Alpha- versus beta-particle radiopeptide therapy in a human prostate cancer model (213Bi-DOTA-PESIN and 213Bi-AMBA versus 177Lu-DOTA-PESIN)

Recurrent prostate cancer presents a challenge to conventional treatment, particularly so to address micrometastatic and small-volume disease. Use of α-radionuclide therapy is considered as a highly effective treatment in such applications due to the shorter range and exquisite cytotoxicity of α-particles as compared with β-particles. (213)Bi is considered an α-emitter with high clinical potential, due to its short half-life (45.6 minutes) being well matched for use in peptide-receptor radionuclide α-therapy; however, there is limited knowledge available within this context of use. In this study, two novel (213)Bi-labeled peptides, DOTA-PEG(4)-bombesin (DOTA-PESIN) and DO3A-CH(2)CO-8-aminooctanoyl-Q-W-A-V-G-H-L-M-NH(2) (AMBA), were compared with (177)Lu (β-emitter)-labeled DOTA-PESIN in a human androgen-independent prostate carcinoma xenograft model (PC-3 tumor). Animals were injected with (177)Lu-DOTA-PESIN, (213)Bi-DOTA-PESIN, or (213)Bi-AMBA to determine the maximum tolerated dose (MTD), biodistribution, and dosimetry of each agent; controls were left untreated or were given nonradioactive (175)Lu-DOTA-PESIN. The MTD of (213)Bi-DOTA-PESIN and (213)Bi-AMBA was 25 MBq (0.68 mCi) whereas (177)Lu-DOTA-PESIN showed an MTD of 112 MBq (3 mCi). At these dose levels, (213)Bi-DOTA-PESIN and (213)Bi-AMBA were significantly more effective than (177)Lu-DOTA-PESIN. At the same time, (177)Lu-DOTA-PESIN showed minimal, (213)Bi-DOTA-PESIN slight, and (213)Bi-AMBA marked kidney damage 20 to 30 weeks posttreatment. These preclinical data indicate that α-therapy with (213)Bi-DOTA-PESIN or (213)Bi-AMBA is more efficacious than β-therapy. Furthermore, (213)Bi-DOTA-PESIN has a better safety profile than (213)Bi-AMBA, and represents a possible new approach for use in peptide-receptor radionuclide α-therapy treating recurrent prostate cancer.

The Flowchart Model of Cluster Policy: The Automobile Industry Cluster in China

This paper proposes a general model of the flowchart approach to industrial cluster policy and applies this model to Guangzhou's automobile industry cluster. The flowchart approach to industrial cluster policy is an action plan for prioritizing policy measures in a time-ordered series. We reached the following two conclusions. First,we clarified the effects of Honda, Nissan, and Toyota on agglomeration in Guangzhou's automobile industry cluster. Second, we established that local governments play a crucial role in successful industrial cluster policy, and that the mayor of the local government should be offered incentives in order to target industrial clustering and implement cluster policy.

Role of hydrophobic interactions and desolvation in determining the structural properties of a model alpha beta peptide.

Model AB, a 20-amino acid peptide that was designed to adopt an alpha beta tertiary structure stabilized by hydrophobic interactions between residues in adjacent helical and extended segments, exhibited large pKa shifts of several ionizable groups and slow hydrogen/deuterium exchange rates of nearly all the peptide amide groups [Butcher, D. J., Bruch, M. D. & Moe, G. T. (1995) Biopolymers 36, 109-120]. These properties, which depend on structure and hydration, are commonly observed in larger proteins but are quite unusual for small peptides. To identify which of several possible features of the peptide design are most important in determining these properties, several closely related analogs of Model AB were characterized by CD and NMR spectroscopy. The results show that hydrophobic interactions between adjacent helical and extended segments are structure-determining and have the additional effect of altering water-peptide interactions over much of the peptide surface. These results may have important implications for understanding mechanisms of protein folding and for the design of independently folding peptides.

Radiobiological model comparison of 3D conformal radiotherapy and IMRT plans for the treatment of prostate cancer

The main aim of radiotherapy is to deliver a dose of radiation that is high enough to destroy the tumour cells while at the same time minimising the damage to normal healthy tissues. Clinically, this has been achieved by assigning a prescription dose to the tumour volume and a set of dose constraints on critical structures. Once an optimal treatment plan has been achieved the dosimetry is assessed using the physical parameters of dose and volume. There has been an interest in using radiobiological parameters to evaluate and predict the outcome of a treatment plan in terms of both a tumour control probability (TCP) and a normal tissue complication probability (NTCP). In this study, simple radiobiological models that are available in a commercial treatment planning system were used to compare three dimensional conformal radiotherapy treatments (3D-CRT) and intensity modulated radiotherapy (IMRT) treatments of the prostate. Initially both 3D-CRT and IMRT were planned for 2 Gy/fraction to a total dose of 60 Gy to the prostate. The sensitivity of the TCP and the NTCP to both conventional dose escalation and hypo-fractionation was investigated. The biological responses were calculated using the Källman S-model. The complication free tumour control probability (P+) is generated from the combined NTCP and TCP response values. It has been suggested that the alpha/beta ratio for prostate carcinoma cells may be lower than for most other tumour cell types. The effect of this on the modelled biological response for the different fractionation schedules was also investigated.

A model for the uptake of E-business by building small and medium enterprises

The adoption of e-business by Small and Medium Enterprises (SMEs) in construction lags from other service and product businesses within the building sector. This paper develops a model to facilitate the uptake of electronic business, especially in relation to SMEs within the Australian construction sector. Ebusiness is defined here as “the undertaking of business-related transactions, communications and information exchanges utilising electronic medium and environment”, the elicited model highlights significant changes needed including skills development, social, economic and cultural issues. The model highlights barriers for SMEs to migrate towards e-transactions, e-bidding, e-tendering and ecollaboration and provides learning and skills development components. The model is derived from case study fieldwork and is to inform diffusion and awareness models for best practice. Empirical techniques included ‘focus group’ interviews and one to one ‘interviews’. Data was transcribed and analysed using cluster analyses. Preliminary results reveal that current models for e-business adoption are not effective within the construction context as they have emerged from other service and product industries - such as retail or tourism. These generic models have largely ignored the nature of the construction industry, and some modifications appears to be required. This paper proposes an alternative adoption model which is more sensitive to the nature of the industry – particularly for e-business uptake in building SME’s.

Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes

The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.

Partial agonists of the [alpha]3[beta]4[ast] neuronal nicotinic acetylcholine receptor reduce ethanol consumption and seeking in rats

Alcohol use disorders (AUDs) impact millions of individuals and there remain few effective treatment strategies. Despite evidence that neuronal nicotinic acetylcholine receptors (nAChRs) have a role in AUDs, it has not been established which subtypes of the nAChR are involved. Recent human genetic association studies have implicated the gene cluster CHRNA3-CHRNA5-CHRNB4 encoding the α3, α5, and β4 subunits of the nAChR in susceptibility to develop nicotine and alcohol dependence; however, their role in ethanol-mediated behaviors is unknown due to the lack of suitable and selective research tools. To determine the role of the α3, and β4 subunits of the nAChR in ethanol self-administration, we developed and characterized high-affinity partial agonists at α3β4 nAChRs, CP-601932, and PF-4575180. Both CP-601932 and PF-4575180 selectively decrease ethanol but not sucrose consumption and operant self-administration following long-term exposure. We show that the functional potencies of CP-601932 and PF-4575180 at α3β4 nAChRs correlate with their unbound rat brain concentrations, suggesting that the effects on ethanol self-administration are mediated via interaction with α3β4 nAChRs. Also varenicline, an approved smoking cessation aid previously shown to decrease ethanol consumption and seeking in rats and mice, reduces ethanol intake at unbound brain concentrations that allow functional interactions with α3β4 nAChRs. Furthermore, the selective α4β2(*) nAChR antagonist, DHβE, did not reduce ethanol intake. Together, these data provide further support for the human genetic association studies, implicating CHRNA3 and CHRNB4 genes in ethanol-mediated behaviors. CP-601932 has been shown to be safe in humans and may represent a potential novel treatment for AUDs.