Ultrasound in Medicine and Biology最新文献

{"title":"Research Progress of Low-Intensity Pulsed Ultrasound in Parkinson's Disease.","authors":"Wangning Peng, Yang Yang, Juehan Wang, Daming Wang","doi":"10.1016/j.ultrasmedbio.2026.09.028","DOIUrl":"https://doi.org/10.1016/j.ultrasmedbio.2026.09.028","url":null,"abstract":"<p><p>Parkinson's disease (PD) is a progressive neurodegenerative disorder where current pharmacological and surgical interventions, including Deep Brain Stimulation, often fail to reverse neuronal degeneration or halt disease progression. Low-intensity pulsed ultrasound (LIPUS) has emerged as a novel, non-invasive neuromodulation technology with significant therapeutic potential. This review synthesizes current research on the key acoustic parameters, biological mechanisms, and clinical applications of LIPUS in PD. Unlike High-Intensity Focused Ultrasound (HIFU), which relies on thermal ablation to destroy target nuclei (e.g., Vim, STN), LIPUS utilizes low-energy mechanical waves (typically <3 W/cm²) to exert non-thermal regulatory effects without damaging tissue. Mechanisms of action include the modulation of mechanosensitive ion channels (e.g., Piezo1), enhancement of neuroplasticity via brain-derived neurotrophic factor (BDNF), promotion of autophagy, and regulation of microglial phenotypes to reduce neuroinflammation. We further compare LIPUS with HIFU, highlighting the superiority of LIPUS regarding safety, reversibility, and the absence of requirement for MRI-based thermal monitoring. While preliminary clinical studies using wearable LIPUS devices indicate improvements in motor and cognitive functions, a unified consensus on optimal stimulation parameters and targeting strategies is lacking. Future research should focus on large-scale, standardized clinical trials to establish precise protocols for LIPUS as a disease-modifying therapy for PD.</p>","PeriodicalId":49399,"journal":{"name":"Ultrasound in Medicine and Biology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149240177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasound Localization Microscopy Combined With Regional Division for Differentiating Benign and Metastatic Lymph Nodes.","authors":"Qing Xie, Ruixue Cui, Zhendan Zhao, Ming Li, Yuehua Gao, Huawei Zhang","doi":"10.1016/j.ultrasmedbio.2026.09.019","DOIUrl":"https://doi.org/10.1016/j.ultrasmedbio.2026.09.019","url":null,"abstract":"<p><strong>Objective: </strong>Ultrasound Localization Microscopy (ULM) enables high-resolution visualization of lymph node microvasculature. However, most previous studies rely on whole-node analysis, which may overlook spatial heterogeneity and be affected by confounding factors. This prospective single-center study aimed to investigate the diagnostic value of ULM combined with regional analysis for differentiating benign and metastatic lymph nodes.</p><p><strong>Methods: </strong>A total of 104 lymph nodes from patients with sonographically suspicious lymph nodes were included. All lymph nodes underwent ultrasound-guided core needle biopsy, which served as the reference standard for classification (benign or metastatic). Each lymph node was divided into central, peripheral, and global regions, and ULM quantitative parameters were extracted. Regional ratio features were calculated. Feature selection was performed using univariate analysis, LASSO regression, and backward stepwise logistic regression. Model performance was evaluated using 10-fold stratified cross-validation, 2000 bootstrap resamples, decision curve analysis (DCA), and clinical impact curve (CIC).</p><p><strong>Results: </strong>Three diagnostic models were constructed: a conventional ultrasound (US) model, a ULM model, and a combined US-ULM model. The combined model demonstrated the best discriminative performance with an AUC of 0.866 (95% CI: 0.7875-0.9320), followed by the ULM model (AUC = 0.833, 95% CI: 0.7464-0.9108) and the US model (AUC = 0.730, 95% CI: 0.6219-0.8285). In multivariable analysis, SD.Density_C_P (OR =0.0578, p = 0.012), FD_C_P (OR = 0.8847 per 0.01 increase, p = 0.003), and peripheral vascular pattern (OR = 10.9501, p < 0.001) were identified as significant predictors of lymph node metastasis. At optimal cut-off values, the combined model achieved a sensitivity of 0.733 and specificity of 0.915.</p><p><strong>Conclusion: </strong>The proposed region-based ULM model combined with conventional ultrasound provides an improved quantitative approach for differentiating benign and metastatic lymph nodes in patients with sonographically suspicious lymphadenopathy.</p>","PeriodicalId":49399,"journal":{"name":"Ultrasound in Medicine and Biology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pilot study of a low-attenuation anechoic gastrointestinal contrast filling agent for enhancing transabdominal ultrasound examination of the pancreas.","authors":"Di Zhang, Guan-Yu Wang, Chang-Tian Li, Tai-Mei Qu, Xue-Ting Bai, Yi Zhao, Han-Jing Gao, Shi-En Xu, Yan Zhang, Cheng-Gang Zhu, Yue-Xiang Wang, Yuan-Yuan Lu","doi":"10.1016/j.ultrasmedbio.2026.09.013","DOIUrl":"https://doi.org/10.1016/j.ultrasmedbio.2026.09.013","url":null,"abstract":"<p><strong>Background: </strong>Transabdominal ultrasound is constrained by gastric gas and collapsed folds when evaluating the pancreas. This study developed a low-attenuation anechoic gastrointestinal filling contrast agent (LAA-GFCA) composed of food-grade ingredients and performed a safety evaluation.</p><p><strong>Methods: </strong>Acoustic characterization of LAA-GFCA was performed using the water-immersion insertion substitute method, with phantom experiments designed to evaluate imaging performance. Pre-clinical safety studies were conducted in rabbits, followed by a clinical pilot study in healthy volunteers to assess safety and the optimal imaging window.</p><p><strong>Results: </strong>Acoustic characterization of LAA-GFCA showed that its speed of sound (1541.2 m/s at 34.9°C) matched human soft tissue, with an ultra-low-attenuation coefficient slope (0.024 dB/(cm·MHz)) at the test frequency of 3.5 MHz. Phantom experiments demonstrated enhanced visualization of cyst-like and gray-scale lesions versus cereal-based echoic gastrointestinal filling contrast agent. Pre-clinical studies in eight rabbits confirmed no acute toxicity. Clinical pilot trials in 10 healthy volunteers verified safety and identified the optimal imaging window as 6-15 min after oral administration, enabling clear visualization of the entire pancreas.</p><p><strong>Conclusion: </strong>LAA-GFCA demonstrates a favorable safety profile in pre-clinical and pilot clinical studies, with promising potential to enhance pancreatic transabdominal ultrasound. Future larger-scale studies are warranted to confirm its diagnostic utility.</p>","PeriodicalId":49399,"journal":{"name":"Ultrasound in Medicine and Biology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-Intensity Pulsed Ultrasound (LIPUS) Mitigates Exercise-Induced Myocardial Fatigue by Optimizing Glucose Oxidation Via the Aldolase/AMPK/PDH Signaling Axis.","authors":"Xindong Liu, Yuting Liu, Yushuo Gao, Ziyi Yi, Nannan Zhao, Hua Cai, Min Li, Lin Xu","doi":"10.1016/j.ultrasmedbio.2026.09.008","DOIUrl":"https://doi.org/10.1016/j.ultrasmedbio.2026.09.008","url":null,"abstract":"<p><strong>Background: </strong>Myocardial energy deficiency is a key bottleneck limiting exercise endurance. Low-intensity pulsed ultrasound (LIPUS) has emerged as a non-invasive metabolic regulator. This study investigates whether LIPUS can activate glucose-centric pathways to alleviate exercise-induced myocardial fatigue.</p><p><strong>Methods: </strong>Sprague-Dawley rats were subjected to a 4-week exhaustive swimming protocol. LIPUS (1 MHz, 30 mW/cm², 20% duty cycle) was administered precordially for 30 min/day. We assessed exercise tolerance, cardiac injury markers (cTnI, NT-proBNP), mitochondrial ultrastructure, and metabolic flux. The activation of the Aldolase/AMPK/PDH axis was evaluated via Western blot and qPCR.</p><p><strong>Results: </strong>LIPUS treatment significantly prolonged exhaustive swimming time and reduced serum injury markers. Ultrastructural analysis revealed preserved mitochondrial cristae and myofiber alignment in LIPUS-treated hearts. Mechanistically, LIPUS promoted a metabolic switch from anaerobic glycolysis to efficient glucose aerobic oxidation by upregulating Aldolase and p-AMPK while reducing inhibitory PDH phosphorylation. Mitochondrial TCA cycle and respiratory chain activities (Complexes I-IV) were systematically restored.</p><p><strong>Conclusions: </strong>LIPUS enhances myocardial glucose oxidation efficiency and exercise endurance by activating the Aldolase/AMPK/PDH signaling axis. These findings highlight LIPUS as a promising non-pharmacological strategy for exercise-induced cardioprotection.</p>","PeriodicalId":49399,"journal":{"name":"Ultrasound in Medicine and Biology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149208257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to 'Evaluating the Role of Point-of-Care Ultrasound in Central Venous Pressure Monitoring for Critically Ill Patients. A Comprehensive Systematic Review and Meta-analysis' [Ultrasound in Medicine & Biology 52 (2026) 473-484].","authors":"Eman E Shaban, Yavuz Yigit, Benny Ponappan, Ahmed Shaban, Amira Shaban, Mohamed Helmi Ahmed, Yasser Osman Abdelaal, Hany A Zaki","doi":"10.1016/j.ultrasmedbio.2026.09.007","DOIUrl":"https://doi.org/10.1016/j.ultrasmedbio.2026.09.007","url":null,"abstract":"","PeriodicalId":49399,"journal":{"name":"Ultrasound in Medicine and Biology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149208225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Isoflurane-Dependent Heart Rate Modulation by Cardiac Focused Ultrasound.","authors":"Yan Wei, Xiangkun Piao, Zhouyang Xu, Jia-Ji Pan, Bingbing Cheng","doi":"10.1016/j.ultrasmedbio.2026.09.010","DOIUrl":"https://doi.org/10.1016/j.ultrasmedbio.2026.09.010","url":null,"abstract":"<p><strong>Objective: </strong>Focused ultrasound (FUS) is an emerging noninvasive neuromodulation technique, yet its effects vary across studies due to multiple factors, including subject physiology, targeting accuracy, FUS exposure conditions and anesthetic state. This study investigated whether isoflurane-induced anesthetic depth modulates heart rate (HR) responses to cardiac FUS (cFUS) in mice.</p><p><strong>Methods: </strong>The acoustic field of a FUS transducer was characterized via hydrophone measurements and numerical simulations. Transthoracic cFUS was applied to adult C57BL/6 mice under 1% or 2% isoflurane, with continuous electrocardiogram (ECG) recording. In acoustic-parameter experiments, cFUS was delivered at 1.5 MHz with peak negative pressures (PNP) of 1.0-3.0 MPa, pulse repetition frequencies (PRF) of 100-1000 Hz and duty cycles (DC) of 3%-20%.</p><p><strong>Results: </strong>The cFUS transducer produced a well-defined focus, and murine ribs had minimal impact on transthoracic focusing. cFUS decreased HR under 1% isoflurane, whereas identical acoustic parameters increased HR under 2% isoflurane. Varying PRF (100, 500 and 1000 Hz), PNP (3, 2.5 and 1 MPa) and DC (20%, 10% and 3%) altered HR response magnitude but did not change the polarity. HR variability analysis suggested that isoflurane-induced anesthetic depth alterations in autonomic state may account for divergent HR responses.</p><p><strong>Conclusion: </strong>The polarity reversal of cFUS-induced HR responses depended on isoflurane-induced anesthetic depth under the tested conditions, with acoustic parameters mainly affecting response magnitude. These findings highlight the need to control and report anesthetic state in cFUS studies.</p>","PeriodicalId":49399,"journal":{"name":"Ultrasound in Medicine and Biology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149208170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robustness of Nomogram Versus Deep Learning Models for Hepatocarcinoma Grading: Insights From a Bilateral Cross-Center Validation.","authors":"Zhijin Zhao, Weibin Zhang, Yuli Zhu, Qihui Guo, Qi Zhang, Hong Ding","doi":"10.1016/j.ultrasmedbio.2026.09.014","DOIUrl":"https://doi.org/10.1016/j.ultrasmedbio.2026.09.014","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to compare the performance of nomograms and deep learning (DL) models in predicting the pre-operative pathological grade of hepatocellular carcinoma (HCC) using clinical data and ultrasound images, with an emphasis on model robustness and generalizability across institutions.</p><p><strong>Methods: </strong>We retrospectively analyzed 2404 pre-operative ultrasound images from 601 patients with pathologically confirmed HCC across two medical centers. Two distinct modeling strategies were employed: two nomograms integrating ultrasound imaging features (B-mode and contrast-enhanced ultrasound [CEUS]) with clinical variables, and a series of DL models based on B-mode, CEUS at arterial phases, portal phases and delayed phases, and a combined B + CEUS modality. Independent predictors of pathological grade were identified using univariate, Least Absolute Shrinkage and Selection Operator and logistic regression and incorporated into the nomograms. To rigorously assess generalizability, bilateral training and a cross-center validation strategy were implemented: data from Center 1 were split 7:3 for training and internal validation, while all data from Center 2 served as an external test set. This process was reversed (i.e., training on Center 2 and testing on Center 1) to complete the bilateral cross-center validation. Model performance was evaluated using the area under the curve (AUC), calibration curves and decision curve analysis.</p><p><strong>Results: </strong>The nomograms demonstrated strong and consistent performance in predicting HCC pathological grade, with AUCs of 0.771 (internal validation) and 0.762 (external validation) when trained on Center 1 data, and AUCs of 0.814 (internal) and 0.818 (external) when trained on Center 2 data. In contrast, DL models showed high internal performance (AUCs: 0.632-0.809 for Center 1; 0.692-0.767 for Center 2) but experienced significant degradation on external validation (AUCs: 0.599-0.646 and 0.620-0.657, respectively).</p><p><strong>Conclusion: </strong>The nomogram exhibited superior robustness and generalizability compared to DL models in heterogeneous clinical populations. These findings highlight the practical advantage of interpretable statistical models in settings with variable data distributions and reinforce the value of feature-based approaches when data diversity is limited.</p>","PeriodicalId":49399,"journal":{"name":"Ultrasound in Medicine and Biology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149208178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Driven Multimodal Integration of Ultrasound and CT for Identifying Central Lymph Node Metastasis in Papillary Thyroid Carcinoma.","authors":"Shidi Miao, Yuxin Jiang, Wenjuan Huang, Yuyang Jiang, Mingxuan Wang, Mengzhuo Sun, Zengyao Liu, Ruitao Wang","doi":"10.1016/j.ultrasmedbio.2026.09.018","DOIUrl":"https://doi.org/10.1016/j.ultrasmedbio.2026.09.018","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the utility of multimodal imaging biomarkers in papillary thyroid carcinoma (PTC), this study developed a deep learning model using ultrasound (US) and computed tomography (CT) imaging to predict central lymph node metastasis (CLNM) in PTC by integrating radiomic features of perivascular adipose tissue (PVAT) at the cervical vertebra 6 (C6) level.</p><p><strong>Methods: </strong>We retrospectively analyzed 477 PTC patients from two centers. Using ResNet101 with Hierarchical Attention Integration (ResHAI-101), we extracted deep learning features of the tumor area from US images. Separately, we extracted radiomic features of PVAT from CT images. Univariate and multivariate analyses were conducted based on clinical data, followed by feature selection and integration.</p><p><strong>Results: </strong>Multimodal prediction model performance was quantified by the area under the receiver operating characteristic curve (AUC). Predictive performance was validated using an external test cohort (ETC). The Multimodal Prediction Model 3, integrating deep learning features, PVAT radiomic features and clinical features, showed superior diagnostic performance for predicting CLNM compared to the Unimodal Model, achieving AUCs of 0.915 in the internal test cohort and 0.890 in the ETC. Binary logistic regression identified gender, age, tumor size and extrathyroidal extension as independent risk factors for CLNM.</p><p><strong>Conclusion: </strong>This study establishes and validates the significant impact of PVAT on CLNM in PTC, providing a non-invasive tool for pre-operative risk assessment to aid in surgical decision-making.</p>","PeriodicalId":49399,"journal":{"name":"Ultrasound in Medicine and Biology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149185493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Segmentation of Intrinsic Foot Muscles in Ultrasound Using Deep Neural Networks: Development and Validation Study.","authors":"Halime Gulle, Furkan Mumcu, Austin G Goodrich, Lokman Bekit, Pinar Kuyulu Haksal, A Wayne Johnson, Irene Davis, Yasin Yilmaz","doi":"10.1016/j.ultrasmedbio.2026.08.027","DOIUrl":"https://doi.org/10.1016/j.ultrasmedbio.2026.08.027","url":null,"abstract":"<p><strong>Objective: </strong>To develop and validate the first automated deep-learning image-processing framework for pixel-level segmentation of the plantar intrinsic foot muscles (PIMs) in ultrasound (US) images. This approach supports objective and reproducible US-based muscle segmentation from conventional B-mode images.</p><p><strong>Methods: </strong>US images of four PIMs (abductor hallucis, flexor digitorum brevis (FDB), quadratus plantae [QP] and abductor digiti minimi) were collected from 200 healthy participants (n = 1099 images). Manual segmentations were used as ground truth. Three deep neural network architectures (U-Net, Feature Pyramid Network [FPN] and DeepLabV3+) with a Mix Transformer backbone were trained and evaluated. Model performance was assessed using the Intersection over Union (IoU) and Dice Similarity Coefficient (DSC), two complementary pixel-level segmentation metrics. Agreement with manual measurements was evaluated using intraclass correlation coefficients (ICC), mean difference and standard error of measurement.</p><p><strong>Results: </strong>All models demonstrated excellent segmentation performance, with mean IoU values ≥0.90 and mean DSC values ≥0.95 across all muscles. The highest accuracy was observed for the FDB (IoU = 0.95; DSC = 0.97), while QP showed slightly lower performance (IoU = 0.90; DSC = 0.95). FPN and DeepLabV3+ exhibited greater stability (SD: ±0.02-0.03) compared to U-Net (up to ±0.08). Agreement with manual segmentation was high for cross-sectional area (ICC: 0.836-0.960) and moderate-to-high for border length (ICC: 0.705-0.944), with minimal systematic bias. Mean model inference time was under 0.01 s per image, reflecting forward-pass model inference only and excluding image loading, resizing and post-processing.</p><p><strong>Conclusion: </strong>This study presents the first automated segmentation framework for the PIMs in US. Built on established segmentation architectures with a Mix Transformer backbone, it enables accurate and reliable pixel-level segmentation in under 0.01 s per image; its novelty lies in extending automated musculoskeletal US analysis to this previously unaddressed muscle group. By automating tissue boundary detection at the pixel level, this framework advances US imaging beyond conventional manual measurement, enabling objective and scalable musculoskeletal assessment with reduced operator dependence at the segmentation stage. These findings establish the feasibility of accurate and reproducible PIM segmentation in healthy adults and provide the necessary foundation for subsequent validation in clinical populations.</p>","PeriodicalId":49399,"journal":{"name":"Ultrasound in Medicine and Biology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149167960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cepharanthine-Mediated Sonodynamic Action Induces Robust Mitochondrial Damage Through Regulation of Drp-1 Phosphorylation Against Lung Cancer.","authors":"Qiu-Yi Zhang, Liu-Gen Li, Mei-Yao Wang, Ge Cheng, Jun Hu, Fan Leng, Cunqing Kong, Ning Han, Hua-Zhen Xu, Mei-Fang Wang, Tong-Fei Li, Chang Xiong","doi":"10.1016/j.ultrasmedbio.2026.09.016","DOIUrl":"https://doi.org/10.1016/j.ultrasmedbio.2026.09.016","url":null,"abstract":"<p><p>Sonodynamic therapy (SDT), an emerging anti-cancer strategy, utilizes ultrasound (US) to activate sonosensitizers derived from natural products, thereby generating singlet oxygen to destroy tumor cells. Cepharanthine (Cep) is a natural phytomedicine with inherent anti-tumor activity, whose efficacy was significantly enhanced by US discovered in the present work. This study aims to further investigate the specific drug target of Cep relevant to SDT/mitochondria and to evaluate the therapeutic effects of Cep-mediated SDT on lung cancer cells and mice. Our findings revealed that Cep binds to dynamin-related protein 1 (Drp1) and inhibits its phosphorylation through SDT, leading to excessive mitochondrial fission. This disruption resulted in severe mitochondrial oxidative stress and subsequent damage. The critical role of Drp1 in this process was confirmed by rescue experiments, wherein overexpression of Drp1 further amplified Cep SDT-induced mitochondrial damage and mitigated its anti-cancer effects in vitro. Ultimately, this mechanism translated into profound therapeutic outcomes: Cep SDT effectively abolished the proliferation and boosted the apoptosis of lung cancer cells. Furthermore, it exhibited a favorable inhibitory effect on tumor growth in mouse models, underscoring its translational potential. To conclude, Cep binds to Drp1 (functional mediator) within lung cancer cells, upon US application, shifts its phosphorylation balance by decreasing inhibitory and increasing activating modifications. This Drp1 hyperactivation triggers excessive mitochondrial fission, which selectively compromises mitochondrial integrity and results in potent SDT destroying of the cancer cells. Our work provides important insights into phytomedicine-based SDT and represents a pioneering exploratory application.</p>","PeriodicalId":49399,"journal":{"name":"Ultrasound in Medicine and Biology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149170919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}