Performance of xenogeneic pulmonary visceral pleura as bioprosthetic heart valve cusps in swine.

Objective: Bovine pericardium is common biological material for bioprosthetic heart valve. There remains a significant need, however, to improve bioprosthetic valves for longer-term outcomes. This study aims to evaluate the chronic performance of bovine pulmonary visceral pleura (PVP) as bioprosthetic valve cusps. Methods: The PVP was extracted from the bovine lung and fixed in 0.625% glutaraldehyde overnight at room temperature. The PVP valve cusps for the bioprosthetic valve were tailored using a laser cutter. Three leaflets were sewn onto a nitinol stent. Six PVP bioprosthetic valves were loaded into the test chamber of the heart valve tester to complete 100 million cycles. Six other PVP bioprosthetic valves were transcardially implanted to replace pulmonary artery valve of six pigs. Fluoroscopy and intracardiac echocardiography were used for in vivo assessments. Thrombosis, calcification, inflammation, and fibrosis were evaluated in the terminal study. Histologic analyses were used for evaluations of any degradation or calcification. Results: All PVP bioprosthetic valves completed 100 million cycles without significant damage or tears. In vivo assessments showed bioprosthetic valve cusps open and coaptation at four months post-implant. No calcification and thrombotic deposits, inflammation, and fibrosis were observed in the heart or pulmonary artery. The histologic analyses showed complete and compact elastin and collagen fibers in the PVP valve cusps. Calcification-specific stains showed no calcific deposit in the PVP valve cusps. Conclusions: The accelerated wear test demonstrates suitable mechanical strength of PVP cusps for heart valve. The swine model demonstrates that the PVP valve cusps are promising for valve replacement.

Rapid identification of wood species using XRF and neural network machine learning.

An innovative approach for the rapid identification of wood species is presented. By combining X-ray fluorescence spectrometry with convolutional neural network machine learning, 48 different wood specimens were clearly differentiated and identified with a 99% accuracy. Wood species identification is imperative to assess illegally logged and transported lumber. Alternative options for identification can be time consuming and require some level of sampling. This non-invasive technique offers a viable, cost-effective alternative to rapidly and accurately identify timber in efforts to support environmental protection laws and regulations.