Inhibition of Iron Uptake Is Responsible for Differential Sensitivity to V-ATPase Inhibitors in Several Cancer Cell Lines

Many cell lines derived from tumors as well as transformed cell lines are far more sensitive to V-ATPase inhibitors than normal counterparts. The molecular mechanisms underlying these differences in sensitivity are not known. Using global gene expression data, we show that the most sensitive responses to HeLa cells to low doses of V-ATPase inhibitors involve genes responsive to decreasing intracellular iron or decreasing cholesterol and that sensitivity to iron uptake is an important determinant of V-ATPase sensitivity in several cancer cell lines. One of the most sensitive cell lines, melanoma derived SK-Mel-5, over-expresses the iron efflux transporter ferroportin and has decreased expression of proteins involved in iron uptake, suggesting that it actively suppresses cytoplasmic iron. SK-Mel-5 cells have increased production of reactive oxygen species and may be seeking to limit additional production of ROS by iron

4D Ultrasound-based knee joint atlas for robotic knee arthroscopy: A feasibility study

In this work, we proved for the first time the feasibility of using high-refresh-rate 3D ultrasound (US) also known as 4D US imaging to create a volumetric atlas of the knee anterior compartment for an autonomous robotic platform for knee arthroscopy. A dataset of 42 4D US sequences (including 94 US volumes) and 25 MRI volumes was collected from seven volunteers, in several leg positions simulating the surgical scenario of knee arthroscopy. MRI-US volume pairs were manually registered, and the knee structures of interest identified on the US volumes. The resulting atlas comprised the femur, tibia and patella surfaces, patellar tendon, femoral cartilage, the anterior parts of the menisci and the ACL, for knee angles between 0 and 90 degrees flexion. The inter-operator reproducibility of the registrations was calculated as the norm of the difference in the translation and the rotation values selected by two experienced orthopaedic surgeons and resulted to be on average of 4.42 mm ± 1.89 mm SD and 7.77 degrees ± 2.80 degrees SD, respectively. A new metric was introduced to measure the overlap of the US volume located at the position selected from the first and the second experts and the agreement resulted to be on average of 87% ± 3 SD. The US scanning protocol adopted could be considered compatible with the arthroscopy procedure, as proved through six cadaver studies. These preliminary results show that 4D US is an excellent candidate for automatic image-based guidance in knee arthroscopy

Deep learning for US image quality assessment based on femoral cartilage boundaries detection in autonomous knee arthroscopy

Knee arthroscopy is a complex minimally invasive surgery that can cause unintended injuries to femoral cartilage or postoperative complications, or both. Autonomous robotic systems using real-time volumetric ultrasound (US) imaging guidance hold potential for reducing significantly these issues and for improving patient outcomes. To enable the robotic system to navigate autonomously in the knee joint, the imaging system should provide the robot with a real-time comprehensive map of the surgical site. To this end, the first step is automatic image quality assessment, to ensure that the boundaries of the relevant knee structures are defined well enough to be detected, outlined, and then tracked. In this article, a recently developed one-class classifier deep learning algorithm was used to discriminate among the US images acquired in a simulated surgical scenario on which the femoral cartilage either could or could not be outlined. A total of 38 656 2-D US images were extracted from 151 3-D US volumes, collected from six volunteers, and were labeled as “1” or as “0” when an expert was or was not able to outline the cartilage on the image, respectively. The algorithm was evaluated using the expert labels as ground truth with a fivefold cross validation, where each fold was trained and tested on average with 15 640 and 6246 labeled images, respectively. The algorithm reached a mean accuracy of 78.4% ± 5.0, mean specificity of 72.5% ± 9.4, mean sensitivity of 82.8% ± 5.8, and mean area under the curve of 85% ± 4.4. In addition, interobserver and intraobserver tests involving two experts were performed on an image subset of 1536 2-D US images. Percent agreement values of 0.89 and 0.93 were achieved between two experts (i.e., interobserver) and by each expert (i.e., intraobserver), respectively. These results show the feasibility of the first essential step in the development of automatic US image acquisition and interpretation systems for autonomous robotic knee arthroscopy.Maria Antico, Damjan Vukovic, Saskia M. Camps, Fumio Sasazawa, Yu Takeda, Anh T. H. Le ... et al

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

Prevalence of insomnia and its relationship to the health habits or status of women living along a city road part 1. epidemiologie study

The prevalence rate of insomnia among 424 married women and its associated factors were surveyed. Insomnia is defined as having one of the following symptoms one or more times per week: difficulty inducing sleep (Fl), difficulty maintaining sleep (F2), early morning awakening (F3), light sleep (F4), or worry about poor sleep quality (F5). Poor sleep as a whole in the past one month (F6) was also inquired about. Percentages of Fl, F2, F3 and F5 among the subjects in their sixties were 21.3%, 13.3%, 6.7% and 10.7%, respectively, relatively higher than those of subjects in their thirties or forties. There was a significant difference in the percentage of F6 among four age categories (p < 0.05), and the percentage of F6 was highest (23.3%) in those in their thirties. Depressive state correlated with six insomnia items, Fl to F6 (rs.=-0.195, -0.161, -0.117,-0.221, -0.176, 0.284, respectively). Perceived health status correlated with Fl (-0.237), F4 (-0.213), F5 (-0.259), and F6 (0.373). Present medical condition correlated with Fl (-0.195), F3 (-0.146), and F5 (-0.220). The prevalence rates of insomnia for subjects in their thirties, forties, fifties and sixties were 16.7%, 17.7%, 25.7%, and 24.0%, respectively. Increases in the percentages of difficulty in inducting and maintaining sleep, early morning awakening and worry about poor sleep quality in the subjects in their sixties, and sleep dissatisfaction of those in their thirties were recognized