An optimal dynamic inversion-based neuro-adaptive approach for treatment of chronic myelogenous leukemia

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.

Quantitation and characterization of glutathionyl haemoglobin as an oxidative stress marker in chronic renal failure by mass spectrometry

Objectives: Glutathionyl haemoglobin (GS-Hb) belonging to the class of glutathionylated proteins has been investigated as a possible marker of oxidative stress in different chronic diseases. The purpose of this study was to examine whether glutathionyl haemoglobin can serve as an oxidative stress marker in non-diabetic chronic renal failure patients on different renal replacement therapies (RRT) through its quantitation, and characterization of the specific binding site of glutathione in haemoglobin molecule by mass spectrometric analysis. Design and methods: The study group consisted of non-diabetic chronic renal failure patients on renal replacement therapy (RRT): hemodialysis (HD), continuous ambulatory peritoneal dialysis (CAPD) and renal allograft transplant (Txp) patients. Haemoglobin samples of these subjects were analyzed by liquid chromatography electrospray ionization mass spectrometry for GS-Hb quantitation. Characterization of GS-Hb was done by tandem mass spectrometry. Levels of erythrocyte glutathione (GSH) and lipid peroxidation (as thiobarbituric acid reacting substances) were measured spectrophotometrically, while glycated baernoglobin (HbA1c) was measured by HPLC. Results: GS-Hb levels were markedly elevated in the dialysis group and marginally in the transplant group as compared to the controls. GS-Hb levels correlated positively with lipid peroxidation and negatively with the erythrocyte glutathione levels in RRT groups indicating enhanced oxidative stress. De novo sequencing of the chymotryptic fragment of GS-Hb established that glutathione is attached to Cys-93 of the beta globin chain. Mass spectrometric quantitation of total glycated haemoglobin showed good agreement with HbA1c estimation by conventional HPLC method. Conclusions: Glutathionyl haemoglobin can serve as a clinical marker of oxidative stress in chronic debilitating therapies like RRT. Mass spectrometry provides a reliable analytical tool for quantitation and residue level characterization of different post-translational modifications of haemoglobin. (c) 2007 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

A neuro-adaptive augmented optimal dynamic inversion approach for effective and efficient treatment of chronic myelogenous leukemia

Combining the advanced techniques of optimal dynamic inversion and model-following neuro-adaptive control design, an efficient technique is presented for effective treatment of chronic myelogenous leukemia (CML). A recently developed nonlinear mathematical model for cell dynamics is used for the control (medication) synthesis. First, taking a set of nominal parameters, a nominal controller is designed based on the principle of optimal dynamic inversion. This controller can treat nominal patients (patients having same nominal parameters as used for the control design) effectively. However, since the parameters of an actual patient can be different from that of the ideal patient, to make the treatment strategy more effective and efficient, a model-following neuro-adaptive controller is augmented to the nominal controller. In this approach, a neural network trained online (based on Lyapunov stability theory) facilitates a new adaptive controller, computed online. From the simulation studies, this adaptive control design approach (treatment strategy) is found to be very effective to treat the CML disease for actual patients. Sufficient generality is retained in the theoretical developments in this paper, so that the techniques presented can be applied to other similar problem as well. Note that the technique presented is computationally non-intensive and all computations can be carried out online.

Successful treatment of biofilm infections using shock waves combined with antibiotic therapy

Many bacteria secrete a highly hydrated framework of extracellular polymer matrix on suitable substrates and embed within the matrix to form a biofilm. Bacterial biofilms are observed on many medical devices, endocarditis, periodontitis and lung infections in cystic fibrosis patients. Bacteria in biofilm are protected from antibiotics and >1,000 times of the minimum inhibitory concentration may be required to treat biofilm infections. Here, we demonstrated that shock waves could be used to remove Salmonella, Pseudomonas and Staphylococcus biofilms in urinary catheters. The studies were extended to a Pseudomonas chronic pneumonia lung infection and Staphylococcus skin suture infection model in mice. The biofilm infections in mice, treated with shock waves became susceptible to antibiotics, unlike untreated biofilms. Mice exposed to shock waves responded to ciprofloxacin treatment, while ciprofloxacin alone was ineffective in treating the infection. These results demonstrate for the first time that, shock waves, combined with antibiotic treatment can be used to treat biofilm infection on medical devices as well as in situ infections.