BACKGROUND:β-Caryophyllene has a variety of pharmacological activities such as antioxidant,anti-inflammatory and anti-apoptotic,which may have a better therapeutic effect on osteoarthritis.OBJECTIVE:To investigate the effect and mechanism of β-caryophyllene on mouse osteoarthritis.METHODS:Forty C57BL/6J mice were randomly divided into sham group,model group,low-dose β-caryophyllene group and high-dose β-caryophyllene group,with 10 mice in each group.Hulth method was used to construct an osteoarthritis model in the latter three groups.Four weeks after modeling,70 and 140 mg/kg/d β-caryophyllene was intragastrically given in the low-and high-dose β-caryophyllene groups,respectively,and normal saline was given by gavage in the sham group and the model group,once a day,for 4 weeks.After administration,knee joint morphological changes were observed by hematoxylin-eosin staining,serum levels of inflammatory factors(tumor necrosis factor-α,interleukin-1β,interleukin-6,and interleukin-10)were detected by ELISA,and oxidative stress indexes(glutathione peroxidase,superoxide dismutase,and malondialdehyde)were detected by chemiluminescence.The expression levels of key proteins in the Sonic hedgehog(Shh)/glioma associated oncogene homolog 1(Gli1)signaling pathway were detected by immunohistochemistry and western blot.RESULTS AND CONCLUSION:(1)Compared with the sham group,a large number of inflammatory cells infiltrated in the knee joint of mice in the model group,cartilage tissue was seriously damaged,serum levels of tumor necrosis factor-α,interleukin-1β,interleukin-6,interleukin-10 and malondialdehyde were significantly increased(P＜0.01),the activities of glutathione peroxidase and superoxide dismutase were significantly decreased(P＜0.01),and the relative expression levels of Shh and Gli1 in the knee joint were significantly increased(P＜0.01).(2)Compared with the model group,in the low-and high-dose β-caryophyllene groups,inflammatory cell infiltration in the mouse knee joint was decreased,cartilage tissue injury was alleviated,serum levels of tumor necrosis factor-α,interleukin-1 β,interleukin-6 and malondialdehyde were significantly decreased(P＜0.05),the activities of glutathione peroxidase and superoxide dismutase were significantly increased(P＜0.01),and the expression levels of Shh and Gli1 in the knee joint were significantly decreased(P＜0.01).The above-mentioned improvements were more significant in the high-dose β-caryophyllene group than the low-dose β-caryophyllene group.To conclude,β-caryophyllene can improve osteoarthritis,and its mechanism may be related to reducing inflammation and oxidative stress damage by regulating the Shh/Gli1 signaling pathway.

BACKGROUND:β-Caryophyllene has a variety of pharmacological activities such as antioxidant,anti-inflammatory and anti-apoptotic,which may have a better therapeutic effect on osteoarthritis.OBJECTIVE:To investigate the effect and mechanism of β-caryophyllene on mouse osteoarthritis.METHODS:Forty C57BL/6J mice were randomly divided into sham group,model group,low-dose β-caryophyllene group and high-dose β-caryophyllene group,with 10 mice in each group.Hulth method was used to construct an osteoarthritis model in the latter three groups.Four weeks after modeling,70 and 140 mg/kg/d β-caryophyllene was intragastrically given in the low-and high-dose β-caryophyllene groups,respectively,and normal saline was given by gavage in the sham group and the model group,once a day,for 4 weeks.After administration,knee joint morphological changes were observed by hematoxylin-eosin staining,serum levels of inflammatory factors(tumor necrosis factor-α,interleukin-1β,interleukin-6,and interleukin-10)were detected by ELISA,and oxidative stress indexes(glutathione peroxidase,superoxide dismutase,and malondialdehyde)were detected by chemiluminescence.The expression levels of key proteins in the Sonic hedgehog(Shh)/glioma associated oncogene homolog 1(Gli1)signaling pathway were detected by immunohistochemistry and western blot.RESULTS AND CONCLUSION:(1)Compared with the sham group,a large number of inflammatory cells infiltrated in the knee joint of mice in the model group,cartilage tissue was seriously damaged,serum levels of tumor necrosis factor-α,interleukin-1β,interleukin-6,interleukin-10 and malondialdehyde were significantly increased(P＜0.01),the activities of glutathione peroxidase and superoxide dismutase were significantly decreased(P＜0.01),and the relative expression levels of Shh and Gli1 in the knee joint were significantly increased(P＜0.01).(2)Compared with the model group,in the low-and high-dose β-caryophyllene groups,inflammatory cell infiltration in the mouse knee joint was decreased,cartilage tissue injury was alleviated,serum levels of tumor necrosis factor-α,interleukin-1 β,interleukin-6 and malondialdehyde were significantly decreased(P＜0.05),the activities of glutathione peroxidase and superoxide dismutase were significantly increased(P＜0.01),and the expression levels of Shh and Gli1 in the knee joint were significantly decreased(P＜0.01).The above-mentioned improvements were more significant in the high-dose β-caryophyllene group than the low-dose β-caryophyllene group.To conclude,β-caryophyllene can improve osteoarthritis,and its mechanism may be related to reducing inflammation and oxidative stress damage by regulating the Shh/Gli1 signaling pathway.

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.

OBJECTIVE: To illustrate the role of dehydrocorydaline (DHC) in chronic constriction injury (CCI)-induced neuropathic pain and the underlying mechanism. METHODS: C57BL/6J mice were randomly divided into 3 groups by using a random number table, including sham group (sham operation), CCI group [intrathecal injection of 10% dimethyl sulfoxide (DMSO)], and CCI+DHC group (intrathecal injection of DHC), 8 mice in each group. A CCI mouse model was conducted to induce neuropathic pain through ligating the right common sciatic nerve. On day 14 after CCI modeling or sham operation, mice were intrathecal injected with 5 µL of 10% DMSO or 10 mg/kg DHC (5 µL) into the 5th to 6th lumbar intervertebral space (L5-L6). Pregnant ICR mice were sacrificed for isolating primary spinal neurons on day 14 of embryo development for in vitro experiment. Pain behaviors were evaluated by measuring the paw withdrawal mechanical threshold (PWMT) of mice. Immunofluorescence was used to observe the activation of astrocytes and microglia in mouse spinal cord. Protein expressions of inducible nitric oxide synthase (iNOS), tumor necrosis factor alpha (TNF-α), interleukin 6 (IL-6), phosphorylation of N-methyl-D-aspartate receptor subunit 2B (p-NR2B), and NR2B in the spinal cord or primary spinal neurons were detected by Western blot. RESULTS: In CCI-induced neuropathic pain model, mice presented significantly decreased PWMT, activation of glial cells, overexpressions of iNOS, TNF-α, IL-6, and higher p-NR2B/NR2B ratio in the spinal cord (P<0.05 or P<0.01), which were all reversed by a single intrathecal injection of DHC (P<0.05 or P<0.01). The p-NR2B/NR2B ratio in primary spinal neurons were also inhibited after DHC treatment (P<0.05). CONCLUSION An intrathecal injection of DHC relieved CCI-induced neuropathic pain in mice by inhibiting the neuroinflammation and neuron hyperactivity.
Animals ; Neuralgia/etiology* ; Mice, Inbred C57BL ; Analgesics/pharmacology* ; Neuroinflammatory Diseases/pathology* ; Constriction ; Male ; Receptors, N-Methyl-D-Aspartate/metabolism* ; Nitric Oxide Synthase Type II/metabolism* ; Mice, Inbred ICR ; Microglia/pathology* ; Spinal Cord/drug effects* ; Female ; Mice ; Tumor Necrosis Factor-alpha/metabolism* ; Disease Models, Animal ; Constriction, Pathologic/complications* ; Interleukin-6/metabolism* ; Astrocytes/metabolism* ; Chronic Disease ; Neurons/metabolism*

Objective:To prepare heparin (Hep)-modified gelatin methacryloyl (GelMA) microspheres and to investigate their application in liver-targeted delivery of adipose-derived mesenchymal stem cells (ADSCs).Methods:GelMA microspheres were modified with Hep to obtain GelMA-Hep microspheres. The surface morphology of the GelMA-Hep microspheres was observed by scanning electron microscopy. The changes of carbon atoms, nitrogen atoms and sulfur atoms on the surface of the GelMA-Hep microspheres were detected by X-ray photoelectron spectroscopy. The surface chemical group composition of the GelMA-Hep microspheres was analyzed by Fourier transform infrared spectrometer. The swelling properties of the GelMA-Hep microspheres were detected by water absorption swelling experiment. Human liver HL-7702 cells transfected with lentivirus were co-cultured with GelMA, GelMA-dopamine (GelMA-dop) and GelMA-Hep microspheres. The effects of microspheres on cell proliferation activity were evaluated by cell counting kit-8 method and live/dead cell staining experiment. The adhesion of microspheres to cells was observed by confocal microscopy. The GelMA-Hep microspheres loaded with ADSCs were injected into C57BL/6 mice through the tail vein, and its efficiency of liver-targeted delivery of ADSCs was observed by a small animal in vivo imaging system. The data were compared by independent sample t test or one-way analysis of variance. Results:The GelMA-Hep microspheres were prepared by modifying the GelMA microspheres with Hep. Compared with the GelMA microspheres, the size of the GelMA-Hep microspheres did not change significantly, and the surface did not collapse and showed some crystalline particles. The binding energy of sulfur atoms on the surface of the GelMA-Hep microspheres increased from 166 eV to 168 eV. On the surface of the GelMA-Hep microspheres, the characteristic peaks of sulfonic acid and sulfate groups of Hep were detected at 1 490 cm ?1 and from 1 135 cm ?1 to 1 050 cm ?1, respectively. The swelling rate of the GelMA-dop microspheres was uniform, while the swelling rate of the GelMA microspheres and the GelMA-Hep microsphere was quite different, but the final swelling mass of the three microspheres tended to be consistent at 5 min. After 12, 24, 36 and 48 h of culture, the relative proliferation of cells in the GelMA-Hep group (1.61±0.29, 1.78±0.05, 2.27±0.08, 2.26±0.33) were higher than those in the negative control group (1.00±0.00, 1.28±0.06, 1.39±0.02, 1.41±0.04) (all P<0.05). After 36 h of culture, the relative proliferation of cells in the GelMA-Hep group was higher than that in the GelMA-dop group (1.63±0.21), with significant difference ( P<0.05). Live/dead cell staining experiment showed that after 12 h of cell culture in the GelMA-Hep group, only a few microspheres had cell adhesion; at 24 h, the cells were densely distributed on the surface of the microspheres. After 36 h, the number of cells increased further. At 48 h, live cells were distributed throughout the microspheres. Confocal microscopy showed that after 24 h of culture, cells adhered to the surface of the microspheres in the GelMA-Hep group and showed a stretched morphology. The liver of the GelMA-Hep+ADSCs group showed strong fluorescence at 0.5 h, and the fluorescence brightness continued to 48.0 h. The number of ADSCs reaching the liver was more than that of ADSCs group and GelMA+ADSCs group. Conclusions:GelMA-Hep microspheres were successfully prepared, which can improve the efficiency of liver-targeted delivery of ADSCs.

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 evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. Methods A retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. Results In the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, spicule sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), spicule sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians’ prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. Conclusion This AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To analyze the imaging features of testicular conventional ultrasonography（US），shear wave elastography（SWE），and contrast-enhanced ultrasonography（CEUS）in subtypes of spermatogenic dysfunction（focal，arrest，and exhausted），and to evaluate the diagnostic efficacy of testicular multimodal ultrasound.Methods:A prospective study enrolled 310 spermatogenic dysfunction patients（focal type group： n=77，arrest type group： n=20，exhaustive type group： n=213）and 30 healthy volunteers（control group）at Shanghai General Hospital between October 2023 and December 2024. All patients underwent preoperative testicular US，SWE，and CEUS examinations，followed by microdissection testicular sperm extraction. Ultrasound parameters were compared among groups，and receiver operating characteristic（ROC）curves were plotted to assess the diagnostic efficacy of multimodal ultrasound for different subtypes. Results:①Testicular volume：The arrest group exhibited significantly greater volume compared to the focal group and the exhausted group（all P<0.05）. No statistically significant difference was observed between the arrest group and the normal group（ P>0.05）. ②Mean Young's modulus（Emean）：The arrest group exhibited significantly lower Emean compared with both the focal and exhausted groups（all P<0.05），but showed no significant difference versus the normal group（ P>0.05）. ③CEUS quantitative parameters：The arrival time（AT）and time to peak（TTP）in the exhausted group were higher than those in the normal group，while the peak intensity（PI）and area under the time-intensity curve（AUC）were lower（all P<0.05）. No significant differences were found in AT，TTP，or AUC among the three spermatogenic dysfunction subgroups（all P>0.05）. ④CEUS perfusion patterns：The focal group predominantly exhibited a mottled pattern（70.13%，54/77）. Both the arrest group（90.00%，18/20）and the normal group（93.33%，28/30）predominantly exhibited an rich pattern. The exhausted group predominantly exhibited a sparse pattern（56.34%，120/213）. ⑤ROC curve analysis showed that for differentiating the arrest group from the focal type group and the exhaustive type group，the combination of Emean，testicular volume，and CEUS perfusion pattern yielded an AUC of 0.931，with a sensitivity of 95.00% and specificity of 85.86%. For differentiating the focal type group from the exhaustive type group，the combination of testicular echogenicity，CEUS perfusion pattern，and Emean yielded an AUC of 0.833，with a sensitivity of 81.82% and specificity of 75.12%. Conclusions:Multimodal ultrasonography can comprehensively characterize the imaging features of different subtypes of spermatogenic dysfunction，holding promise as a noninvasive predictive tool.