Biological therapy management from the initial selection of biologics to switching between biologics in severe asthma.

The aim of this review is to elaborate the management of biologic therapy from initial selection to switching biologics in severe asthma. A nonsystematic review was performed for biological therapy management in severe asthma. Depending on clinical characteristics and biomarkers, selecting the preferred biologic based on super-responder criteria from previous studies may result in adequate clinical efficacy in most patients. On the other hand, no matter how carefully the choice is made, in some patients, it may be necessary to discontinue the drug due to suboptimal clinical response or even no response. This may result in the need to switch to a different biological therapy. How long the biological treatment of patients whose asthma is controlled with biologics will be continued and according to which criteria they will be terminated remains unclear. It has been shown that in patients with a long history of good response to biologics, asthma control may be impaired when biologics are discontinued, while it may persist in others. Therefore, discontinuation of biologics may be a viable strategy in a particular patient group. Clinicians should make the best use of all predictive factors to identify patients who will most benefit from each biologic. Patients who do not meet a predefined response criterion after sufficient time for response evaluation and who are eligible for one or more alternative biological agents should be offered the opportunity to switch to another biologic. There is no consensus on when the biologics used in severe asthma that produce favorable results should be discontinued. In our opinion, treatment should continue for at least five years, as premature termination may potentially deteriorate asthma control.

Neural network modelling of an electrochemical process.

Artificial Neural Network (ANN) methodology has gained popularity in chemistry in recent years as a result of its ability to solve problems for various purposes. In particular, ANN shows a very high performance in the modelling of the experimental measurements. To this aim, the trained ANN is used to predict unknown values of measurement system. For a given trial set of parameters, the experimental response may be predicted by the model. In recent decades, several investigations were based on the electrooxidation of D-glucose owing to its many applications such as detection systems (glucose sensors), fuel cells and synthesis of economically interesting products. In the present work based on the electrochemical process modelling, the estimation of peak current densities of the D-glucose electrooxidation on palladium electrode in alkaline medium was investigated as a function of potential sweep rate and D-glucose concentration by using a three layer feed-forward ANN with error propagation learning algorithm.