RESUMO
In the United Arab Emirates (UAE), lung cancer (LC) was the third leading cause of deaths due to cancer in 2017. Around 80% of the patients in the UAE are diagnosed at a late stage, rendering the treatment less effective in improving survival outcomes. Lack of awareness of disease symptomatology, deficient screening initiatives, misdiagnosis, and delayed referral to the specialist are contributing factors for delayed diagnosis. Effective screening at a primary care setting can be crucial for early diagnosis, referral to specialists, and enhancing patient outcomes. It is important to establish screening and referral guidelines through which each suspected case can be identified and provided timely intervention. Although the international screening and referral pathway framework are comprehensive, several regional barriers need to be addressed before they can be adapted at the national level. A group of LC experts from the UAE deliberated on issues like delayed diagnosis of LC and strategic recommendations for overcoming the challenges. The discussion was based on the review of the published evidence, international and regional guidelines for screening and early diagnosis of LC. Herein, we present a guideline, endorsed by the esteemed panel of experts, for aiding early diagnosis and optimizing the management of LC in the UAE.
RESUMO
Although gastrointestinal stromal tumors (GISTs) are rare, with an incidence of 1/100000 per year, they are the most common sarcomas in the peritoneal cavity. Despite considerable progress in the diagnosis and treatment of GIST, about half of all patients are estimated to experience recurrence. With only two drugs, sunitinib and regorafenib, approved by the Food and Drug Administration, selecting treatment options after imatinib failure and coordinating multidisciplinary care remain challenging. In addition, physicians across the Middle East face some additional and unique challenges such as lack of published local data from clinical trials, national disease registries and regional scientific research, limited access to treatment, lack of standardization of care, and limited access to mutational analysis. Although global guidelines set a framework for the management of GIST, there are no standard local guidelines to guide clinical practice in a resource-limited environment. Therefore, a group of 11 experienced medical oncologists from across the Gulf and Levant region, part of the Rare Tumors Gastrointestinal Group, met over a period of one year to conduct a narrative review of the management of GIST and to describe regional challenges and gaps in patient management as an essential step to proposing local clinical practice recommendations.
RESUMO
<p><b>OBJECTIVE</b>During present investigation the data of a laboratory-scale anoxic sulfide oxidizing (ASO) reactor were used in a neural network system to predict its performance.</p><p><b>METHODS</b>Five uncorrelated components of the influent wastewater were used as the artificial neural network model input to predict the output of the effluent using back-propagation and general regression algorithms. The best prediction performance is achieved when the data are preprocessed using principal components analysis (PCA) before they are fed to a back propagated neural network.</p><p><b>RESULTS</b>Within the range of experimental conditions tested, it was concluded that the ANN model gave predictable results for nitrite removal from wastewater through ASO process. The model did not predict the formation of sulfate to an acceptable manner.</p><p><b>CONCLUSION</b>Apart from experimentation, ANN model can help to simulate the results of such experiments in finding the best optimal choice for ASObased denitrification. Together with wastewater collection and the use of improved treatment systems and new technologies, better control of wastewater treatment plant (WTP) can lead to more effective maneuvers by its operators and, as a consequence, better effluent quality.</p>
