We examined the potential of ex vivo gene therapy to enhance bone repair using lentiviral vectors encoding either enhanced green fluorescent protein (EGFP) as a reporter gene or bone morphogenetic protein-2 (BMP-2) downstream of either the cytomegalovirus immediate early (CMV) promoter or the murine leukemia virus long terminal repeat (RhMLV) promoter derived from a murine retrovirus adapted to replicate in a rhesus macaque. In vitro, rat bone marrow stromal cells (BMSCs) transduced with Lenti-CMV-EGFP or Lenti-RhMLV-EGFP demonstrated over 90% transduction efficiency at 1 week and continued to demonstrate stable expression for 8 weeks. ELISA results demonstrated that lentivirus-mediated gene transfer into BMSCs induced stable BMP-2 production in vitro for 8 weeks. Increased EGFP and BMP-2 production was noted with the RhMLV promoter. In addition, we implanted BMSCs transduced with Lenti-RhMLV-BMP-2 into a muscle pouch in the hind limbs of severe combined immune deficient mice. Robust bone formation was noted in animals that received Lenti-RhMLV-BMP-2 cells at 3 weeks. These results demonstrate that lentiviral vectors expressing BMP-2 can induce long-term gene expression in vitro and new bone formation in vivo under the control of the RhMLV promoter. Prolonged gene expression may be advantageous when developing tissue engineering strategies to repair large bone defects.
Ex-vivo regional gene therapy with bone marrow cells (BMCs) overexpressing bone morphogenetic protein-2 (BMP-2) has demonstrated efficacy in healing critical sized bone defects in preclinical studies. The purpose of this preclinical study was to compare the osteoinductive potential of a novel "same day" ex-vivo regional gene therapy versus a traditional two-step approach, which involves culture expansion of the donor cells before implantation. In the "same day" strategy buffy coat cells were harvested from the rat bone marrow, transduced with a lentiviral vector-expressing BMP-2 for 1 hour and implanted into a rat femoral defect in the same sitting. There was no significant difference (P = 0.22) with respect to the radiographic healing rates between the femoral defects treated with the "same day" strategy (13/13; 100%) versus the traditional two-step approach (11/14; 78%). However, the femoral defects treated with the "same day" strategy induced earlier radiographic bone healing (P = 0.004) and higher bone volume (BV) [micro-computed tomography (micro-CT); P < 0.001]. The "same day" strategy represents a significant advance in the field of ex-vivo regional gene therapy because it offers a solution to limitations associated with the culture expansion process required in the traditional ex vivo approach. This strategy should be cost-effective when adapted for human use.
We developed a computer-aided diagnosis (CADx) method for classification between benign nodule, primary lung cancer, and metastatic lung cancer and evaluated the following: (i) the usefulness of the deep convolutional neural network (DCNN) for CADx of the ternary classification, compared with a conventional method (hand-crafted imaging feature plus machine learning), (ii) the effectiveness of transfer learning, and (iii) the effect of image size as the DCNN input. Among 1240 patients of previously-built database, computed tomography images and clinical information of 1236 patients were included. For the conventional method, CADx was performed by using rotation-invariant uniform-pattern local binary pattern on three orthogonal planes with a support vector machine. For the DCNN method, CADx was evaluated using the VGG-16 convolutional neural network with and without transfer learning, and hyperparameter optimization of the DCNN method was performed by random search. The best averaged validation accuracies of CADx were 55.9%, 68.0%, and 62.4% for the conventional method, the DCNN method with transfer learning, and the DCNN method without transfer learning, respectively. For image size of 56, 112, and 224, the best averaged validation accuracy for the DCNN with transfer learning were 60.7%, 64.7%, and 68.0%, respectively. DCNN was better than the conventional method for CADx, and the accuracy of DCNN improved when using transfer learning. Also, we found that larger image sizes as inputs to DCNN improved the accuracy of lung nodule classification.
The time of onset of MPO-ANCA-associated vasculitis and the dose at onset varied. The severity and number of organs involved were not correlated with the MPO-ANCA titer, indicating a need for vigilance even when the MPO-ANCA titer is only weakly positive.
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