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

Health status and lifestyle factors as predictors of depression in middle-aged and elderly Japanese adults: a seven-year follow-up of the Komo-Ise cohort study

<p>Abstract</p> <p>Background</p> <p>Depression is a common mental disorder. Several studies suggest that lifestyle and health status are associated with depression. However, only a few large-scale longitudinal studies have been conducted on this topic.</p> <p>Methods</p> <p>The subjects were middle-aged and elderly Japanese adults between the ages of 40 and 69 years. A total of 9,650 respondents completed questionnaires for the baseline survey and participated in the second wave of the survey, which was conducted 7 years later. We excluded those who complained of depressive symptoms in the baseline survey and analyzed data for the remaining 9,201 individuals. In the second-wave survey, the DSM-12D was used to determine depression. We examined the risks associated with health status and lifestyle factors in the baseline survey using a logistic regression model.</p> <p>Results</p> <p>An age-adjusted analysis showed an increased risk of depression in those who had poor perceived health and chronic diseases in both sexes. In men, those who were physically inactive also had an increased risk of depression. In women, the analysis also showed an increased risk of depression those with a BMI of 25 or more, in those sleeping 9 hours a day or more and who were current smokers. A multivariate analysis showed that increased risks of depression still existed in men who had chronic diseases and who were physically inactive, and in women who had poor perceived health and who had a BMI of 25 or more.</p> <p>Conclusions</p> <p>These results suggest that lifestyle and health status are risk factors for depression. Having a chronic disease and physical inactivity were distinctive risk factors for depression in men. On the other hand, poor perceived health and a BMI of 25 or more were distinctive risk factors for depression in women. Preventive measures for depression must therefore take gender into account.</p

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