ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Background/Aims: Dexamethasone (DEX) is a widely used exogenous therapeutic glucocorticoid in clinical settings. Its long-term use leads to many side effects. However, its effect on metabolic disorders in individuals on a high-fat diet (HFD) remains poorly understood. Methods: In this study, HFD-fed mice were intraperitoneally injected with DEX 2.5 mg/kg/day for 30 days. Lipid metabolism, adipocyte proliferation, and inflammation were assayed using typical approaches. Results: DEX increased the epididymal fat index and epididymal adipocyte size in HFD-fed mice. The number of epididymal adipocytes with diameters > 70 μm accounted for 0.5% of the cells in the control group, 30% of the cells in the DEX group, 19% of the cells in the HFD group, and 38% of all the cells in the D+H group. Adipocyte proliferation in the D+H group was inhibited by DEX treatment. Adipocyte enlargement in the D+H group was associated with increased the lipid accumulation but not the adipocyte proliferation. In contrast, the liver triglyceride and total cholesterol levels and their metabolism were downregulated by the same treatment, indicating the therapeutic potential of DEX for nonalcoholic fatty liver disease. Conclusions DEX synergizes with HFD to promote lipid deposition in adipose tissues. A high risk of obesity development in patients receiving HFD and DEX treatment is suggested.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Background/Aims: Dexamethasone (DEX) is a widely used exogenous therapeutic glucocorticoid in clinical settings. Its long-term use leads to many side effects. However, its effect on metabolic disorders in individuals on a high-fat diet (HFD) remains poorly understood. Methods: In this study, HFD-fed mice were intraperitoneally injected with DEX 2.5 mg/kg/day for 30 days. Lipid metabolism, adipocyte proliferation, and inflammation were assayed using typical approaches. Results: DEX increased the epididymal fat index and epididymal adipocyte size in HFD-fed mice. The number of epididymal adipocytes with diameters > 70 μm accounted for 0.5% of the cells in the control group, 30% of the cells in the DEX group, 19% of the cells in the HFD group, and 38% of all the cells in the D+H group. Adipocyte proliferation in the D+H group was inhibited by DEX treatment. Adipocyte enlargement in the D+H group was associated with increased the lipid accumulation but not the adipocyte proliferation. In contrast, the liver triglyceride and total cholesterol levels and their metabolism were downregulated by the same treatment, indicating the therapeutic potential of DEX for nonalcoholic fatty liver disease. Conclusions DEX synergizes with HFD to promote lipid deposition in adipose tissues. A high risk of obesity development in patients receiving HFD and DEX treatment is suggested.

Background/Aims: Dexamethasone (DEX) is a widely used exogenous therapeutic glucocorticoid in clinical settings. Its long-term use leads to many side effects. However, its effect on metabolic disorders in individuals on a high-fat diet (HFD) remains poorly understood. Methods: In this study, HFD-fed mice were intraperitoneally injected with DEX 2.5 mg/kg/day for 30 days. Lipid metabolism, adipocyte proliferation, and inflammation were assayed using typical approaches. Results: DEX increased the epididymal fat index and epididymal adipocyte size in HFD-fed mice. The number of epididymal adipocytes with diameters > 70 μm accounted for 0.5% of the cells in the control group, 30% of the cells in the DEX group, 19% of the cells in the HFD group, and 38% of all the cells in the D+H group. Adipocyte proliferation in the D+H group was inhibited by DEX treatment. Adipocyte enlargement in the D+H group was associated with increased the lipid accumulation but not the adipocyte proliferation. In contrast, the liver triglyceride and total cholesterol levels and their metabolism were downregulated by the same treatment, indicating the therapeutic potential of DEX for nonalcoholic fatty liver disease. Conclusions DEX synergizes with HFD to promote lipid deposition in adipose tissues. A high risk of obesity development in patients receiving HFD and DEX treatment is suggested.

This study summarizes the holistic nursing management of high-output stomas in three cases of refractory Crohn's disease following double stoma surgery.The nursing highlights encompassed management of high-output stomas(including assessment of high stoma output,use of octreotide ace-tate combined with Bifidobacterium and montmorillonite powder to reduce high stoma output,and dual stoma management),dynamic assessment of nutritional risk,stepped nutritional support,psychological intervention,and continued care at home.After treatment and nursing,the high stoma output in all three patients was effectively controlled,their nutritional status gradually improved,and they were dis-charged in an improved condition.

Background/Aims: Dexamethasone (DEX) is a widely used exogenous therapeutic glucocorticoid in clinical settings. Its long-term use leads to many side effects. However, its effect on metabolic disorders in individuals on a high-fat diet (HFD) remains poorly understood. Methods: In this study, HFD-fed mice were intraperitoneally injected with DEX 2.5 mg/kg/day for 30 days. Lipid metabolism, adipocyte proliferation, and inflammation were assayed using typical approaches. Results: DEX increased the epididymal fat index and epididymal adipocyte size in HFD-fed mice. The number of epididymal adipocytes with diameters > 70 μm accounted for 0.5% of the cells in the control group, 30% of the cells in the DEX group, 19% of the cells in the HFD group, and 38% of all the cells in the D+H group. Adipocyte proliferation in the D+H group was inhibited by DEX treatment. Adipocyte enlargement in the D+H group was associated with increased the lipid accumulation but not the adipocyte proliferation. In contrast, the liver triglyceride and total cholesterol levels and their metabolism were downregulated by the same treatment, indicating the therapeutic potential of DEX for nonalcoholic fatty liver disease. Conclusions DEX synergizes with HFD to promote lipid deposition in adipose tissues. A high risk of obesity development in patients receiving HFD and DEX treatment is suggested.