ABSTRACT
Osteochondral tissue engineering aims to regenerate functional tissue-mimicking physiological properties of injured cartilage and its subchondral bone. Given the distinct structural and biochemical difference between bone and cartilage, bilayered scaffolds, and bioreactors are commonly employed. We present an osteochondral culture system which cocultured ATDC5 and MC3T3-E1 cells on an additive manufactured bilayered scaffold in a dual-chamber perfusion bioreactor. Also, finite element models (FEM) based on the microcomputed tomography image of the manufactured scaffold as well as on the computer-aided design (CAD) were constructed; the microenvironment inside the two FEM was studied and compared. In vitro results showed that the coculture system supported osteochondral tissue growth in terms of cell viability, proliferation, distribution, and attachment. In silico results showed that the CAD and the actual manufactured scaffold had significant differences in the flow velocity, differentiation media mixing in the bioreactor and fluid-induced shear stress experienced by the cells. This system was shown to have the desired microenvironment for osteochondral tissue engineering and it can potentially be used as an inexpensive tool for testing newly developed pharmaceutical products for osteochondral defects.
Subject(s)
Bone and Bones , Cartilage , Cell Culture Techniques , Cellular Microenvironment , Computer Simulation , Tissue Scaffolds/chemistry , X-Ray Microtomography , Animals , Bone and Bones/chemistry , Bone and Bones/diagnostic imaging , Bone and Bones/metabolism , Cartilage/cytology , Cartilage/diagnostic imaging , Cartilage/metabolism , Cell Differentiation , Cell Line , Cell Proliferation , Coculture Techniques , MiceABSTRACT
PURPOSE: To demonstrate possible superiority in the performance of a radiologist who is tasked with detecting actionable nodules and aided by the bone suppression and soft-tissue visualization algorithm of a new software program that produces a modified image by suppressing the ribs and clavicles, filtering noise, and equalizing the contrast in the area of the lungs. MATERIALS AND METHODS: The study and use of anonymized and deidentified data received approval from the MedStar-Georgetown University Oncology Institutional Review Board. Informed consent was obtained from 15 study radiologists. The study radiologists participated as observers in a reader study of 368 patients in an approximately 2:1 cancer-free-to-cancer ratio. The localized receiver operating characteristic (LROC) method was used for analyses. Images were rerandomized for each radiologist. Each patient image was sequentially read, first with the standard radiograph and then with the software-aided image. Normal studies were confirmed with computed tomography (CT), follow-up, and/or panel consensus. RESULTS: Each reader and the combined scores of the 15 readers showed improvement. The area under the combined LROC curve increased significantly from 0.460 unaided to 0.558 aided by visualization software (P = .0001). When measured according to the reader's indication that a case should be sent or not sent for CT or biopsy, sensitivity for cancer detection increased from 49.5% unaided to 66.3% aided by software (P < .0001); specificity decreased from 96.1% to 91.8% (P = .004). Seventy-four percent of the aided detections occurred in cancers with 70% or greater overlap of the bone and the nodule. CONCLUSION: The radiologists using visualization software significantly increased their detection of lung cancers and benign nodules.
