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Biomedical data obtained during cell experiments, laboratory animal research, or human studies often display a complex distribution. Statistical identification of subgroups in research data poses an analytical challenge. Here were introduce an interactive R-based bioinformatics tool, called “AdaptGauss”. It enables a valid identification of a biologically-meaningful multimodal structure in the data by fitting a Gaussian mixture model (GMM) to the data. The interface allows a supervised selection of the number of subgroups. This enables the expectation maximization (EM) algorithm to adapt more complex GMM than usually observed with a noninteractive approach. Interactively fitting a GMM to heat pain threshold data acquired from human volunteers revealed a distribution pattern with four Gaussian modes located at temperatures of 32.3, 37.2, 41.4, and 45.4 °C. Noninteractive fitting was unable to identify a meaningful data structure. Obtained results are compatible with known activity temperatures of different TRP ion channels suggesting the mechanistic contribution of different heat sensors to the perception of thermal pain. Thus, sophisticated analysis of the modal structure of biomedical data provides a basis for the mechanistic interpretation of the observations. As it may reflect the involvement of different TRP thermosensory ion channels, the analysis provides a starting point for hypothesis-driven laboratory experiments.
In contrast to several smaller studies, which demonstrate that remote ischemic preconditioning (RIPC) reduces myocardial injury in patients that undergo cardiovascular surgery, the RIPHeart study failed to demonstrate beneficial effects of troponin release and clinical outcome in propofol-anesthetized cardiac surgery patients. Therefore, we addressed the potential biochemical mechanisms triggered by RIPC. This is a predefined prospective sub-analysis of the randomized and controlled RIPHeart study in cardiac surgery patients (n = 40) that was recently published. Blood samples were drawn from patients prior to surgery, after RIPC of four cycles of 5 min arm ischemia/5 min reperfusion (n = 19) and the sham (n = 21) procedure, after connection to cardiopulmonary bypass (CPB), at the end of surgery, 24 h postoperatively, and 48 h postoperatively for the measurement of troponin T, macrophage migration inhibitory factor (MIF), stromal cell-derived factor 1 (CXCL12), IL-6, CXCL8, and IL-10. After RIPC, right atrial tissue samples were taken for the measurement of extracellular-signal regulated kinase (ERK1/2), protein kinase B (AKT), Glycogen synthase kinase 3 (GSK-3β), protein kinase C (PKCε), and MIF content. RIPC did not significantly reduce the troponin release when compared with the sham procedure. MIF serum levels intraoperatively increased, peaking at intensive care unit (ICU) admission (with an increase of 48.04%, p = 0.164 in RIPC; and 69.64%, p = 0.023 over the baseline in the sham procedure), and decreased back to the baseline 24 h after surgery, with no differences between the groups. In the right atrial tissue, MIF content decreased after RIPC (1.040 ± 1.032 Arbitrary units [au] in RIPC vs. 2.028 ± 1.631 [au] in the sham procedure, p < 0.05). CXCL12 serum levels increased significantly over the baseline at the end of surgery, with no differences between the groups. ERK1/2, AKT, GSK-3β, and PKCɛ phosphorylation in the right atrial samples were no different between the groups. No difference was found in IL-6, CXCL8, and IL10 serum levels between the groups. In this cohort of cardiac surgery patients that received propofol anesthesia, we could not show a release of potential mediators of signaling, nor an effect on the inflammatory response, nor an activation of well-established protein kinases after RIPC. Based on these data, we cannot exclude that confounding factors, such as propofol, may have interfered with RIPC.