Ultrasound Molecular Imaging for Multiple Biomarkers by Serial Collapse of Targeting Microbubbles with Distinct Acoustic Pressures.
Zhenzhou LiManlin LaiShuai ZhaoYi ZhouJingna LuoYongsheng HaoLiting XieYaru WangFei YanPublished in: Small (Weinheim an der Bergstrasse, Germany) (2022)
Ultrasound molecular imaging (UMI) has shown promise for assessing the expression levels of biomarkers for the early detection of various diseases. However, it remains difficult to simultaneously image multiple biomarkers in a single systemic administration, which is important for the accurate diagnosis of diseases and for understanding the dynamic intermolecular mechanisms that drive their malignant progression. The authors develop an ultrasound molecular imaging method by serial collapse of targeting microbubbles with distinct acoustic pressures for the simultaneous detection of two biomarkers. To test this, α v β 3 -targeting lipid microbubbles (L-MB α ) and VEGFR2-targeting lipid-PLGA microbubbles (LP-MB v ) are fabricated and simultaneously injected into tumor-bearing mice at 7 and 14 days, followed by the low-intensity acoustic collapse of L-MB α and high-intensity acoustic collapse of LP-MB v . The UMI signals of L-MB α and LP-MB v are obtained by subtracting the first post-burst signals from the first pre-burst signals, and subtracting the second post-burst signals from the first post-burst signals, respectively. Interestingly, the signal intensities from UMI agree with the immunohistochemical staining results for α v β 3 and VEGFR2. Importantly, they find a better fit for the invasive behavior of MDA-MB-231 breast tumors by analyzing the ratio of α v β 3 integrin to VEGFR2, but not the single α v β 3 or VEGFR2 levels.
Keyphrases
- high intensity
- magnetic resonance imaging
- high frequency
- cancer therapy
- vascular endothelial growth factor
- poor prognosis
- deep learning
- metabolic syndrome
- ultrasound guided
- computed tomography
- machine learning
- adipose tissue
- cell death
- long non coding rna
- resistance training
- insulin resistance
- body composition
- breast cancer cells
- big data
- contrast enhanced ultrasound
- flow cytometry