Chronic liver diseases are a group of diseases in which the liver is subjected to a variety of injuries over a long period of time， resulting in irreversible pathological changes that last longer than 6 months. Emodin （EMO） is a natural anthraquinone derivative derived from Rheum officinale， and its pharmacological effect has been extensively studied， exhibiting a variety of biological properties and involving multiple signaling molecules and pathways. Western medicine or surgical treatment is currently the main treatment regimen for chronic liver diseases， and the advance in treatment is limited by various reasons such as side effects and high costs. Due to its natural origin and efficacy， EMO has unique advantages in the treatment of chronic liver diseases and has now become a research hotspot. This article summarizes the therapeutic effect of EMO on chronic liver diseases and its mechanism， in order to provide a certain scientific basis for the traditional Chinese medicine treatment of chronic liver diseases and the development of drugs in clinical practice.

ObjectiveWe reviewed the existing experimental studies about renal injury-inducing Chinese medicine and systematically analyzed the toxicity mechanisms， toxic components， toxicity attenuation measures， and modern evaluation methods of renal injury-inducing Chinese medicine. The results are expected to provide new ideas for the modern research on kidney injury-inducing Chinese medicine， offer new breakthrough points for the toxicity attenuation of Chinese medicine by compatibility and processing， and give insights into the future research of Chinese medicine toxicology on the basis of ensuring the safety and scientific application of Chinese medicine. MethodsThe animal， cell， and clinical studies of kidney injury-inducing Chinese medicine were retrieved from CNKI， Wanfang Data， VIP， PubMed， and Web of Science. The names and toxic components of renal injury-inducing Chinese medicine， renal injury sites， toxicity mechanisms， toxicity attenuation measures， and related evaluation methods were summarized. ResultsThe toxicity mechanisms of kidney injury-inducing Chinese medicine mainly involved oxidative stress， endoplasmic reticulum stress， inflammatory cell infiltration， and organic anion transporters. Processing and compatibility were the main toxicity attenuation measures. The evaluation methods encompassed animal experiments， cell models， network pharmacology， metabolomics， toxicology genomics， and fluorescent probe technology. ConclusionAt present， the toxicological verification of kidney injury-inducing Chinese medicine starts from toxic components and combines various experimental methods， which is more comprehensive and systematic than the previous studies based on only animal experiments. According to the classical theories of traditional Chinese medicine， the toxicity of kidney injury-inducing Chinese medicine is mainly attenuated by decocting in water， steaming， and frying. With the progress of science and technology， new processing methods for toxicity attenuation are emerging， and structural transformation， fermentation， and microwave methods are the key research directions of toxicity attenuation of Chinese medicine in recent years.

ObjectiveWe reviewed the existing experimental studies about renal injury-inducing Chinese medicine and systematically analyzed the toxicity mechanisms， toxic components， toxicity attenuation measures， and modern evaluation methods of renal injury-inducing Chinese medicine. The results are expected to provide new ideas for the modern research on kidney injury-inducing Chinese medicine， offer new breakthrough points for the toxicity attenuation of Chinese medicine by compatibility and processing， and give insights into the future research of Chinese medicine toxicology on the basis of ensuring the safety and scientific application of Chinese medicine. MethodsThe animal， cell， and clinical studies of kidney injury-inducing Chinese medicine were retrieved from CNKI， Wanfang Data， VIP， PubMed， and Web of Science. The names and toxic components of renal injury-inducing Chinese medicine， renal injury sites， toxicity mechanisms， toxicity attenuation measures， and related evaluation methods were summarized. ResultsThe toxicity mechanisms of kidney injury-inducing Chinese medicine mainly involved oxidative stress， endoplasmic reticulum stress， inflammatory cell infiltration， and organic anion transporters. Processing and compatibility were the main toxicity attenuation measures. The evaluation methods encompassed animal experiments， cell models， network pharmacology， metabolomics， toxicology genomics， and fluorescent probe technology. ConclusionAt present， the toxicological verification of kidney injury-inducing Chinese medicine starts from toxic components and combines various experimental methods， which is more comprehensive and systematic than the previous studies based on only animal experiments. According to the classical theories of traditional Chinese medicine， the toxicity of kidney injury-inducing Chinese medicine is mainly attenuated by decocting in water， steaming， and frying. With the progress of science and technology， new processing methods for toxicity attenuation are emerging， and structural transformation， fermentation， and microwave methods are the key research directions of toxicity attenuation of Chinese medicine in recent years.

Objective: To investigate the role of butyric acid-producing bacteria in long bone homeostasis in mice with periodontitis under a high-fat/high-sugar diet and to provide new insights for the prevention and treatment of periodontitis and related bone metabolic diseases. Methods: This study has been approved by the Animal Welfare and Ethics Committee of the Experimental Animal Center. Initially, 14 mice were randomly divided into the CON group (the control group) and the LIG group (the periodontitis group). Mice in the LIG group had experimental periodontitis induced by ligating the second maxillary molars bilaterally and were fed a high-fat and high-sugar diet. After 8 weeks, samples were collected. Micro-computed tomography (Micro-CT) was used to analyze alveolar bone resorption and various parameters of the proximal tibia trabecular bone, including bone mineral density (BMD), bone volume per tissue volume (BV/TV), trabecular number (Tb.N), trabecular thickness (Tb.Th), and trabecular separation (Tb.Sp). After decalcification, hematoxylin and eosin (HE) staining was performed on maxillary bone sections to assess periodontal tissue inflammation and connective tissue destruction. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect related genes in the distal femur and proximal tibia bone tissues, including osteocalcin (OCN), osteogenic transcription factor (Osterix), osteoprotegerin (OPG), tartrate resistant acid phosphatase (TRAP), osteoclast-associated receptor (OSCAR), receptor activator of nuclear factor kappa-B (RANK), and receptor activator of nuclear factor kappa-B ligand (RANK-L). Subsequently, the other 28 mice were randomly divided into the CON group (the control group), LIG group (the periodontitis group), CON + butyric acid-producing bacteria (BP) group, and LIG + BP group. The breeding, sampling, and sample detection methods remained the same. Finally, the other 28 mice were randomly divided into the CON group (the control group), LIG group (the periodontitis group), CON + sodium butyrate (SB) group, and LIG + SB group. The breeding, sampling, and sample detection methods remained the same. Results: ①Periodontitis modeling was successful. Compared with the CON group, the LIG group exhibited significant alveolar bone resorption of the maxillary second molar, aggravated periodontal tissue inflammation, and connective tissue destruction. ②Periodontitis exacerbated long bone resorption in mice fed a high-fat high-sugar diet. Compared with the CON group, the LIG group had significantly lower BMD, BV/TV, Tb.N, and Tb.Th (P<0.05), and significantly higher Tb.Sp (P<0.05). HE staining of the proximal tibia showed that the trabeculae in the LIG group were sparse and disordered, with some areas showing fractures or dissolution. The expression of osteoblast markers (OCN, Osterix, OPG) was significantly lower in the LIG group (P<0.05), while the expression of the osteoclast marker TRAP showed an increasing trend (P>0.05). The ratio of RANK-L/OPG was significantly higher in the LIG group compared with the CON group (P<0.05). ③ Supplementation with butyric acid-producing bacteria alleviates periodontitis-induced disruption of long bone homeostasis in mice fed a high-fat/high-sugar diet. Compared with the LIG group, BMD and Tb.Th were significantly higher in the LIG + BP group. HE staining of the proximal tibia showed that bone resorption was mitigated in the LIG + BP group compared with the LIG group. The expression of OCN and Osterix was significantly higher in the LIG + BP group, while the expression of osteoclast-specific genes (OSCAR, RANK, RANK-L) was significantly lower (P<0.05). ④ Supplementation with butyrate alleviates periodontitis-induced disruption of long bone homeostasis in mice fed a high-fat/high-sugar diet. Compared with the LIG group, BV/TV and Tb.N were significantly higher in the LIG + SB group, and Tb.Sp was significantly lower (P<0.05). HE staining of the proximal tibia showed that bone resorption was mitigated in the LIG + SB group compared with the LIG group. The expression of Osterix, OPG, OSCAR, TRAP, and RANK was significantly lower in the LIG + SB group compared with the LIG group (P<0.05). Conclusion Periodontitis disrupts the long bone homeostasis of mice fed a high-fat high-sugar diet, aggravating long bone resorption. Supplementation with butyric acid-producing bacteria or butyrate can effectively alleviate the disruption of long bone homeostasis caused by periodontitis.

Objectives: This study evaluates the efficacy of a new temporomandibular joint (TMJ) capsule suspension technique for stabilizing the TMJ after free fibular flap reconstruction of the mandibular condyle. Patients and Methods: Patients undergoing the TMJ capsule suspension technique during free fibular flap reconstruction after mandibulectomy with condylectomy (study group; n=9) were compared with a control group (n=9). Mandibular movement trajectory and surface electromyographic signals of bilateral masseters were recorded. The neocondyle–disc relationship was examined with magnetic resonance imaging (MRI) at 6 months after surgery. Results: Maximal mouth opening and bilateral marginal movement distances were comparable between the two groups (P>0.05). The asymmetry index of the condyle path length was significantly higher in controls (P=0.02). Bilateral mouth opening trajectories were symmetric in 7 patients and deviated to the affected side in 2 patients in the study group; they deviated to the affected side in all controls. The mean electromyographic values of the masseter on the affected side in resting, maximum bite, and chewing states were comparable between the two groups (P=0.13, P=0.65, and P=0.82, respectively). On MRI at 6 months, the thicknesses of the anterior, medial, and posterior bands and TMJ disc length were similar on the affected and normal sides in the study group (P=0.57, P=0.13, P=0.48, and P=0.87, respectively). Conclusion The proposed TMJ capsule suspension technique could improve postoperative TMJ structure and function after fibular free flap reconstruction following mandibulectomy with condylectomy.

Objectives: This study evaluates the efficacy of a new temporomandibular joint (TMJ) capsule suspension technique for stabilizing the TMJ after free fibular flap reconstruction of the mandibular condyle. Patients and Methods: Patients undergoing the TMJ capsule suspension technique during free fibular flap reconstruction after mandibulectomy with condylectomy (study group; n=9) were compared with a control group (n=9). Mandibular movement trajectory and surface electromyographic signals of bilateral masseters were recorded. The neocondyle–disc relationship was examined with magnetic resonance imaging (MRI) at 6 months after surgery. Results: Maximal mouth opening and bilateral marginal movement distances were comparable between the two groups (P>0.05). The asymmetry index of the condyle path length was significantly higher in controls (P=0.02). Bilateral mouth opening trajectories were symmetric in 7 patients and deviated to the affected side in 2 patients in the study group; they deviated to the affected side in all controls. The mean electromyographic values of the masseter on the affected side in resting, maximum bite, and chewing states were comparable between the two groups (P=0.13, P=0.65, and P=0.82, respectively). On MRI at 6 months, the thicknesses of the anterior, medial, and posterior bands and TMJ disc length were similar on the affected and normal sides in the study group (P=0.57, P=0.13, P=0.48, and P=0.87, respectively). Conclusion The proposed TMJ capsule suspension technique could improve postoperative TMJ structure and function after fibular free flap reconstruction following mandibulectomy with condylectomy.

Objectives: This study evaluates the efficacy of a new temporomandibular joint (TMJ) capsule suspension technique for stabilizing the TMJ after free fibular flap reconstruction of the mandibular condyle. Patients and Methods: Patients undergoing the TMJ capsule suspension technique during free fibular flap reconstruction after mandibulectomy with condylectomy (study group; n=9) were compared with a control group (n=9). Mandibular movement trajectory and surface electromyographic signals of bilateral masseters were recorded. The neocondyle–disc relationship was examined with magnetic resonance imaging (MRI) at 6 months after surgery. Results: Maximal mouth opening and bilateral marginal movement distances were comparable between the two groups (P>0.05). The asymmetry index of the condyle path length was significantly higher in controls (P=0.02). Bilateral mouth opening trajectories were symmetric in 7 patients and deviated to the affected side in 2 patients in the study group; they deviated to the affected side in all controls. The mean electromyographic values of the masseter on the affected side in resting, maximum bite, and chewing states were comparable between the two groups (P=0.13, P=0.65, and P=0.82, respectively). On MRI at 6 months, the thicknesses of the anterior, medial, and posterior bands and TMJ disc length were similar on the affected and normal sides in the study group (P=0.57, P=0.13, P=0.48, and P=0.87, respectively). Conclusion The proposed TMJ capsule suspension technique could improve postoperative TMJ structure and function after fibular free flap reconstruction following mandibulectomy with condylectomy.

Objective To explore the efficiency improvement in segmenting neural network with the application of Transformer + U-Net artificial intelligence (AI) and modeling with the application of Python scripts in three-dimensional (3D) printing brachytherapy. Methods A Transformer + U-Net AI neural network model was constructed, and Adam optimizer was used to ensure rapid gradient descent. Computed tomography or magnetic resonance imaging data of patients were standardized and processed as self-made data sets. The training set was used to train AI and the optimal result weight parameters were saved. The test set was used to evaluate the AI ability. Python programming language was used to write an automated script to obtain the output segmentation image and convert it to the STL file for import. The source applicator and needle could be automatically modeled. The time of automatic segmentation and modeling and the time of manual segmentation and modeling were entered by two people, and the difference was verified by paired t-test. Results Dice similarity coefficient (DSC), mean intersection over union (MIOU), and Hausdorff distance (HD95) were used for evaluation. DSC was 0.9341, MIOU was 0.8762, and mean HD95 was 2.516. The mean time of manual segmentation was 1187 s, and the mean time of AI segmentation was 145 s (P < 0.01). The mean time of manual modeling was 321 s, and the mean time of modeling with Python automated scripts was 18 s (P < 0.01). Conclusion The application of Transformer + U-Net automatic segmentation and Python automated scripts can effectively improve work efficiency in the 3D printing brachytherapy for cervical cancer.

ObjectiveThis study proposes a novel single-cell protein localization method based on a class perception graph convolutional network (CP-GCN) to overcome several critical challenges in protein microscopic image analysis, including the scarcity of cell-level annotations, inadequate feature extraction, and the difficulty in achieving precise protein localization within individual cells. The methodology involves multiple innovative components designed to enhance both feature extraction and localization accuracy. MethodsFirst, a class perception module (CPM) is developed to effectively capture and distinguish semantic features across different subcellular categories, enabling more discriminative feature representation. Building upon this, the CP-GCN network is designed to explore global features of subcellular proteins in multicellular environments. This network incorporates a category feature-aware module to extract protein semantic features aligned with label dimensions and establishes a subcellular relationship mining module to model correlations between different subcellular structures. By doing so, it generates co-occurrence embedding features that encode spatial and contextual relationships among subcellular locations, thereby improving feature representation. To further refine localization, a multi-scale feature analysis approach is employed using the K-means clustering algorithm, which classifies multi-scale features within each subcellular category and generates multi-cell class activation maps (CAMs). These CAMs highlight discriminative regions associated with specific subcellular locations, facilitating more accurate protein localization. Additionally, a pseudo-label generation strategy is introduced to address the lack of annotated single-cell data. This strategy segments multicellular images into single-cell images and assigns reliable pseudo-labels based on the CAM-predicted regions, ensuring high-quality training data for single-cell analysis. Under a transfer learning framework, the model is trained to achieve precise single-cell-level protein localization, leveraging both the extracted features and pseudo-labels for robust performance. ResultsExperimental validation on multiple single-cell test datasets demonstrates that the proposed method significantly outperforms existing approaches in terms of robustness and localization accuracy. Specifically, on the Kaggle 2021 dataset, the method achieves superior mean average precision (mAP) metrics across 18 subcellular categories, highlighting its effectiveness in diverse protein localization tasks. Visualization of the generated CAM results further confirms the model’s capability to accurately localize subcellular proteins within individual cells, even in complex multicellular environments. ConclusionThe integration of the CP-GCN network with a pseudo-labeling strategy enables the proposed method to effectively capture heterogeneous cellular features in protein images and achieve precise single-cell protein localization. This advancement not only addresses key limitations in current protein image analysis but also provides a scalable and accurate solution for subcellular protein studies, with potential applications in biomedical research and diagnostic imaging. The success of this method underscores the importance of combining advanced deep learning architectures with innovative training strategies to overcome data scarcity and improve localization performance in biological image analysis. Future work could explore the extension of this framework to other types of microscopic imaging and its application in large-scale protein interaction studies.

Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large language models, large visual models, and multimodal large models, offer unprecedented capabilities in processing and interpreting complex medical data across various imaging modalities. By leveraging self-supervised pretraining on vast unlabeled datasets, cross-modal representation learning, and domain-specific medical knowledge adaptation through fine-tuning, large models can achieve higher diagnostic accuracy and more efficient workflows for key clinical tasks. This review summarizes the concepts, methods, and progress of large models in medical imaging, highlighting their potential in precision medicine. The article first outlines the integration of multimodal data under large model technologies, approaches for training large models with medical datasets, and the need for robust evaluation metrics. It then explores how large models can revolutionize applications in critical tasks such as image segmentation, disease diagnosis, personalized treatment strategies, and real-time interactive systems, thus pushing the boundaries of traditional imaging analysis. Despite their potential, the practical implementation of large models in medical imaging faces notable challenges, including the scarcity of high-quality medical data, the need for optimized perception of imaging phenotypes, safety considerations, and seamless integration with existing clinical workflows and equipment. As research progresses, the development of more efficient, interpretable, and generalizable models will be critical to ensuring their reliable deployment across diverse clinical environments. This review aims to provide insights into the current state of the field and provide directions for future research to facilitate the broader adoption of large models in clinical practice.
Humans ; Diagnostic Imaging/methods* ; Precision Medicine/methods* ; Image Processing, Computer-Assisted/methods*