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.

Autoimmune hepatitis (AIH) frequently coexists with extrahepatic autoimmune diseases (EADs), but their prevalence, characteristics, progression, and treatment effect in the Han Chinese population remain unclear. This study aimed to evaluate the prevalence and spectrum of EADs and to assess their clinical features, disease course, and treatment outcomes in Han Chinese patients with AIH.

Medical records of 371 Han Chinese patients with AIH (diagnosed from March 2016 to October 2023) were retrospectively analyzed.

Among the 371 AIH patients, 304 (81.94%) were female, with a median age of 52.5 years (interquartile range, 46.0–61.0). A total of 23.98% (89/371) had at least one EAD, including 27.06% (82/303) in type 1 AIH, 11.11% (7/63) in antibody-negative AIH, and none in type 2. A single EAD was the most common (20.21%, 75/371). The most frequent EADs were Sjogren’s syndrome (8.63%) and autoimmune thyroid disease (8.36%). Compared with patients without EADs, those with EADs had lower alanine aminotransferase, red blood cell, and hemoglobin levels, but higher aspartate aminotransferase/alanine aminotransferase ratio and antinuclear antibody (ANA) positivity (all P < 0.05). ANA positivity was independently associated with EADs (odds ratio = 2.209, 95% confidence interval = 1.242–3.927, P = 0.007). After three months of treatment, the complete biochemical response rate was lower in the EADs group than in the non-EADs group (40.0% vs. 55.3%, P = 0.024), whereas no significant differences were observed at 6, 12, 24, or 36 months (all P > 0.05).

In the Han Chinese population, 23.98% of AIH patients had EADs, with Sjogren’s syndrome and autoimmune thyroid disease being the most common. ANA positivity was a significant risk factor for EADs. EAD patients had a poorer initial treatment response at three months, but comparable long-term biochemical response from six months.

Lung cancer remains the leading cause of cancer-related mortality worldwide. Early detection of pulmonary nodules is crucial for timely diagnosis and effective treatment. Conventional computer-aided detection systems have shown limitations, including high false-positive rates and low sensitivity. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have shown great potential in improving the accuracy and reliability of nodule detection and classification. This study aimed to develop and evaluate an automatic method for lung nodule detection and classification using a CNN-based architecture applied to computed tomography images from the publicly available LIDC-IDRI database.

This retrospective study was conducted on 82 patients (10,496 computed tomography slices) selected from the LIDC-IDRI database. The proposed method consists of five main steps: image preprocessing, lung parenchyma segmentation using Otsu’s thresholding and morphological operations, detection of nodule candidates, feature extraction, and classification using a CNN model. The CNN architecture includes two convolutional layers (20 and 30 filters, 3×3 kernel), ReLU activation, max-pooling layers, and a Softmax output layer. The network was trained with a mini-batch size of 32 for 50 epochs using the Stochastic Gradient Descent with Momentum optimizer (learning rate = 0.001, momentum = 0.9). Model performance was evaluated in terms of sensitivity, specificity, precision, and accuracy.

The proposed CNN model successfully detected pulmonary nodules and achieved accurate classification between benign and malignant nodules. On the LIDC-IDRI dataset, the model achieved a sensitivity of 98.7%, specificity of 97.5%, precision of 97.9%, and accuracy of 98.4%. Comparative analysis with recent studies, including hybrid CNN-long short-term memory and ResNet-based models, demonstrated that the proposed method provides competitive performance while maintaining lower computational complexity. The classification of nodule subtypes (solid, partially frosted, totally frosted) showed satisfactory discrimination results.

The proposed CNN-based system demonstrates the feasibility and robustness of deep learning for automatic lung nodule detection and classification. Despite strong results, the study acknowledges limitations such as single-database validation and a relatively small training size. Future work will focus on validating the model across other datasets (e.g., ELCAP, NELSON) and optimizing multi-class classification performance to enhance generalizability and clinical applicability.

Primary biliary cholangitis (PBC) is a chronic cholestatic disorder in which symptoms exert a direct influence on patients’ quality of life. Beyond pruritus and fatigue, patients with PBC are also prone to developing osteoporosis (OP). This skeletal condition not only heightens the likelihood of fractures but is also associated with elevated mortality. With the overall prevalence of PBC rising, a parallel increase in OP incidence among these patients can be anticipated. Early recognition, preventive strategies, and appropriate therapeutic approaches are essential for preserving patients’ quality of life. Nevertheless, current data on the management of OP in PBC remain limited. Most existing recommendations are extrapolated from studies on postmenopausal OP. However, these findings have not been effectively adapted into practical management protocols for PBC-related OP, largely due to distinct pathophysiological mechanisms between the two conditions. The absence of well-established preventive and therapeutic measures continues to represent a major obstacle in addressing OP among patients with PBC. This review offers a detailed synthesis of the epidemiology, underlying mechanisms, and therapeutic considerations of OP linked to PBC.

Nutrition plays a pivotal role in the prevention and management of gastrointestinal and hepatic diseases, yet dietary guidance remains generic, limiting its effectiveness. Conditions such as inflammatory bowel disease, irritable bowel syndrome, metabolic dysfunction-associated steatotic liver disease, celiac disease, and gastroesophageal reflux disease are significantly influenced by dietary factors. Personalized nutrition has emerged as a promising strategy to tailor interventions, but conventional approaches fail to account for individual metabolic, genetic, and microbiome variability, limiting their clinical impact. The rapid rise of artificial intelligence (AI) has transformed precision nutrition by integrating genomics, microbiome profiles, metabolic markers, and real-time dietary tracking to generate individualized recommendations. AI-driven systems are advancing dietary assessment, condition-specific nutrition optimization, and continuous monitoring through tools such as wearable devices and natural language processing-based diet analysis. These innovations hold transformative potential in gastroenterology and hepatology, offering dynamic, patient-specific strategies that may enhance clinical outcomes. However, challenges remain, including the lack of standardized AI-driven protocols, ethical concerns such as bias and data privacy, limited clinical validation, and the underrepresentation of nutrition in many current AI applications. Opportunities for progress include developing federated learning models, expanding real-world validation studies, and designing regulatory and ethical frameworks for safe implementation. This narrative review synthesizes literature published between 2015 and 2025 across five databases, highlighting key applications, limitations, and future directions of AI-driven personalized nutrition in gastroenterology and hepatology. It provides insights into how AI could reshape patient-centered care through more individualized, effective, and scalable dietary strategies.

Thalassemia is a group of anemias that result from inherited defects in the production of the beta chain of hemoglobin. It is stabilized by gamma globin, which combines to form fetal hemoglobin. One therapeutic approach is to target histone deacetylase (HDAC), which plays an important role in controlling beta thalassemia. This study sought to identify a natural inducer for treating this disease.

Twenty-five Andrographis paniculata compounds were screened using Schrödinger Suite 2020 (Maestro 12.3) for ligand preparation, grid generation, glide extra precision docking and molecular mechanics/generalized born surface area scoring. The HDAC2 crystal structure (Protein Data Bank ID: 4LXZ) was prepared by removing crystallographic water molecules and performing restrained minimization. Top-scoring complexes were subjected to 5-ns molecular dynamics simulations in GROMACS 2019 using the optimized potentials for liquid simulations force field, three interaction site point charge solvation, and standard neutralization and equilibration protocols. Absorption, distribution, metabolism, and excretion properties were predicted using QikProp.

Among the twenty five screened compounds, SRJ09 derivative of andrographolide, ranked among the top candidates based on glide extra precision docking and molecular mechanics/generalized born surface area scores and was therefore selected for further analysis. SRJ09 showed favorable binding to the HDAC2 active site, with interactions comparable to the reference inhibitor 20Y. Absorption, distribution, metabolism, and excretion predictions indicated acceptable drug-likeness, and molecular dynamics simulations demonstrated stable SRJ09–HDAC2 complex behavior over 5 ns.

We concluded that beta thalassemia may benefit from the use of andrographolide, and SRJ 09 as prospective HDAC2 inhibitor drugs that are favourable and efficacious and that generate fetal hemoglobin. Therefore, this bioactive compound is worth further investigation using in vitro and in vivo studies.

The Chinese Society of Hepatology of the Chinese Medical Association has invited experts in relevant fields to revise and rename the 2019 “Chinese Guidelines on the Management of Liver Cirrhosis” to “Chinese Guidelines for Clinical Diagnosis, Treatment, and Management of Cirrhosis (2025)”. These updated guidelines are aimed at providing recommendations for the clinical diagnosis and management of liver cirrhosis across the compensated, decompensated, and recompensated stages, as well as guidance on cirrhosis reversal and associated complications.

Human immunodeficiency virus (HIV)/hepatitis C virus (HCV) coinfection leads to severe systemic inflammation, increasing non-AIDS morbidity and mortality risk. CD39 ectoenzyme on T-cells, which catalyzes the conversion of pro-inflammatory purines to immunosuppressive adenosine, plays an important role in inflammation control. The role of CD39+ T-cells in systemic inflammation during HIV/HCV coinfection under antiretroviral therapy (ART) remains unexplored. This study aimed to identify specific patterns of CD39 expression on T-cells in ART-treated HIV/HCV coinfected patients and assess their relationship with systemic inflammation.

We conducted a case-control study that enrolled 41 HIV/HCV coinfected patients on stable ART (cases) and 23 healthy controls. CD39 expression on blood CD4+ and CD8+ T-cells, including CD45RA+ and CD45RA– subsets, was quantified using flow cytometry. Cytokines were assessed using multiplex and enzyme-linked immunosorbent assays.

A significant proportion of CD4+ T-cells expressed CD39 in both groups (cases – 24.0%; controls – 16.1%). That was not true for CD8+ T-cells (cases – 3.2%; controls – 2.8%). CD39 expression was higher on CD45RA+ than CD45RA– CD4+ T-cells (cases – 39.4% vs. 19.0%; controls – 24.6% vs. 9.2%). HIV/HCV coinfected patients exhibited a significantly increased proportion of CD39+ CD4+ T-cells compared to uninfected controls (P < 0.01). A negative correlation was observed between the percentage of CD39+ CD4+ CD45RA– T-cells and levels of pro-inflammatory chemokines monocyte chemoattractant protein 1 (R = –0.392; P < 0.01) and eotaxin (R = –0.325; P < 0.05).

The data suggest a compensatory expansion of cells with regulatory properties that is ultimately insufficient to control systemic immune activation.

Cardiac pacemaker implantation is a primary therapy for various arrhythmic disorders; however, safety concerns persist in India. This study aimed to evaluate two-year safety outcomes of cardiac pacemakers, implantable cardioverter-defibrillators, and cardiac resynchronization therapy devices in a tertiary care setting.

In this prospective cohort study, data collection was conducted over a one-year enrolment period (February 2023 to January 2024), encompassing patient demographics, pacemaker implantation details, indications, and comorbidities. Patients were prospectively followed for a total of two years from enrolment—during the data collection period and for an additional year, to record device-associated adverse events. Ethical approval was obtained (IECJNMC/1662), and data were analyzed using SPSS.

Among 183 patients, 95% received cardiac pacemakers, 3% cardiac resynchronization therapy devices, and 2% implantable cardioverter-defibrillators. The data comprised 58% males (mean age, 63 years). The adverse event rate was 5.5% (10/183), distributed as 3.8% device infection, 1.09% lead dislodgement, and 0.54% generator dysfunction, with no statistical difference between males and females (P > 0.05). Different age groups, various indications, and several comorbidities showed no significant disparities (P > 0.05) between males and females. The Cox model showed no significant effect of several predictors on the occurrence of adverse events (P > 0.05). The Kaplan–Meier survival curve revealed a higher incidence of adverse events in the first six months, followed by stabilization. Adverse events were appropriately documented and reported to the Indian Pharmacopoeia Commission.

The observed adverse event rate of 5.5% supports previous Indian and international data; however, the smaller sample size and short follow-up duration warrant further investigation for more specific outcomes.