Pemphigus can cause complications during pregnancy and may cause serious harm to a fetus. For this study, a comprehensive review of common treatments of pemphigus and their adverse effects associated with pregnancy and male fertility was conducted. We concluded that a period of remission with minimal or no therapy before conception could significantly reduce the risk of the disease flaring up, at least in the first trimester. The period of remission causes a delay in the flare-up of the disease, which means lower cumulative doses and the prevention of possible congenital abnormalities caused by corticosteroid or immunosuppressant treatments. All common treatments of pemphigus-azathioprine, mycophenolate mofetil, and methotrexate-should be avoided during pregnancy. However, it appears that systemic corticosteroids in a safe dose with a topical form of corticosteroids may be used without serious risk. Due to the lack of data associated with rituximab therapy, it is recommended that this drug be avoided 12 months before conception. It appears that the safest treatment of pemphigus is intravenous immunoglobulin (IVIg), which may be more effective when used with topical corticosteroids. Due to the delayed effect of IVIg, it should be used some months prior to conception.

Purpose Sustainable development is the management and conservation of the basic natural resources through which organizational and technological changes are lead to meet present and future needs of humans. In developing and analyzing the solutions based on sustainable development principles, an integrated and holistic approach needs to be pursued. Not only system dynamics has the essential tools for systemic analysis, but also it is an appropriate approach for perceiving problems and offering solutions. The aim of this study is to present an integrated and systemic model to analyze the existent dynamics in sustainable development of Iran’s farming industry. Design/methodology/approach To achieve the mathematical equations and values of model’s variables, a simulation is carried out using the data gathered from Damavand city, Tehran, Iran. The parameters of the model are selected and calculated considering the specifications of this case study. After modeling the system, Vensim simulation software has been employed, followed by identifying the leverage points of the model; then, a set of scenarios have been generated and tested through simulation to achieve a much improved understanding of the system’s dynamic behavior. Findings The results show that two factors are among the most important leverage points: “profit gained from agriculture” and “required water”. The authors could also observe that the main issue in Damavand is the lack of water for which saving policies would be a major step toward agriculture’s sustainable development in this area. Originality/value The paper shows how System Dynamics simulation approach can provide deep insights into the field of sustainable development and present efficient policies for agriculture sustainability.

A key and challenging step toward personalized/precision medicine is the ability to redesign dose-finding clinical trials. This work studies a problem of fully response-adaptive Bayesian design of phase II dose-finding clinical trials with patient information, where the decision maker seeks to identify the right dose for each patient type (often defined as an effective target dose for each group of patients) by minimizing the expected (over patient types) variance of the right dose. We formulate this problem by a stochastic dynamic program and exploit a few properties of this class of learning problems. Because the optimal solution is intractable, we propose an approximate policy by an adaptation of a one-step look-ahead framework. We show the optimality of the proposed policy for a setting with homogeneous patients and two doses and find its asymptotic rate of sampling. We adapt a number of commonly applied allocation policies in dose-finding clinical trials, such as posterior adaptive sampling, and test their performance against our proposed policy via extensive simulations with synthetic and real data. Our numerical analyses provide insights regarding the connection between the structure of the dose-response curve for each patient type and the performance of allocation policies. This paper provides a practical framework for the Food and Drug Administration and pharmaceutical companies to transition from the current phase II procedures to the era of personalized dose-finding clinical trials.

We study the classical ranking and selection problem, where the ultimate goal is to find the unknown best alternative in terms of the probability of correct selection or expected opportunity cost. However, this paper adopts an alternative sampling approach to achieve this goal, where sampling decisions are made with the objective of maximizing information about the unknown best alternative, or equivalently, minimizing its Shannon entropy. This adaptive learning is formulated via a Bayesian stochastic dynamic programming problem, by which several properties of the learning problem are presented, including the monotonicity of the optimal value function in an information-seeking setting. Since the state space of the stochastic dynamic program is unbounded in the Gaussian setting, a one-step look-ahead approach is used to develop a policy. The proposed policy seeks to maximize the one-step information gain about the unknown best alternative, and therefore, it is called information gradient (IG). It is also proved that the IG policy is consistent, that is, as the sampling budget grows to infinity, the IG policy finds the true best alternative almost surely. Later, a computationally efficient estimate of the proposed policy, called approximated information gradient (AIG), is introduced and in the numerical experiments its performance is tested against recent benchmarks alongside several sensitivity analyses. Results show that AIG performs competitively against other algorithms from the literature.