Abstract
Calcification and mechanical failure are the major causes of the loss of cardiac bioprostheses. The chemical treatments used to stabilize the tissue employed are considered to play a fundamental role in the development of these two phenomena, although the problem is multifactorial and the underlying causes are yet to be fully identified. Currently, there is an ongoing search for chemical treatments capable of reducing or eliminating the process of calcification while preserving the mechanoelastic characteristics of the tissue. One of the approaches to this effort is the elimination of the phospholipid component from the biological tissue employed in prosthesis construction. There is evidence that this component may be responsible for the precipitation of calcium salts. The present study compares two delipidating chemical treatments involving chloroform/methanol and sodium dodecyl sulfate (SDS) with the use of glutaraldehyde (GA) alone. For this purpose, porcine pericardial tissue was subjected to tensile strength testing employing a hydraulic simulator. A total of 234 samples were studied 90 treated with GA, 72 treated with chloroform/methanol and 72 treated with SDS. The mean breaking strength was significantly higher in the samples treated with GA (between 43.29 and 63.01 MPa) when compared with those of tissue treated with chloroform/methanol (29.92–42.30 MPa) or with SDS (13.49–19.06 MPa). In a second phase of the study, selection criteria based on morphological and mechanical factors were applied to the pericardial membranes employing a system of paired samples. The mathematical analysis of the findings in one fragment will aid in determining the mechanical behavior of its adjacent twin sample. In conclusion, the anticalcification chemical treatments tested in the experimental model conferred a lesser mechanical resistance than that obtained with GA. On the other hand, the utilization of paired samples was found to be useful in the prediction of the mechanical behavior of porcine pericardial tissue. Nevertheless, in order for our method of selection to be considered the most adequate approach, it will be necessary to validate these findings in dynamic studies involving a real, functional model.
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