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.

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 genome tagging project (GTP) plays a pivotal role in addressing a critical gap in the understanding of protein functions. Within this framework, we successfully generated a human influenza hemagglutinin-tagged sperm-specific protein 411 (HA-tagged Ssp411) mouse model. This model is instrumental in probing the expression and function of Ssp411. Our research revealed that Ssp411 is expressed in the round spermatids, elongating spermatids, elongated spermatids, and epididymal spermatozoa. The comprehensive examination of the distribution of Ssp411 in these germ cells offers new perspectives on its involvement in spermiogenesis. Nevertheless, rigorous further inquiry is imperative to elucidate the precise mechanistic underpinnings of these functions. Ssp411 is not detectable in metaphase II (MII) oocytes, zygotes, or 2-cell stage embryos, highlighting its intricate role in early embryonic development. These findings not only advance our understanding of the role of Ssp411 in reproductive physiology but also significantly contribute to the overarching goals of the GTP, fostering groundbreaking advancements in the fields of spermiogenesis and reproductive biology.
Animals ; Female ; Humans ; Male ; Mice ; Spermatids/metabolism* ; Spermatogenesis/physiology* ; Spermatozoa/metabolism* ; Thioredoxins/genetics*

OBJECTIVE: To investigate the clinical characteristics and prognosis of peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS). METHODS: Clinical data of 10 patients with PTCL-NOS in Gansu Provincial Hospital from May 2016 to June 2023 were collected. The treatment outcomes were evaluated, and the factors affecting prognosis were analyzed. RESULTS: The median age of onset for the 10 patients was 60.7 (47-75) years, with 7 males and 3 females. Nine cases received chemotherapy, while one case died suddenly after diagnosis, and the median course of chemotherapy was 6.9 (1-13) courses. Assessing the efficacy, 3 patients achieved complete remission (CR) while 7 patients showed progression. Age, sex, lactate dehydrogenase (LDH) level, Ki-67 and the presence of hemophagocytic lymphohistocytosis (HLH) were not statistically correlated with CR rate ( P >0.05). Patients with IPI score 3-5, and Ann Arbor stage III-IV had statistically lower CR rates (both P <0.05). Age, B symptoms, LDH level ,hemoglobin, Ki-67 index and PLR value were not statistically correlated with overall survival (OS) time ( P >0.05). Male, platelet <150×10⁹/L, IPI score 3-5, Ann Arbor stage III-IV, presence of HLH, NLR≥4.05, and LMR <2.81 were statistically correlated with shorter OS (all P <0.05). Among the 10 patients, 3 cases have survived and are still in CR status, while 7 cases have died, with a median survival time of 7.5 (1-85) months. CONCLUSIONS Patients with IPI score 3-5 and Ann Arbor stage III-IV have low CR rate and poor prognosis. The OS of patients who are male, with platelet <150×10⁹/L, IPI score 3-5, Ann Arbor stage III-IV, complication of HLH, NLR≥4.05, and LMR <2.81 is short, and prognosis is poor.
Humans ; Lymphoma, T-Cell, Peripheral/diagnosis* ; Male ; Prognosis ; Middle Aged ; Female ; Aged

Genetic transformation is a fundamental tool in molecular biology research of medicinal plants. Tailoring transgenic technologies to each distinct medicinal plant would necessitate a substantial investment of time and effort. Here, we present a simple hairy root transformation method that does not require sterile conditions, utilizing Agrobacterium rhizogenes strain K599 and the visible RUBY reporter system. Transgenic hairy roots were obtained for six tested medicinal plant species, roots or rhizomes of which have recognized medicinal value, spanning four botanical families and six genera (Platycodon grandiflorus, Atractylodes macrocephala, Scutellaria baicalensis, Codonopsis pilosula, Astragalus membranaceus, and Glycyrrhiza uralensis). Furthermore, two previously identified Glycyrrhiza uralensis UGTs that convert liquiritigenin into liquiritin in heterologous systems were studied in planta using the method. Our results indicate that overexpression of GuUGT1 but not GuUGT10 and Cas9-mediated knockout of GuUGT1 profoundly influenced the accumulation of liquiritin and isoliquiritin in licorice roots. Therefore, the method described here represents a simple, rapid and widely applicable hairy root transformation method that enables fast gene functional study in medicinal plants.

Objective To evaluate the effects of fasting and high-fat diet on the pharmacokinetics of rabeprazole sodium enteric-coated tablets in healthy Chinese subjects.Methods A single-center,randomized,open,two-agent,two-sequence,four-cycle,fully repeated crossover,single-dose trial design was used in this study,healthy subjects were assigned to receive single dose of rabeprazole sodium enteric-coated tablets 0.1 g in either fasting or high-fat diet state,and blood samples were taken at different time points,respectively.The concentrations of rabeprazole sodium enteric-coated in plasma were determined by liquid chromatography-tandem mass spectrometry(LC-MS/MS),the model method of the non-compartmental was used to calculate the pharmacokinetic parameters by Phoenix WinNonlin 8.2.Results The main pharmacokinetic parameters of rabeprazole sodium enteric-coated tablets in fasting state and high-fat diet state were as follows:Cmax were(339.63±156.47)and(318.86±132.13)ng·mL-1;t1/2 were(2.34±0.68)and(3.60±2.40)h;AUC0_t were(556.62±251.65)and(528.50±201.78)ng·mL-1·h;AUC0-∞ were(563.39±255.69)and(535.15±203.24)ng·mL-1·h;tmax were 3.65 and 6.99 h.After high-fat diet,the Cmax and AUC of rapeprazole sodium after high-fat and high-calorie diet decreased,Cmax decreased by 6.12％,AUC0-t decreased by 5.05％,AUC0-∞ decreased by 5.01％,andtmaxwas delayed by about 3.34 h.Cmax,AUC0-t and AUC0-∞ 90％confidence interval were 73.13％-115.10％,83.22％-112.28％and 83.40％-112.13％,respectively.Neither was between 85.00％-125.00％.Conclusion High-fat diet affects the absorption rate and degree of rabeprazole sodium enteric-coated,so it is suitable to be administered on an empty stomach.

Objective:To prepare decellularized extracellular matrix (dECM)-gelatin methacryloyl (GelMA) composite hydrogels and to study their effects on hepatocyte proliferation.Methods:Hepatic dECM was prepared by elution, and GelMA hydrogel and 10%, 30% and 50% dECM-GelMA composite hydrogels were prepared by pepsin solubilization. The morphology of normal liver and dECM liver was observed by eyes and scanning electron microscopy using hematoxylin-eosin, Sirius red and periodate-Schiff staining, respectively. The internal structure of the dECM-GelMA composite hydrogels was observed by scanning electron microscopy, and the pore diameter was measured. Liver HL-7702 cells were co-cultured with GelMA hydrogel and 10%, 30% and 50% dECM-GelMA composite hydrogels, and the cell proliferation viability was determined by cell counting kit-8. The expression of proliferating cell nuclear antigen (PCNA), Wnt family protein 5a (Wnt5a), β-catenin, extracellular-regulated protein kinase 1/2 (ERK1/2) and phosphorylated ERK1/2 (p-ERK1/2) were detected by Western blotting. Comparisons were made using independent sample t-test or one-factor analysis of variance. Results:After decellularization, the hepatocyte morphology showed rounded depressions, and the extracellular matrix structure was intact. The GelMA hydrogel and 10%, 30% and 50% dECM-GelMA composite hydrogels showed inernally porous structures. The pore diameter increased from (3.06±1.35) μm in the GelMA hydrogel to (16.01±4.02) μm in the 50% dECM-GelMA composite hydrogel. On the 3rd, 5th and 7th day, the relative cell proliferation was higher in the 50% dECM-GelMA composite hydrogel group than that in the GelMA hydrogel group (1.89±0.04 vs 1.53±0.01, 9.36±0.04 vs 3.89±0.09, 7.15±0.27 vs 4.89±0.15, all P<0.05). The relative expression levels of PCNA, Wnt5a, β-catenin, and p-ERK1/2/ERK1/2 proteins in the 50% dECM-GelMA composite hydrogel group were higher than those in the GelMA hydrogel group (2.14±0.04 vs 1.00±0.03, 2.36±0.09 vs 1.00±0.08, 1.45±0.03 vs 1.00±0.04, 1.43±0.04 vs 1.00±0.01, all P<0.05). Conclusions:A dECM-GelMA composite hydrogel can be prepared, which may promote hepatocyte proliferation by upregulating the phosphorylation of ERK1/2 and activating Wnt/β-catenin signaling pathway.

AIM To study the chemical constituents from Euphorbia humifusa Willd.and their in vitro anti-hepatoma activity.METHODS Silica gel,D101 macroporous adsorption resin and semi-preparative RP-HPLC were used for isolated and purified,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The anti-hepatocellular carcinoma activity was determined by MTT mothod.RESULTS Eighteen compounds were isolated and identified as 22-O-angeloyl-R1-barrigenol(1),dimethyl 3,3'-[oxybis(4,1-phenylene)](2E,2'E)-diacrylate(2),N-(3-methoxy-1,3-dioxopropyl)-D-tryptophan methyl ester(3),N-acetyltryptophan methyl ester(4),N-(methoxycarbonyl)-tryptophan methyl ester(5),(3β,5α,17β)-4,4,8,14-tetramethyl-18-norandrostane-3,17-diol(6),3β,18,19β-trihydroxylupane(7),pregnenolone(8),3-hydroxy-5,6-epoxy-7-megastigmen-9-one(9),dehydrovomifoliol(10),loliolide(11),2,2'-oxybis(1,4-di-tert-butylbenzene)(12),dibutyl phthalate(13),4-methoxycinnamic acid(14),3,4-dimethoxycinnamic acid(15),methyl 4-hydroxybenzoate(16),kaempferol(17),quercetin(18).The IC50 values of compounds 1,7 and 8 on HepG2 cells were(17.27±0.92),(19.11±2.14)and(7.53±1.09)μmol/L,respectively.CONCLUSION Compounds 1-16 are first isolated from this plant.Compounds 1,7 and 8 have anti-hepatoma activity.

Objective:To evaluate the accuracy and feasibility of an automated scanning and uptake system to assist pathologists with hu-man epidermal growth factor receptor 2(HER2)FISH interpretation.Methods:HER2 gene amplification is detected using FISH,and"result interpretation by independent pathologists"is regarded as the"gold standard."The consistency of"human-machine dialogue results"(use of a CytoVision* system combined with manual interpretation)and"CytoVision*-based automated interpretation"with the"gold standard"was assessed.Results:Consistency between"human-machine dialogue results"and the"gold standard"can surpass 91%,with the former method saving up to 50%of the manual operation time.The tendency of each cell nucleus's HER2 copy number to be"underestimated"is the main reason for the low sensitivity observed in cases with low copy number amplification and HER2 heterogeneous expression cases in"human-machine dialogue interpretation."Conclusions:Automatic FISH image analysis and uptake systems simulate the process of manu-ally interpreted cell selection,ensure random cell selection,and improve work efficiency.With its accurate selection of the hybridization re-gion and"human-computer dialogue,"the system is expected to"replace"interpretation by independent pathologists.