ObjectiveTo explore the effects of Yimei Baijiang formula （YMBJF） on colitis-associated colorectal cancer （CAC） and the nuclear factor kappaB （NF-κB） signaling pathway in mice. MethodsSixty male Balb/c mice of 4-6 weeks old were randomized into 6 groups： Normal， model， capecitabine （0.83 g·kg^-1）， and low-， medium-， and high-dose （4.63， 9.25， 18.50 g·kg^-1， respectively） YMBJF. The mouse model of CAC was established by combining azoxymethane （AOM， 10 mg·kg^-1） and dextran sulfate sodium （DSS， 2%）. At the 5^th week after the start of modeling， the capecitabine group and the YMBJF groups were respectively administrated with the corresponding doses of drugs by gavage once a day for a total of 6 weeks. The body weights and general conditions of mice were measured and observed， and the disease activity index （DAI） was scored. The tumors in the colorectal tissue were counted， and the colorectal length was measured. The histological features of the colorectal tissue in each group of mice were observed by hematoxylin-eosin （HE） staining. The levels of inflammatory cytokines including interleukin-6 （IL-6）， interleukin-1β （IL-1β）， and tumor necrosis factor-α （TNF-α） in the serum were determined by enzyme-linked immunosorbent assay （ELISA）. The expression and localization of proliferating cell nuclear antigen-67 （Ki67）， proliferating cell nuclear antigen （PCNA）， and phosphorylated nuclear transcription factor-κB p65 （p-NF-κB p65） proteins were observed by immunohistochemistry （IHC）. The apoptosis of tumor cells was observed by the TUNEL assay. The mRNA levels of IL-6，IL-1β， TNF-α， nuclear transcription factor-κB p65 （NF-κB p65）， NF-κB inhibitor alpha （IκBα）， B-cell lymphoma-2 （Bcl-2）， and Bcl-2-associated X protein （Bax） in the colorectal tissue were quantified by Real-time polymerase chain reaction（Real-time PCR）. The protein levels of NF-κB p65， phosphorylated （p）-NF-κB p65， IκBα， p-IκBα， Bcl-2， and Bax in the colorectal tissue were determined by Western blot. ResultsCompared with the model group， each YMBJF group showed decreases in DAI and tumor number （P0.01）， and the medium-dose and high-dose YMBJF groups showed increases in colorectal length （P0.05）. In addition， each YMBJF group showed alleviated colorectal mucosal pathological injury， declined levels of IL-6， IL-1β， and TNF-α in the serum （P0.05）， reduced expression of Ki67 and PCNA （P0.01）， and down-regulated expression of p-NF-κB p65 （P0.05）. The apoptosis in the medium-dose and high-dose YMBJF groups increased （P0.01）. Each YMBJF group and the capecitabine group showed down-regulated mRNA levels of IL-1β， TNF-α， IL-6， NF-κB p65， and Bcl-2 （P0.05） and up-regulated mRNA levels of IκBα and Bax （P0.05）. The mRNA level of IκBα was up-regulated， and that of Bcl-2 was down-regulated （P0.05） in the high-dose YMBJF group. The protein levels of p-IκBα and Bcl-2 were down-regulated （P0.05） in each YMBJF group. The protein levels of IκBα and Bax were up-regulated in the medium-dose and high-dose YMBJF groups （P0.01）， while those of NF-κB p65 and p-NF-κB p65 were down-regulated （P0.05）. ConclusionYMBJF can reduce the number of tumors， reduce the levels of inflammatory factors， promote tumor cell apoptosis， and inhibit tumor cell proliferation by regulating the direct effector molecules of the NF-κB signaling pathway in the mouse model of CRC.

ObjectiveTo explore the effects of Yimei Baijiang formula （YMBJF） on colitis-associated colorectal cancer （CAC） and the nuclear factor kappaB （NF-κB） signaling pathway in mice. MethodsSixty male Balb/c mice of 4-6 weeks old were randomized into 6 groups： Normal， model， capecitabine （0.83 g·kg^-1）， and low-， medium-， and high-dose （4.63， 9.25， 18.50 g·kg^-1， respectively） YMBJF. The mouse model of CAC was established by combining azoxymethane （AOM， 10 mg·kg^-1） and dextran sulfate sodium （DSS， 2%）. At the 5^th week after the start of modeling， the capecitabine group and the YMBJF groups were respectively administrated with the corresponding doses of drugs by gavage once a day for a total of 6 weeks. The body weights and general conditions of mice were measured and observed， and the disease activity index （DAI） was scored. The tumors in the colorectal tissue were counted， and the colorectal length was measured. The histological features of the colorectal tissue in each group of mice were observed by hematoxylin-eosin （HE） staining. The levels of inflammatory cytokines including interleukin-6 （IL-6）， interleukin-1β （IL-1β）， and tumor necrosis factor-α （TNF-α） in the serum were determined by enzyme-linked immunosorbent assay （ELISA）. The expression and localization of proliferating cell nuclear antigen-67 （Ki67）， proliferating cell nuclear antigen （PCNA）， and phosphorylated nuclear transcription factor-κB p65 （p-NF-κB p65） proteins were observed by immunohistochemistry （IHC）. The apoptosis of tumor cells was observed by the TUNEL assay. The mRNA levels of IL-6，IL-1β， TNF-α， nuclear transcription factor-κB p65 （NF-κB p65）， NF-κB inhibitor alpha （IκBα）， B-cell lymphoma-2 （Bcl-2）， and Bcl-2-associated X protein （Bax） in the colorectal tissue were quantified by Real-time polymerase chain reaction（Real-time PCR）. The protein levels of NF-κB p65， phosphorylated （p）-NF-κB p65， IκBα， p-IκBα， Bcl-2， and Bax in the colorectal tissue were determined by Western blot. ResultsCompared with the model group， each YMBJF group showed decreases in DAI and tumor number （P0.01）， and the medium-dose and high-dose YMBJF groups showed increases in colorectal length （P0.05）. In addition， each YMBJF group showed alleviated colorectal mucosal pathological injury， declined levels of IL-6， IL-1β， and TNF-α in the serum （P0.05）， reduced expression of Ki67 and PCNA （P0.01）， and down-regulated expression of p-NF-κB p65 （P0.05）. The apoptosis in the medium-dose and high-dose YMBJF groups increased （P0.01）. Each YMBJF group and the capecitabine group showed down-regulated mRNA levels of IL-1β， TNF-α， IL-6， NF-κB p65， and Bcl-2 （P0.05） and up-regulated mRNA levels of IκBα and Bax （P0.05）. The mRNA level of IκBα was up-regulated， and that of Bcl-2 was down-regulated （P0.05） in the high-dose YMBJF group. The protein levels of p-IκBα and Bcl-2 were down-regulated （P0.05） in each YMBJF group. The protein levels of IκBα and Bax were up-regulated in the medium-dose and high-dose YMBJF groups （P0.01）， while those of NF-κB p65 and p-NF-κB p65 were down-regulated （P0.05）. ConclusionYMBJF can reduce the number of tumors， reduce the levels of inflammatory factors， promote tumor cell apoptosis， and inhibit tumor cell proliferation by regulating the direct effector molecules of the NF-κB signaling pathway in the mouse model of CRC.

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.

Background: Despite the fact that an increasing number of studies have focused on developing therapies for acute lung injury, managing acute respiratory distress syndrome (ARDS) remains a challenge in intensive care medicine.Whether the pathology of animal models with acute lung injury in prior studies differed from clinical symptoms of ARDS, resulting in questionable management for human ARDS. To evaluate precisely the therapeutic effect of trans‑ planted stem cells or medications on acute lung injury, we developed an animal model of severe ARDS with lower lung function, capable of keeping the experimental animals survive with consistent reproducibility. Establishing this animal model could help develop the treatment of ARDS with higher efficiency. Results: In this approach, we intratracheally delivered bleomycin (BLM, 5 mg/rat) into rats’ left trachea via a needle connected with polyethylene tube, and simultaneously rotated the rats to the left side by 60 degrees. Within sevendays after the injury, we found that arterial blood oxygen saturation ­(SpO2 ) significantly decreased to 83.7%, partial pressure of arterial oxygen ­(PaO2 ) markedly reduced to 65.3 mmHg, partial pressure of arterial carbon dioxide ­(PaCO2 )amplified to 49.2 mmHg, and the respiratory rate increased over time. Morphologically, the surface of the left lung appeared uneven on Day 1, the alveoli of the left lung disappeared on Day 2, and the left lung shrank on Day 7. A his‑ tological examination revealed that considerable cell infiltration began on Day 1 and lasted until Day 7, with a larger area of cell infiltration. Serum levels of IL-5, IL-6, IFN-γ, MCP-1, MIP-2, G-CSF, and TNF-α substantially rose on Day 7. Conclusions This modified approach for BLM-induced lung injury provided a severe, stable, and one-sided (left-lobe) ARDS animal model with consistent reproducibility. The physiological symptoms observed in this severe ARDS animal model are entirely consistent with the characteristics of clinical ARDS. The establishment of this ARDS animal model could help develop treatment for ARDS.

PURPOSE: To investigate the protective effect of sub-hypothermic mechanical perfusion combined with membrane lung oxygenation on ischemic hypoxic injury of yorkshire brain tissue caused by traumatic blood loss. METHODS: This article performed a random controlled trial. Brain tissue of 7 yorkshire was selected and divided into the sub-low temperature anterograde machine perfusion group (n = 4) and the blank control group (n = 3) using the random number table method. A yorkshire model of brain tissue injury induced by traumatic blood loss was established. Firstly, the perfusion temperature and blood oxygen saturation were monitored in real-time during the perfusion process. The number of red blood cells, hemoglobin content, NA⁺, K⁺, and Ca²⁺ ions concentrations and pH of the perfusate were detected. Following perfusion, we specifically examined the parietal lobe to assess its water content. The prefrontal cortex and hippocampus were then dissected for histological evaluation, allowing us to investigate potential regional differences in tissue injury. The blank control group was sampled directly before perfusion. All statistical analyses and graphs were performed using GraphPad Prism 8.0 Student t-test. All tests were two-sided, and p value of less than 0.05 was considered to indicate statistical significance. RESULTS: The contents of red blood cells and hemoglobin during perfusion were maintained at normal levels but more red blood cells were destroyed 3 h after the perfusion. The blood oxygen saturation of the perfusion group was maintained at 95% - 98%. NA⁺ and K⁺ concentrations were normal most of the time during perfusion but increased significantly at about 4 h. The Ca²⁺ concentration remained within the normal range at each period. Glucose levels were slightly higher than the baseline level. The pH of the perfusion solution was slightly lower at the beginning of perfusion, and then gradually increased to the normal level. The water content of brain tissue in the sub-low and docile perfusion group was 78.95% ± 0.39%, which was significantly higher than that in the control group (75.27% ± 0.55%, t = 10.49, p < 0.001), and the difference was statistically significant. Compared with the blank control group, the structure and morphology of pyramidal neurons in the prefrontal cortex and CA1 region of the hippocampal gyrus were similar, and their integrity was better. The structural integrity of granulosa neurons was destroyed and cell edema increased in the perfusion group compared with the blank control group. Immunofluorescence staining for glail fibrillary acidic protein and Iba1, markers of glial cells, revealed well-preserved cell structures in the perfusion group. While there were indications of abnormal cellular activity, the analysis showed no significant difference in axon thickness or integrity compared to the 1-h blank control group. CONCLUSIONS Mild hypothermic machine perfusion can improve ischemia and hypoxia injury of yorkshire brain tissue caused by traumatic blood loss and delay the necrosis and apoptosis of yorkshire brain tissue by continuous oxygen supply, maintaining ion homeostasis and reducing tissue metabolism level.
Animals ; Perfusion/methods* ; Disease Models, Animal ; Brain Injuries/etiology* ; Swine ; Male ; Hypothermia, Induced/methods*

OBJECTIVE: To detect and analyze the expression and clinical significance of long non-coding RNA tyrosine kinase non-catalytic region adaptor protein 1-antisense RNA1 (NCK1-AS1) in patients with acute myeloid leukemia (AML). METHODS: 89 AML patients and 23 healthy controls were included from the People's Hospital Affiliated to Jiangsu University. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression levels of NCK1-AS1 and NCK1 in bone marrow samples. The relationship between the expression of NCK1-AS1 and the clinical characteristics of patients were analyzed, as well as the correlation between NCK1-AS1 and NCK1. RESULTS: The expression level of NCK1-AS1 in all AML, non-M3 AML and cytogenetically normal AML (CN-AML) patients was significantly higher than that in the control group (P < 0.01, P < 0.05, P < 0.01, respectively). In non-M3 AML, patients with high NCK1-AS1 expression had a significantly lower hemoglobin level than those with low NCK1-AS1 expression (P =0.036), furthermore, NCK1-AS1 ^high patients had shorter overall survival than NCK1-AS1^low patients (P =0.0378). Multivariate analysis showed that NCK1-AS1 expression was an independent adverse factor in patients with non-M3 AML ( HR =2.392, 95% CI :1.089-5.255, P =0.030). In addition, NCK1 expression was also significantly upregulated in all AML, non-M3 AML and CN-AML patients compared with controls (P < 0.01, P < 0.01, P < 0.001, respectively). There was a certain correlation between NCK1-AS1 and NCK1 expression (r =0.37, P =0.0058). CONCLUSION High expression of NCK1-AS1 in AML indicates poor prognosis of AML patients.
Humans ; Leukemia, Myeloid, Acute/genetics* ; RNA, Long Noncoding/genetics* ; Oncogene Proteins/genetics* ; Adaptor Proteins, Signal Transducing/genetics* ; Prognosis ; Male ; Female ; Middle Aged ; Adult ; Case-Control Studies ; Clinical Relevance

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.