Oxygen is one of the essential requirements for cell survival, retention, and proliferation. The field of regenerative medicine and tissue engineering (TE) has realized considerable achievements for the regeneration of tissues. However, tissue regeneration still lacks the full functionality of solid organ implantations; limited cell survival and retention due to oxidative stress and hypoxia in the deeper parts of tissues remains a perpetual challenge. Especially prior to neovascularization, hypoxia is a major limiting factor, since oxygen delivery becomes crucial for cell survival throughout the tissue-engineered construct. Oxygen diffusion is generally limited in the range 100–200 μm of the thickness of a scaffold, and the cells located beyond this distance face oxygen deprivation, which ultimately leads to hypoxia. Furthermore, before achieving functional anastomosis, implanted tissues will be depleted of oxygen, resulting in hypoxia (<5% dissolved oxygen) followed by anoxic (<0.5% dissolved oxygen) microenvironments. Different types of approaches have been adopted to establish a sustained oxygen supply both in vitro and in vivo. In this review, we have summarized the recent developments in oxygen-generating and/or releasing biomaterials for enhancing cell survival in vitro, as well as for promoting soft and hard tissue repair, including skin, heart, nerve, pancreas, muscle, and bone tissues in vivo. In addition, redox-scavenging biomaterials and oxygenated scaffolds have also been highlighted. The surveyed results have shown significant promise in oxygen-producing biomaterials and oxygen carriers for enhancing cell functionality for regenerative medicine and TE applications. Taken together, this review provides a detailed overview of newer approaches and technologies for oxygen production, as well as their applications for bio-related disciplines.

Objective: To evaluate the performance of contrast-enhanced ultrasound in the diagnosis of small, solid, TR3–5 benign and malignant thyroid nodules (≤1 cm). Methods: From January 2016 to March 2018, 185 thyroid nodules from 154 patients who underwent contrast enhanced ultrasound (CEUS) and fine-needle aspiration or thyroidectomy in Shanghai General Hospital were included. The χ2 test was used to compare the CEUS characteristics of benign and malignant thyroid nodules, and the CEUS features of malignant nodules assigned scores. The total score of the CEUS features and the scores of the above nodules were evaluated according to the latest 2017 version of the Thyroid Imaging Reporting and Data System (TI-RADS). The diagnostic performance of the two were compared based on the receiver operating characteristic curves generated for benign and malignant thyroid nodules. Results: The degree, enhancement patterns, boundary, shape, and homogeneity of enhancement in thyroid small solid nodules were significantly different (p＜0.05). No significant differences were seen between benign and malignant thyroid nodules regarding completeness of enhancement and size of enhanced lesions (p＞0.05). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the TI-RADS classification TR5 in diagnosis of malignant nodules were 90.10%, 55.95%, 74.59%, 72.22%, and 82.46%, respectively (area under the curve [AUC]=0.738; 95% confidence interval[CI], 0.663–0.813). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the total score of CEUS qualitative analysis indicators were 86.13%, 89.29%, 87.57%, 90.63%, and 84.27% respectively (AUC = 0.916; 95% CI, 0.871–0.961). Conclusion: CEUS qualitative analysis is superior to TI-RADS in evaluating the diagnostic performance of small, solid thyroid nodules. Qualitative analysis of CEUS has a significantly higher specificity for diagnosis of malignant thyroid nodules than TI-RADS. Advances in knowledge: The 2017 version of TI-RADS has recently suggested the malignant stratification of thyroid nodules by ultrasound. In this paper we applied this system and CEUS to evaluate 185 nodules and compare the results with pathological findings to access the diagnostic performance.

Objective: This study aimed to compare the diagnostic performance of contrast-enhanced ultrasound (CEUS), MRI, and the combined use of the two modalities for differentiating breast lesions of different sizes. Methods: A total of 406 patients with 406 solid breast masses detected by conventional ultrasound underwent both CEUS and MRI scans. Histological results were used as reference standards. The lesions were categorized into three groups according to size (Group 1, ≤ 20 mm; Group 2, > 20 mm, Group 3: total lesions). Sensitivity, specificity, accuracy, and receiver operating characteristic (ROC) curve analysis were used to assess the diagnostic performance of these imaging methods for breast lesions. Results: There were 194 benign and 212 malignant breast lesions according to the histological diagnosis. Compared with MRI, CEUS demonstrated similar sensitivity in detecting breast cancer (p = 1.0000 for all) in all the three groups. With regard to specificity, accuracy, and the area under the ROC curve (Az) values, MRI showed a better performance than that shown by CEUS (p ＜0.05 for all), and the combination of the two modalities improved the diagnostic performance of CEUS alone significantly (p ＜0.05 for all) in all the three groups. However, the diagnostic specificity and accuracy of the combined method was not superior to that of MRI alone except for Group 2. Conclusion: CEUS demonstrated good sensitivity in detecting breast cancer, and the combined use with MRI can optimize the diagnostic specificity and accuracy in breast cancer prediction. Advances in knowledge: Few studies have compared the diagnostic efficacy of CEUS and MRI, and this study is the first attempt to seek out the diagnostic values for breast lesions of variable sizes (lesions with ≤20 mm and >20 mm).

Objectives: To develop, validate, and evaluate a predictive model for breast cancer diagnosis using conventional ultrasonography (US), shear wave elastography (SWE), and contrast-enhanced US (CEUS).Materials and methods: This retrospective study included 674 patients with 674 breast lesions. The data, a main and an independent datasets, were divided into three cohorts. Cohort 1 (80% of the main dataset; n = 448) was analyzed by logistic regression analysis to identify risk factors and establish the predictive model. The area under the receiver operating characteristic curve (AUC) was analyzed in Cohort 2 (20% of the main dataset; n = 119) to validate and in Cohort 3 (the independent dataset; n = 107) to evaluate the predictive model.Results: Multivariable regression analysis revealed nine independent breast cancer risk factors, including age > 40 years; ill-defined margin, heterogeneity, rich blood flow, and abnormal axillary lymph nodes on US; enhanced area enlargement, contrast agent retention, and irregular shape on CEUS; mean SWE higher than the cutoff value (P < 0.05 for all). The diagnostic performance of the model was good, with AUC values of 0.847, 0.857, and 0.774 for Cohorts 1, 2, and 3, respectively. The model increased the diagnostic specificity (from 31% to 81.3% and 7.3% to 73.1% in cohorts 2 and 3, respectively) without a significant loss in sensitivity (from 100.0% to 90.1% and 100.0% to 81.8% in cohorts 2 and 3, respectively).Conclusion: The multi-parameter US-based model showed good performance in breast cancer diagnosis, improving specificity without a significant loss in sensitivity. Using the model could reduce unnecessary biopsies and guide clinical diagnosis and treatment.