Persistent liver injury halts the regenerative capacity of hepatocytes and activates mechanisms that result in the replacement of normal hepatic parenchyma with extracellular matrix deposits. As liver fibrosis develops, the liver undergoes architectural changes and alterations in microcirculation that lead to increased intrahepatic vascular resistance and portal hypertension. Thrombocytopenia is a prevalent condition in patients with chronic liver disease and portal hypertension. Multiple mechanisms related to increased platelet destruction or decreased platelet production contribute to thrombocytopenia. Increased platelet destruction occurs due to splenic sequestration caused by hypersplenism or immune-mediated conditions. Decreased platelet production results from a decline in thrombopoietin production, bone marrow suppression by medications, or toxic insults. Therapies aimed at improving thrombocytopenia are controversial, and individual factors must be considered. Although hepatic venous pressure gradient measurement is the gold standard for diagnosing portal hypertension, non-invasive tests show adequate correlation with hepatic venous pressure gradients. Various clinical risk scores consider platelet counts as independent predictors of adverse liver outcomes, such as the development of esophageal varices and the presence of advanced fibrosis. Nonselective beta-blockers are the cornerstone of long-term management for clinically significant portal hypertension. Indications for transjugular intrahepatic portosystemic shunt placement include failure to control portal hypertension-related bleeding, early rebleeding, and refractory or recurrent ascites. Ultimately, liver transplantation is the only definitive cure for portal hypertension and its major complications, including thrombocytopenia. Understanding the mechanisms underlying thrombocytopenia in patients with portal hypertension and chronic liver disease is essential for accurate diagnosis and effective patient management. This review aimed to evidence on the pathophysiological mechanisms linking chronic liver disease, portal hypertension, and thrombocytopenia, and to discuss their diagnostic and therapeutic implications.

Chronic subdural hematoma (CSDH) is a common disease in neurosurgery, with epidemiological characteristics showing an overall annual incidence of 1.7–20.6 per 100,000 people and a higher prevalence in the elderly. However, despite the increased disease burden, there have been limited breakthroughs in treatment options over the past 20 years. A significant gap exists in our understanding of the exact pathophysiological mechanism of CSDH, leading to a lack of specific clinical treatment options based on a clear pathological mechanism. Current research suggests that the development of CSDH involves dual mechanisms of trauma and inflammation, and that these pathologic processes together promote pathological changes such as angiogenesis, inflammatory response, and neovascularization. Therapies for CSDH encompass both surgical (e.g., twist-drill drainage, burr-hole drainage, craniotomy) and non-surgical approaches (e.g., clinical observation, medication, intracranial pressure monitoring, anticoagulation). Meanwhile, middle meningeal artery embolization, as an emerging minimally invasive interventional technique, has shown good prospects for clinical application. This review aims to bridge the gap between current treatment options and the need for effective strategies by providing a comprehensive summary of the epidemiological trends, pathophysiological advances, and optimization of therapeutic strategies for CSDH.

Characteristic signs of alopecia are gradual thinning, disruption of structural features, and the hair development cycle (anagen, catagen, telogen) against the background of miniaturization of hair follicles, which leads to baldness and psychological stress in patients. Despite the rapid development and clinical application of synthetic pharmacological, cellular/acellular, and molecular drugs, no effective therapeutic agent against alopecia has yet been developed. Great hopes are pinned on the improvement of therapeutic strategies with the introduction of exosomes into practical application, which contain a wide array of active substances for the targeted stimulation of hair follicle activity (anagen inducers) through the regulation of intracellular signaling cascades, growth factors, and microRNAs. The review discusses in detail the microRNAs and their intracellular targets that control hair follicle morphogenesis. It also focuses on the prospects of using stem cell exosomes from various sources for the treatment of alopecia, providing a clinical rationale for potential benefits and risks.

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.

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.

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.

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.