[Research progress of mesenchymal stem cell-derived exosomes in the treatment of inflammatory bowel disease].

Inflammatory bowel disease (IBD) is a chronic nonspecific inflammatory disease of the intestine, with unknown etiology and the incidence is increasing year by year. Traditional treatment has limited effect. Mesenchymal stem cell-derived exosomes (MSC-Exos) are a group of nano-sized extracellular vesicles. Their function is equivalent to that of mesenchymal stem cells (MSCs), with no tumorigenicity and high safety. They represent a novel cell-free therapy. It has been shown that MSC-Exos can improve IBD by effects including anti-inflammation, antioxidant stress, repairing intestinal mucosal barrier and immune regulation. However, their clinical application still faces some problems, such as the lack of standardized production technology, lack of specific IBD diagnostic molecules and anti-intestinal fibrosis.

Ultrasound-based radiomics for the evaluation of fetal rat lung maturity: A noninvasive assessment method (Ultrasound-based radiomics in fetal rat lung).

OBJECTIVE: To establish a classification model for the evaluation of rat fetal lung maturity (FLM) using radiomics technology. METHOD: A total of 430 high-throughput features were extracted per fetal lung image from 134 fetal lung ultrasound images (four-cardiac-chamber views) of 67 Sprague-Dawley (SD) fetal rats with a gestational age of 16-21 days. The detection of fetal lung tissues included histopathological staining and the expression of surface proteins SP-A, SP-B, and SP-C. A machine learning classification model was established using a support vector machine based on histopathological results to analyze the relationship between fetal lung texture characteristics and FLM. RESULTS: The rat fetal lungs were divided into two groups: terminal sac period (SD1) and canalicular period (SD2). The mRNA transcription and protein expression level of SP-C protein were significantly higher in the SD1 group than in the SD2 group (p < 0.05). The diagnostic performance of the rat FLM classification model was measured as follows: area under the receiver operating characteristic curve (AUC), 0.93 (training set) and 0.89 (validation set); sensitivity, 89.26% (training set) and 87.10% (validation set); specificity, 85.87% (training set) and 79.17% (validation set); and accuracy, 87.79% (training set) and 83.64% (validation set). CONCLUSION: Ultrasound-based radiomics technology can be used to evaluate the FLM of rats, which lays a foundation for further research on this technology in human fetal lungs.

Ultrasound-based radiomics technology in fetal lung texture analysis prediction of neonatal respiratory morbidity.

To develop a novel method for predicting neonatal respiratory morbidity (NRM) by ultrasound-based radiomics technology. In this retrospective study, 430 high-throughput features per fetal-lung image were extracted from 295 fetal lung ultrasound images (four-chamber view) in 295 single pregnancies. Images had been obtained between 28+3 and 37+6 weeks of gestation within 72 h before delivery. A machine-learning model built by RUSBoost (Random under-sampling with AdaBoost) architecture was created using 20 radiomics features extracted from the images and 2 clinical features (gestational age and pregnancy complications) to predict the possibility of NRM. Of the 295 standard fetal lung ultrasound images included, 210 in the training set and 85 in the testing set. The overall performance of the neonatal respiratory morbidity prediction model achieved AUC of 0.88 (95% CI 0.83-0.92) in the training set and 0.83 (95% CI 0.79-0.97) in the testing set, sensitivity of 84.31% (95% CI 79.06-89.44%) in the training set and 77.78% (95% CI 68.30-87.43%) in the testing set, specificity of 81.13% (95% CI 78.16-84.07%) in the training set and 82.09% (95% CI 77.65-86.62%) in the testing set, and accuracy of 81.90% (95% CI 79.34-84.41%) in the training set and 81.18% (95% CI 77.33-85.12%) in the testing set. Ultrasound-based radiomics technology can be used to predict NRM. The results of this study may provide a novel method for non-invasive approaches for the prenatal prediction of NRM.

Prenatal prediction of neonatal respiratory morbidity: a radiomics method based on imbalanced few-shot fetal lung ultrasound images.

BACKGROUND: To develop a non-invasive method for the prenatal prediction of neonatal respiratory morbidity (NRM) by a novel radiomics method based on imbalanced few-shot fetal lung ultrasound images. METHODS: A total of 210 fetal lung ultrasound images were enrolled in this study, including 159 normal newborns and 51 NRM newborns. Fetal lungs were delineated as the region of interest (ROI), where radiomics features were designed and extracted. Integrating radiomics features selected and two clinical features, including gestational age and gestational diabetes mellitus, the prediction model was developed and evaluated. The modelling methods used were data augmentation, cost-sensitive learning, and ensemble learning. Furthermore, two methods, which embed data balancing into ensemble learning, were employed to address the problems of imbalance and few-shot simultaneously. RESULTS: Our model achieved sensitivity values of 0.82, specificity values of 0.84, balanced accuracy values of 0.83 and area under the curve values of 0.87 in the test set. The radiomics features extracted from the ROIs at different locations within the lung region achieved similar classification performance outcomes. CONCLUSION: The feature set we designed can efficiently and robustly describe fetal lungs for NRM prediction. RUSBoost shows excellent performance compared to state-of-the-art classifiers on the imbalanced few-shot dataset. The diagnostic efficacy of the model we developed is similar to that of several previous reports of amniocentesis and can serve as a non-invasive, precise evaluation tool for NRM prediction.

Combination of ultrasound elastography with TI-RADS in the diagnosis of small thyroid nodules (≤10 mm): A new method to increase the diagnostic performance.

OBJECTIVE: To evaluate the diagnostic performance of a new method of combined ultrasound elastography (UE) and thyroid imaging-reporting and data system (TI-RADS) in the differential diagnosis of small thyroid nodules. METHODS: Ultrasonography(US), TI-RADS, Elasticity Imaging (EI), Virtual Touch Tissue Imaging (VTI) and Virtual Touch Tissue Quantification (VTQ) features of 142 thyroid nodules (maximum diameter ≤10 mm according to conventional ultrasound measurement) confirmed by surgery or FNA were retrospectively analyzed. Different elastographic methods in small benign and malignant thyroid nodules were compared. The diagnostic efficiency of three adjustment methods of conventional ultrasound TI-RADS classification and ultrasound elastography (VTI and/or VTQ) were compared. RESULTS: The pathological examination showed that 70 thyroid nodules were benign and 72 were malignant. The differential ability of UE to diagnose the small benign and malignant thyroid nodules alone is not better than that of conventional ultrasound TI-RADS classification. The sensitivity of conventional ultrasound TI-RADS classification were higher than that of VTI and equal to VTQ(91.67% vs83.33%、91.67%), while the specificity of VTI was much higher than that of TI-RADS and VTQ(91.43% vs 75.71%、60.00%). The diagnostic sensitivity and specificity of TI-RADS classification plus UE (VTI + VTQ) were 94.44% and 87.14% respectively. TI-RADS classification combined with UE (VTI + VTQ) was the highest diagnostic efficiency. CONCLUSION: Conventional ultrasound TI-RADS classification is the basis for the diagnosis of small thyroid nodules. The combination of TI-RADS with VTI and VTQ can significantly improve the differential diagnosis of small benign and malignant thyroid nodules. It may provide a new and reliable method for the clinical diagnosis of small thyroid nodules.

Application of contrast-enhanced ultrasound in the diagnosis of small breast lesions.

BACKGROUND: Breast cancer is the most common cancer in women worldwide. The purpose of the study was to observe the features of contrast-enhanced ultrasound (CEUS) and the combination with Breast Imaging-Reporting and Data System (BI-RADS) of conventional ultrasound for assessing small breast lesions. OBJECTIVES: The study was to explore the small breast lesions' features of contrast-enhanced ultrasound (CEUS) and the combination with Breast Imaging-Reporting and Data System (BI-RADS) of conventional ultrasound, in order to improve the diagnostic accuracy of early breast cancer. METHODS: 105 lesions were subject to conventional US (ultrasound) and CEUS before operations or biopsies. Among 105 breast lesions, six patient diagnoses were established by thick core-needle biopsy, while the rest were all confirmed by surgery and pathology. RESULTS: Significant differences were found between benign and malignant lesions in qualitative and quantitative indexes (peak) of CEUS (P < 0.05). The qualitative features of malignant small breast lesions were as follows: (1) enhanced intensity within the lesion was not uniform (61/61,100%); (2) the speed of wash-in was earlier than the surrounding tissue (58/61, 95.1%); (3) lesion interior and the surrounding tissues had contrast vessel performance (61/61,100%). Peak of malignant lesions (35.77±11.45) was higher than that of benign lesions (31.96±10.76) (P < 0.05). The diagnostic performance of BI-RADS-US plus qualitative indexes (method one) in terms of area under receiver operating characteristic curve (AUROC) were the highest (i.e., AUROC = 0.817), in comparison with other combined diagnostic methods. The associated sensitivity, specificity and accuracy were 78.69%, 84.09% and 80.95%, respectively. With method one, however, was similar with US-BI-RADS in specificity, 11 malignant breast lesions were regarded as a higher classification of BI-RADS and classified into malignant group, which were identified as benign on US-BI-RADS originally. CONCLUSIONS: CEUS was useful to differentiate benign from malignant small breast lesions, and the combination of CEUS and BI-RADS-US can improve the early diagnosis of breast cancers.