Recent advancements in cancer immunotherapy have highlighted glypican-3 (GPC3) as a prominent target for treating hepatocellular carcinoma (HCC). However, approximately 10% to 30% of HCC patients exhibit low or absent GPC3 expression on the surface of tumor cells, which limits the feasibility of GPC3-targeted therapies. Consequently, it is essential for patients to undergo pre-diagnostic assessments of GPC3 expression in tumor cells to evaluate their suitability for GPC3-directed therapy. Although various methods have been developed to specifically detect GPC3 as a biomarker for treatment and prognosis, the diagnostic approaches currently employed in clinical studies remain relatively limited. Here, we provide a comprehensive overview of the clinical development of GPC3-targeted therapeutics, clinical trials in GPC3-positive HCC, and current methods for detecting GPC3 expression, highlighting their advantages and limitations. Furthermore, we explore the potential of integrating targeted therapy with various GPC3 detection modalities tailored to different pathological stages. This integration not only provides insights into the selection of effective methods for detecting GPC3 expression but also has the potential to significantly improve the clinical outcomes of patients with liver cancer. By simultaneously assessing the advantages and disadvantages of these methods, this review aims to establish a theoretical foundation for the clinical selection of appropriate GPC3 detection strategies for targeted therapy.

With the widespread application of systemic treatments for hepatocellular carcinoma, liver injury caused by molecular targeted drugs and immune checkpoint inhibitors has become a common clinical problem. The Chinese Society of Hepatology, Chinese Medical Association, organized domestic experts to summarize and analyze adverse liver reactions, as well as advances in the diagnosis and treatment related to systemic therapy for liver cancer, both domestically and internationally. Based on this work, we formulated the “Consensus on the Management of Liver Injury Associated with Targeted Drugs and Immune Checkpoint Inhibitors for Hepatocellular Carcinoma”, aiming to provide practical recommendations and decision-making guidance for clinicians in hepatology and related specialties. This guidance focuses on the monitoring, diagnosis, prevention, and treatment of liver injury during targeted and immune checkpoint inhibitor therapy, ultimately helping more liver cancer patients benefit from targeted immunotherapy.

Chronic hepatitis B virus (HBV) infection remains a major cause of liver diseases, including cirrhosis and hepatocellular carcinoma. Reliable biomarkers for assessing viral replication, liver damage, and predicting clinical outcomes are essential for effective patient management. This review focuses on two promising biomarkers: serum HBV RNA and hepatitis B core-related antigen, both of which show strong correlations with viral replication and disease progression. Serum HBV RNA levels reflect the quantity and transcriptional activity of intrahepatic covalently closed circular DNA, providing insights into viral replication. They also correlate with other markers of replicative activity and have predictive value for key clinical outcomes, including hepatitis B e antigen and hepatitis B surface antigen seroconversion, relapse after therapy cessation, and liver fibrosis. Similarly, hepatitis B core-related antigen is closely associated with covalently closed circular DNA levels, correlates with markers of viral replication, and shows promise in predicting liver fibrosis, cirrhosis, and the risk of hepatocellular carcinoma. This review highlights the potential of both biomarkers for monitoring disease progression and guiding therapeutic decisions, particularly in the context of personalized treatment strategies and risk assessment for liver-related complications.

Macromolecular-based gene delivery systems have emerged as viable alternatives to non-viral vectors for gene therapy due to their versatility, biocompatibility, and capacity to efficiently deliver therapeutic cargo. These systems, primarily based on synthetic and natural polymers, offer significant advantages in terms of safety, controlled gene release, and targeted delivery. This review explores the design and synthesis of macromolecular carriers, focusing on their chemical and physical architectures, which play a key role in improving gene delivery. Catanionic polymers and their derivatives (comb, brush, and star polymers) have been extensively researched for their capacity to condense and protect genetic material. Furthermore, natural polymers like chitosan and hyaluronic acid have been modified to enhance gene delivery capabilities. These macromolecular carriers are engineered to boost circulation time, increase cellular uptake, and facilitate the controlled release of genetic material at the target site. Strategies such as incorporating targeting ligands, stimuli-responsive elements, and reducing cytotoxicity are being pursued to improve the overall efficiency and specificity of these systems. This review highlights the current state of macromolecular gene delivery systems, their applications, and the ongoing research aimed at overcoming existing challenges, paving the way for more effective non-viral gene therapies.

Acetaminophen (APAP) is one of the most commonly used analgesic and antipyretic medications and is generally considered safe at therapeutic doses. However, overdose remains a leading cause of acute liver failure, primarily characterized by centrilobular (zone 3) hepatic necrosis, oxidative stress, mitochondrial dysfunction, and sterile inflammation. The hepatotoxic effects of APAP are localized to the centrilobular region, where cytochrome P450 2E1 is highly expressed. Cytochrome P450 2E1 catalyzes the conversion of APAP to a toxic metabolite, N-acetyl-p-benzoquinone imine. During overdose, the liver’s detoxification capacity is overwhelmed and excess N-acetyl-p-benzoquinone imine binds to cellular proteins, initiating oxidative stress and mitochondrial injury that culminate in hepatocyte death. A central component of APAP-induced hepatotoxicity is the activation of innate immune responses, particularly via inflammasome pathways. Inflammasomes are cytosolic multiprotein complexes that detect cellular damage and trigger inflammation. Among these, the NOD-, LRR-, and pyrin domain-containing 3 (NLRP3) inflammasome plays a significant role in APAP-induced liver injury. Upon activation, the NLRP3 inflammasome promotes autocatalytic cleavage of procaspase-1 into its active form, caspase-1, which subsequently processes the pro-inflammatory cytokines pro-interleukin-1β and pro-interleukin-18 into their mature forms. These cytokines recruit additional immune cells and amplify liver inflammation, exacerbating tissue injury. Thus, the NLRP3 inflammasome serves as a key mechanistic link between the initial toxic insult and the ensuing inflammatory response in APAP hepatotoxicity. This review aimed to explore the molecular mechanisms underlying APAP-induced liver injury, particularly inflammasome activation, and evaluate the current and emerging therapeutic strategies.

Organoids are derived from self-organizing stem cells and form three-dimensional structures that are structurally and functionally similar to in vivo tissues. With the ability to replicate the in vivo microenvironment and maintain genetic stability, organoids have become a powerful tool for elucidating developmental mechanisms, accurately modeling disease processes, and efficiently screening drug candidates, and have also demonstrated significant value in the field of traditional Chinese medicine (TCM)-including applications in screening active components of TCM, studying TCM pharmacodynamic mechanisms, evaluating TCM safety, and verifying the effects of traditional non-pharmacological therapies such as acupuncture and yoga. Organoids can be cultured using air-liquid interface systems, bioreactors, and vascularization techniques. They are widely used in drug screening, disease modeling, precision medicine, and toxicity assessment. However, current limitations include high costs, difficulty in accurately replicating the microenvironment, and ethical concerns. In this review, we systematically retrieve, synthesize, and analyze relevant literature to elucidate the culture methods of organoid technology, its diverse applications across various fields, and the challenges it faces. In the future, integration with artificial intelligence may provide new insights and strategies for drug development and disease research and the modernization of TCM.

Neurodegenerative diseases (NDs) represent a major global health challenge in aging populations, with their incidence continuing to rise worldwide. Although substantial progress has been made in elucidating the clinical features and molecular underpinnings of these disorders, the precise mechanisms driving neurodegeneration remain incompletely understood. This review examines the increasing significance of the gut–brain–immune triad in the pathogenesis of NDs, with particular attention to Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, and multiple sclerosis. It explores how disruptions in gut microbiota composition and function influence neuroinflammation, blood–brain barrier integrity, and immune modulation through microbial-derived metabolites, including short-chain fatty acids, lipopolysaccharides, and bacterial amyloids. In both Alzheimer’s and Parkinson’s diseases, a reduced abundance of short-chain fatty acid-producing bacterial taxa has been consistently associated with heightened pro-inflammatory signaling, thereby facilitating disease progression. Although detailed mechanistic understanding remains limited, experimental evidence—primarily from rodent models—indicates that microbial metabolites derived from a dysbiotic gut may initiate or aggravate central nervous system dysfunctions, such as neuroinflammation, synaptic dysregulation, neuronal degeneration, and disruptions in neurotransmitter signaling via vagal, humoral, and immune-mediated pathways. The review further highlights how gut microbiota alterations in amyotrophic lateral sclerosis and multiple sclerosis contribute to dysregulated T cell polarization, glial cell activation, and central nervous system inflammation, implicating microbial factors in disease pathophysiology. In addition to identifying critical knowledge gaps, the review emphasizes the need for sustained, multifactorial research efforts, including the development of physiologically relevant brain–gut organoid models and the implementation of standardized experimental protocols. A major limitation in the field remains the difficulty of establishing causality, as clinical manifestations often arise after extended preclinical phases—lasting years or decades—during which aging, dietary patterns, pharmacological exposures, environmental factors, and comorbidities collectively modulate the gut microbiome. Finally, the review discusses how microbial influences on host epigenetic regulation may offer innovative avenues for modulating neuroimmune dynamics, underscoring the therapeutic potential of targeted microbiome-based interventions in neurodegenerative diseases.

The global integration of traditional medicine (TM) and modern medicine reflects a fundamental shift in healthcare aimed at delivering more holistic, culturally sensitive, and patient-centered care. With over 80% of the global population relying on some form of TM, especially in Asia, Africa, and Latin America, there is growing momentum to institutionalize TM alongside evidence-based biomedicine. Countries like India, China, and Korea have led integration through formal education, government-supported research, and clinical frameworks, while high-income countries are increasingly adopting complementary and integrative medicine models. However, this convergence faces substantial challenges, including differences in epistemology, regulatory standards, evidence hierarchies, and practitioner training. Limited clinical trials, quality assurance concerns, and issues related to intellectual property rights and biopiracy further complicate harmonization. Despite these barriers, the World Health Organization’s Traditional Medicine Strategy (2014–2023) and its newly established Global Centre for Traditional Medicine (India) underscore a growing international commitment to evidence-based integration. Opportunities lie in promoting collaborative research, strengthening regulatory frameworks, enhancing digital health platforms for TM documentation, and fostering intercultural dialogue between health systems. If guided ethically and scientifically, integration can improve access to care, reduce treatment costs, and offer personalized health solutions for chronic and lifestyle-related diseases. This review explored global integration models, evaluated emerging challenges, and identified strategies to support an inclusive, pluralistic, and sustainable healthcare future that respects both traditional wisdom and modern science.

Artificial intelligence (AI) is profoundly transforming the paradigm of solid tumor drug development. By integrating multi-omics data, spatial transcriptomics, and advanced computational models, AI has significantly accelerated the discovery and validation of new targets, compressing the traditional ten-year research and development cycle to two to three years. Generative AI platforms have optimized small molecule inhibitors, biologics, and messenger RNA vaccines, achieving breakthroughs in overcoming tumor heterogeneity, improving efficacy, and predicting drug resistance. However, clinical translation still faces challenges such as data bias, algorithm transparency, and the validation gap between models and real-world human experience. This review aims to systematically elaborate on the transformative role of AI in solid tumor drug development and to promote interdisciplinary cooperation as well as the construction of ethical frameworks to enable the full realization of precision oncology.

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.