Therapeutic response of benzodiazepine, orphenadrine citrate and occlusal splint association in TMD pain

Loss of function, muscle inflammation, and pain are some of the signs and symptoms of temporomandibular dysfunction (TMD). Pharmacological strategies to minimize the clinical manifestation of these disorders often focus on blocking or inhibiting the pain-causing symptom. Resources such as muscle-relaxants, anxiety-relief drugs, and splint therapy are often used to reduce muscular hyperactivity related to TMD muscle pain. This study compares the effect of a randomly ordered association of occlusal splint therapy (S), nonsteroid anti-inflammatory with a muscle-relaxant drug (orphenadrine citrate) (O), and an anxiety-relief drug (benzodiazepine) (B), to ease painful TMD muscle symptoms. Clinical and anamnestic analyses were recorded in accordance with the Helkimo TMD index and applied before and after treatments. Twenty-one group two Helkimo TMD adult female patients were treated, all of whom were subjected to the three random therapeutic associations proposed: SBO, BOS, and OSB. The same operator applied the three specific associations over a period of 21 days in the proposed sequence, seven days for each therapy. The results show that all the groups presented the best results in terms of relief from pain after the therapeutic association (28.5% showed a decrease and 47.6% showed an absence of symptoms). No significant difference was observed among association therapeutic protocols. Copyright © 2003 by CHROMA, Inc.

Farmacología en la tercera edad: Medicamentos de uso continuo y peligros de la interacción medicamentosa

Introduction: It was observed a considerable growth of elderly people. They are who use more medicines. The physiological changes associated with the age advancing can make pharmacokinetic and pharmacodynamic alterations. The cognitive decline, physical limitations and associate chronic pathology affect the medications appropriately use ability. Aims: Based in a literature review, appoint the main pharmacological groups prescribed to the elderly and the drug-drug interaction risks. Conclusion: The most of elderly use continually at least 3 medicines, the most prescribed are to cardiovascular and psychic diseases treatment.

Lyprinol : is it a useful anti-inflammatory agent?

The New Zealand green lipped mussel preparation Lyprinol is available without a prescription from a supermarket, pharmacy or Web. The Food and Drug Administration have recently warned Lyprinol USA about their extravagant anti-inflammatory claims for Lyprinol appearing on the web. These claims are put to thorough review. Lyprinol does have anti-inflammatory mechanisms, and has anti-inflammatory effects in some animal models of inflammation. Lyprinol may have benefits in dogs with arthritis. There are design problems with the clinical trials of Lyprinol in humans as an anti-inflammatory agent in osteoarthritis and rheumatoid arthritis, making it difficult to give a definite answer to how effective Lyprinol is in these conditions, but any benefit is small. Lyprinol also has a small benefit in atopic allergy. As anti-inflammatory agents, there is little to choose between Lyprinol and fish oil. No adverse effects have been reported with Lyprinol. Thus, although it is difficult to conclude whether Lyprinol does much good, it can be concluded that Lyprinol probably does no major harm.

Load–frequency control : a GA-based multi-agent reinforcement learning

The load–frequency control (LFC) problem has been one of the major subjects in a power system. In practice, LFC systems use proportional–integral (PI) controllers. However since these controllers are designed using a linear model, the non-linearities of the system are not accounted for and they are incapable of gaining good dynamical performance for a wide range of operating conditions in a multi-area power system. A strategy for solving this problem because of the distributed nature of a multi-area power system is presented by using a multi-agent reinforcement learning (MARL) approach. It consists of two agents in each power area; the estimator agent provides the area control error (ACE) signal based on the frequency bias estimation and the controller agent uses reinforcement learning to control the power system in which genetic algorithm optimisation is used to tune its parameters. This method does not depend on any knowledge of the system and it admits considerable flexibility in defining the control objective. Also, by finding the ACE signal based on the frequency bias estimation the LFC performance is improved and by using the MARL parallel, computation is realised, leading to a high degree of scalability. Here, to illustrate the accuracy of the proposed approach, a three-area power system example is given with two scenarios.