TCam-2 seminoma cell line exhibits characteristic foetal germ cell responses to TGF-beta ligands and retinoic acid.

Germ cell testicular cancer is understood to arise during embryogenesis, based on the persistence of embryonic germ cell markers in carcinoma in situ and seminoma. In this study, we examine the potential of the seminoma-derived TCam-2 cell line to be used as representative in functional analyses of seminoma. We demonstrate expression of several early germ cell markers, including BLIMP1, OCT3/4, AP2γ, NANOG and KIT. Many TGF-beta superfamily receptors and downstream transcription factors are also present in these cells including the normally foetal ACTRIIA receptor, indicating potential responsiveness to TGF-beta superfamily ligands. Treatment with BMP4 or RA induces a significant increase in ACTRIA, ACTRIIA and ACTRIIB transcripts, whereas activin A decreases ACTRIB. BMP4 and RA each support TCam-2 survival and/or proliferation. In addition, despite increased KIT mRNA levels induced by BMP4, RA and activin A, activin A does not improve survival or proliferation. The capacity for BMP4 and retinoic acid to enhance foetal germ cell survival and proliferation/self-renewal has been demonstrated in mice, but not previously tested in humans. This study is the first to demonstrate a functional response in seminoma cells, using a well-characterized cell line, consistent with their foetal germ cell-like identity.

Deep Learning-Based Femoral Cartilage Automatic Segmentation in Ultrasound Imaging for Guidance in Robotic Knee Arthroscopy.

Knee arthroscopy is a minimally invasive surgery used in the treatment of intra-articular knee pathology which may cause unintended damage to femoral cartilage. An ultrasound (US)-guided autonomous robotic platform for knee arthroscopy can be envisioned to minimise these risks and possibly to improve surgical outcomes. The first necessary tool for reliable guidance during robotic surgeries was an automatic segmentation algorithm to outline the regions at risk. In this work, we studied the feasibility of using a state-of-the-art deep neural network (UNet) to automatically segment femoral cartilage imaged with dynamic volumetric US (at the refresh rate of 1 Hz), under simulated surgical conditions. Six volunteers were scanned which resulted in the extraction of 18278 2-D US images from 35 dynamic 3-D US scans, and these were manually labelled. The UNet was evaluated using a five-fold cross-validation with an average of 15531 training and 3124 testing labelled images per fold. An intra-observer study was performed to assess intra-observer variability due to inherent US physical properties. To account for this variability, a novel metric concept named Dice coefficient with boundary uncertainty (DSCUB) was proposed and used to test the algorithm. The algorithm performed comparably to an experienced orthopaedic surgeon, with DSCUB of 0.87. The proposed UNet has the potential to localise femoral cartilage in robotic knee arthroscopy with clinical accuracy.