Desensitization variability

Interleukin-1 (IL-1) produced by monocytes and several other cell types [70, 146] has a wide array of biological properties, including T cell activation and inflammatory interactions with muscle, liver, fibroblasts, brain and bone [70, 146], IL-1, both natural and recombinant, has been shown to release histamine from human basophils and from human adenoidal mast cells [70,146,151] and this release was abolished by an IL-1 antibody. However, the average release produced by 10 units of IL-1 was less than 20% and there was considerable variability between populations of basophils in the extent of histamine release. Moreover, the secretory response elicited was quite slow (within 15 min) compared with that of other peptides [151]. Desensitization of the basophils by anti-IgE serum had no effect on the subsequent IL-1 response, suggesting different mechanisms of action [ 151], as has been the case with other peptides. Interestingly, the portion of the IL-1 molecule that is responsible for its immu-nostimulatory activity appears to be separate from that portion responsible for its proinflammatory effects [152]. However, that portion of the molecule responsible for eliciting basophil and mast-cell histamine release has not as yet been defined. 

Denture stomatitis. Coconut soap, associated with 05% sodium hypochlorite, used by patients for 15 days, significantly reduced clinical signs of denture stomatitis and was effective in controlling denture biofilm . Desensitization effect. Saline extract of the dried pollen, administered subcutaneously to 96 allergic adults at variable doses, produced a clinical improvement and decreased IgE levels ". 

The efficacy of odour recognition is very variable, not only in different species but also in individuals. The condition, (anosmia), where an individual cannot perceive an odour at concentrations that are detected readily by others is fi equent. Desensitization on longtime exposure to odorants and resensitization ate probably similar to the corresponding processes occurring in other sensory systems, although these processes are not as well-defined as in the hormonal and the visual systems. 

FIGURE 5.14 Temporal desensitization of agonist response. (A) Patterns of response for a concentration of agonist producing 80% maximal response. Curve 1 no desensitization. For concentration of agonist [A] — 5x EC50, first-order rate of onset kt = sec-1 mol-1, k2 = 10-3 sec-1. Curve 2 constant desensitization rate — k(, .sl. — 10-3. Curve 3 variable desensitization rate equals where p equals fractional receptor occupancy. (B) Complete dose-response curves... 

The numerical study of the four-variable system (5.9a-d) reveals that it is capable of sustained oscillatory behaviour. These results, developed in further detail in the following section, also indicate that ATP remains practically constant in the course of cAMP oscillations (fig. 5.30, below). Thus, in contrast with the allosteric model considered above, the model based on receptor desensitization can account for the experimental observation (fig. 5.22) on the limited variation of ATP in the course of cAMP oscillations. Once we have established that the model predicts this characteristic feature of the experimental system, we may consider, as a first approximation, that ATP remains constant in time at the value given by eqn (5.11), in view of the relative smallness of the maximum rate of adenylate cyclase compared to the accumulative rate of ATP utilization in other pathways ... 

Fig. 5.30. Sustained oscillations of cAMP in the model based on receptor desensitization. (a) The evolution of intracellular cAMP (/3), ATP (a), the total fraction of receptor in active state (pr), and extracellular cAMP (-y). The latter is represented, on an enlarged scale, in (b) together with the total fraction of receptor in the desensitized state (Sp) and the saturation function (Y) measuring binding of cAMP to the two receptor states. The curves are obtained by numerical integration of the four-variable system (5.9) for the parameter values indicated in table 5.3 most of these values are those determined experimentally and available in the literature (see table 5.2). Similar curves are obtained by integration of the three-variable system (5.12) when ATP is maintained constant at the value a = 3 (Martiel Goldbeter, 1987a).

Fig. 5.38. Adaptation to constant stimuli. At time zero, the system is subjected to an increase in extracellular cAMP, y, whose level rises from zero to 0.1 (curve a), 1 (curve b) and 10 (curve c). For = 10" M, these stimuli correspond to the passage from the cAMP level from zero to 10" M, 10 M, and lO M, respectively. The synthesis of intracellular cAMP resulting from these stimulations is indicated the responses are characterized by the phenomenon of adaptation. In inset, the evolution of fraction Sj indicates that adaptation correlates with receptor modification. The magnitude of receptor desensitization increases with the intensity of stimulation. The curves are obtained by numerical integration of the two-variable system (5.16) for the parameter values given in table 5.3 (Martiel Goldbeter, 1987a).

Fig. 5.45. Sustained oscillations in the five-variable model for cAMP signalling in D. discoideum, incorporating receptor desensitization as well as the two types of G-protein (Halloy, 1995 Halloy Goldbeter, 1995).

Fig. 6.11. Aperiodic oscillations (chaos) in cAMP synthesis predicted by the model based on receptor desensitization. The chaotic behaviour is obtained by numerical integration of the seven-variable system (6.2), for v = 7.545 x 10 s, other parameter values are those of fig. 6.1, divided by 10 for constants expressed in s (Martiel Goldbeter, 1985a).

Fig. 8.1. Response to pulsatile stimulation by extracellular cAMP in the model for cAMP synthesis based on receptor desensitization. Equations (5.16) of the two-variable model are integrated in the case where periodic stimulation by extracellular cAMP (y) takes the form of a square wave. In (a), the stimulus consists in raising y from 0 to 10 for 5 min at 5 min intervals. In (b), the same pulse is applied at 1 min intervals. In each case the variation of intracellular cAMP (j8) is represented, as well as the variation of the total fraction of active receptor (pp). Parameter values are those of fig. 5.38 (Martiel Goldbeter, 1987a).

A most plausible, additional variable is IP3. The fact that IP3 variations do not accompany Ca oscillations in some cells does not mean that IP3 oscillations could not occur in other cells, concomitantly with Ca transients. To allow for IP3 oscillations we have to couple the variation of IP3 with that of Ca. As evidenced in chapters 4 and 6, complex oscillations can originate from the interplay between two (or more) endogenous oscillatory mechanisms. Such an interplay should occur when incorporating into the CICR-based model the activation of IP3 synthesis by cytosolic Ca - which process is at the core of the oscillatory mechanism proposed by Meyer Stryer (1988) - and desensitization of the IP3 receptor (see Meyer Stryer, 1990 Pietri, Hilly Mauger, 1990 Payne Potter, 1991 Hajnoczky Thomas, 1994). The variables in this extended model can be as many as five and are listed in table 9.1, together with the variables of the minimal model considered above. 

Fig. 9.23. Oscillations of cytosolic Ca (in p,M, solid line) following sustained stimulation by an external signal in the extended version of the model based on Ca -induced Ca release (CICR). Also represented are the normalized concentration of IP3 (dashed line) and the fraction of active, nondesensitized IP3 receptors (dotted line), which are both driven here passively by oscillations based on CICR. In the extended model, which contains the five variables listed in table 9.1, IP3-mediated release of Ca" and CICR are supplemented with the following additional regulatory processes activation of IP3 synthesis by Ca, and desensitization of the IP3 receptor induced by IP3 (as in the present simulations) or cytosolic Ca (Dupont et al, 1991).

The most stringent need for wavenumber axis calibration is in determinations based on band position. For this reason, qualitative analyses are likely to be affected by drifts or inaccuracy in the wavenumber axis [14]. Likewise, quantitative determinations based on band position, such as strain in diamond films [6], will be affected similarly. Other quantitative analyses may also be affected by band-position error. It is common to use the raw spectral intensities (intensity at every wavenumber) in a multivariate analysis. Although this approach can be very powerful, any unexpected shift in wavenumber calibration can cause severe error in the model. In essence, the spectral pattern to which the model has been trained has been shifted. The mathematics of the model are expecting a particular relationship of intensity between adjacent variables (wavenumbers) and cannot usually account for shifts [31], To some extent, multivariate models can be desensitized to inaccuracy and imprecision by assuring that the calibration samples also exhibit some of the same shifting features, but model sensitivity may suffer as a result. Although not in common use, other deconvolution methods have been introduced which may be applicable to removing shift effects of inaccurate wavenumber calibrations [37]. 

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