Biomedical subjects
Publications and source records attributed to A Sadana.
The acquired immunodeficiency syndrome is associated with the development of opportunistic infections. We report the cases of two HIV antibody positive male partners who developed pneumonia responding to antituberculous therapy.
A patient is described with both Reiter's syndrome and cutaneous vasculitis characterised by livedo reticularis. The development of the Reiter's syndrome immediately preceded the onset of vasculitis. It is suggested that Reiter's syndrome is a previously unrecognised potential cause of vasculitis in which infection may initiate the vasculitic reaction.
Enzyme deactivation kinetics is often first-order. Different examples of first-order deactivation kinetics exhibited by different enzymes under a wide variety of conditions are presented. Examples of both soluble and immobilized enzymes are presented. The influence of different parameters, chemical modification of specific residues, inhibitors, inactivators, protecting agents, induced conformational changes by external agents, enzyme concentration, and different substrates on the first-order inactivation kinetics of different enzymes is analyzed. The different examples presented from a variety of different areas provides a judicious framework and collection demonstrating the wide applicability of first-order deactivation kinetics. Examples of reversible first-order deactivation kinetics and deactivation-disguise kinetics are also presented. Different mechanisms are also presented to model complex enzyme deactivations. The non-series type mechanisms are emphasized and these involve the substrate and chemical modifiers. Substrate-dependent deactivation rate expressions that are of "separable" and "non-separable" type are presented. Rate expressions involving time-dependent rate constants along with their corresponding mechanisms are presented. Examples of enzymes that exhibit a deactivation-free grace period are also given. An interesting case of enzyme inactivation is the loss of activity in the presence of an auto-decaying reagent. The method is presented by which the intrinsic inactivation rate constants may be obtained. Examples of pH-dependent enzyme inactivation are presented that may be modelled by a five-step (or a simplified two-step) mechanism, and also by a single-step mechanism involving residual activity for the final state. Appropriate examples of enzyme inactivation are presented in each case to highlight the different mechanisms involved.
A series deactivation model is utilized to theoretically examine the influence of different modifying agents on enzyme deactivation kinetics. A form of the Hill-type equation is used to describe the effect of the modifying agents on the model parameters. Modification-induced inactivation equations are presented for the acetylation and succinylation of E. Coli asparaginase, for the site-specific reagent and substrate modification of flavocytochrome b(2) from Baker's yeast, and for the guanidinium chloride inactivation of cathepsin D. The analysis of more data for these and other enzymes would help further substantiate the technique presented and enhance the applicability of the model.
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Enzymes are placed in different categories depending on the effect of chemical modification on their inactivation kinetics and residual activity. This is done using a series-type mechanism involving degraded but stable enzyme states. The major distinction in the three basic categories is the effect of modification on residual activity. Each category is further sub-divided depending on the effect of modification on the values of the deactivation rate constants. The classification provides for a framework for comparison of a wide variety of enzyme deactivation data. Structure-function relations are provided wherever possible.
A series-type enzyme deactivation/activation model involving active enzyme states is utilized to theoretically quantify the influence of regional brain development and drugs on enzyme activity levels in rats. Continuous hexachlorobenzene administration with, or without, phenobarbitone pretreatment has different effects on the deactivation/activation kinetics of porphyrinogen carboxylase, delta-aminolaevulinate synthase and delta-aminolaevulinate dehydratase. The deactivation/activation kinetics exhibited by pyruvate dehydrogenase, citrate synthase, and D-3-hydroxybutyrate dehydrogenase during the development of the medulla oblongata, mid-brain, striatum, and hypothalamus sections exhibit similarities as well as discrepancies. These are identified and made more quantitative.
A series-type enzyme deactivation model is utilized to examine and to quantify the influence of aging on the deactivation kinetics of various enzymes. The effect of in vivo aging as well as the influence of "dwell time" on the deactivation kinetics of enzymes from organisms and cells of different age is examined. The influence of chemical modifiers on the deactivation kinetics of enzymes from cells of different age is elucidated. Attempts have been made to suggest and provide reasonable explanations for the structure-function relations for aging-influenced enzymes.
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The diffusion-limited binding kinetics of antigen (analyte), in solution with antibody (receptor) immobilized on a biosensor surface, is analyzed within a fractal framework. Most of the data presented is adequately described by a single-fractal analysis. This was indicated by the regression analysis provided by Sigmaplot. A single example of a dual-fractal analysis is also presented. It is of interest to note that the binding-rate coefficient (k) and the fractal dimension (Df) both exhibit changes in the same and in the reverse direction for the antigen-antibody systems analyzed. Binding-rate coefficient expressions, as a function of the Df developed for the antigen-antibody binding systems, indicate the high sensitivity of the k on the Df when both a single- and a dual-fractal analysis are used. For example, for a single-fractal analysis, and for the binding of antibody Mab 0.5 beta in solution to gp120 peptide immobilized on a BIAcore biosensor, the order of dependence on the Df was 4.0926. For a dual-fractal analysis, and for the binding of 25-100 ng/mL TRITC-LPS (lipopolysaccharide) in solution with polymyxin B immobilized on a fiberoptic biosensor, the order of dependence of the binding-rate coefficients, k1 and k2, on the fractal dimensions, Df1 and Df2, were 7.6335 and -11.55, respectively. The fractional order of dependence of the k(s) on the Df(s) further reinforces the fractal nature of the system. The k(s) expressions developed as a function of the Df(s) are of particular value, since they provide a means to better control biosensor performance, by linking it to the heterogeneity on the surface, and further emphasize, in a quantitative sense, the importance of the nature of the surface in biosensor performance.
This work presents a theoretical analysis of the influence of multivalency of antigen on external mass transfer-limited binding kinetics to divalent antibody for biosensor applications to polycyclic-aromatic systems. Both cases are considered wherein the antigen is in solution and the antibody is either covalently or noncovalently attached to a cylindrical fiber-optic biosensor, and the antibody is in solution and the antigen is attached to the surface. Both single-step and dual-step binding processes are considered. The rate of attachment of antigen to antibody (or vice versa) is linear for the valencies (or reaction orders) analyzed in the time frame (100 min) considered. The rate of attainment of saturation levels of antigen or antibody in solution close to the surface is very rapid (within 20 min). An increase in the valency of the antigen in solution has the effect of decreasing the order of reaction (for valency, v > or = 1). An increase in the number of steps increases the order of reaction, as expected. An increase in the valency of the antigen in solution decreases the saturation level of the antigen close to the surface and the rate of antigen attachment to the antibody on the surface for all Damkohler numbers. A decrease in the diffusional limitations decreases the effect of valency (or reaction order) on saturation levels of Cs/C0. Nondimensional plots presented in the analysis help extend the analysis to different antigen-antibody systems. An increase in the valency of the antibody in solution has the effect of increasing the order of reaction (for v > 2). The effects in this case are reverse to those described earlier. For valency greater than 2, the reaction order is dependent on the antigen valency, whether it is in solution or immobilized on the surface. The general analysis presented here should be applicable to most surface reactions that involve ligand-receptor binding wherein multiple-binding sites are involved on either the receptor or the ligand.
The diffusion-limited binding kinetics of antigen (or antibody) in solution to antibody (or antigen) immobilized on a biosensor surface is analyzed within a fractal framework. The fit obtained by a dual-fractal analysis is compared with that obtained from a single-fractal analysis. In some cases, the dual-fractal analysis provides an improved fit when compared with a single-fractal analysis. This was indicated by the regression analysis provided by Sigmaplot (San Rafael, CA). These examples are presented. It is of interest to note that the state of disorder (or the fractal dimension) and the binding rate coefficient both increase (or decrease, a single example is presented for this case) as the reaction progresses on the biosensor surface. For example, for the binding of monoclonal antibody MAb 49 in solution to surface-immobilized antigen, a 90.4% increase in the fractal dimension (Df1 to Df2) from 1.327 to 2.527 leads to an increase in the binding rate coefficient (k1 to k2) by a factor of 9.4 from 11.74 to 110.3. The different examples analyzed and presented together provide a means by which the antigen-antibody reactions may be better controlled by noting the magnitude of the changes in the fractal dimension and in the binding rate coefficient as the reaction progresses on the biosensor surface.
Kinetic rate expressions are developed for the binding kinetics of antigen by immobilized antibody or of antibody by immobilized antigen. The influence of lateral interactions leads to variable rate coefficients for adsorption, and these provide a more realistic picture of the events occurring on the surface. Emphasis is placed on the similarities and dissimilarities present in the kinetic rate expressions. A general trend in the rate expressions is observed on going from dual-step binding to dual-step binding with lateral interactions. This provides physical insights into the reactions occurring at the surface and helps in their control and stability at the surface.
The diffusion-limited binding kinetics of antigen (or antibody) in solution to antibody (or antigen) immobilized on a biosensor surface is analyzed within a fractal framework. The estimated fractal dimension increases linearly from 2.28 to 2.75 for the antirabbit IgG (antibody) (Nygren, H.; Stenberg, M. J. Colloid Interface Sci. 1985, 107, 560-566) as the R1S1 antibody concentration in solution increases 28-fold from 6 to 170 micrograms/mL. Similarly, for the binding of ferritin (antigen) in solution by a "sandwich" immunoassay to antiferritin (antibody) immobilized on a surface (Bluestein, B. I.; Craig, M.; Slovacek, R., Stundter, L.; Urciuoli, C.; Walizak, I.; Luderer, A. In Biosensors with Fiberoptics; Wise, D., Wingard, L. B., Jr., Eds.; The Humana Press, Inc.: Clifton, NJ, 1991; pp 181-223), the estimated fractal dimension decreases from 1.33 to 1.02 as the initial concentration of ferritin increases from 20 to 1000 micrograms/mL. The analysis attempts to provide novel physical insights into the reactions occurring on the surface and the structure or nature of the surface. Diffusional limitations inherent in such systems need to be uncoupled from reaction kinetics and complexities, such as heterogeneity, that exist on the surface.
The diffusion-limited binding kinetics of antigen (or antibody or substrate) in solution to antibody (or antigen or enzyme) immobilized on a biosensor surface is analyzed within a fractal framework. The change in the fractal dimension, Df, is in the same direction as that in the forward binding rate coefficient, k1. An increase or decrease in the fractal dimension, Df leads to an increase or decrease in the forward binding rate coefficient, k1, respectively, irrespective of whether the antigen or the antibody is immobilized on the biosensor surface. For example, an increase in the concentration of IgG (MRC OX-19) (antigen) in solution from 1 to 10 micrograms/mL bound to polyclonal sheep anti-mouse IgG (antibody) immobilized on immunomagnetic beads leads to decreases in Df from 1.93 to 0.517 and in k1 from 2.72 to 0.064, respectively. The different examples analyzed and presented together indicate one means by which the forward binding rate coefficient, k1, may be controlled, that is, by changing the fractal dimension or the disorder on the surface. The versatility of the analysis is indicated by applying it to different examples for both types of systems, wherein either the antigen or antibody is immobilized on the surface. The analysis should assist in improving the stability, sensitivity, and response time of biosensors.
The diffusion-limited binding kinetics of antigen (or antibody) in solution to antibody (or antigen) immobilized on a biosensor and other surfaces is analyzed within a fractal framework. Often, the binding kinetics may be described by a single-fractal analysis. In some cases, the binding curve exhibits complexities. Then, for these cases, the dual-fractal analysis provides an improved fit when compared with a single-fractal analysis. This indicates a change in the reaction mechanism on the surface. It is of interest to note that the state of disorder (or the fractal dimension) and the binding rate coefficient both increase as the reaction progresses on the biosensor surface. For example, for the binding of 10 nM insulin growth factor-1 in solution to insulin growth factor binding protein-1 immobilized on a biosensor surface, a 64% increase in the fractal dimension from 1.73 (Df1) to 2.85 (Df2) leads to an increase in the binding rate coefficient by a factor of 31.8 from 3.92 (k1) to 125 (k2). Furthermore, as the IGF-1 concentration in solution increases from 10 to 80 nM in solution, k2 and Df2 exhibit a linear increase. k1 and Df1 exhibit a linear increase with the reciprocal of the IGF-1 concentration in solution. The different examples analyzed and presented together provide a means by which the antigen-antibody reactions may be better controlled by noting the magnitude of the changes in the fractal dimension and in the binding rate coefficient as the reaction progresses on the biosensor surface. Also, the magnitude of the changes in the binding rate coefficients (k1 and k2) and in the fractal dimensions (Df1 and Df2) as different parameters are changed for the different biosensor applications are of particular value, since they provide us with a measure or extent of changes in the binding rate coefficient on changing different experimental parameter values. It is of interest to note the effect of different parameters on the extent or heterogeneity that exists on the surface and how this influences the binding rate coefficients. This may be one method to help manipulate or control the binding rate coefficients on the reaction surface.
The diffusion-limited hybridization kinetics of analyte in solution to a receptor immobilized on a biosensor or immunosensor surface is analyzed within a fractal framework. The data may be analyzed by a single- or a dual-fractal analysis. This was indicated by the regression analysis provided by Sigmaplot. It is of interest to note that the binding rate coefficient and the fractal dimension both exhibit changes in the same direction for both the single-fractal and the dual-fractal analysis examples presented. For example, for a single-fractal analysis and for the hybridization of 10 nM 16CFl (oligonucleotide) to 16B immobilized via sulfosuccinimidyl-6-(biotinamido)hexanoate and streptavidin using chemical and thermal regeneration (Abel, A. P.; Weller, M. G.; Duveneck, G. L.; Ehrat, M. Widmer, H. M. Anal. Chem. 1996, 68, 2905-2912), an increase in the fractal dimension, Df from 1.211 (chemical regeneration) to 1.394 (thermal regeneration), leads to an increase in the binding rate coefficient, k, from 86.53 (chemical regeneration) to 100.0 (thermal regeneration). An increase in the degree of heterogeneity on the biosensor surface leads to an increase in the binding rate coefficient. When a dual-fractal analysis was utilized, an increase in the fractal dimension value from Df1 to Df2 leads to an increase in the binding rate coefficient value from k1 to k2. The fractional order of dependence of the binding rate coefficient, k1, on (a) the analyte (rRNA) concentration in solution and (b) on the fractal dimension, Df1, for the hybridization kinetics to detect Listeria species (Fliss, R.; St-Laurent, M.; Emond, E.; Simard, R. E.; Lemieux, R.; Ettriki, A.; Pandian, S. Appl. Microbiol. Biotechnol. 1995, 43, 717-724.) further reinforces the fractal nature of the system. The binding rate coefficient(s) expressions developed as a function of the analyte concentration in solution and the fractal dimension are of particular value since they provide a means to better control of biosensor or immunosensor performance.
Protein inactivations at liquid-liquid, gas-liquid, and liquid-solid interfaces are presented. Wherever possible the mechanisms of protein inactivation, the extent of inactivation, and means by which this inactivation may be minimized are presented. Emphasis is placed on the 'quality' or the heterogeneity of the protein absorbed at the different types of interfaces. The analysis of the adsorption of proteins at different types of interfaces presented together provides novel physical insights into protein interactions at interfaces. The influence of protein adsorption at interfaces on bioseparations is analyzed by discussing examples on two-phase separations, fermentation systems, membrane separation systems, and chromatographic separations. Valuable knowledge gained during protein adsorption for biomedical applications may be applied with caution to bioseparation systems wherever appropriate. Future theoretical and experimental analysis on protein adsorption in bioseparation systems should pay more attention to the 'quality' of the protein adsorbed at the interface.