A new model in medicine education: smart model education set.

PURPOSE: Education set consisting of three-dimensional smart interactive models with audio, visual, and light features and an application program that allows learning branches of science such as anatomy and histology at the same time can bring innovation to medicine and health education. Our study aims to show contributions of this education set, which we have patented, to student academic success and medical education. METHODS: The students participating in study consisted of three groups. Students were divided into Group 1 (classical education), Group 2 (smart model education set and theoretical expression), and Group 3 (smart model education set). Pre-test, post-test, and state anxiety scale applications were made to all groups before and after education. Trait anxiety scale was administered before education. Education set used in study includes hardware and software parts. RESULTS: Post-education state anxiety scale in Group 1 mean was significantly higher than Group 2 and 3 mean; Group 2 mean was significantly higher than Group 3 mean. There was no significant difference between groups regarding trait anxiety levels. It was observed that there was a significant difference between the pre-test and post-test in all three groups. The increase in post-test achievement level of Group 2 and 3 was significantly higher than Group 1. CONCLUSIONS: Smart model education set integrates basic and clinical information. Mobile application will ensure continuity of theoretical and practical education at desired place and time. Invention will bring a new breath to basic medical education by preventing inequalities in medical and health sciences education.

The Mechanisms of Action of Cationic Antimicrobial Peptides Refined by Novel Concepts from Biophysical Investigations.

Even 30 years after the discovery of magainins, biophysical and structural investigations on how these peptides interact with membranes can still bear surprises and add new interesting detail to how these peptides exert their antimicrobial action. Early on, using oriented solid-state NMR spectroscopy, it was found that the amphipathic helices formed by magainins are active when being oriented parallel to the membrane surface. More recent investigations indicate that this in-planar alignment is also found when PGLa and magainin in combination exert synergistic pore-forming activities, where studies on the mechanism of synergistic interaction are ongoing. In a related manner, the investigation of dimeric antimicrobial peptide sequences has become an interesting topic of research which bears promise to refine our views how antimicrobial action occurs. The molecular shape concept has been introduced to explain the effects of lipids and peptides on membrane morphology, locally and globally, and in particular of cationic amphipathic helices that partition into the membrane interface. This concept has been extended in this review to include more recent ideas on soft membranes that can adapt to external stimuli including membrane-disruptive molecules. In this manner, the lipids can change their shape in the presence of low peptide concentrations, thereby maintaining the bilayer properties. At higher peptide concentrations, phase transitions occur which lead to the formation of pores and membrane lytic processes. In the context of the molecular shape concept, the properties of lipopeptides, including surfactins, are shortly presented, and comparisons with the hydrophobic alamethicin sequence are made.

Revealing the Mechanisms of Synergistic Action of Two Magainin Antimicrobial Peptides.

The study of peptide-lipid and peptide-peptide interactions as well as their topology and dynamics using biophysical and structural approaches have changed our view how antimicrobial peptides work and function. It has become obvious that both the peptides and the lipids arrange in soft supramolecular arrangements which are highly dynamic and able to change and mutually adapt their conformation, membrane penetration, and detailed morphology. This can occur on a local and a global level. This review focuses on cationic amphipathic peptides of the magainin family which were studied extensively by biophysical approaches. They are found intercalated at the membrane interface where they cause membrane thinning and ultimately lysis. Interestingly, mixtures of two of those peptides namely magainin 2 and PGLa which occur naturally as a cocktail in the frog skin exhibit synergistic enhancement of antimicrobial activities when investigated together in antimicrobial assays but also in biophysical experiments with model membranes. Detailed dose-response curves, presented here for the first time, show a cooperative behavior for the individual peptides which is much increased when PGLa and magainin are added as equimolar mixture. This has important consequences for their bacterial killing activities and resistance development. In membranes that carry unsaturations both peptides align parallel to the membrane surface where they have been shown to arrange into mesophases involving the peptides and the lipids. This supramolecular structuration comes along with much-increased membrane affinities for the peptide mixture. Because this synergism is most pronounced in membranes representing the bacterial lipid composition it can potentially be used to increase the therapeutic window of pharmaceutical formulations.

Biophysical Investigations Elucidating the Mechanisms of Action of Antimicrobial Peptides and Their Synergism.

Biophysical and structural investigations are presented with a focus on the membrane lipid interactions of cationic linear antibiotic peptides such as magainin, PGLa, LL37, and melittin. Observations made with these peptides are distinct as seen from data obtained with the hydrophobic peptide alamethicin. The cationic amphipathic peptides predominantly adopt membrane alignments parallel to the bilayer surface; thus the distribution of polar and non-polar side chains of the amphipathic helices mirror the environmental changes at the membrane interface. Such a membrane partitioning of an amphipathic helix has been shown to cause considerable disruptions in the lipid packing arrangements, transient openings at low peptide concentration, and membrane disintegration at higher peptide-to-lipid ratios. The manifold supramolecular arrangements adopted by lipids and peptides are represented by the 'soft membranes adapt and respond, also transiently' (SMART) model. Whereas molecular dynamics simulations provide atomistic views on lipid membranes in the presence of antimicrobial peptides, the biophysical investigations reveal interesting details on a molecular and supramolecular level, and recent microscopic imaging experiments delineate interesting sequences of events when bacterial cells are exposed to such peptides. Finally, biophysical studies that aim to reveal the mechanisms of synergistic interactions of magainin 2 and PGLa are presented, including unpublished isothermal titration calorimetry (ITC), circular dichroism (CD) and dynamic light scattering (DLS) measurements that suggest that the peptides are involved in liposome agglutination by mediating intermembrane interactions. A number of structural events are presented in schematic models that relate to the antimicrobial and synergistic mechanism of amphipathic peptides when they are aligned parallel to the membrane surface.

Prostate Cancer Predictive Simulation Modelling, Assessing the Risk Technique (PCP-SMART): Introduction and Initial Clinical Efficacy Evaluation Data Presentation of a Simple Novel Mathematical Simulation Modelling Method, Devised to Predict the Outcome of Prostate Biopsy on an Individual Basis.

INTRODUCTION: We developed a mathematical "prostate cancer (PCa) conditions simulating" predictive model (PCP-SMART), from which we derived a novel PCa predictor (prostate cancer risk determinator [PCRD] index) and a PCa risk equation. We used these to estimate the probability of finding PCa on prostate biopsy, on an individual basis. MATERIALS AND METHODS: A total of 371 men who had undergone transrectal ultrasound-guided prostate biopsy were enrolled in the present study. Given that PCa risk relates to the total prostate-specific antigen (tPSA) level, age, prostate volume, free PSA (fPSA), fPSA/tPSA ratio, and PSA density and that tPSA ≥ 50 ng/mL has a 98.5% positive predictive value for a PCa diagnosis, we hypothesized that correlating 2 variables composed of 3 ratios (1, tPSA/age; 2, tPSA/prostate volume; and 3, fPSA/tPSA; 1 variable including the patient's tPSA and the other, a tPSA value of 50 ng/mL) could operate as a PCa conditions imitating/simulating model. Linear regression analysis was used to derive the coefficient of determination (R2), termed the PCRD index. To estimate the PCRD index's predictive validity, we used the χ2 test, multiple logistic regression analysis with PCa risk equation formation, calculation of test performance characteristics, and area under the receiver operating characteristic curve analysis using SPSS, version 22 (P < .05). RESULTS: The biopsy findings were positive for PCa in 167 patients (45.1%) and negative in 164 (44.2%). The PCRD index was positively signed in 89.82% positive PCa cases and negative in 91.46% negative PCa cases (χ2 test; P < .001; relative risk, 8.98). The sensitivity was 89.8%, specificity was 91.5%, positive predictive value was 91.5%, negative predictive value was 89.8%, positive likelihood ratio was 10.5, negative likelihood ratio was 0.11, and accuracy was 90.6%. Multiple logistic regression revealed the PCRD index as an independent PCa predictor, and the formulated risk equation was 91% accurate in predicting the probability of finding PCa. On the receiver operating characteristic analysis, the PCRD index (area under the curve, 0.926) significantly (P < .001) outperformed other, established PCa predictors. CONCLUSION: The PCRD index effectively predicted the prostate biopsy outcome, correctly identifying 9 of 10 men who were eventually diagnosed with PCa and correctly ruling out PCa for 9 of 10 men who did not have PCa. Its predictive power significantly outperformed established PCa predictors, and the formulated risk equation accurately calculated the probability of finding cancer on biopsy, on an individual patient basis.