Assessment of toxicity using dehydrogenases activity and mathematical modeling

Dehydrogenase activity is frequently used to assess the general condition of microorganisms in soil and activated sludge. Many studies have investigated the inhibition of dehydrogenase activity by various compounds, including heavy metal ions. However, the time after which the measurements are carried out is often chosen arbitrarily. Thus, it can be difficult to estimate how the toxic effects of compounds vary during the reaction and when the maximum of the effect would be reached. Hence, the aim of this study was to create simple and useful mathematical model describing changes in dehydrogenase activity during exposure to substances that inactivate enzymes. Our model is based on the Lagergrens pseudo-first-order equation, the rate of chemical reactions, enzyme activity, and inactivation and was created to describe short-term changes in dehydrogenase activity. The main assumption of our model is that toxic substances cause irreversible inactivation of enzyme units. The model is able to predict the maximum direct toxic effect (MDTE) and the time to reach this maximum (T_MDTE). In order to validate our model, we present two examples: inactivation of dehydrogenase in microorganisms in soil and activated sludge. The model was applied successfully for cadmium and copper ions. Our results indicate that the predicted MDTE and T_MDTE are more appropriate than EC₅₀ and IC₅₀ for toxicity assessments, except for long exposure times.     

Representation,Exemplification, and Risk: Resonance of Tobacco Graphic Health Warnings Across Diverse Populations

As countries implement Article 11 of the World Health Organization (WHO) Framework Convention on Tobacco Control, graphic warning labels that use images of people and their body parts to illustrate the consequences of smoking are being added to cigarette packs. According to exemplification theory, these case examples—exemplars—can shape perceptions about risk and may resonate differently among demographic subpopulations. Drawing on data from eight focus groups (N = 63) with smokers and nonsmokers from vulnerable populations, this qualitative study explores whether people considered exemplars in their reactions to and evaluations of U.S. graphic health warning labels initially proposed by the Food and Drug Administration. Participants made reference to prior and concurrent mass media messages and exemplars during the focus groups and used demographic cues in making sense of the images on the warning labels. Participants were particularly sensitive to age of the exemplars and how it might affect label effectiveness and beliefs about smoking. Race and socioeconomic status also were salient for some participants. We recommend that exemplars and exemplification be considered when selecting and evaluating graphic health warnings for tobacco labels and associated media campaigns.     

Use of a Checklist During Observation of a Simulated Cardiac Arrest Scenario Does Not Improve Time to CPR and Defibrillation Over Observation Alone for Subsequent Scenarios

Theory: Immersive simulation is a common mode of education for medical students. Observation of clinical simulations prior to participation is believed to be beneficial, though this is often a passive process. Active observation may be more beneficial. Hypotheses: The hypothesis tested in this study was that the active use of a simple checklist during observation of an immersive simulation would result in better participant performance in a subsequent scenario compared with passive observation alone. Methods: Medical students were randomized to either passive or active (with checklist) observation of an immersive simulation involving cardiac arrest prior to participating in their own simulation. Performance measures included time to cardiopulmonary resuscitation (CPR) and time to defibrillation and were compared between first and second scenarios as well as between passive and active observers. Results: Seventy-nine simulations involving 232 students were conducted. Mean time to CPR was 18 seconds (SD = 11.6) for those using the checklist and 24 seconds (SD = 15.8) for those who observed passively (M difference = 6 seconds), t(35) = 1.46, p =.153. Time to defibrillation was 94 seconds (SD = 26.4) for those using the checklist and 92 seconds (SD = 23.8) for those who observed passively (M difference = –2 seconds), t(38) =.21, p =.837. Time to CPR was 24 seconds (SD = 15.8) for passive observers and 31 seconds (SD = 21.0; M difference = 7 seconds), t(35) = 1.13, p =.265, for their first scenario counterparts. Time to CPR was 18 seconds (SD = 11.6) for active observers and 36 seconds (SD = 26.2; M difference = 18 seconds), t(24) = 2.81, p =.010, for their first scenario counterparts. Time to defibrillation was 92 seconds (SD = 23.8) for passive observers and 125 seconds (SD = 32.2; M difference = 33 seconds), t(33) = 3.63, p =.001, for their first scenario counterparts. Time to defibrillation was 94 seconds (SD = 26.4) for the active observers and 132 seconds (SD = 52.9; M difference = 38 seconds), t(28) =.46, p =.008, for their first scenario counterparts. Conclusions: Observation alone leads to improved performance in the management of a simulated cardiac arrest. The active use of a simple skills-based checklist during observation did not appear to improve performance over passive observation alone.