Effective drug dosage design for treatment of infectious diseases using dynamic inversion

A generic nonlinear mathematical model describing the human immunological dynamics is used to design an effective automatic drug administration scheme. Even though the model describes the effects of various drugs on the dynamic system, this work is confined to the drugs that kill the invading pathogen and heal the affected organ. From a system theoretic point of view, the drug inputs can be interpreted as control inputs, which can be designed based on control theoretic concepts. The controller is designed based on the principle of dynamic inversion and is found to be effective in curing the �nominal model patient� by killing the invading microbes and healing the damaged organ. A major advantage of this technique is that it leads to a closed-form state feedback form of control. It is also proved from a rigorous mathematical analysis that the internal dynamics of the system remains stable when the proposed controller is applied. A robustness study is also carried out for testing the effectiveness of the drug administration scheme for parameter uncertainties. It is observed from simulation studies that the technique has adequate robustness for many �realistic model patients� having off-nominal parameter values as well.

Herbal medicines used in the treatment of diabetes mellitus in Arunachal Himalaya, northeast, India

Ethnopharmacological relevance: Medicinal plants have played an important role in treating and preventing a variety of diseases throughout the world. Khampti tribal people living in the far-flung Lohit district of the Eastern Arunachal Himalaya, India still depend on medicinal plants and most of them have a general knowledge of medicinal plants which are used for treating a variety of ailments. This survey was undertaken in Lohit district in order to inventory the medicinal plants used in folk medicine to treat diabetes mellitus. Materials and methods: Field investigations were conducted in seventeen remote villages of Lohit district starting from April 2002 to May 2004 through interviews among 251 key informants who were selected randomly during our household survey. To elucidate community domains and determine differences in indigenous traditional knowledge of medicinal plants with anti-diabetic efficacy, we repeated our field survey starting from April 2008 to May 2010 with one hundred traditional healers locally called as ``Chau ya'' in Khampti of Lohit district. ``Chau ya'' traditional healers who know and use medicinal plants for treating diabetes mellitus were interviewed using a semi-structured questionnaire. Results: This study reports an ethnobotanical survey of medicinal plants in Lohit district of Arunachal Pradesh reputed for the treatment of diabetes mellitus. Forty-six plant species were identified in the study area to treat diabetes mellitus by the Khamptis ``Chau ya'' traditional healers. Comparative published literature survey analysis of this study with other ethnobotanical surveys of plants used traditionally in treating diabetes mellitus suggests that eleven plant species make claims of new reports on antidiabetic efficacy. These plant species are Begonia roxburghii, Calamus tenuis, Callicarpa arborea, Cuscuta reflexa, Dillenia indica, Diplazium esculentum, Lectuca gracilis, Millingtonia hortensis, Oxalis griffithii, Saccharum spontaneum, and Solanum viarum. Some of the plants reported in this study have an antidiabetic effect on rodent models but none have sufficient clinical evidence of effectiveness. Conclusions: The wide variety of medicinal plants that are used to treat diabetes mellitus in this area supports the importance of plants in the primary healthcare system of the rural people of Lohit district of Arunachal Pradesh. The finding of new plant uses in the current study reveals the importance of the documentation of such ethnobotanical knowledge. (C) 2012 Elsevier Ireland Ltd. All rights reserved.

Biodiesel Exhaust Treatment with Dielectric Barrier Discharges Coupled with Industrial Waste Byproducts

Biodiesel run engines are gaining popularity since the last few years as a viable alternative to conventional petro-diesel based engines. In biodiesel exhaust the content of volatile organic compounds, oil mist, and mass of particulate matter is considerably lower. However, the concentration of oxides of nitrogen (NOx) is relatively higher. In this paper the biodiesel exhaust from a stationary engine is treated under controlled laboratory conditions for removal of NOx using dielectric barrier discharge plasma in cascade with adsorbents prepared from abundantly available industrial waste byproducts like red mud and copper slag. Results were compared with gamma-alumina, a commercial adsorbent. Two different dielectric barrier discharge (DBD) reactors were tested for their effectiveness under Repetitive pulses /AC energization. NOx removal as high as 80% was achieved with pulse energized reactors when cascaded with red mud as adsorbent.

Biodiesel Exhaust Treatment with HFAC Plasma supported by Red Mud: Study on DeNOx and power consumption

The concentration of Nitrogen Oxides (NOx) in engines which use biodiesel as fuel is higher compared to conventional diesel engine exhaust. In this paper, an attempt has been made to treat this exhaust using a combination of High frequency AC (HFAC) plasma and an industrial waste, Red Mud which shows proclivity towards Nitrogen dioxide (NO2) adsorption. The high frequency AC source in combination with the proposed compact double dielectric plasma reactors is relatively more efficient in converting Nitric Oxide (NO) to NO2. It has been shown that the plasma treated gas enhances the activity of red mud as an adsorbent/catalyst and about 60-72% NOx removal efficiency was observed at a specific energy of 250 J/L. The advantage in this method is the cost effectiveness and abundant availability of the waste red mud in the industry. Further, power estimation studies were carried out using Manley's equation for the two reactors employed in the experiment and a close agreement between experimental and predicted powers was observed. (C) 2015 The Authors. Published by Elsevier Ltd.