ObjectiveTo observe the effects of Yiqi Huoxue Jiedu formula（YHJF） on immune cell exhaustion in the spleen of septic mice and to explore and validate its potential intervention targets. MethodsMice were randomly divided into the sham-operated， model， low-dose YHJF（4.1 g·kg^-1）， and high-dose YHJF（8.2 g·kg^-1） groups. Except for the sham-operated group， a cecal ligation and puncture（CLP） procedure was performed to establish a mouse sepsis model. The treatment groups received oral administration of the corresponding doses， while the sham-operated and model groups received an equal volume of physiological saline. After the intervention， the 7-day survival rate of each group was recorded， and spleen samples were collected 72 h post-intervention， and the spleen index was calculated. Terminal deoxynucleotidyl transferase deoxyuridine triphosphate（dUTP） nick end labeling（TUNEL） staining was used to detect apoptosis in spleen cells. Enzyme-linked immunosorbent assay（ELISA） was performed to measure the levels of interleukin（IL）-4 and IL-10 in the serum. Transcriptomics and metabolomics were used to screen for differentially expressed genes（DEGs） and differential metabolites in the spleen， followed by bioinformatics analysis to identify key targets. Real-time quantitative polymerase chain reaction（Real-time PCR）， flow cytometry， and multiplex immunofluorescence were used to verify the expressions of key genes and proteins. ResultsThe high-dose YHJF group significantly improved the 7-day survival rate of septic mice（P0.05）. Compared with the sham-operated group， the model group showed a significant increase in apoptosis of spleen cells and a decrease in the spleen index at 72 h post-modeling， with markedly elevated peripheral serum IL-4 and IL-10 levels（P0.01）. Compared with the model group， the high-dose YHJF group showed a reduction in apoptosis of spleen cells， an increase in the spleen index， and a significant decrease in peripheral serum IL-4 and IL-10 levels（P0.05）. Spleen transcriptomics identified 255 DEGs between groups， potentially serving as intervention targets for YHJF. Gene Ontology（GO） enrichment analysis revealed that DEGs were mainly involved in biological processes such as natural killer（NK） cell-mediated positive immune regulation， cell killing， cytokine production， positive regulation of innate immune cells， and interferon production. Kyoto Encyclopedia of Genes and Genomes（KEGG） pathway enrichment analysis showed that DEGs were mainly involved in cytokine-cytokine receptor interactions， viral protein interactions with cytokines and cytokine receptors， chemokine signaling pathway， and nuclear transcription factor-κB（NF-κB） signaling pathway. Protein-protein interaction（PPI） network analysis identified CD160， granzyme B（GZMB）， and chemokine ligand 4（CCL4） as key targets for YHJF in treating sepsis. Metabolomics identified 46 differential metabolites that were significantly reversed by YHJF intervention， and combined transcriptomics and metabolomics analysis identified 17 differential metabolites closely related to CD160. Pathway enrichment revealed that these metabolites were mainly involved in glycerophospholipid metabolism， arachidonic acid metabolism， glycosylphosphatidylinositol（GPI） anchor biosynthesis， linoleic acid metabolism， and α-linolenic acid metabolism pathways. Verification results showed that， compared with the sham-operated group， the model group exhibited significantly elevated CD160 mRNA expression level in the spleen， along with markedly decreased CCL4 and GZMB mRNA expression， and had a significant increase in CD160 expression on the surface of natural killer T（NKT） cells in the spleen（P0.01）. Compared with the model group， the high-dose YHJF group had a significant decrease in CD160 mRNA expression in the spleen， a significant increase in CCL4 and GZMB mRNA expressions. Further flow cytometry and immunofluorescence revealed that compared with the sham-operated group， CD160 expression on the surface of splenic NKT cells in the model group was significantly increased（P0.01）， while high-dose YHJF intervention significantly reduced CD160 expression（P0.01）. ConclusionYHJF may alleviate NKT cell exhaustion in sepsis by downregulating the expression of the negative co-stimulatory molecule CD160， and this regulatory effect is closely related to fatty acid metabolism pathways. This study provides new insights and targets for further exploration of strengthening vital Qi and detoxifying strategy to improve immune cell exhaustion in acute deficiency syndrome of sepsis.

ObjectiveTo observe the effects of Yiqi Huoxue Jiedu formula（YHJF） on immune cell exhaustion in the spleen of septic mice and to explore and validate its potential intervention targets. MethodsMice were randomly divided into the sham-operated， model， low-dose YHJF（4.1 g·kg^-1）， and high-dose YHJF（8.2 g·kg^-1） groups. Except for the sham-operated group， a cecal ligation and puncture（CLP） procedure was performed to establish a mouse sepsis model. The treatment groups received oral administration of the corresponding doses， while the sham-operated and model groups received an equal volume of physiological saline. After the intervention， the 7-day survival rate of each group was recorded， and spleen samples were collected 72 h post-intervention， and the spleen index was calculated. Terminal deoxynucleotidyl transferase deoxyuridine triphosphate（dUTP） nick end labeling（TUNEL） staining was used to detect apoptosis in spleen cells. Enzyme-linked immunosorbent assay（ELISA） was performed to measure the levels of interleukin（IL）-4 and IL-10 in the serum. Transcriptomics and metabolomics were used to screen for differentially expressed genes（DEGs） and differential metabolites in the spleen， followed by bioinformatics analysis to identify key targets. Real-time quantitative polymerase chain reaction（Real-time PCR）， flow cytometry， and multiplex immunofluorescence were used to verify the expressions of key genes and proteins. ResultsThe high-dose YHJF group significantly improved the 7-day survival rate of septic mice（P0.05）. Compared with the sham-operated group， the model group showed a significant increase in apoptosis of spleen cells and a decrease in the spleen index at 72 h post-modeling， with markedly elevated peripheral serum IL-4 and IL-10 levels（P0.01）. Compared with the model group， the high-dose YHJF group showed a reduction in apoptosis of spleen cells， an increase in the spleen index， and a significant decrease in peripheral serum IL-4 and IL-10 levels（P0.05）. Spleen transcriptomics identified 255 DEGs between groups， potentially serving as intervention targets for YHJF. Gene Ontology（GO） enrichment analysis revealed that DEGs were mainly involved in biological processes such as natural killer（NK） cell-mediated positive immune regulation， cell killing， cytokine production， positive regulation of innate immune cells， and interferon production. Kyoto Encyclopedia of Genes and Genomes（KEGG） pathway enrichment analysis showed that DEGs were mainly involved in cytokine-cytokine receptor interactions， viral protein interactions with cytokines and cytokine receptors， chemokine signaling pathway， and nuclear transcription factor-κB（NF-κB） signaling pathway. Protein-protein interaction（PPI） network analysis identified CD160， granzyme B（GZMB）， and chemokine ligand 4（CCL4） as key targets for YHJF in treating sepsis. Metabolomics identified 46 differential metabolites that were significantly reversed by YHJF intervention， and combined transcriptomics and metabolomics analysis identified 17 differential metabolites closely related to CD160. Pathway enrichment revealed that these metabolites were mainly involved in glycerophospholipid metabolism， arachidonic acid metabolism， glycosylphosphatidylinositol（GPI） anchor biosynthesis， linoleic acid metabolism， and α-linolenic acid metabolism pathways. Verification results showed that， compared with the sham-operated group， the model group exhibited significantly elevated CD160 mRNA expression level in the spleen， along with markedly decreased CCL4 and GZMB mRNA expression， and had a significant increase in CD160 expression on the surface of natural killer T（NKT） cells in the spleen（P0.01）. Compared with the model group， the high-dose YHJF group had a significant decrease in CD160 mRNA expression in the spleen， a significant increase in CCL4 and GZMB mRNA expressions. Further flow cytometry and immunofluorescence revealed that compared with the sham-operated group， CD160 expression on the surface of splenic NKT cells in the model group was significantly increased（P0.01）， while high-dose YHJF intervention significantly reduced CD160 expression（P0.01）. ConclusionYHJF may alleviate NKT cell exhaustion in sepsis by downregulating the expression of the negative co-stimulatory molecule CD160， and this regulatory effect is closely related to fatty acid metabolism pathways. This study provides new insights and targets for further exploration of strengthening vital Qi and detoxifying strategy to improve immune cell exhaustion in acute deficiency syndrome of sepsis.

Based on spleen deficiency-phlegm dampness as the core pathogenesis of obesity， and integrating recent advances in modern medicine regarding the key role of immune inflammation in obesity， this paper proposes a multidimensional pathogenic network of "obesity-spleen deficiency-phlegm dampness-immune imbalance". Various traditional Chinese medicine （TCM） herbs that strengthen the spleen， regulate qi， and resolve phlegm and dampness can treat obesity by improving spleen-stomach transport and transformation， promoting water-damp metabolism， and regulating immune homeostasis. This highlights immune inflammation as an important entry point to elucidate the TCM concepts of "spleen deficiency-phlegm dampness" and the therapeutic principle of "strengthening the spleen and eliminating dampness to treat obesity". By systematically analyzing the intrinsic connection between "spleen deficiency generating dampness， internal accumulation of phlegm dampness" and immune dysregulation in obesity， this paper aims to provide theoretical support for TCM treatment of obesity based on dampness.

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.

Objective: To investigate the epidemiological characteristics of lung cancer in cancer registration areas of Guangdong Province in 2020, so as to provide the evidence for improving prevention and control strategies of lung cancer. Methods: Data of incidence and mortality in 2020 from 30 cancer registries in Guangdong Province were collected from the Cancer Follow-up Registration System and the All-Cause Mortality Registration Reporting System of the Guangdong Provincial Center for Disease Control and Prevention. The crude incidence, crude mortality, and cumulative rate for 0 to 74 years were calculated. The Chinese population-standardized rate and world population-standardized rate were calculated using the age structure of the standard population from the Fifth National Population Census in 2000 and Segi's world standard population. The incidence and mortality characteristics of lung cancer in different genders, urban/rural areas and ages were described. Results: In 2020, there were 25 357 new cases of lung cancer in Guangdong Province. The crude incidence, Chinese population-standardized incidence, world population-standardized incidence, and cumulative incidence for 0 to 74 years were 60.40/100 000, 43.75/100 000, 43.26/100 000, and 5.30%, respectively. There were 14 366 lung cancer deaths. The crude mortality, Chinese population-standardized mortality, world population-standardized mortality, and cumulative mortality for 0 to 74 years were 38.82/100 000, 24.49/100 000, 24.36/100 000, and 2.88%, respectively. The crude incidence and crude mortality of lung cancer in males were higher than those in females (71.19/100 000 vs. 49.42/100 000, 52.94/100 000 vs. 24.36/100 000, both P<0.05). The crude incidence and crude mortality of lung cancer in urban areas were higher than those in rural areas (66.37/100 000 vs. 45.95/100 000, 40.68/100 000 vs. 35.07/100 000, both P<0.05). The crude incidence and crude mortality of lung cancer exhibited upward trends with increasing age (both P<0.05), peaking in the age of 80-<85 years (347.97/100 000 and 342.14/100 000). Conclusions Comparing to the national data, the incidence of lung cancer in registration areas of Guangdong Province remained relatively high, while mortality remained relatively low. Males, urban residents and the elderly constitute the key populations for lung cancer prevention and control. It is recommend to optimize the allocation of medical resources between urban and rural areas and strengthen lung cancer screening among high-risk groups.

Objective : To establish an interleukin-1β (Il-1β) induced inflammatory model of rat articular chondro- cytes (ACs) , and to investigate the relationship between the expression of lysine acetyltransferase 7 (KAT7) under inflammatory stimulation and the senescence of ACs. Methods: Primary ACs were obtained by digestion of rat knee cartilage with collagenase type Ⅱ and identified. The inflammatory model of ACs was induced by IL-1β . KAT7 was over-expressed or knocked down in ACs by adeno-associated virus infection or small interfering RNA transfection , respectively. A negative control group was set up. Transwell assay was used to detect cell migration ability. Senes- cent cells were stained with senescence-associated β-galactosidase (SA-β-Gal) . Western blot ( WB) was used to detect the protein expression levels of KAT7 , collagen type II (Col Ⅱ ) , matrix metalloproteinase 13 (MMP13) , tumor protein p53 (p53) and cyclin-dependent kinase inhibitor 1A (p21) . The cells of negative control group and KAT7 over-expression group were performed for RNA sequencing , and WB was used to verify the related signaling pathways obtained by Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis. Results: Compared with the control group , the SA-β-Gal staining was enhanced , the protein expression of Col Ⅱ decreased , the pro- tein expression of MMP13 and p53 increased , the cell migration ability decreased , and the expression of KAT7 also increased in the ACs of rats after IL-1β stimulation. Compared with the negative control group , the SA-β-Gal stai- ning was enhanced , the protein expression of Col Ⅱ decreased , the protein expression of MMP13 , p53 and p21 in- creased , and the cell migration ability decreased in the KAT7 over-expression group. Compared with the negative control group , the SA-β-Gal staining was weakened , the protein expression of Col Ⅱ increased , the protein expres- sion of MMP13 , p53 and p21 decreased , and the cell migration ability was enhanced in the KAT7 knockdown inflammatory model of ACs. KEGG enrichment analysis showed that phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) signaling pathway was activated. Compared with the negative control group , the relative protein ex⁃pression levels of phosphorylated protein kinase B (p⁃AKT)/AKT and phosphorylated mammalian target of rapamy⁃cin (p⁃mTOR)/mTOR in KAT7 over⁃expression group increased. The relative protein expression levels of p ⁃AKT/AKT and p ⁃mTOR/mTOR in KAT7 knockdown cells decreased. Conclusion Rat ACs with high expression of KAT7 exhibit senescence and osteoarthritis phenotype , and the mechanism may be related to the activation of PI3K/AKT/mTOR signaling pathway by KAT7.

Recently,diffusion models have emerged as a promising paradigm for molecular design and optimization.However,most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geom-etries,with limited research on molecular sequence diffusion models.The International Union of Pure and Applied Chemistry(IUPAC)names are more akin to chemical natural language than the simplified molecular input line entry system(SMILES)for organic compounds.In this work,we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language(SMILES)and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language.We propose DiffIUPAC,a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings.Evaluation results demonstrate that our model out-performs existing methods and successfully captures the semantic rules of both chemical languages.Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints.Additionally,to illustrate the model's applicability in drug design,we conducted case studies in functional group editing,analogue design and linker design.