Purpose To determine the feasibility of using a deep learning approach to detect cartilage lesions (including cartilage softening, fibrillation, fissuring, focal defects, diffuse thinning due to cartilage degeneration, and acute cartilage injury) within the knee joint on MR images. Materials and Methods A fully automated deep learning-based cartilage lesion detection system was developed by using segmentation and classification convolutional neural networks (CNNs). Fat-suppressed T2-weighted fast spin-echo MRI data sets of the knee of 175 patients with knee pain were retrospectively analyzed by using the deep learning method. The reference standard for training the CNN classification was the interpretation provided by a fellowship-trained musculoskeletal radiologist of the presence or absence of a cartilage lesion within 17 395 small image patches placed on the articular surfaces of the femur and tibia. Receiver operating curve (ROC) analysis and the κ statistic were used to assess diagnostic performance and intraobserver agreement for detecting cartilage lesions for two individual evaluations performed by the cartilage lesion detection system. Results The sensitivity and specificity of the cartilage lesion detection system at the optimal threshold according to the Youden index were 84.1% and 85.2%, respectively, for evaluation 1 and 80.5% and 87.9%, respectively, for evaluation 2. Areas under the ROC curve were 0.917 and 0.914 for evaluations 1 and 2, respectively, indicating high overall diagnostic accuracy for detecting cartilage lesions. There was good intraobserver agreement between the two individual evaluations, with a κ of 0.76. Conclusion This study demonstrated the feasibility of using a fully automated deep learning-based cartilage lesion detection system to evaluate the articular cartilage of the knee joint with high diagnostic performance and good intraobserver agreement for detecting cartilage degeneration and acute cartilage injury. © RSNA, 2018 Online supplemental material is available for this article .

Cysteine protease inhibitors kill malaria parasites and are being pursued for development as antimalarial agents. Because they have multiple targets within bloodstream-stage parasites, workers have assumed that resistance to these inhibitors would not be acquired easily. In the present study, we used in vitro selection to generate a parasite resistant to growth inhibition by leupeptin, a broad-profile cysteine and serine protease inhibitor. Resistance was not associated with upregulation of cysteine protease activity, reduced leupeptin sensitivity of this activity, or expression level changes for putative cysteine or serine proteases in the parasite genome. Instead, it was associated with marked changes in the plasmodial surface anion channel (PSAC), an ion channel on infected erythrocytes that functions in nutrient and bulky organic solute uptake. Osmotic fragility measurements, electrophysiological recordings, and leupeptin uptake studies revealed selective reductions in organic solute permeability via PSAC, altered single-channel gating, and reduced inhibitor affinity. These changes yielded significantly reduced leupeptin uptake and could fully account for the acquired resistance. PSAC represents a novel route for the uptake of bulky hydrophilic compounds acting against intraerythrocytic parasite targets. Drug development based on such compounds should proceed cautiously in light of possible resistance development though the selection of PSAC mutants.

No abstract

Introduction: Robotic surgery, with more precise and defined movements compared to manual human surgery, is the wave of the future. The purpose of this project is to move the focus of the HIFU transducer using a 3-axis robotic work cell in order to treat the whole volume of a tumor in a precise manner. Methods: A 3-axis robotic work cell from Arricks Robotics was chosen based on price ($3300 USD), precision, range of movement, and payload capacity (up to 2Kg). A LabVIEW program serves as the entire surgery interface including movement options, treatment algorithms with up to 25 points within the tumor, and a guidance system via ultrasound imaging and the hyperechoic spot of the HIFU focus. Results: The robotic work cell could achieve 0.5mm per 10mm precision and speeds of 10mm/sec in real-time within a cell of 180mm by 180mm by 50mm with a HIFU transducer mounted. Testing with native MD-2xp software has resulted in distance testing with less than 2% error in all three axes (0.7% in X, 1.5% in Y, 1.3% in Z). Conclusion: The final outcome will provide a robust, computer-assisted, cost-effective way to perform robotic HIFU surgery.