https://www.selleckchem.com/products/milademetan.html DC P  less then  0.05). In the MP, ChAT-IR neurons were 44% in AC and 54% in DC (P  less then  0.05), nNOS-IR neurons were 50% in both, and ChAT/nNOS-IR neurons were 12 and 18%, respectively. The ENS architecture with multilayered submucosal plexuses and the distribution of functionally distinct groups of neurons in the pig colon are similar to humans, supporting the suitability of the pig as a model and providing the platform for investigating the mechanisms underlying human colonic diseases.In cell biology, pharmacology and toxicology dose-response and concentration-response curves are frequently fitted to data with statistical methods. Such fits are used to derive quantitative measures (e.g. EC[Formula see text] values) describing the relationship between the concentration of a compound or the strength of an intervention applied to cells and its effect on viability or function of these cells. Often, a reference, called negative control (or solvent control), is used to normalize the data. The negative control data sometimes deviate from the values measured for low (ineffective) test compound concentrations. In such cases, normalization of the data with respect to control values leads to biased estimates of the parameters of the concentration-response curve. Low quality estimates of effective concentrations can be the consequence. In a literature study, we found that this problem occurs in a large percentage of toxicological publications. We propose different strategies to tackle the problem, including complete omission of the controls. Data from a controlled simulation study indicate the best-suited problem solution for different data structure scenarios. This was further exemplified by a real concentration-response study. We provide the following recommendations how to handle deviating controls (1) The log-logistic 4pLL model is a good default option. (2) When there are at least two concentrations in the no-ef