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.

Various vaccines have been reported as triggers of Autoimmune hepatitis (AIH). Recently, with the ongoing COVID-19 pandemic and widespread vaccination worldwide, COVID-19 vaccination-associated AIH (CA-AIH) occurring without COVID-19 infection have been reported. However, only a handful of CA-AIH cases have been reported in patients with COVID-19 infection. Therefore, we report such a case and summarize the CA-AIH with or without COVID-19 infection.

In this report, we describe a 66-year-old female who developed biopsy-proven acute-onset autoimmune hepatitis after receiving four doses of the COVID-19 vaccine and experiencing one COVID-19 infection in 2022. The patient was immediately treated with prednisone. Her liver enzymes gradually decreased to the normal range after treatment. In addition, we reviewed 20 cases of CA-AIH reported from multiple countries. The summarized data of these cases showed that CA-AIH and classic AIH share some clinical, serological, and histopathological features, such as female predominance and a middle-aged distribution. All patients had some positive circulating autoantibodies, including anti-nuclear antibody and/or positive anti-smooth muscle antibody. Histologically, CA-AIH showed a more acute onset compared to classic AIH, which typically presents with more chronic hepatitis. However, only 5 (23.8%) of the 21 cases had COVID-19 infection.

This case report provides additional evidence supporting an association of COVID-19 vaccination and/or infection with AIH, suggesting a more causal than coincident relationship. The majority of the patients of COVID-19 vaccination associated AIH show acute disease onset and may not have COVID-19 infection.

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.

Mesonephric carcinoma (MC) is a rare type of cervical carcinoma that arises from mesonephric remnants. It is characterized by a mixture of a wide variety of growth patterns and typically exhibits positive immunoreactivity for GATA binding protein 3, thyroid transcription factor 1, and apical common acute lymphoblastic leukemia antigen. A subset of adenocarcinomas in the uterine corpus and ovary with similar morphology and immunophenotype is classified as mesonephric-like adenocarcinoma (MLA) in the current World Health Organization classification. This review aimed to summarize the clinicopathological features of mesonephric remnants, mesonephric hyperplasia, and MC, provide an update on the current understanding of MLA, and highlight the molecular differences between MC and MLA.

A literature review was conducted on mesonephric remnants, mesonephric hyperplasia, MC, and MLA. The clinicopathological and molecular features were summarized from previously published studies and compared across these entities.

Both MC and MLA exhibit a mixture of growth patterns and show immunoreactivity for GATA binding protein 3, thyroid transcription factor 1, and common acute lymphoblastic leukemia antigen. They commonly harbor genetic alterations in KRAS and NRAS. However, key differences exist between these two entities. MC is associated with mesonephric remnants, whereas no such association has been identified for MLA. Additionally, although KRAS and NRAS mutations are common in both, a subset of MLA cases also harbors PIK3CA and/or PTEN mutations, genetic alterations commonly seen in endometrioid adenocarcinoma.

Although the exact pathogenesis of MLA remains unclear, it is favored to originate from Müllerian-derived epithelium undergoing differentiation along the mesonephric pathway, rather than from true mesonephric remnants. Both MC and MLA tend to follow a relatively aggressive clinical course, underscoring the importance of accurate diagnosis.

Endometrial cancer is a common malignant tumor of the female reproductive system, and its incidence is increasing worldwide. The underlying causes of endometrial cancer are multifactorial. In recent years, the role of diet and lifestyle has received considerable attention and has become a key area of research for cancer prevention. Available literature suggests that different dietary patterns, such as the Mediterranean diet or a plant-based diet, along with moderate physical activity, are associated with a reduced risk of this cancer. Despite these findings, significant gaps in knowledge remain, particularly regarding the specific foods, lifestyle choices, and mechanisms of action that can help mitigate the risk of cancer. Furthermore, the effects of cultural and genetic differences among subpopulations make this issue even more complex. In this context, this review aimed to assess the existing literature on the potential role of diet and lifestyle factors in preventing endometrial cancer, evaluate the available data, and highlight areas that require further investigation to provide concrete evidence and recommendations for prevention.

Craniopharyngioma (CP), although histologically benign, is a surgically challenging sellar-region tumor for which stereotactic irradiation is increasingly used as an alternative or adjuvant strategy. This review summarizes the role of stereotactic radiosurgery (SRS) in managing CP, with a focus on treatment outcomes, technical advances, and emerging strategies to support evidence-based clinical practice. Literature reports indicate that Gamma Knife radiosurgery achieves variable tumor control rates (36–100%), with optimal outcomes (79.6–91.4%) when marginal doses ≥12 Gy are delivered and patients receive adequate follow-up. Smaller tumors (<5 cm3) and those with higher solid components show particularly favorable outcomes. SRS demonstrates a favorable safety profile, with visual impairment occurring in approximately 4% of cases and endocrine dysfunction in 6%. Compared to conventional radiotherapy, SRS significantly reduces the risk of hypothalamic obesity in pediatric patients. The identification of BRAF mutations in papillary CPs has created novel opportunities for combining targeted therapies with SRS. Collectively, these advances underscore the role of SRS as an essential component of multidisciplinary CP management, particularly in the treatment of residual or recurrent lesions. It offers a more favorable toxicity profile and may improve quality of life outcomes compared to conventional radiotherapy. Further studies are needed to optimize patient selection, dosing strategies, and integration with novel systemic therapies.

Intrahepatic cholangiocarcinoma (iCCA) is the second most prevalent primary liver cancer, characterized by insidious onset and high malignancy. Many patients are diagnosed at an inoperable stage, and the effectiveness of chemotherapy and radiotherapy remains limited. This study aimed to provide a comprehensive review of the histological classification, genetic alterations, molecular subtypes, and corresponding imaging signatures of iCCA, highlighting its heterogeneity and offering insights into targeted therapy and personalized treatment. The heterogeneity of iCCA poses significant challenges to both targeted therapy and immunotherapy, necessitating in-depth exploration at the molecular and subtyping levels. Investigating genetic variations, signaling pathway alterations, and molecular subtypes can aid in patient stratification. Stratifying iCCA patients allows for more precise treatment selection, ultimately improving survival outcomes. Imaging, as a non-invasive tool, holds substantial potential for predicting subtypes and molecular profiles. It is possible to infer histological and molecular features from imaging, or to interpret imaging signatures in light of known histological and molecular data. This integrative approach, combining external imaging with internal molecular insights, fosters a comprehensive understanding of iCCA’s characteristics and enhances clinical management.

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.

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.