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.

Combined seawater Hf and Nd isotope compositions have been applied as reliable tracers of water mass mixing and weathering input changes.The establishment of standardized synchronous extraction methods for Fe-Mn oxyhydroxide in sediments,which serve as effective carriers for isotopic signals of ancient seawater,is a prerequisite and key for paleoceanography research.The research material of this study was the surface sediments of the Western Arctic Ocean obtained from China's 7th Arctic Scientific Expedition,and a reliable simultaneous Nd-Hf isotope extraction method in Fe-Mn oxyhydroxide fraction was established through systematic optimization of pretreatment procedures and key extraction parameters.This research focused on analyzing the effects of pretreatment(Milli-Q water pre-wash)and key extraction parameters(leaching time,solution/solid ratios,concentration of EDTA-2Na,and concentration of mixed solution of hydroxyamine hydrochloride(HH)and acetic acid(HAc))on element extraction efficiency and isotopic composition.The experimental results indicated that the Milli-Q water pre-wash was not necessary,Fe-Mn oxyhydroxide extraction could be directly performed.Nd extraction amount in Fe-Mn oxyhydroxide fraction was not sensitive to changes in extraction conditions,while the Hf extraction amount was significantly affected by leaching time,solution/solid ratios and concentration of EDTA-2Na.The optimal conditions for synchronously extracting Nd-Hf isotopes were as follows:adding 20 mL of a mixed solution of 0.005 mol/L HH+1.5%HAc+0.03 mol/L EDTA-2Na per gram of sample,and reaction at room temperature for 3 h under shaking.Through systematic verification of 87Sr/86Sr ratio,εNd value,εHf value,Al/Nd ratio and Al/Hf ratio,it was confirmed that this method could effectively avoid interferences from residual components and accurately obtain Nd-Hf isotope signals of ancient seawater preserved in sediments.The standardized method established in this work provided reliable technical support for reconstructing water mass mixing between the Arctic,North Atlantic,and North Pacific Oceans and riverine input histories.It also provided important reference for the extraction methods of Nd and Hf isotopes from marine sources in sediments from other sea areas around the world.

Objective: To investigate the efficacy, safety, and traditional Chinese medicine (TCM) medication patterns of rituximab (RTX) combined with TCM on treating children with steroid-dependent nephrotic syndrome (SDNS). Methods: One hundred and forty-three children with SDNS who visited the Pediatric Nephrology Department of the First Affiliated Hospital of Henan University of Chinese Medicine from January 2018 to December 2022 were enrolled. A cohort study design was adopted, with " RTX treatment" as the exposure factor. Children who met this exposure factor were assigned to the RTX cohort (RTX, glucocorticoid, immunosuppressive agent, combined with traditional Chinese medicine syndrome differentiation treatment), whereas those who did not were assigned to the basic treatment cohort (glucocorticoid, immunosuppressive agent, combined with traditional Chinese medicine syndrome differentiation treatment ), and followed up for 6 months. The frequency of urinary protein recurrences, urinary protein remission duration, proportion and duration of steroid reduction and cessation, cumulative usage of steroids, proportion of recurrence, recurrence amount of steroid used, efficacy of TCM syndrome, and laboratory and safety indicators after treatment, and height and CD19+ B cell count before and after treatment were compared between the two cohorts. The medication patterns of TCM in the two cohorts were analyzed using frequency statistics, association rule analysis, and systematic clustering analysis. Results: Compared with the basic treatment cohort, the RTX cohort showed a decrease in the frequency of urinary protein recurrence, extended sustained remission of urinary protein, an increase in the proportion of steroid reduction and cessation, a shorter duration of steroid reduction and cessation, a decrease in cumulative steroid dosage, a lower recurrence rate, a decrease in CD19+ B cell count, and a decrease in 24-h urinary total protein quantification and the level of cholesterol (P<0.05). No significant difference in the recurrence amount of steroid used, height, TCM syndrome efficacy, albumin, aspartate transaminase, blood urea nitrogen, platelet count, and safety indicators between the two cohorts. Children with SDNS were mostly characterized by qi and yin deficiency syndrome, followed by spleen and kidney yang deficiency syndrome. A total of 175 TCMs were included, including 28 high-frequency drugs such as Huangqi, Fuling, Gancao, Baizhu, Dangshen, and Jiuyurou. The primary use of medication is to nourish the qi and spleen, nourish the kidney, and warm yang. The analysis of association rules yielded eight binary associations and ten three-phase associations, with Huangqi, Baizhu, Fuling, and Dangshen, being the most closely related. Cluster analysis identified four TCM combinations, primarily focusing on tonifying kidney and replenishing essence, benefiting qi and nourishing yin, and removing blood stasis. Conclusion RTX combined with TCM syndrome differentiation treatment can reduce the recurrence frequency of SDNS, prolong the remission period, reduce the glucocorticoid dosage, and have no marked effect on height growth. No apparent adverse reactions were observed. TCM should focus on nourishing qi and yin while removing blood stasis.

Abelmoschi Corolla, the dried corolla of Abelmoschus manihot, has anti-inflammatory, antioxidant, and anti-fibrosis activities. Its chemical constituents mainly include flavonoids, organic acids, steroids, and polysaccharides. This study reviewed the research progress in the chemical constituents and pharmacological activities of Abelmoschi Corolla in recent 20 years. According to the concept of quality marker(Q-marker), the Q-markers of Abelmoschi Corolla were predicted from plant phylogeny, chemical constituent specificity, traditional efficacy, chemical constituent measurability, and absorbed constituents. The primary Q-markers for Abelmoschi Corolla were anticipated to include quercetin-3'-O-β-D-glucopyranoside, gossypetin-8-O-β-D-glucuronide, isoquercetin, myricetin,quercetin, and hyperoside, with the aim of providing reference data for improving the quality evaluation system of Abelmoschi Corolla.
Abelmoschus/chemistry* ; Drugs, Chinese Herbal/pharmacology* ; Flowers/chemistry* ; Humans ; Animals ; Quality Control ; Flavonoids/chemistry*

Coronary heart disease is a cardiovascular disease that affects coronary arteries. It presents high incidence and high mortality worldwide, bringing a serious threat to human health and quality of life. Salviae Miltiorrhizae Radix et Rhizoma derived from Salvia miltiorrhiza is widely used in the treatment of cardiovascular diseases, such as coronary heart disease. Salvianolic acid B is an active component in Salviae Miltiorrhizae Radix et Rhizoma extracts, and studies have shown that it has anti-inflammatory, antioxidant, apoptosis-and autophagy-regulating, anti-fibrosis, and metabolism-modulating effects. This article reviews the research progress regarding the therapeutic effect of salvianolic acid B on coronary heart disease in the recent decade. It elaborates on the role and mechanism of salvianolic acid B in treating coronary heart disease from multiple perspectives, such as the inhibition of thrombosis, improvement of blood circulation, reduction of myocardial cell injury, and inhibition of cardiac remodeling. This article provides a theoretical basis for the application of Chinese medicinal materials and TCM prescriptions containing salvianolic acid B in the treatment of coronary heart disease.
Humans ; Benzofurans/administration & dosage* ; Coronary Disease/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Salvia miltiorrhiza/chemistry* ; Animals ; Depsides

Based on the interleukin-17(IL-17) signaling pathway, this study explores the effect and mechanism of Bufei Decoction on Klebsiella pneumoniae pneumonia in rats. SD rats were randomly divided into the control group, model group, Bufei Decoction low-dose group(6.68 g·kg~(-1)·d~(-1)), Bufei Decoction high-dose group(13.36 g·kg~(-1)·d~(-1)), and dexamethasone group(1.04 mg·kg~(-1)·d~(-1)), with 10 rats in each group. A pneumonia model was established by tracheal drip injection of K. pneumoniae. After successful model establishment, the improvement in lung tissue damage was observed following drug administration. Core targets and signaling pathways were screened using transcriptomics techniques. Real-time fluorescence quantitative polymerase chain reaction was used to detect the mRNA expression of core targets interleukin-6(IL-6), interleukin-1β(IL-1β), tumor necrosis factor-α(TNF-α), and chemokine CXC ligand 6(CXCL6). Western blot was used to assess key proteins in the IL-17 signaling pathway, including interleukin-17A(IL-17A), nuclear transcription factor-κB activator 1(Act1), tumor necrosis factor receptor-associated factor 6(TRAF6), and downstream phosphorylated p38 mitogen-activated protein kinase(p-p38 MAPK), and phosphorylated nuclear factor-κB p65(p-NF-κB p65). Apoptosis of lung tissue cells was detected by terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling(TUNEL). The results showed that, compared with the control group, the model group exhibited significant pathological damage in lung tissue. The mRNA expression of IL-6, IL-1β, TNF-α, and CXCL6, as well as the protein levels of IL-17A, Act1, TRAF6, p-p38 MAPK/p38 MAPK, and p-NF-κB p65/NF-κB p65, were significantly increased, and the number of apoptotic cells was notably higher, indicating successful model establishment. Compared with the model group, both low-and high-dose groups of Bufei Decoction showed reduced pathological damage in lung tissue. The mRNA expression levels of IL-6, IL-1β, TNF-α, and CXCL6, and the protein levels of IL-17A, Act1, TRAF6, p-p38 MAPK/p38 MAPK, and p-NF-κB p65/NF-κB p65, were significantly decreased, with a significant reduction in apoptotic cells in the high-dose group. In conclusion, Bufei Decoction can effectively improve lung tissue damage and reduce inflammation in rats with K. pneumoniae. The mechanism may involve the regulation of the IL-17 signaling pathway and the reduction of apoptosis.
Animals ; Interleukin-17/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Signal Transduction/drug effects* ; Rats ; Male ; Klebsiella pneumoniae/physiology* ; Klebsiella Infections/immunology* ; Humans ; Lung/drug effects*

This study selected 6 529 plant-based Chinese materia medica(PCMM) from Chinese Materia Medica as research subjects and applied a random permutation test to explore the overall correlation characteristics between nature and flavor, as well as the correlation characteristics after distinguishing different medicinal parts and harvest seasons. The results showed that the overall correlation characteristics between nature and flavor in PCMM were significantly associated in the following pairs: cold and bitter, cool and bitter, cool and astringent, cool and light, neutral and sweet, neutral and astringent, neutral and light, neutral and sour, hot and pungent, and warm and pungent. When analyzing the data by distinguishing medicinal parts and/or harvest seasons, new correlation patterns emerged, characterized by the disappearance of some significant correlations and the emergence of new ones. When analyzing by medicinal parts alone, significant correlations were found in the following cases: cold and light in leaves, cold and salty in barks, cool and sweet in fruits and seeds, neutral and pungent in whole herbs, neutral and salty in stems, and warm and salty in flowers. However, no significant correlations were found between cool and bitter in stems and other types of herbs, cool and astringent in fruits, seeds, flowers, and other types of herbs, cool and light in leaves, fruits, seeds, barks, flowers and other types of herbs, neutral and sweet in barks, neutral and astringent in whole herbs and stems, neutral and light in leaves, fruits, seeds, and flowers, neutral and sour in whole herbs, stems, barks, flowers, and other types of herbs, and hot and pungent in whole herbs, stems, flowers, and other types of herbs. When analyzing by harvest season alone, significant correlations were found in the following cases: cold and salty, and cool and sour in herbs harvested in winter, and neutral and salty in herbs harvested year-round. However, no significant correlation was found between cool and light in herbs harvested in winter. When considering both medicinal parts and harvest seasons, compared to the independent influence of medicinal parts, 14 new significant correlations emerged(e.g., the correlation between cool and bitter in stems harvested in spring), while 53 previously significant correlations disappeared(e.g., the correlation between cool and bitter in barks harvested in summer). Compared to the independent influence of harvest seasons, 11 new significant correlations appeared(e.g., the correlation between cold and light in barks harvested in autumn), while 50 previously significant correlations disappeared(e.g., the correlation between hot and pungent in leaves harvested in winter). This study is the first to reveal the influence of medicinal parts and harvest seasons on the correlation between nature and flavor in PCMM, which highlights that these two factors can interact and jointly affect nature-flavor correlations. Further research is needed to explore the underlying mechanisms. This study provides a deeper understanding of the inherent scientific connotations of herbal properties and offers a theoretical foundation for the cultivation and harvesting of PCMM.
Seasons ; Plants, Medicinal/growth & development* ; Drugs, Chinese Herbal/chemistry* ; Taste

Baihuasheshecao(Hedyotis diffusa) is a commonly used traditional Chinese medicine derived from the whole herb of H. diffusa and has been widely utilized in folk medicine. It possesses anti-tumor, antibacterial, and anti-inflammatory properties, making it one of the frequently used herbs in TCM clinical practice. However, Shuixiancao(H. corymbosa) and Xianhuaercao(H. tenelliflora), species of the same genus, are often used as substitutes for Baihuasheshecao. To substantiate the medicinal basis of Baihuasheshecao, this study systematically reviewed classical herbal texts and modern literature, examining its nomenclature, botanical origin, harvesting, processing, properties, meridian tropism, pharmacological effects, and clinical applications. The results indicate that Baihuasheshecao was initially recorded as "Shuixiancao" in Preface to the Indexes to the Great Chinese Botany(Zhi Wu Ming Shi Tu Kao). Based on its morphological characteristics and habitat description, it was identified as H. diffusa in the Rubiaceae family. Subsequent records predominantly refer to it as Baihuasheshecao as its official name. In most regions, Baihuasheshecao is recognized as the authentic medicinal material, distinct from Shuixiancao and Xianhuaercao. Baihuasheshecao is harvested in late summer and early autumn, and the dried whole plant, including its roots, is used medicinally. The standard processing method involves cutting. It is known for its effects in clearing heat, removing toxins, reducing swelling and pain, and promoting diuresis to resolve abscesses. Initially, it was mainly used for treating appendicitis, intestinal abscesses, and venomous snake bites, and later, it became a treatment for cancer. The excavation of its clinical value followed a process in which overseas Chinese introduced the herb from Chinese folk medicine to other countries. After its unique anti-cancer effects were recognized abroad, it was reintroduced to China and gradually became a crucial TCM for cancer treatment. The findings of this study help clarify the historical and contemporary uses of Baihuasheshecao, providing literature support and a scientific basis for its rational development and precise clinical application.
Humans ; China ; Drugs, Chinese Herbal/chemistry* ; Hedyotis/classification* ; Medicine, Chinese Traditional/history*

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.