Narrow Results

Today, with real-life problems, modeling is a primary step in organizing, analyzing and optimizing them. Queueing theory is a particular approach used to model this category of issues. Space constraints, feedback, service dependency, etc. are often inseparable from the issues they create. In light of this objective, this research presents a model and analysis of the steady-state behavior of an $M/G/1$ feedback retrial queue with two dependent phases of service under a Bernoulli vacation policy. The service times for the two stages are often independent in normal queueing frameworks. We presume that they are dependent random variables in this case. Indeed, this dependence is one-way (i.e., the service time of the second phase has no effect on the service time of the first phase). Yet, the first phase service time has an impact on the second phase service time. In order to determine the steady-state probabilities and probability-generating functions (PGF) for the different states, the supplementary variable technique (SVT) was utilized. Furthermore, a broad range of performance metrics had been established. The generated metrics are then envisioned and validated with the aid of graphs and tables. Additionally, a nonlinear cost function is constructed, which is subsequently minimized by distinct approaches like particle swarm optimization (PSO), artificial bee colony (ABC) and genetic algorithm (GA). Furthermore, we used certain figures to examine the convergence of these optimization methods. Finally, validation outcomes are compared with neuro-fuzzy results generated with the “adaptive neuro-fuzzy inference system” (ANFIS).

Purpose: Cancer patients widely use complementary alternative medicines. Although some remedies have been shown to be of benefit, there is also a risk of potentially serious interactions with conventional cancer therapies and diagnostic procedures. The aim of this review is to identify the main factors which might make complementary medicines potentially unsafe in cancer.

Method: Systematic review of potential interactions with chemo- and radiotherapy and review of the purported mechanisms of action.

Results: Four factors were identified. These included the potential modification of the clinical course, interaction with the pharmacodynamics and pharma-cokinetics of conventional therapies and potential alterations of investigations. Complementary immunostimulants may be contraindicated in lymphomas and other cancers in which suppression of the immune system is desired. Phytoestrogens could stimulate growth of hormone sensitive cancer cells. Antioxidants should not be used in chemotherapies whose mechanisms of action rely on cell damage through oxidative stress. Many remedies can interact with the cytochrome P450 system thereby potentially changing plasma levels of conventional medicines. However, in vitro effects or findings from animal studies may not translate into clinically relevant effects. Some remedies may interfere with the membrane transporter proteins thereby contributing to multi-drug resistance. Finally some complementary medicines remedies may interfere with unsealed source radiotherapy or nuclear scans.

Conclusions: Predicting the safety profile of complementary medicines is complex and may depend on personal and genetic factors. In cancer therapy, where the therapeutic margin of chemotherapies is very narrow, potential risks and benefits need to be meticulously evaluated.