An in vivo study of the biodistribution of gold nanoparticles after intervaginal space injection in the tarsal tunnel

The biodistribution of gold nanoparticles （AuNPs） is closely related to toxicological effects and is of great concern because of their potential application in diverse biomedical areas. However, with the discovery of novel anatomic and histological structures for fluid transport, the underlying mechanisms involved in the in vivo transport and biodistribution of AuNPs require further in-depth investigations. In the current study, we investigated the biodistribution of 10-nm AuNPs in rats after intervaginal space injection （ISI） in the tarsal tunnel, where a focal point of tendons, vessels, and nerve fibers may optimally connect to other remote connective tissues. The intravenous injection （IVI） of AuNPs served as a control. The blood and organs were collected at 5, 15, and 30 min and at 1, 4, 12, and 24 h after injection for quantitative analysis of Au distribution with inductively coupled plasma mass spectrometry （ICP-MS）. IVI and ISI yielded significantly different results： The AuNP content in the blood after ISI was much lower than that after IVI; was similar in the lungs, heart, and intestines; and was higher in the skin and muscle. These findings were supported by the ratios of AuNP content and relative organ AuNP distribution proportions. Our results demonstrated a fast, direct, and the circulation-independent AuNP-organ transport pathway, which may improve our understanding of physiological and pathological biodistribution processes in biological systems. Furthermore, these results provide novel insights into the in vivo transport and biodistribution of AuNPs, which may lead to novel and efficient therapeutic and administration strategies.

The application of artificial intelligence to chest medical image analysis

The aim of this article is to review recent progress in the application of artificial intelligence to chest medical image analysis.The lungs,bone,and mediastinum were included in terms of anatomy,while X-ray and computed tomography(CT),with and without contrast enhancement,were considered regarding imaging modalities.Four key components of deep learning were summarized,namely,network architectures,learning strategies,optimiza-tion methods,and vision tasks.Disease-specific applications were discussed in detail with respect to the dimen-sion of the data input,network architecture,and modality:lung cancer,pneumonia,tuberculosis,pulmonary embolism,chronic obstructive pulmonary disease,and interstitial lung disease for lung;traumatic fractures,os-teoporosis,osteoporotic fractures,and bone metastases for bone;and coronary artery calcification and aortic dissection for vascular diseases.Finally,five promising research directions and possible solutions were presented for future work.