ObjectiveTo review the application of cell derived decellularized extracellular matrix (CDM) in tissue engineering. Methods The literature related to the application of CDM in tissue engineering was extensively reviewed and analyzed. Results CDM is a mixture of cells and their secretory products obtained by culturing cells in vitro for a period of time, and then the mixture is treated by decellularization. Compared with tissue derived decellularized extracellular matrix (TDM), CDM can screen and utilize pathogen-free autologous cells, effectively avoiding the possible shortcomings of TDM, such as immune response and limited sources. In addition, by selecting the cell source, controlling the culture conditions, and selecting the template scaffold, the composition, structure, and mechanical properties of the scaffold can be controlled to obtain the desired scaffold. CDM retains the components and microstructure of extracellular matrix and has excellent biological functions, so it has become the focus of tissue engineering scaffolds. ConclusionCDM is superior in the field of tissue engineering because of its outstanding adjustability, safety, and high bioactivity. With the continuous progress of technology, CDM stents suitable for clinical use are expected to continue to emerge.
ObjectiveTo conduct a meta-analysis comparing the accuracy of artificial intelligence (AI)-assisted diagnostic systems based on 18F-fluorodeoxyglucose PET/CT (18F-FDG PET/CT) and structural MRI (sMRI) in the diagnosis of Alzheimer's disease (AD). MethodsOriginal studies dedicated to the development or validation of AI-assisted diagnostic systems based on 18F-FDG PET/CT or sMRI for AD diagnosis were retrieved from the Web of Science, PubMed, and Embase databases. Studies meeting the inclusion criteria were collected, and the risk of bias and clinical applicability of the included studies were assessed using the PROBAST checklist. The pooled sensitivity, specificity, and area under the summary receiver operating characteristic (SROC) curve (AUC) were calculated using a bivariate random-effects model. ResultsTwenty-six studies met the inclusion criteria, yielding a total of 38 2×2 contingency tables related to diagnostic performance. Specifically, 24 contingency tables were based on 18F-FDG PET/CT to distinguish AD patients from normal cognitive (NC) controls, and 14 contingency tables were based on sMRI for the same purpose. The meta-analysis results showed that for 18F-FDG PET/CT, the AI-assisted diagnostic systems had a pooled sensitivity, specificity, and SROC-AUC of 89% (95%CI 88% to 91%), 93% (95%CI 91% to 94%), and 0.96 (95%CI 0.93 to 0.97), respectively. For sMRI, the AI-assisted diagnostic systems had a pooled sensitivity, specificity, and SROC-AUC of 88% (95%CI 85% to 90%), 90% (95%CI 87% to 92%), and 0.94 (95%CI 0.92 to 0.96), respectively. ConclusionAI-assisted diagnostic systems based on either 18F-FDG PET/CT or sMRI demonstrated similar performance in the diagnosis of AD, with both showing high accuracy.