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Objectives: To assess the severity of intrauterine adhesion (IUA) after endometrial injury via ultrasound molecular imaging (USMI) of vascular endothelial growth factor receptor 2 (VEGFR2) expression on the uterine endothelium.
Methods: MB was constructed via "biotin-avidin-biotin" bridging technology. A rat IUA animal model was constructed via chemical injury. Fourteen days after the model was constructed, MB and MB contrast agents were injected into the standard group and the model group of rats, and simultaneous ultrasound molecular imaging was carried out and ultrasound molecular imaging was performed and the changes in ultrasound signals in vivo were normalized to differential targeted enhancement (dTE). In addition, immunohistochemistry was used to detect the expression levels of VEGFR2 and α-SMA in the uterine tissues of the rats in both groups.
Results: Targeted-VEGFR2 microbubble (MB) was successfully constructed, the average particle size of the targeted VEGFR2 microbubble contrast agent was 2309.6 ± 234.3 nm, with the average zeta potential -16.9 ± 0.15 mV and the dispersity coefficient less than 0.4. In addition, its essential performance and biosafety were examined, which revealed that the MB contrast agent had an appropriate size, stable imaging performance, good target adhesion, and good biocompatibility. In vivo, ultrasound imaging results revealed that after the injection of the targeted MB, the contrast ultrasound signal intensity of uterine tissues in the model group (62.5 ± 17.9 dB) was higher than in the normal group (33.5 ± 12.5 dB), and the difference was statistically significant (P < .05). The immunohistochemistry results revealed that the expression of VEGFR2 and α-SMA in the uterine tissue of the model group was greater than that in the standard group (P < .05), further indicating that the signal intensity of the MB contrast agent can reflect the expression level of VEGFR2 molecules.
Conclusions: Ultrasound molecular imaging based on MB can be used to evaluate the expression level of VEGFR2 in uterine vascular endothelial cells. This provides a potential approach for the early diagnosis and treatment monitoring of intrauterine adhesions after endometrial injury.
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http://dx.doi.org/10.1002/jum.16710 | DOI Listing |
Biosens Bioelectron
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College of Chemistry, Jilin Province Research Center for Engineering and Technology of Spectral Analytical Instruments, Jilin University, Qianjin Street 2699, Changchun, 130012, China. Electronic address:
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Department of Computer Science, Università degli Studi di Bari Aldo Moro, Bari, Italy.
Fetal standard plane detection is essential in prenatal care, enabling accurate assessment of fetal development and early identification of potential anomalies. Despite significant advancements in machine learning (ML) in this domain, its integration into clinical workflows remains limited-primarily due to the lack of standardized, end-to-end operational frameworks. To address this gap, we introduce FetalMLOps, the first comprehensive MLOps framework specifically designed for fetal ultrasound imaging.
View Article and Find Full Text PDFFront Mol Biosci
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Department of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Recent advances in artificial intelligence (AI) are reshaping the diagnostic and therapeutic of primary aldosteronism (PA). For screening, machine learning models integrate multidimensional data to improve the efficiency of PA detection, facilitating large-scale population screening. For diagnosis, AI-driven algorithms have further enhanced the specificity of PA identification.
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