Applicability of the National Comprehensive Cancer Network/Multinational Association of Supportive Care in Cancer Guidelines for Prevention and Management of Chemotherapy-Induced Nausea and Vomiting in Southeast Asia: A Consensus Statement.

A meeting of regional experts was convened in Manila, Philippines, to develop a resource-stratified chemotherapy-induced nausea and vomiting (CINV) management guideline. In patients treated with highly emetogenic chemotherapy in general clinical settings, triple therapy with a serotonin (5-hydroxytryptamine-3 [5-HT3]) antagonist (preferably palonosetron), dexamethasone, and aprepitant is recommended for acute CINV prevention. In resource-restricted settings, triple therapy is still recommended, although a 5-HT3 antagonist other than palonosetron may be used. In both general and resource-restricted settings, dual therapy with dexamethasone (days 2 to 4) and aprepitant (days 2 to 3) is recommended to prevent delayed CINV. In patients treated with moderately emetogenic chemotherapy, dual therapy with a 5-HT3 antagonist, preferably palonosetron, and dexamethasone is recommended for acute CINV prevention in general settings; any 5-HT3 antagonist can be combined with dexamethasone in resource-restricted environments. In general settings, for the prevention of delayed CINV associated with moderately emetogenic chemotherapy, corticosteroid monotherapy on days 2 and 3 is recommended. If aprepitant is used on day 1, it should be continued on days 2 and 3. Prevention of delayed CINV with corticosteroids is preferred in resource-restricted settings. The expert panel also developed CINV management guidelines for anthracycline plus cyclophosphamide combination schedules, multiday cisplatin, and chemotherapy with low or minimal emetogenic potential, and its recommendations are detailed in this review. Overall, these regional guidelines provide definitive guidance for CINV management in general and resource-restricted settings. These consensus recommendations are anticipated to contribute to collaborative efforts to improve CINV management in Southeast Asia

Analysis Of Boiler Tube Leakage Using Artificial Neural Network

Artificial neural network (ANN) models, developed by training the network with data from an existing plant, are very useful especially for large systems such as Thermal Power Plant. The project is focusing on the ANN modeling development and to examine the relative importance of modeling and processing variables in investigating the unit trip due to steam boiler tube leakage. The modeling and results obtained will be used to overcome the effect of the boiler tube leakage which influenced the boiler to shutdown if the tube leakage continuously producing the mixture of steam and water to escape from the risers into the furnace. The Artificial Intelligent-ANN has been chosen as the system to evaluate the behavior of the boiler because it has the ability to forecast the trips. Hence, the objective of this study has been developed to design an ANN to detect and diagnosis the boiler tube leakage and to simulate the ANN using real data obtained from Thermal Power Plant. The feed-forward with back-propagation, (BP) ANN model will be trained with the real data obtained from the plant. Training and validation of ANN models, using real data from an existing plant, are very useful to minimize or avoid the trip occurrence in the plants. The study will focus on investigating the unit trip due to tube leakage of risers in the boiler furnace and developing the ANN model to forecast the trip