22 resultados para Treatment guidelines


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The change in energy during hydrogen abstraction by ketones is estimated for different electronic states as a function of the intermolecular orbital overlap employing perturbation theory. The results suggest that ketones preferentially undergo the in-plane reaction and abstract a hydrogen atom in their triplet nπ* state. For ketones where the triplet ππ* state lies below the triplet nπ* state, hydrogen abstraction can take place in the ππ* state owing to the crossing of the zero order reaction surfaces of the nπ* and ππ* states.

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In this study we investigated the possibility of treating Heymann's Nephritis (HN) by destroying antibody producing cells by targetting a toxin, gelonin - conjugated to gp330, the renal brush border antigen. HN was induced in rats by immunizing them with purified gp330. The gelonin-gp330 conjugate was administered 12 days after the antigenic challenge. Serum was screened for circulating antibodies. Proteinurea was estimated. The gp330-gelonin conjugate-treated animals had a circulating antibody titre in the serum much lower than that of diseased (untreated) animals. Proteinurea seen in diseased animals was not observed in treated animals. This work suggests the possibility of using a toxin-antigen conjugate for immunomodulating antibody mediated autoimmune renal disease.

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An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

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Combining the advanced techniques of optimal dynamic inversion and model-following neuro-adaptive control design, an innovative technique is presented to design an automatic drug administration strategy for effective treatment of chronic myelogenous leukemia (CML). A recently developed nonlinear mathematical model for cell dynamics is used to design the controller (medication dosage). First, a nominal controller is designed based on the principle of optimal dynamic inversion. This controller can treat the nominal model patients (patients who can be described by the mathematical model used here with the nominal parameter values) effectively. However, since the system parameters for a realistic model patient can be different from that of the nominal model patients, simulation studies for such patients indicate that the nominal controller is either inefficient or, worse, ineffective; i.e. the trajectory of the number of cancer cells either shows non-satisfactory transient behavior or it grows in an unstable manner. Hence, to make the drug dosage history more realistic and patient-specific, a model-following neuro-adaptive controller is augmented to the nominal controller. In this adaptive approach, a neural network trained online facilitates a new adaptive controller. The training process of the neural network is based on Lyapunov stability theory, which guarantees both stability of the cancer cell dynamics as well as boundedness of the network weights. From simulation studies, this adaptive control design approach is found to be very effective to treat the CML disease for realistic patients. Sufficient generality is retained in the mathematical developments so that the technique can be applied to other similar nonlinear control design problems as well.

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STOAT has been extensively used for the dynamic simulation of an activated sludge based wastewater treatment plant in the Titagarh Sewage Treatment Plant, near Kolkata, India. Some alternative schemes were suggested. Different schemes were compared for the removal of Total Suspended Solids (TSS), b-COD, ammonia, nitrates etc. A combination of IAWQ#1 module with the Takacs module gave best results for the existing scenarios of the Titagarh Sewage Treatment Plant. The modified Bardenpho process was found most effective for reducing the mean b-COD level to as low as 31.4 mg/l, while the mean TSS level was as high as 100.98 mg/l as compared to the mean levels of TSS (92 62 mg/l) and b-COD (92.0 mg/l) in the existing plant. Scheme 2 gave a better scenario for the mean TSS level bringing it down to a mean value of 0.4 mg/l, but a higher mean value for the b-COD level at 54.89 mg/l. The Scheme Final could reduce the mean TSS level to 2.9 mg/l and the mean b-COD level to as low as 38.8 mg/l. The Final Scheme looks to be a technically viable scheme with respect to the overall effluent quality for the plant. (C) 2009 Elsevier B.V. All rights reserved.

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A creep resistant Mg alloy MRI 230D was subjected to laser surface treatment using Nd:YAG laser equipped with a fiber optics beam delivery system in argon atmosphere. The laser surface treatment produced a fine dendritic microstructure and this treatment was beneficial for the corrosion and wear resistance of the alloy. Long-term linear polarisation resistance and Electrochemical Impedance Spectroscopy measurements confirmed that the polarisation resistance values of laser treated material were twice as high as that for the untreated material. This improved behaviour was due to the finer and more homogenous microstructure of the laser treated surface. The laser treatment also increased surface hardness two times and reduced the wear rate by 25% due to grain refinement and solid solution strengthening.

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Osteoporosis is a disease of low bone mass most often caused by an increase in bone resorption that is not sufficiently compensated for by a corresponding increase in bone formation(1). As gut-derived serotonin (GDS) inhibits bone formation(2), we asked whether hampering its biosynthesis could treat osteoporosis through an anabolic mechanism (that is, by increasing bone formation). We synthesized and used LP533401, a small molecule inhibitor of tryptophan hydroxylase-1 (Tph-1), the initial enzyme in GDS biosynthesis. Oral administration of this small molecule once daily for up to six weeks acts prophylactically or therapeutically, in a dose-dependent manner, to treat osteoporosis in ovariectomized rodents because of an isolated increase in bone formation. These results provide a proof of principle that inhibiting GDS biosynthesis could become a new anabolic treatment for osteoporosis.