Synthol systemic complications: Hypercalcemia and pulmonary granulomatosis. A case report

Abstract

Introduction Synthol injection for body contouring has been used by bodybuilders for some time. We report two extremely rare systemic complications; pulmonary granulomatosis and hypercalcemia, in a woman who has received Synthol injections for buttocks augmentation. Presentation of case The case discussed in this report is of a 36-year-old lady who presented for severe hypercalcemia and nephrocalcinosis. Subsequent workup revealed granulomas in the buttocks and in the lungs. Upon questioning, it was discovered that she had received Synthol injection for buttocks augmentation a few months earlier. Labs were consistent with calcitriol mediated hypercalcemia, a phenomenon observed in granulomatous diseases. A diagnosis of foreign body granulomatosis with pulmonary migration and secondary hypercalcemia was made. The patient was started on prednisone and showed an initial positive response. Discussion Reported complications of Synthol include pain, muscle deformity, and ulceration at the injection site. Hypercalcemia secondary to foreign body granulomatosis after Synthol injection has been reported only once previously, and here we report a second case. The hypercalcemia is thought to be calcitriol mediated, where overexpression of CYP27B1 in the macrophages forming the granulomas leads to pathological extrarenal calcitriol production. Pulmonary granulomatosis, theorized to be secondary to hematologic migration of the injected material, has never been reported previously with Synthol use. Conclusion Synthol injection for body contouring may be a cause of extensive local and pulmonary foreign body granulomatosis leading to calcitriol mediated hypercalcemia. History of cosmetic injections should not be disregarded during history taking.

Evaluation of computed tomography images under deep learning in the diagnosis of severe pulmonary infection

“…Deep learning is a deep nonlinear structure that is based on the human neural network mechanism, layered feature extraction, and recognition. Ideally, as long as the amount of data is sufficient and the network is deep enough, an ideal effect can be achieved, and the accuracy rate of human beings can even be exceeded (Diab et al, 2020;Alimoradi et al, 2021). Due to its excellent quality, deep learning is also widely used in medical image processing.…”