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.

Despite significant advances in the field of critical care medicine over the past three decades, veno-arterial extracorporeal membrane oxygenation (V-A ECMO) remains the primary temporary mechanical circulatory support modality for patients with acute severe circulatory failure. With the accumulation of clinical experience and the increasing maturity of operational techniques in V-A ECMO, its technical management—particularly hemodynamic management—has become a key factor influencing patient outcomes. To further improve patient survival, the Chinese Critical Care Ultrasound Study Group, in collaboration with the Hemodynamic Therapy of Critical Care Collaborative Group and the Critical Care Medicine Branch of the China International Exchange and Promotive Association for Medical and Health Care, organized experts in critical care medicine to develop the Consensus on Hemodynamic Management in Adult Veno-Arterial Extracorporeal Membrane Oxygenation (2026 Edition). Based on years of clinical experience and the latest evidence-based medical evidence, this consensus formulates 15 systematic recommendations covering the entire process of V-A ECMO, including initiation, management during support, and weaning, aiming to promote standardized application of hemodynamic management in V-A ECMO.

Background Per- and polyfluoroalkyl substances (PFAS), a group of persistent organic pollutants associated with adverse health effects including hepatotoxicity, immunosuppression, and carcinogenicity, have undergone risk assessments by multiple international organizations, with dietary exposure being the primary pathway. Objective To characterize the exposure to PFAS from food and drinking water sources of elderly residents in Songjiang District of Shanghai and to evaluate associated health risk and health effects. Methods A cross-sectional study was conducted from May to July 2024 in Songjiang District based on the Shanghai Suburban Adult Cohort and Biobank (SSACB) cohort. Dietary surveys were administered via face-to-face interviews among older adults aged 65 years and above, yielding 4 583 valid questionnaires. The estimated daily intake (EDI) of PFAS was calculated by integrating data from the Sixth National Dietary Survey and recent literature on PFAS concentrations in food and drinking water in Shanghai. Health risk assessment was performed using health-based guideline values (HBGV) proposed by various institutions and studies. Additionally, correlation analysis and linear regression modeling of EDI and biochemical indicators in the elderly were conducted to evaluate potential adverse health effects. Results The elderly population in Songjiang District exhibited dietary characteristics consistent with the Eastern Healthy Diet Pattern. Among PFAS compounds, PFOA showed the highest level of oral exposure [mean: 1.495 ng·(kg·d⁻¹)], followed by PFOS [mean: 0.637 ng·(kg·d⁻¹)], PFHxS [mean: 0.636 ng·(kg·d⁻¹)], and PFBS [mean: 0.273 ng·(kg·d⁻¹)]. Specifically, drinking water was the primary source of PFOA [1.415 ng·(kg·d⁻¹), accounting for 94.60%], while aquatic products were the major source of PFOS [0.278 ng·(kg·d⁻¹), accounting for 43.66%]. Using the HBGV derived by China's epidemiological studies, the mean hazard index (HI) for PFAS exposure was 1.39, indicating 54.35% of the population had potential health risks (HI>1). Following the 2024 standard established by the Food Safety Commission of Japan (FSCJ), the HI value dropped to 0.11, suggesting negligible risk. PFAS exposure was negatively associated with triglyceride levels and the indicators of liver and kidney function, but positively associated with low-density lipoprotein cholesterol (LDL-C) and lung cancer markers in the elderly residents. Conclusion PFAS exposure among the elderly residents in Songjiang District is predominantly attributed to PFOA, PFOS, PFHxS, and PFBS, with drinking water and aquatic products identified as primary exposure sources. Current exposure levels demonstrate significant associations with biomarkers of lipid metabolism and lung cancer markers, suggesting potential population health risks. These findings underscore the urgent need to establish HBGV for PFAS compounds based on Chinese population-specific metabolic characteristics.

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.

Objective To explore the feasibility of long non-coding RNAs (lncRNAs) as biomarkers for radiation-induced intestinal injury. Methods Mice were exposed to 15 Gy of ⁶⁰Co γ-rays to the abdominal area. The pathological changes in intestinal tissues were analyzed at 72 h post-irradiation to confirm the successful establishment of the radiation-induced intestinal injury model. Real-time quantitative PCR was conducted to detect the expression of candidate radiation-responsive lncRNAs in the jejunum, jejunal crypts, colon tissues, and plasma of irradiated mice. Human intestinal epithelial cell line HIEC-6 and human colon epithelial cell line NCM460 were exposed to 0, 5, 10, and 15 Gy of ⁶⁰Co γ-rays. The expression levels of candidate lncRNAs were measured at 4, 24, 48, and 72 h post-irradiation to observe their changes with the irradiation dose. Results Pathological analysis showed that abdominal irradiation with 15 Gy successfully established an acute radiation-induced intestinal injury mouse model. Real-time quantitative PCR showed that Dino, Lncpint, Meg3, Dnm3os, Trp53cor1, Pvt1, and Neat1 were significantly upregulated following the occurrence of radiation-induced intestinal injury (P < 0.05). Among them, Meg3 and Dnm3os in mouse plasma were significantly upregulated (P < 0.05), while Gas5 was significantly downregulated (P < 0.05). In HIEC-6 and NCM460 cells, the expression levels of DINO, MEG3, DNM3OS, and GAS5 showed dose-dependent patterns at certain time points (P < 0.05). Conclusion The lncRNAs encoded by MEG3, DNM3OS, and GAS5 in intestinal epithelial cells are responsive to ionizing radiation. Consistent differential expression changes were detected in mouse plasma and intestinal tissues, indicating their potential as biomarkers for radiation-induced intestinal injury.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Peroxisome proliferator-activated receptor γ coactivator 1 α (PGC-1α) is a core member of the PGC-1 family and serves as a transcriptional coactivator, playing a crucial regulatory role in various diseases. Mitochondria, the main site of cellular energy metabolism, are essential for maintaining cell growth and function. Their function is regulated by various transcription factors and coactivators. PGC-1α regulates the biogenesis, dynamics, energy metabolism, calcium homeostasis, and autophagy processes of mitochondria by interacting with multiple nuclear transcription factors, thereby exerting significant effects on mitochondrial function. This review explores the biological functions of PGC-1α and its regulatory effects and related mechanisms on mitochondria, providing important information for our in-depth understanding of the role of PGC-1α in cellular metabolism. The potential role of PGC-1α in metabolic diseases, cardiovascular diseases, and neurodegenerative diseases was also discussed, providing a theoretical basis for the development of new treatment strategies.
Humans ; Mitochondria/metabolism* ; Peroxisome Proliferator-Activated Receptor Gamma Coactivator 1-alpha/physiology* ; Animals ; Energy Metabolism/physiology* ; Neurodegenerative Diseases/physiopathology* ; Autophagy/physiology* ; Transcription Factors/physiology* ; Metabolic Diseases/physiopathology* ; Cardiovascular Diseases/physiopathology*

The present study aimed to investigate the effects of eccentric treadmill exercise on adaptive hypertrophy of skeletal muscle in rats. Thirty-two 3-month-old Sprague Dawley (SD) rats were selected and randomly assigned to one of the four groups based on their body weights: 2-week quiet control group (2C), 2-week downhill running exercise group (2E), 4-week quiet control group (4C), and 4-week downhill running exercise group (4E). The downhill running protocol for rats in the exercise groups involved slope of -16°, running speed of 16 m/min, training duration of 90 min, and 5 training sessions per week. Twenty-four hours after the final session of training, all the four groups of rats underwent an exhaustion treadmill exercise. After resting for 48 h, all the rats were euthanized and their gastrocnemius muscles were harvested for analysis. HE staining was used to measure the cross-sectional area (CSA) and diameter of muscle fibers. Transmission electron microscope was used to observe the ultrastructural changes in muscle fibers. Purithromycin surface labeling translation method was used to measure protein synthesis rate. Immunofluorescence double labeling was used to detect the colocalization levels of lysosomal-associated membrane protein 2 (Lamp2)-leucyl-tRNA synthetase (LARS) and Lamp2-mammalian target of rapamycin (mTOR). Western blot was used to measure the protein expression levels of myosin heavy chain (MHC) IIb and LARS, as well as the phosphorylation levels of mTOR, p70 ribosomal protein S6 kinase (p70S6K), and eukaryotic translation initiation factor 4E binding protein 1 (4E-BP1). The results showed that, compared with the 2C group rats, the 2E group rats showed significant increases in wet weight of gastrocnemius muscle, wet weight/body weight ratio, running distance, running time, pre- and post-exercise blood lactate levels, myofibrillar protein content, colocalization levels of Lamp2-LARS and Lamp2-mTOR, and LARS protein expression. Besides these above changes, compared with the 4C group, the 4E group further exhibited significantly increased fiber CSA, fiber diameter, protein synthesis rate, and phosphorylation levels of mTOR, p70S6K, and 4E-BP1. Compared with the quiet control groups, the exercise groups exhibited ultrastructural damage of rat gastrocnemius muscle, which was more pronounced in the 4E group. These findings suggest that eccentric treadmill exercise may promote mTOR translocation to lysosomal membrane, activating mTOR signaling via up-regulating LARS expression. This, in turn, increases protein synthesis rate through the mTOR-p70S6K-4E-BP1 signaling pathway, promoting protein deposition and inducing adaptive skeletal muscle hypertrophy. Although the ultrastructural changes of skeletal muscle are more pronounced, the relatively long training cycles during short-term exercise periods have a more significant effect on promoting gastrocnemius muscle protein synthesis and adaptive hypertrophy.
Animals ; Rats, Sprague-Dawley ; Physical Conditioning, Animal/physiology* ; Rats ; Muscle, Skeletal/metabolism* ; TOR Serine-Threonine Kinases/metabolism* ; Male ; Hypertrophy ; Adaptation, Physiological/physiology* ; Adaptor Proteins, Signal Transducing/metabolism* ; Ribosomal Protein S6 Kinases, 70-kDa/metabolism* ; Intracellular Signaling Peptides and Proteins

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.