultimately achieving precise prevention and treatment of liver diseases based on gut–liver axis regulation.

Sterol regulatory element-binding protein 1 (SREBP1), a key regulator of lipogenesis, is highly expressed in tumors, but the mechanisms sustaining its elevated levels remain unclear. The role of UFMylation, a posttranslational modification, in modulating SREBP1 stability and tumor progression has not been explored. This study aimed to investigate the role of UFMylation in the progression of liver cancer.

Liquid chromatography-tandem mass spectrometry was employed to investigate the interacting proteins of ubiquitin-fold modifier 1-specific ligase 1 (UFL1). Knockdown of UFL1 and DDRGK domain-containing protein 1 (DDRGK1) was performed to assess SREBP1 stability. In vitro and in vivo models of hepatocellular carcinoma (HCC) were used to evaluate tumor progression. Clinical correlations between UFL1/DDRGK1 and SREBP1 levels were analyzed in HCC patient samples.

SREBP1 undergoes UFMylation, which synergizes with ubiquitination to reduce its stability. Depletion of UFL1 or DDRGK1 increased SREBP1 stability, driving HCC progression. Clinically, UFL1 and DDRGK1 levels were reduced in HCC tissues and inversely correlated with SREBP1 expression. Fatostatin (an SREBP1 inhibitor) enhanced the therapeutic effect of Lenvatinib in HCC models with low UFL1 expression.

UFMylation is a critical posttranslational modification that destabilizes SREBP1, and its dysregulation contributes to HCC progression. Targeting the UFMylation-SREBP1 axis, particularly through Fatostatin and Lenvatinib combination therapy, represents a novel therapeutic strategy for HCC.