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.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

The auxin/indole-3-acetic acid (Aux/IAA) gene family is an important regulator for plant growth hormone signaling, involved in plant growth, development, as well as response to environmental stresses. In the present study, we identified SmIAA7 which is potentially associated with Salvia miltiorrhiza leaf development through comparatively analyzed the transcriptome data from different pinnate leaves. SmIAA7 was successfully isolated from S. miltiorrhiza using the specific primers. Then subsequent bioinformatic analysis, prokaryotic expression and purification, subcellular localization, and induction expression analysis under auxin and abiotic stress were performed. The full-length of SmIAA7 contained an ORF of 684 bp encoding a protein of 227 amino acid with a molecular weight of 25.3 kD. Conserved domain analysis showed that SmIAA7 contains the conserved Aux_IAA domain (pfam02309). Sequence analysis and phylogenetic tree analysis results indicated SmIAA7 was phylogenetically close to IAA7 and IAA14 from other plants, suggesting SmIAA7 involved in plant growth and development as well as response to environmental stresses. The prokaryotic expression vector pET28a-SmIAA7 was constructed and SmIAA7 recombinant protein was successfully expressed in E. coli Rosetta (DE3) strain. Subcellular localization experiment demonstrated that SmIAA7 localized in the nucleus of plant cells. Real-time fluorescence quantitative PCR results showed that the expression level of SmIAA7 was upregulated in response to auxin. Drought, low temperature, and salt stress significantly increased the transcript level of SmIAA7 gene. This study lays a foundation for further elucidating the role of SmIAA7 in leaf development, signal transduction, and stress defense in S. miltiorrhiza.

AIM To study the chemical constituents from dichloromethane fraction of Dalbergia odorifera T.Chen heartwood.METHODS Separation and purification were performed using silica gel,Sephadex LH-20,thin-layer chromatography,and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.RESULTS Twenty-four compounds were isolated and identified as 7,2′-dihydroxy-4′-methoxy-isoflavanol(1),vanillin(2),2,2′-oxybis-(1,4-di-tert-butylbenzene)(3),7-hydroxy-6-methoxyflavone(4),sativan(5),5-hydroxy-4′,7-dimethoxyisoflavone(6),2-hydroxy-4,4′-dimethoxychalcone(7),7,2′,3′,4′-tetramethoxydihydroisoflavone(8),2,4,2′-trihydroxy-4′-methoxybenzil(9),ethyl-3-hydroxy-3-phenyl-2-propenoate(10),6,7-dimethoxy-2,3-dihydr-ochromen-4-one(11),sophorophenolone(12),apocynin(13),ethyl-2,4-dihydroxybenzoate(14),ethylparaben(15),methyl-2,4-dihydroxybenzoate(16),5,7-dihydroxy-6-methoxyflavanone(17),7-hydroxyflavanone(18),mimosifoliol(19),7-hydroxy-4′-methoxyisoflavane(20),virolane(21),5-hydroxy-7-methoxychromone(22),3-hydroxyl-5-methoxy-stilbene(23),2′,4′-dihydroxydihydrochalcone(24).CONCLUSION Compound 8 is new natural product,2-6,15,17-18 are isolated from this plant for the first time,7,9-14,16,20-24 are first isolated from genus Dalbergia.

Objective:To explore the feasibility of shortening the time of long exercise test (LET) from 120 to 60 minutes by analyzing the positive rate within 60 minutes among periodic paralysis (PP) patients who were positive in 120-minute test.Methods:The data of patients undergoing 120-minute LET from January 2015 to October 2021 in Beijing Tiantan Hospital, Capital Medical University were retrospectively analyzed, with 30%, 33%, and 40% as diagnostic cut-off values, respectively. PP patients with positive results within 120 minutes after exercise were enrolled in the study. The positive rate within 30 minutes and 60 minutes after exercise was calculated. The change rates of compound muscle action potential (CMAP) amplitude and the sensitivity and specificity of LET at 30 minutes, 60 minutes, and 120 minutes after exercise were analyzed. The change rate of CMAP amplitude in PP patients who did not show positive results within 60 minutes was further calculated.Results:A total of 254 patients were examined, including 114 PP patients. With 30%, 33%, and 40% as diagnostic cut-off values, the results showed that there were 88, 88, and 82 positive PP patients, respectively. Under each diagnostic cut-off values, the age of positive PP patients was (32±10) years, with a male proportion of 98% (86/88), 98% (86/88), and 99% (81/82), respectively; the positive rate of PP patients within 30 minutes after exercise was 60% (53/88), 58% (51/88), and 41% (34/82), respectively; the positive rate of PP patients within 60 minutes after exercise was 91% (80/88), 86% (76/88), and 83% (68/82), respectively. At the cut-off values of 30%, 33% and 40%, the change rate of CMAP amplitude at 30 minutes [-36% (-49%, -23%), -36% (-49%, -23%), -37% (-51%, -24%)], 60 minutes [-51% (-66%, -40%), -51% (-66%, -40%), -53% (-66%, -42%)] and 120 minutes [-57% (-67%, -45%), -57% (-67%, -45%), -58% (-67%, -46%)] after exercise showed statistically significant difference among 3 time points ( H=57.764, 57.764, 59.616, respectively, all P<0.001); the further comparison between time points showed that there was statistically significant difference in the change rate of CMAP amplitude between 60 minutes ( Z=5.419, 5.419, 5.531, respectively, all P<0.001), 120 minutes ( Z=7.325, 7.325, 7.431, respectively, all P<0.001) and 30 minutes after exercise, but there was no statistically significant difference in the change rate of CMAP amplitude between 120 minutes and 60 minutes after exercise ( Z=1.906, 1.906, 1.899, respectively, all P>0.05); the sensitivity of LET for the diagnosis of PP at 60 minutes after exercise was 70.2% (80/114), 66.7% (76/114) and 59.6% (68/114), and the specificity of LET for the diagnosis of PP was 77.9% (109/140), 84.3% (118/140) and 91.4%(128/140), respectively. When 30%, 33% and 40% were used as the diagnostic cut-off values, and the change rate of CMAP amplitude at 60 minutes after exercise fell below these cut-off values but showed a decline of ≥20%, ≥22% and ≥24%, respectively, the detection time should be extended to 120 minutes. Conclusions:Whether using 30%, 33%, or 40% as diagnostic cut-off values, it is feasible to shorten the LET time from 120 minutes to 60 minutes. The 60-minute LET has good sensitivity and specificity for the diagnosis of PP. It is recommended to extend the detection time to 120 minutes for patients with a ≥20%, ≥22%, or ≥24% decline in CMAP amplitude at 60 minutes after exercise while falling short of corresponding diagnostic cut-off values when 30%, 33%, and 40% are used as diagnostic cut-off values. This method can not only improve the examination efficiency of LET, but also minimize the missed diagnosis as much as possible.

Antibody-drug conjugates (ADC) are one of the most popular types of anti-tumor drugs nowadays. Monomethyl auristatin E (MMAE), as a tubulin binder, is the most common applied payload in ADC and is also the main component that causes peripheral neuropathy (PN). By describing the clinical characteristics of 3 cases with MMAE ADC associated severe PN complications and analyzing reported references, this article summarizes that the occurrence of MMAE ADC associated PN is correlated with therapeutic cycles and duration of ADC, MMAE ADC associated PN is different from traditional chemotherapy-induced PN in the clinical presentation. Patients with MMAE ADC associated PN may present with a length-dependent involvement of peripheral motor and sensory nerves, and generally their weakness and deep sensory deficiency symptoms are more serious compared with traditional chemotherapy-induced PN. Two of the 3 patients achieved a relatively rapid recovery after treated with plasma exchange or immunoglobulin intravenous infusions.