Fagantempleton7408

Diabetic retinopathy (DR) is characterized by the presence of red lesions (RLs), such as microaneurysms and hemorrhages, and bright lesions, such as exudates (EXs). Early DR diagnosis is paramount to prevent serious sight damage. Computer-assisted diagnostic systems are based on the detection of those lesions through the analysis of fundus images. In this paper, a novel method is proposed for the automatic detection of RLs and EXs. As the main contribution, the fundus image was decomposed into various layers, including the lesion candidates, the reflective features of the retina, and the choroidal vasculature visible in tigroid retinas. We used a proprietary database containing 564 images, randomly divided into a training set and a test set, and the public database DiaretDB1 to verify the robustness of the algorithm. Lesion detection results were computed per pixel and per image. Using the proprietary database, 88.34% per-image accuracy (ACCi), 91.07% per-pixel positive predictive value (PPVp), and 85.25% per-pixel sensitivity (SEp) were reached for the detection of RLs. Using the public database, 90.16% ACCi, 96.26% PPV_p, and 84.79% SEp were obtained. As for the detection of EXs, 95.41% ACCi, 96.01% PPV_p, and 89.42% SE_p were reached with the proprietary database. Using the public database, 91.80% ACCi, 98.59% PPVp, and 91.65% SEp were obtained. The proposed method could be useful to aid in the diagnosis of DR, reducing the workload of specialists and improving the attention to diabetic patients.Cisplatin resistance remains a significant obstacle for improving the clinical outcome of ovarian cancer patients. Recent studies have demonstrated that cisplatin is an important inducer of intracellullar reactive oxygen species (ROS), triggering cancer cell death. Sirtuin 2 (SIRT2), a member of class III NAD+ dependent histone deacetylases (HDACs), has been reported to be involved in regulating cancer hallmarks including drug response. In this study, we aimed to identify the role of SIRT2 in oxidative stress and cisplatin response in cancer. Two ovarian cancer cell lines featuring different sensitivities to cisplatin were used in this study. We found different expression patterns of SIRT2 in cisplatin-sensitive (A2780/S) and cisplatin-resistant (A2780/CP) cancer cells with cisplatin treatment, where SIRT2 expression was augmented only in A2780/S cells. Furthermore, cisplatin-induced ROS generation was responsible for the upregulation of SIRT2 in A2780/S cells, whereas overexpression of SIRT2 significantly enhanced the sensitivity of cisplatin-resistant counterpart cells to cisplatin. Our study proposes that targeting SIRT2 may provide new strategies to potentiate platinum-based chemotherapy in ovarian cancer patients.Introduction of checkpoint inhibitors resulted in durable responses and improvements in overall survival in advanced RCC patients, but the treatment efficacy is widely variable, and a considerable number of patients are resistant to PD-1/PD-L1 inhibition. This variability of clinical response makes necessary the discovery of predictive biomarkers for patient selection. Previous findings showed that the epigenetic modifications, including an extensive microRNA-mediated regulation of tumor suppressor genes, are key features of RCC. Based on this biological background, we hypothesized that a miRNA expression profile directly identified in the peripheral lymphocytes of the patients before and after the nivolumab administration could represent a step toward a real-time monitoring of the dynamic changes during cancer evolution and treatment. Interestingly, we found a specific subset of miRNAs, called "lymphocyte miRNA signature", specifically induced in long-responder patients (CR, PR, or SD to nivolumab >18 months). DL-Buthionine-Sulfoximine Focusing on the clinical translational potential of miRNAs in controlling the expression of immune checkpoints, we identified the association between the plasma levels of soluble PD-1/PD-L1 and expression of some lymphocyte miRNAs. These findings could help the development of novel dynamic predictive biomarkers urgently needed to predict the potential response to immunotherapy and to guide clinical decision-making in RCC patients.Osteoporosis is the second most common disease only secondary to cardiovascular disease, with the risk of fracture increasing with age. Osteoporosis is caused by an imbalance between osteoblastogenesis and osteoclastogenesis processes. Osteoclastogenesis may be enhanced, osteoblastogenesis may be reduced, or both may be evident. Inflammation and high reactive oxygen enhance osteoclastogenesis while reducing osteoblastogenesis by inducing osteoblast apoptosis and suppressing osteoblastic proliferation and differentiation. Catechins, the main polyphenols found in green tea with potent anti-oxidant and anti-inflammatory properties, can counteract the deleterious effects of the imbalance of osteoblastogenesis and osteoclastogenesis caused by osteoporosis. Green tea catechins can attenuate osteoclastogenesis by enhancing apoptosis of osteoclasts, hampering osteoclastogenesis, and prohibiting bone resorption in vitro. Catechin effects can be directly exerted on pre-osteoclasts/osteoclasts or indirectly exerted via the modulation of mesenchymal stem cells (MSCs)/stromal cell regulation of pre-osteoclasts through activation of the nuclear factor kB (RANK)/RANK ligand (RANKL)/osteoprotegerin (OPG) system. Catechins also can enhance osteoblastogenesis by enhancing osteogenic differentiation of MSCs and increasing osteoblastic survival, proliferation, differentiation, and mineralization. The in vitro effects of catechins on osteogenesis have been confirmed in several animal models, as well as in epidemiological observational studies on human subjects. Even though randomized control trials have not shown that catechins provide anti-fracture efficacy, safety data in the trials are promising. A large-scale, placebo-controlled, long-term randomized trial with a tea regimen intervention of optimal duration is required to determine anti-fracture efficacy.This paper proposes a time difference of arrival (TDOA) passive positioning sensor selection method based on tabu search to balance the relationship between the positioning accuracy of the sensor network and system consumption. First, the passive time difference positioning model, taking into account the sensor position errors, is considered. Then, an approximate closed-form constrained total least-squares (CTLS) solution and a covariance matrix of the positioning error are provided. By introducing a Boolean selection vector, the sensor selection problem is transformed into an optimization problem that minimizes the trace of the positioning error covariance matrix. Thereafter, the tabu search method is employed to solve the transformed sensor selection problem. The simulation results show that the performance of the proposed sensor optimization method considerably approximates that of the exhaustive search method. Moreover, it can significantly reduce the running time and improve the timeliness of the algorithm.