Mutation-specific differences in arrhythmias and drug responses in CPVT patients : simultaneous patch clamp and video imaging of iPSC derived cardiomyocytes

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is an inherited cardiac disease characterized by arrhythmias under adrenergic stress. Mutations in the cardiac ryanodine receptor (RYR2) are the leading cause for CPVT. We characterized electrophysiological properties of CPVT patient-specific induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) carrying different mutations in RYR2 and evaluated effects of carvedilol and flecainide on action potential (AP) and contractile properties of hiPSC-CMs. iPSC-CMs were generated from skin biopsies of CPVT patients carrying exon 3 deletion (E3D) and L4115F mutation in RYR2. APs and contractile movement were recorded simultaneously from the same hiPSC-CMs. Differences in AP properties of ventricular like CMs were seen in CPVT and control CMs: APD90 of both E3D (n = 20) and L4115F (n = 25) CPVT CMs was shorter than in control CMs (n = 15). E3D-CPVT CMs had shortest AP duration, lowest AP amplitude, upstroke velocity and more depolarized diastolic potential than controls. Adrenaline had positive and carvedilol and flecainide negative chronotropic effect in all hiPSC CMs. CPVT CMs had increased amount of delayed after depolarizations (DADs) and early after depolarizations (EADs) after adrenaline exposure. E3D CPVT CMs had the most DADs, EADs, and tachyarrhythmia. Discordant negatively coupled alternans was seen in L4115F CPVT CMs. Carvedilol cured almost all arrhythmias in L4115F CPVT CMs. Both drugs decreased contraction amplitude in all hiPSC CMs. E3D CPVT CMs have electrophysiological properties, which render them more prone to arrhythmias. iPSC-CMs provide a unique platform for disease modeling and drug screening for CPVT. Combining electrophysiological measurements, we can gain deeper insight into mechanisms of arrhythmias.Peer reviewe

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part II: an illustrative example

<p>Abstract</p> <p>Background</p> <p>Popular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework. The study is divided into two parts. In the first part modelling techniques and intrinsic strengths and weaknesses of different approaches were discussed from a theoretical point of view. In this second part the performances of the same models are evaluated in an illustrative example.</p> <p>Methods</p> <p>Eight models were developed: Bayes linear and quadratic models, <it>k</it>-nearest neighbour model, logistic regression model, Higgins and direct scoring systems and two feed-forward artificial neural networks with one and two layers. Cardiovascular, respiratory, neurological, renal, infectious and hemorrhagic complications were defined as morbidity. Training and testing sets each of 545 cases were used. The optimal set of predictors was chosen among a collection of 78 preoperative, intraoperative and postoperative variables by a stepwise procedure. Discrimination and calibration were evaluated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test, respectively.</p> <p>Results</p> <p>Scoring systems and the logistic regression model required the largest set of predictors, while Bayesian and <it>k</it>-nearest neighbour models were much more parsimonious. In testing data, all models showed acceptable discrimination capacities, however the Bayes quadratic model, using only three predictors, provided the best performance. All models showed satisfactory generalization ability: again the Bayes quadratic model exhibited the best generalization, while artificial neural networks and scoring systems gave the worst results. Finally, poor calibration was obtained when using scoring systems, <it>k</it>-nearest neighbour model and artificial neural networks, while Bayes (after recalibration) and logistic regression models gave adequate results.</p> <p>Conclusion</p> <p>Although all the predictive models showed acceptable discrimination performance in the example considered, the Bayes and logistic regression models seemed better than the others, because they also had good generalization and calibration. The Bayes quadratic model seemed to be a convincing alternative to the much more usual Bayes linear and logistic regression models. It showed its capacity to identify a minimum core of predictors generally recognized as essential to pragmatically evaluate the risk of developing morbidity after heart surgery.</p

A bootstrap approach for assessing the uncertainty of outcome probabilities when using a scoring system

Background: Scoring systems are a very attractive family of clinical predictive models, because the patient score can be calculated without using any data processing system. Their weakness lies in the difficulty of associating a reliable prognostic probability with each score. In this study a bootstrap approach for estimating confidence intervals of outcome probabilities is described and applied to design and optimize the performance of a scoring system for morbidity in intensive care units after heart surgery. Methods: The bias-corrected and accelerated bootstrap method was used to estimate the 95% confidence intervals of outcome probabilities associated with a scoring system. These confidence intervals were calculated for each score and each step of the scoring-system design by means of one thousand bootstrapped samples. 1090 consecutive adult patients who underwent coronary artery bypass graft were assigned at random to two groups of equal size, so as to define random training and testing sets with equal percentage morbidities. A collection of 78 preoperative, intraoperative and postoperative variables were considered as likely morbidity predictors. Results: Several competing scoring systems were compared on the basis of discrimination, generalization and uncertainty associated with the prognostic probabilities. The results showed that confidence intervals corresponding to different scores often overlapped, making it convenient to unite and thus reduce the score classes. After uniting two adjacent classes, a model with six score groups not only gave a satisfactory trade-off between discrimination and generalization, but also enabled patients to be allocated to classes, most of which were characterized by well separated confidence intervals of prognostic probabilities. Conclusions: Scoring systems are often designed solely on the basis of discrimination and generalization characteristics, to the detriment of prediction of a trustworthy outcome probability. The present example demonstrates that using a bootstrap method for the estimation of outcome-probability confidence intervals provides useful additional information about score-class statistics, guiding physicians towards the most convenient model for predicting morbidity outcomes in their clinical context

Sustainable Forest Management Preferences of Interest Groups in Three Regions with Different Levels of Industrial Forestry: An Exploratory Attribute-Based Choice Experiment

The challenge of sustainable forest management is to integrate diverse and sometimes conflicting management objectives. In order to achieve this goal, we need a better understanding of the aspects influencing the preferences of diverse groups and how these groups make trade-offs between different attributes of SFM. We compare the SFM preferences of interest groups in regions with different forest use histories based on the reasoning that the condition of the forest reflects the forest use history of the area. The condition of the forest also shapes an individual’s forest values and attitudes. These held values and attitudes are thought to influence SFM preferences. We tested whether the SFM preferences vary amongst the different interest groups within and across regions. We collected data from 252 persons using a choice experiment approach, where participants chose multiple times among different options described by a combination of attributes that are assigned different levels. The novelty of our approach was the use of choice experiments in the assessment of regional preference differences. Given the complexity of interregional comparison and the small sample size, this was an exploratory study based on a purposive rather than random sample. Nevertheless, our results suggest that the aggregation of preferences of all individuals within a region does not reveal all information necessary for forest management planning since opposing viewpoints could cancel each other out and lead to an interpretation that does not reflect possibly polarised views. Although based on a small\ud sample size, the preferences of interest groups within a region are generally statistically significantly different from each other; however preferences of interest groups across regions are also significantly different. This illustrates the potential importance of assessing heterogeneity by region and by group

Epidural anesthesia and postoperative analgesia with ropivacaine and fentanyl in off-pump coronary artery bypass grafting: a randomized, controlled study

<p>Abstract</p> <p>Background</p> <p>Our aim was to assess the efficacy of thoracic epidural anesthesia (EA) followed by postoperative epidural infusion (EI) and patient-controlled epidural analgesia (PCEA) with ropivacaine/fentanyl in off-pump coronary artery bypass grafting (OPCAB).</p> <p>Methods</p> <p>In a prospective study, 93 patients were scheduled for OPCAB under propofol/fentanyl anesthesia and randomized to three postoperative analgesia regimens aiming at a visual analog scale (VAS) score < 30 mm at rest. The control group (n = 31) received intravenous fentanyl 10 μg/ml postoperatively 3-8 mL/h. After placement of an epidural catheter at the level of Th₂-Th₄before OPCAB, a thoracic EI group (n = 31) received EA intraoperatively with ropivacaine 0.75% 1 mg/kg and fentanyl 1 μg/kg followed by continuous EI of ropivacaine 0.2% 3-8 mL/h and fentanyl 2 μg/mL postoperatively. The PCEA group (n = 31), in addition to EA and EI, received PCEA (ropivacaine/fentanyl bolus 1 mL, lock-out interval 12 min) postoperatively. Hemodynamics and blood gases were measured throughout 24 h after OPCAB.</p> <p>Results</p> <p>During OPCAB, EA decreased arterial pressure transiently, counteracted changes in global ejection fraction and accumulation of extravascular lung water, and reduced the consumption of propofol by 15%, fentanyl by 50% and nitroglycerin by a 7-fold, but increased the requirements in colloids and vasopressors by 2- and 3-fold, respectively (<it>P </it>< 0.05). After OPCAB, PCEA increased PaO₂/FiO₂at 18 h and decreased the duration of mechanical ventilation by 32% compared with the control group (<it>P </it>< 0.05).</p> <p>Conclusions</p> <p>In OPCAB, EA with ropivacaine/fentanyl decreases arterial pressure transiently, optimizes myocardial performance and influences the perioperative fluid and vasoactive therapy. Postoperative EI combined with PCEA improves lung function and reduces time to extubation.</p> <p>Trial Registration</p> <p><a href="http://www.clinicaltrials.gov/ct2/show/NCT01384175">NCT01384175</a></p

Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer.

Tumor-stroma ratio (TSR) is a prognostic factor for many types of solid tumors. In this study, we propose a method for automated estimation of TSR from histopathological images of colorectal cancer. The method is based on convolutional neural networks which were trained to classify colorectal cancer tissue in hematoxylin-eosin stained samples into three classes: stroma, tumor and other. The models were trained using a data set that consists of 1343 whole slide images. Three different training setups were applied with a transfer learning approach using domain-specific data i.e. an external colorectal cancer histopathological data set. The three most accurate models were chosen as a classifier, TSR values were predicted and the results were compared to a visual TSR estimation made by a pathologist. The results suggest that classification accuracy does not improve when domain-specific data are used in the pre-training of the convolutional neural network models in the task at hand. Classification accuracy for stroma, tumor and other reached 96.1% on an independent test set. Among the three classes the best model gained the highest accuracy (99.3%) for class tumor. When TSR was predicted with the best model, the correlation between the predicted values and values estimated by an experienced pathologist was 0.57. Further research is needed to study associations between computationally predicted TSR values and other clinicopathological factors of colorectal cancer and the overall survival of the patients