Epidemiological and clinical characteristics of malignant melanoma in Southeast Anatolia in Turkey

Introduction: The present study aimed to establish the epidemiological and clinical characteristics of patients who were histopathologically diagnosed with malignant melanoma (MM). Methods: The present study retrospectively analyzed the data of 78 patients who were histopathologically diagnosed with MM in Dicle University Medical Faculty, Dermatology and Medical Oncology departments between 2005 and 2014. Results: The study included 78 patients in total with 44 (56.4%) male and 34 (43.6%) female. Median age of the patients was 62.50 years (range: 27 - 84 years). Of the patients, 78.2% (n=61) had cutaneous melanoma, 8.9% had solid organ melanoma, and 2.5% had ocular and mucosal melanoma. The most common tumor localization among the patients was the lower extremities with 29.4% (n=23). The most common histopathological type was nodular malignant melanoma with 35.8% (n=28). Based on TNM, Clark and Breslow classifications, 26.9% (n=21) of the patients were stage 4, 26.9% (n=21) were Clark stage 4, and 37.1% (n=29) were Breslow stage 4. Median overall survival in all patients was 14.9 months (95% CI 10.9 - 18.8 months). In the multivariate Cox analysis, only stage statistically significantly affecting survival [odds ratio (OR): 0.54; (95% CI 0.16-1.82, p=0.02)]. Conclusion: Malignant melanoma data are also important for the optimal utilization of effective methods and healthcare resources to prevent the disease. In order to minimize MM mortality and morbidity, not only the society but also physicians from primary and secondary care hospitals should become familiar with melanoma.Pan African Medical Journal 2016; 2

Biosimilar filgrastim vs filgrastim: a multicenter nationwide observational bioequivalence study in patients with chemotherapy-induced neutropenia

Background: We studied the comparative effectiveness of biosimilar filgrastim vs original filgrastim in patients with chemotherapy-induced neutropenia.Patients and methods: This multicenter, observational study was conducted at 14 centers. The study included 337 patients experiencing neutropenia under chemotherapy. Patients were given either filgrastim 30 MIU or 48 MIU (Neupogen (R)) or biosimilar filgrastim 30 MIU (Leucostim (R)). Data regarding age, chemotherapeutic agents used, number of chemotherapy courses, previous diagnosis of neutropenia, neutrophil count of patients after treatment, medications used for the treatment of neutropenia, and duration of neutropenia were collected. Time to absolute neutrophil count (ANC) recovery was the primary efficacy measure.Results: Ambulatory and hospitalized patients comprised 11.3% and 45.1% of the enrolled patients, respectively, and a previous diagnosis of neutropenia was reported in 49.3% of the patients, as well. Neutropenia occurred in 13.7% (n=41), 45.5% (n=136), 27.4% (n=82), 11.4% (n=34), and 2.0% (n=6) of the patients during the first, second, third, fourth, and fifth cycles of chemotherapy, respectively. While the mean neutrophil count was 0.53 +/- 0.48 before treatment, a significant increase to 2.44 +/- 0.66 was observed after treatment (p=0.0001). While 90.3% of patients had a neutrophil count,1.49 before treatment, all patients had a neutrophil count >= 1.50 after treatment. Neutropenia resolved within <= 4 days of filgrastim therapy in 60.1%, 56.7%, and 52.6% of the patients receiving biosimilar filgrastim 30 MIU, original filgrastim 30 MIU, and original filgrastim 48 MIU, respectively. However, there was no significant difference between the three arms (p=0.468). Similarly, time to ANC recovery was comparable between the treatment arms (p=0.332).Conclusion: The results indicate that original filgrastim and biosimilar filgrastim have comparable efficacy in treating neutropenia. Biosimilar filgrastim provides a valuable alternative; however, there is need for further studies comparing the two products in different patient subpopulations

COMPLEMENTARY AND ALTERNATIVE MEDICINE USAGE IN CANCER PATIENTS IN SOUTHEAST OF TURKEY

The aim of this study was to investigate the frequency of complementary and alternative medicine (CAM) methods and clinical characteristics in cancer patients in southeast of Turkey. A total of 324 patients (173 female) were enrolled to this study. Questionnaire was applied to all patients individually for approximately 15 minutes by a doctor. At least one CAM method was used by 62% (n=201) of the patients. 82.5% (n=166) of patients treated with CAM were using at least one herbal species. Likewise, 40.9% (68/166) of these patients were using herbal mixtures and 39.8% (66/166) of them were using single herbal as nettle (Urtica dioica) or its seed, 19.3 % (32/166) of them were using other herbals. CAM methods were preferred more frequently by the patients with metastatic stage (p=0.005), receiving palliative treatment (

Dynamic multi-objective optimisation using deep reinforcement learning::benchmark, algorithm and an application to identify vulnerable zones based on water quality

Dynamic multi-objective optimisation problem (DMOP) has brought a great challenge to the reinforcement learning (RL) research area due to its dynamic nature such as objective functions, constraints and problem parameters that may change over time. This study aims to identify the lacking in the existing benchmarks for multi-objective optimisation for the dynamic environment in the RL settings. Hence, a dynamic multi-objective testbed has been created which is a modified version of the conventional deep-sea treasure (DST) hunt testbed. This modified testbed fulfils the changing aspects of the dynamic environment in terms of the characteristics where the changes occur based on time. To the authors’ knowledge, this is the first dynamic multi-objective testbed for RL research, especially for deep reinforcement learning. In addition to that, a generic algorithm is proposed to solve the multi-objective optimisation problem in a dynamic constrained environment that maintains equilibrium by mapping different objectives simultaneously to provide the most compromised solution that closed to the true Pareto front (PF). As a proof of concept, the developed algorithm has been implemented to build an expert system for a real-world scenario using Markov decision process to identify the vulnerable zones based on water quality resilience in São Paulo, Brazil. The outcome of the implementation reveals that the proposed parity-Q deep Q network (PQDQN) algorithm is an efficient way to optimise the decision in a dynamic environment. Moreover, the result shows PQDQN algorithm performs better compared to the other state-of-the-art solutions both in the simulated and the real-world scenario

Automatically creating spatiotemporal river maps of the world using remotely sensed images

Rivers are essential to the Earth's water cycle and deeply impact many human societies and ecosystems, yet they are currently monitored poorly at the global scale. In-situ gauging stations are distributed sparsely and heterogeneously and do not cover much of the world, whereas remotely sensed images are spatially and temporally dense and available globally. Remotely sensed multispectral images, such as the ones acquired by Landsat missions, are available to enable the analysis and surveying of rivers using suitable algorithms. However, existing algorithms are limited in ways that restrict the coverage of the produced results and that prevent the automated analysis of river networks at large scales over short periods of time. Ideally, river maps should be as \live" as possible, e.g., computed quickly and continuously as new Earth imaging data becomes available. Towards advancing progress on this problem, we describe automated tools for creating large-scale hydrography datasets from remotely sensed data in a short period of time. First, we describe an automated river analysis and mapping engine, RivaMap, that uses hand-crafted features to extract rivers from multispectral remotely sensed images. RivaMap delineates rivers and estimates their widths by using optimized filters that have been shown to be effective for enhancing water features and extracting curvilinear structures. Second, we propose a data-driven, deep learning based approach to surface water and river mapping. We describe a fully convolutional neural network architecture that learns the characteristics of water bodies across the globe and extracts rivers from multispectral imagery using both natural and synthetic data. Unlike existing methods, our tools do not require any human intervention or ancillary data and do not exclude complex river network structures, such as deltaic systems in coastal areas and heavily braided rivers. Therefore, they can be used to monitor water resources over large spatiotemporal extents. As a practical application of our tools, we present a global-scale river centerline and width dataset, that is automatically computed on Landsat imagery. The outcomes of this research, the software and the dataset, are publicly availableElectrical and Computer Engineerin