Receptor mechanisms and their role in drug interactions:Effects of anaesthetics on G-protein-activated intracellular signalling pathways

Different types of receptor-mediated mechanism play a key role in cellular transmembrane communication. The majority of plasma membrane receptors mediate the effects of neurotransmitters and hormones through activation of GTP-binding proteins (G-proteins). Coupling of the activated receptor to a G-protein initiates (occasionally inhibits) a cascade of enzyme-catalysed reactions leading to the production of one or more second messengers, eventually leading to the physiological response. The most commonly known cascades are the phosphoinositide and the cAMP route. This paper will describe the key concepts of G-protein-mediated signalling of both cascades and introduce the concept of 'cross-talk'. Further, the effects of anaesthetics on the intracellular components of these signalling pathways will be reviewed.</p

Pirates and Samaritans: A Decade of Measurements on Peer Production and their Implications for Net Neutrality and Copyright

This study traces the evolution of commons-based peer production by a measurementbased analysis of case studies and disusses the impact of peer production on net neutrality and copyright law. The measurements include websites such asSuprnova. org, Youtube.com, and Facebook.com, and the Peer-to-Peer (P2P) systems Kazaa, Bittorrent, and Tribler. The measurements show the two sides of peer production, the pirate side with free availability of Hollywood movies on these P2P systems and the samaritan side exhibited by the quick joining of 400,000+ people in a community to organize protests against events in Burma. The telecommunications and content industry are disrupted by this way of peer production. As a consequence, revenues of both industries are likely to suffer in the coming years. On the other hand, innovative P2P systems could win the battle on merit over classical distribution technologies. As a result, a continuation is expected of both legal actions against P2P and possible blocking actions of P2P traffic, violating net neutrality. It is argued that this hinders innovation and causes a large discrepancy between legal and user perspectives. A reform of copyright laws are clearly needed, otherwise they will be unenforceable around 2010. Key words: P2P, collaboration, commons-based peer production, copyright

Inotropic effects of propofol, thiopental, midazolam, etomidate, and ketamine on isolated human atrial muscle

Background: Cardiovascular instability after intravenous induction of anesthesia may be explained partly by direct negative inotropic effects. The direct inotropic influence of etomidate, ketamine, midazolam, propofol, and thiopental on the contractility of isolated human atrial tissue was determined. Effective concentrations were compared with those reported clinically. Methods: Atrial tissue was obtained from 16 patients undergoing coronary bypass surgery. Each fragment was divided into three strips, and one anesthetic was tested per strip in increasing concentrations (10 -6 to 10 - 2 M). Strips were stimulated at 0.5 Hz, and maximum isometric force was measured. Induction agents were studied in two groups, group 1 (n = 7) containing thiopental, midazolam, and propofol, and group 2 (n = 9) consisting of etomidate, ketamine, and propofol. Results: The tested anesthetics caused a concentration-dependent depression of contractility resulting in complete cessation of contractions at the highest concentrations. The IC 50s (mean ± SEM; μM) for inhibition of the contractility were: thiopental 43 ± 7.6, propofol 235 ± 48 (group 1), and 246 ± 42 (group 2), midazolam 145 ± 54, etomidate 133 ± 13, and ketamine 303 ± 54. Conclusions: This is the first study demonstrating a concentration-dependent negative inotropic effect of intravenous anesthetics in isolated human atrial muscle. NO inhibition of myocardial contractility was found in the clinical concentration ranges of propofol, midazolam, and etomidate. In contrast, thiopental showed strong and ketamine showed slight negative inotropic properties. Thus, negative inotropic effects may explain in part the cardiovascular depression on induction of anesthesia with thiopental but not with propofol, midazolam, and etomidate. Improvement of hemodynamics after induction of anesthesia with ketamine cannot be explained by intrinsic cardiac stimulation

Design space exploration for providing QoS within the HARMONY framework

ABSTRACT The HARMONY architectur

Mild hypothermia during cardiopulmonary bypass assisted CABG is associated with improved short- and long-term survival, a 18- year cohort study

Data substantiating the optimal patient body temperature during cooling procedures in cardiac operations are currently unavailable. To explore the optimal temperature strategy, we examined the association between temperature management and survival among patients during cardiopulmonary bypass assisted coronary artery bypass grafting (CABG) procedures on 30-days and 5-year postoperative survival. Adult patients (n = 5,672, 23.6% female and mean (SD) age of 66 (10) years) operated between 1997 and 2015 were included, with continuous measured intraoperative nasopharyngeal temperatures. The association between mortality and patient characteristics, laboratory parameters, the lowest intraoperative plateau temperature and intraoperative cooling/rewarming rates were examined by multivariate Cox regression analysis. Machine learning-based cluster analysis was used to identify patient subgroups based on pre-cooling parameters and explore whether specific subgroups benefitted from a particular temperature management. Mild hypothermia (32- 35°C) was independently associated with improved 30-days and 5-year survival compared to patients in other temperature categories regardless of operation year. 30 days and 5-year survival were 98% and 88% in the mild hypothermia group, whereas it amounted 93% and 80% in the severe hypothermia (<30°C). Normothermia (35-37°C) showed the lowest survival after 30 days and 5 years amounting 93% and 72%, respectively. Cluster analysis identified 8 distinct patient subgroups principally defined by gender, age, kidney function and weight. The full cohort and all patient subgroups displayed the highest survival at a temperature of 32°C. Given these associations, further prospective randomized controlled trials are needed to ascertain optimal patient temperatures during CPB

Comparison of Machine Learning Models Including Preoperative, Intraoperative, and Postoperative Data and Mortality After Cardiac Surgery

Importance: A variety of perioperative risk factors are associated with postoperative mortality risk. However, the relative contribution of routinely collected intraoperative clinical parameters to short-term and long-term mortality remains understudied. Objective: To examine the performance of multiple machine learning models with data from different perioperative periods to predict 30-day, 1-year, and 5-year mortality and investigate factors that contribute to these predictions. Design, Setting, and Participants: In this prognostic study using prospectively collected data, risk prediction models were developed for short-term and long-term mortality after cardiac surgery. Included participants were adult patients undergoing a first-time valve operation, coronary artery bypass grafting, or a combination of both between 1997 and 2017 in a single center, the University Medical Centre Groningen in the Netherlands. Mortality data were obtained in November 2017. Data analysis took place between February 2020 and August 2021. Exposure: Cardiac surgery. Main Outcomes and Measures: Postoperative mortality rates at 30 days, 1 year, and 5 years were the primary outcomes. The area under the receiver operating characteristic curve (AUROC) was used to assess discrimination. The contribution of all preoperative, intraoperative hemodynamic and temperature, and postoperative factors to mortality was investigated using Shapley additive explanations (SHAP) values. Results: Data from 9415 patients who underwent cardiac surgery (median [IQR] age, 68 [60-74] years; 2554 [27.1%] women) were included. Overall mortality rates at 30 days, 1 year, and 5 years were 268 patients (2.8%), 420 patients (4.5%), and 612 patients (6.5%), respectively. Models including preoperative, intraoperative, and postoperative data achieved AUROC values of 0.82 (95% CI, 0.78-0.86), 0.81 (95% CI, 0.77-0.85), and 0.80 (95% CI, 0.75-0.84) for 30-day, 1-year, and 5-year mortality, respectively. Models including only postoperative data performed similarly (30 days: 0.78 [95% CI, 0.73-0.82]; 1 year: 0.79 [95% CI, 0.74-0.83]; 5 years: 0.77 [95% CI, 0.73-0.82]). However, models based on all perioperative data provided less clinically usable predictions, with lower detection rates; for example, postoperative models identified a high-risk group with a 2.8-fold increase in risk for 5-year mortality (4.1 [95% CI, 3.3-5.1]) vs an increase of 11.3 (95% CI, 6.8-18.7) for the high-risk group identified by the full perioperative model. Postoperative markers associated with metabolic dysfunction and decreased kidney function were the main factors contributing to mortality risk. Conclusions and Relevance: This study found that the addition of continuous intraoperative hemodynamic and temperature data to postoperative data was not associated with improved machine learning-based identification of patients at increased risk of short-term and long-term mortality after cardiac operations

Fast download but eternal seeding: The reward and punishment of Sharing Ratio Enforcement

Many private BitTorrent communities employ Sharing Ratio Enforcement (SRE) schemes to incentivize users to contribute their upload resources. It has been demonstrated that communities that use SRE are greatly oversupplied, i.e., they have much higher seeder-to-leecher ratios than communities in which SRE is not employed. The first order effect of oversupply under SRE is a positive increase in the average downloading speed. However, users are forced to seed for extremely long times to maintain adequate sharing ratios to be able to start new downloads. In this paper, we propose a fluid model to study the effects of oversupply under SRE, which predicts the average downloading speed, the average seeding time, and the average upload capacity utilization for users in communities that employ SRE. We notice that the phenomenon of oversupply has two undesired negative effects: a) Peers are forced to seed for long times, even though their seeding efforts are often not very productive (in terms of low upload capacity utilization); and b) SRE discriminates against peers with low bandwidth capacities and forces them to seed for longer durations than peers with high capacities. To alleviate these problems, we propose four different strategies for SRE, which have been inspired by ideas in social sciences and economics. We evaluate these strategies through simulations. Our results indicate that these new strategies release users from needlessly long seeding durations, while also being fair towards peers with low capacities and maintaining high system-wide downloading speeds. © 2011 IEEE

Systemic Risk and User-Level Performance in Private P2P Communities

Many peer-to-peer communities, including private BitTorrent communities that serve hundreds of thousands of users, utilize credit-based or sharing ratio enforcement schemes to incentivize their members to contribute. In this paper, we analyze the performance of such communities from both the system-level and the user-level perspectives. We show that both credit-based and sharing ratio enforcement policies can lead to system-wide 'crunches' or 'crashes,' where the system seizes completely due to too little or too much credit, respectively. We present a theoretical model that identifies the conditions that lead to these system pathologies and we design an adaptive credit system that automatically adjusts credit policies to maintain sustainability. Given private communities that are sustainable, it has been demonstrated that they are greatly oversupplied in terms of excessively high seeder-to-leecher ratios. We further analyze the user-level performance by studying the effects of oversupply. We show that although achieving an increase in the average downloading speed, the phenomenon of oversupply has three undesired effects: long seeding times, low upload capacity utilizations, and an unfair playing field for late entrants into swarms. To alleviate these problems, we propose four different strategies, which have been inspired by ideas in social sciences and economics. We evaluate these strategies through simulations and demonstrate their positive effects. © 1990-2012 IEEE

Ensemble machine learning prediction and variable importance analysis of 5-year mortality after cardiac valve and CABG operations

Despite having a similar post-operative complication profile, cardiac valve operations are associated with a higher mortality rate compared to coronary artery bypass grafting (CABG) operations. For long-term mortality, few predictors are known. In this study, we applied an ensemble machine learning (ML) algorithm to 88 routinely collected peri-operative variables to predict 5-year mortality after different types of cardiac operations. The Super Learner algorithm was trained using prospectively collected peri-operative data from 8241 patients who underwent cardiac valve, CABG and combined operations. Model performance and calibration were determined for all models, and variable importance analysis was conducted for all peri-operative parameters. Results showed that the predictive accuracy was the highest for solitary mitral (0.846 [95% CI 0.812-0.880]) and solitary aortic (0.838 [0.813-0.864]) valve operations, confirming that ensemble ML using routine data collected perioperatively can predict 5-year mortality after cardiac operations with high accuracy. Additionally, post-operative urea was identified as a novel and strong predictor of mortality for several types of operation, having a seemingly additive effect to better known risk factors such as age and postoperative creatinine.Peer reviewe

Deep Learning for Identification of Acute Illness and Facial Cues of Illness

Background: The inclusion of facial and bodily cues (clinical gestalt) in machine learning (ML) models improves the assessment of patients' health status, as shown in genetic syndromes and acute coronary syndrome. It is unknown if the inclusion of clinical gestalt improves ML-based classification of acutely ill patients. As in previous research in ML analysis of medical images, simulated or augmented data may be used to assess the usability of clinical gestalt. Objective: To assess whether a deep learning algorithm trained on a dataset of simulated and augmented facial photographs reflecting acutely ill patients can distinguish between healthy and LPS-infused, acutely ill individuals. Methods: Photographs from twenty-six volunteers whose facial features were manipulated to resemble a state of acute illness were used to extract features of illness and generate a synthetic dataset of acutely ill photographs, using a neural transfer convolutional neural network (NT-CNN) for data augmentation. Then, four distinct CNNs were trained on different parts of the facial photographs and concatenated into one final, stacked CNN which classified individuals as healthy or acutely ill. Finally, the stacked CNN was validated in an external dataset of volunteers injected with lipopolysaccharide (LPS). Results: In the external validation set, the four individual feature models distinguished acutely ill patients with sensitivities ranging from 10.5% (95% CI, 1.3–33.1% for the skin model) to 89.4% (66.9–98.7%, for the nose model). Specificity ranged from 42.1% (20.3–66.5%) for the nose model and 94.7% (73.9–99.9%) for skin. The stacked model combining all four facial features achieved an area under the receiver characteristic operating curve (AUROC) of 0.67 (0.62–0.71) and distinguished acutely ill patients with a sensitivity of 100% (82.35–100.00%) and specificity of 42.11% (20.25–66.50%). Conclusion: A deep learning algorithm trained on a synthetic, augmented dataset of facial photographs distinguished between healthy and simulated acutely ill individuals, demonstrating that synthetically generated data can be used to develop algorithms for health conditions in which large datasets are difficult to obtain. These results support the potential of facial feature analysis algorithms to support the diagnosis of acute illness