Use of diagnostic imaging studies for evaluation of pregnant patients with medical conditions not related to pregnancy poses a persistent and recurring dilemma. Although a theoretical risk of carcinogenesis exists, there are no known risks for development of congenital malformations or mental retardation in a fetus exposed to ionizing radiation at the levels typically used for diagnostic imaging. An understanding of the effects of ionizing radiation on the fetus at different gestational stages and the estimated exposure dose received by the fetus from various imaging modalities facilitates appropriate choices for diagnostic imaging of pregnant patients with nonobstetric conditions. Other aspects of imaging besides radiation (ie, contrast agents) also carry potential for fetal injury and must be taken into consideration. Imaging algorithms based on a review of the current literature have been developed for specific nonobstetric conditions: pulmonary embolism, acute appendicitis, urolithiasis, biliary disease, and trauma. Imaging modalities that do not use ionizing radiation (ie, ultrasonography and magnetic resonance imaging) are preferred for pregnant patients. If ionizing radiation is used, one must adhere to the principle of using a dose that is as low as reasonably achievable after a discussion of risks versus benefits with the patient.

No abstract

Molecular weight distribution, which is characterized by its averages like number average (Mn) and weight average (Mw), is one of the important properties of polybutadiene rubber (PBR), and it is difficult to measure. The objective of this work is to develop models to predict Mn and Mw from readily available process variables. Neural networks that are capable of mapping highly complex and non-linear dependencies have been adapted to develop models for the Mn and Mw of PBR. The molecular weight distribution and its averages of PBR samples collected over a wide range of operating conditions were measured by the conventional Gel Permeable Chromatograph (GPC) method. Neural networks were trained with relevant data to predict Mn and Mw from process variables. The trained networks were found to generalize well when tested with new data.

No abstract