Controversies in the management of advanced prostate cancer

For advanced prostate cancer, the main hormone treatment against which other treatments are assessed is surgical castration. It is simple, safe and effective, however it is not acceptable to all patients. Medical castration by means of luteinizing hormone-releasing hormone (LH-RH) analogues such as goserelin acetate provides an alternative to surgical castration. Diethylstilboestrol, previously the only non-surgical alternative to orchidectomy, is no longer routinely used. Castration reduces serum testosterone by around 90%, but does not affect androgen biosynthesis in the adrenal glands. Addition of an anti-androgen to medical or surgical castration blocks the effect of remaining testosterone on prostate cells and is termed combined androgen blockade (CAB). CAB has now been compared with castration alone (medical and surgical) in numerous clinical trials. Some trials show advantage of CAB over castration, whereas others report no significant difference. The author favours the view that CAB has an advantage over castration. No study has reported that CAB is less effective than castration. Of the anti-androgens which are available for use in CAB, bicalutamide may be associated with a lower incidence of side-effects compared with the other non-steroidal anti-androgens and, in common with nilutamide, has the advantage of once-daily dosing. Only one study has compared anti-androgens within CAB: bicalutamide plus LH-RH analogue and flutamide plus LH-RH analogue. At 160-week follow-up, the groups were equivalent in terms of survival and time to progression. However, bicalutamide caused significantly less diarrhoea than flutamide. Withdrawal and intermittent therapy with anti-androgens extend the range of treatment options. © 1999 Cancer Research Campaig

Intravesical Treatments of Bladder Cancer: Review

For bladder cancer, intravesical chemo/immunotherapy is widely used as adjuvant therapies after surgical transurethal resection, while systemic therapy is typically reserved for higher stage, muscle-invading, or metastatic diseases. The goal of intravesical therapy is to eradicate existing or residual tumors through direct cytoablation or immunostimulation. The unique properties of the urinary bladder render it a fertile ground for evaluating additional novel experimental approaches to regional therapy, including iontophoresis/electrophoresis, local hyperthermia, co-administration of permeation enhancers, bioadhesive carriers, magnetic-targeted particles and gene therapy. Furthermore, due to its unique anatomical properties, the drug concentration-time profiles in various layers of bladder tissues during and after intravesical therapy can be described by mathematical models comprised of drug disposition and transport kinetic parameters. The drug delivery data, in turn, can be combined with the effective drug exposure to infer treatment efficacy and thereby assists the selection of optimal regimens. To our knowledge, intravesical therapy of bladder cancer represents the first example where computational pharmacological approach was used to design, and successfully predicted the outcome of, a randomized phase III trial (using mitomycin C). This review summarizes the pharmacological principles and the current status of intravesical therapy, and the application of computation to optimize the drug delivery to target sites and the treatment efficacy

Linearized Bregman Iterations for Automatic Optical Fiber Fault Analysis

Supervision of the physical layer of optical networks is an extremely relevant subject. To detect fiber faults, single-ended solutions, such as the optical time-domain reflectometry (OTDR), allow for precise measurements of fault profiles. Combining the OTDR with a signal processing approach for high-dimensional sparse parameter estimation allows for automated and reliable results in reduced time. In this paper, a measurement system composed of a photon-counting OTDR data acquisition unit and a processing unit based on a linearized Bregman iterations' algorithm for automatic fault finding is proposed. An in-depth comparative study of the proposed algorithm's fault-finding prowess in the presence of noise is presented. Characteristics, such as sensitivity, specificity, processing time, and complexity, are analyzed in simulated environments. Real-life measurements that are conducted using the photon-counting OTDR subsystem for data acquisition and the linearized Bregman-based processing unit for automated data analysis demonstrated accurate results. It is concluded that the proposed measurement system is particularly well-suited to the task of fault finding. The natural characteristic of the algorithm fosters embedding the solution in digital hardware, allowing for reduced costs and processing time.Accepted Author ManuscriptQID/Tittel GroupQuTec

FPGA-embedded Linearized Bregman Iteration algorithm for trend break detection

Detection of level shifts in a noisy signal, or trend break detection, is a problem that appears in several research fields, from biophysics to optics and economics. Although many algorithms have been developed to deal with such a problem, accurate and low-complexity trend break detection is still an active topic of research. The Linearized Bregman Iterations have been recently presented as a low-complexity and computationally efficient algorithm to tackle this problem, with a formidable structure that could benefit immensely from hardware implementation. In this work, a hardware architecture of the Linearized Bregman Iteration algorithm is presented and tested on a Field Programmable Gate Array (FPGA). The hardware is synthesized in different-sized FPGAs, and the percentage of used hardware, as well as the maximum frequency enabled by the design, indicate that an approximately 100 gain factor in processing time, concerning the software implementation, can be achieved. This represents a tremendous advantage in using a dedicated unit for trend break detection applications. The proposed architecture is compared with a state-of-the-art hardware structure for sparse estimation, and the results indicate that its performance concerning trend break detection is much more pronounced while, at the same time, being the indicated solution for long datasets.</p