Genome-wide profiling of the cardiac transcriptome after myocardial infarction identifies novel heart-specific long non-coding RNAs

Aim Heart disease is recognized as a consequence of dysregulation of cardiac gene regulatory networks. Previously, unappreciated components of such networks are the long non-coding RNAs (lncRNAs). Their roles in the heart remain to be elucidated. Thus, this study aimed to systematically characterize the cardiac long non-coding transcriptome post-myocardial infarction and to elucidate their potential roles in cardiac homoeostasis. Methods and results We annotated the mouse transcriptome after myocardial infarction via RNA sequencing and ab initio transcript reconstruction, and integrated genome-wide approaches to associate specific lncRNAs with developmental processes and physiological parameters. Expression of specific lncRNAs strongly correlated with defined parameters of cardiac dimensions and function. Using chromatin maps to infer lncRNA function, we identified many with potential roles in cardiogenesis and pathological remodelling. The vast majority was associated with active cardiac-specific enhancers. Importantly, oligonucleotide-mediated knockdown implicated novel lncRNAs in controlling expression of key regulatory proteins involved in cardiogenesis. Finally, we identified hundreds of human orthologues and demonstrate that particular candidates were differentially modulated in human heart disease. Conclusion These findings reveal hundreds of novel heart-specific lncRNAs with unique regulatory and functional characteristics relevant to maladaptive remodelling, cardiac function and possibly cardiac regeneration. This new class of molecules represents potential therapeutic targets for cardiac disease. Furthermore, their exquisite correlation with cardiac physiology renders them attractive candidate biomarkers to be used in the clini

A Statistical Design for Testing Transgenerational Genomic Imprinting in Natural Human Populations

Genomic imprinting is a phenomenon in which the same allele is expressed differently, depending on its parental origin. Such a phenomenon, also called the parent-of-origin effect, has been recognized to play a pivotal role in embryological development and pathogenesis in many species. Here we propose a statistical design for detecting imprinted loci that control quantitative traits based on a random set of three-generation families from a natural population in humans. This design provides a pathway for characterizing the effects of imprinted genes on a complex trait or disease at different generations and testing transgenerational changes of imprinted effects. The design is integrated with population and cytogenetic principles of gene segregation and transmission from a previous generation to next. The implementation of the EM algorithm within the design framework leads to the estimation of genetic parameters that define imprinted effects. A simulation study is used to investigate the statistical properties of the model and validate its utilization. This new design, coupled with increasingly used genome-wide association studies, should have an immediate implication for studying the genetic architecture of complex traits in humans

Lung epithelium as a sentinel and effector system in pneumonia – molecular mechanisms of pathogen recognition and signal transduction

Pneumonia, a common disease caused by a great diversity of infectious agents is responsible for enormous morbidity and mortality worldwide. The bronchial and lung epithelium comprises a large surface between host and environment and is attacked as a primary target during lung infection. Besides acting as a mechanical barrier, recent evidence suggests that the lung epithelium functions as an important sentinel system against pathogens. Equipped with transmembranous and cytosolic pathogen-sensing pattern recognition receptors the epithelium detects invading pathogens. A complex signalling results in epithelial cell activation, which essentially participates in initiation and orchestration of the subsequent innate and adaptive immune response. In this review we summarize recent progress in research focussing on molecular mechanisms of pathogen detection, host cell signal transduction, and subsequent activation of lung epithelial cells by pathogens and their virulence factors and point to open questions. The analysis of lung epithelial function in the host response in pneumonia may pave the way to the development of innovative highly needed therapeutics in pneumonia in addition to antibiotics

Interdependence between transportation system and power distribution system: a comprehensive review on models and applications

The rapidly increasing penetration of electric vehicles in modern metropolises has been witnessed during the past decade, inspired by financial subsidies as well as public awareness of climate change and environment protection. Integrating charging facilities, especially high-power chargers in fast charging stations, into power distribution systems remarkably alters the traditional load flow pattern, and thus imposes great challenges on the operation of distribution network in which controllable resources are rare. On the other hand, provided with appropriate incentives, the energy storage capability of electric vehicle offers a unique opportunity to facilitate the integration of distributed wind and solar power generation into power distribution system. The above trends call for thorough investigation and research on the interdependence between transportation system and power distribution system. This paper conducts a comprehensive survey on this line of research. The basic models of transportation system and power distribution system are introduced, especially the user equilibrium model, which describes the vehicular flow on each road segment and is not familiar to the readers in power system community. The modelling of interdependence across the two systems is highlighted. Taking into account such interdependence, applications ranging from long-term planning to short-term operation are reviewed with emphasis on comparing the description of traffic-power interdependence. Finally, an outlook of prospective directions and key technologies in future research is summarized.fi=vertaisarvioitu|en=peerReviewed

Effects of total fat intake on body fatness in adults

Background: The ideal proportion of energy from fat in our food and its relation to body weight is not clear. In order to prevent overweight and obesity in the general population, we need to understand the relationship between the proportion of energy from fat and resulting weight and body fatness in the general population. Objectives: To assess the effects of proportion of energy intake from fat on measures of body fatness (including body weight, waist circumference, percentage body fat and body mass index) in people not aiming to lose weight, using all appropriate randomised controlled trials (RCTs) of at least six months duration. Search methods: We searched CENTRAL, MEDLINE, Embase, Clinicaltrials.gov and the WHO International Clinical Trials Registry Platform (ICTRP) to October 2019. We did not limit the search by language. Selection criteria: Trials fulfilled the following criteria: 1) randomised intervention trial, 2) included adults aged at least 18 years, 3) randomised to a lower fat versus higher fat diet, without the intention to reduce weight in any participants, 4) not multifactorial and 5) assessed a measure of weight or body fatness after at least six months. We duplicated inclusion decisions and resolved disagreement by discussion or referral to a third party. Data collection and analysis: We extracted data on the population, intervention, control and outcome measures in duplicate. We extracted measures of body fatness (body weight, BMI, percentage body fat and waist circumference) independently in duplicate at all available time points. We performed random-effects meta-analyses, meta-regression, subgrouping, sensitivity, funnel plot analyses and GRADE assessment. Main results: We included 37 RCTs (57,079 participants). There is consistent high-quality evidence from RCTs that reducing total fat intake results in small reductions in body fatness; this was seen in almost all included studies and was highly resistant to sensitivity analyses (GRADE high-consistency evidence, not downgraded). The effect of eating less fat (compared with higher fat intake) is a mean body weight reduction of 1.4 kg (95% confidence interval (CI) -1.7 to -1.1 kg, in 53,875 participants from 26 RCTs, I2 = 75%). The heterogeneity was explained in subgrouping and meta-regression. These suggested that greater weight loss results from greater fat reductions in people with lower fat intake at baseline, and people with higher body mass index (BMI) at baseline. The size of the effect on weight does not alter over time and is mirrored by reductions in BMI (MD -0.5 kg/m2, 95% CI -0.6 to -0.3, 46,539 participants in 14 trials, I2 = 21%), waist circumference (MD -0.5 cm, 95% CI -0.7 to -0.2, 16,620 participants in 3 trials; I2 = 21%), and percentage body fat (MD -0.3% body fat, 95% CI -0.6 to 0.00, P = 0.05, in 2350 participants in 2 trials; I2 = 0%). There was no suggestion of harms associated with low fat diets that might mitigate any benefits on body fatness. The reduction in body weight was reflected in small reductions in LDL (-0.13 mmol/L, 95% CI -0.21 to -0.05), and total cholesterol (-0.23 mmol/L, 95% CI -0.32 to -0.14), with little or no effect on HDL cholesterol (-0.02 mmol/L, 95% CI -0.03 to 0.00), triglycerides (0.01 mmol/L, 95% CI -0.05 to 0.07), systolic (-0.75 mmHg, 95% CI -1.42 to -0.07) or diastolic blood pressure(-0.52 mmHg, 95% CI -0.95 to -0.09), all GRADE high-consistency evidence or quality of life (0.04, 95% CI 0.01 to 0.07, on a scale of 0 to 10, GRADE low-consistency evidence). Authors' conclusions: Trials where participants were randomised to a lower fat intake versus a higher fat intake, but with no intention to reduce weight, showed a consistent, stable but small effect of low fat intake on body fatness: slightly lower weight, BMI, waist circumference and percentage body fat compared with higher fat arms. Greater fat reduction, lower baseline fat intake and higher baseline BMI were all associated with greater reductions in weight. There was no evidence of harm to serum lipids, blood pressure or quality of life, but rather of small benefits or no effect

Reviewing next of kin regrets in surgical decision-making: cross-sectional analysis of systematically searched literature

Background: Decision-making concerning relatives undergoing surgery is challenging. It remains unclear to what extent implicated next of kin eventually regret their decisions and how this regret is assessed. Our aim was to systematically review the literature on decisional regret of next of kin and to describe the assessment tools used and the surgical populations studied. Methods: We included interventional or observational, quantitative or qualitative studies reporting the measurement of decisional regret of next of kin concerning relatives undergoing surgery. We searched a variety of databases without restriction on publication year. We assessed the quality of reporting of quantitative studies using the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies and of qualitative studies using the Critical Appraisal Skills Program Checklist. Results: Thirteen cross-sectional, five prospective cohorts and five qualitative studies matched our inclusion criteria. In 18 studies (78%), patients were children, in five (22%), young or middle-aged adults. No study included elderly or frail patients. Thirteen studies (57%) used the original Decision Regret Scale which was validated for patients, but not for next of kin. Only 3 of the 18 (17%) quantitative studies and only one of the 4 (25%) qualitative studies were rated as "good" in the quality assessment. Conclusion: None of the retrieved studies used validated tools to assess the decisional regret of next of kin and none of them examined this issue in elderly or frail surgical patients.</p

Local anaesthetics risks perception : a web-based survey

Background: The use of local anaesthetics (LAs) is usually associated with few adverse effects, but local anaesthetic systemic toxicity (LAST) can result in serious harm and even death. However, practitioner awareness regarding this risk has been little studied. Methods: This was a closed, web-based study carried out at two Swiss university hospitals using a fully automated questionnaire. The main objective was to evaluate LAST awareness and LA use among various medical practitioners. The secondary objective was to determine whether these physicians felt that a tool designed to compute maximum safe LA doses should be developed. Results: The overall participation rate was 40.2 % and was higher among anaesthesiologists (154/249, 61.8 % vs 159/530, 30.0 %; P &lt; .001). Anaesthesiologists identified the risk of LAST and the systems involved more frequently than non-anaesthesiologists (85.1 % vs 43.4 %, P &lt; .001). After adjusting for years of clinical experience, age, country of diploma, frequency of LA use, clinical position and being an anaesthesiologist, the only significant associations were this latter factor (P &lt; .001) and clinical position (P = .016 for fellows and P = .046 for consultants, respectively). Most respondents supported the development of a tool designed to compute maximum safe LA doses (251/313, 80.2 %) and particularly of a mobile app (190/251, 75.7 %). Conclusions: LAST awareness is limited among practitioners who use LAs on a regular basis. Educational interventions should be created, and tools designed to help calculate maximum safe LA doses developed. The actual frequency of unsafe LA doses administration would also deserve further study.</p

Impact of a Mobile App (LoAD Calc) on the Calculation of Maximum Safe Doses of Local Anesthetics : Protocol for a Randomized Controlled Trial

Background Local anesthetics (LAs) are regularly used to alleviate pain during medical or surgical procedures. Their use is generally considered safe, but exceeding the maximum recommended doses can lead to LA systemic toxicity, a rare but potentially lethal complication. Determining maximum safe doses is therefore mandatory before performing local anesthesia, but rules are often unclear and the factors affecting dose calculation are numerous. Mobile health apps have been shown to help clinical decision-making, but most currently available apps present significant limitations. The Local Anesthetics Dose Calculator (LoAD Calc) app was designed to overcome these limitations by taking all relevant parameters into account. Before deploying this app in a clinical setting, it should be tested to determine its effectiveness and whether clinicians would be willing to use it. Objective The primary objective will be to evaluate the effectiveness of the LoAD Calc app through written simulated cases. The secondary objective will be to determine whether physicians find this app easier, faster, and safer than the methods they generally use. Methods We describe a parallel-group randomized controlled trial protocol. Anesthesiologists working at the Geneva University Hospitals will be invited to participate. Participants will be asked to compute the maximum dose of LA in 10 simulated clinical cases using 3 different LAs. The maximum safe dose will be determined manually using the same calculation rules that were used to develop LoAD Calc, without using the app itself. An overdose will be considered any dose higher than the correct dose, rounded to the superior integer, while an underdose will be defined as the optimal calculated dose minus 20%, rounded to the inferior integer. Randomization will be stratified according to current position (resident vs registrar). The participants allocated to the LoAD Calc (experimental) group will use the LoAD Calc app to compute the maximum safe LA doses. Those allocated to the control group will be asked to use the method they generally use. The primary outcome will be the overall overdose rate. Secondary outcomes will include the overdose rate according to ideal and actual body weight and to each specific LA, the overall underdose rate, and the time taken to complete these calculations. The app’s usability will also be assessed. Results A sample size of 46 participants will be needed to detect a difference of 10% with a power of 90%. Thus, a target of 50 participants was set to allow for attrition and exclusion criteria. We expect recruitment to begin during the winter of 2023, data analysis in the spring of 2024, and results by the end of 2024. Conclusions This study should determine whether LoAD Calc, a mobile health app designed to compute maximum safe LA doses, is safer and more efficient than traditional LA calculation methods. International Registered Report Identifier (IRRID) PRR1-10.2196/53679</p

Development and Preliminary Validation of LoAD Calc, a Mobile App for Calculating the Maximum Safe Single Dose of Local Anesthetics

Local anesthetics systemic toxicity can lead to life-threatening situations. Correct calculation of the maximum safe dose is therefore paramount in preventing such complications. Different solutions have already emerged to support anesthesiologists but are seldom used in clinical practice as they require either access to a computer or specific documents to be at hand. A mobile app could provide an easy and practical solution; however, the few apps already created for this purpose often lack key elements, allowing invalid data to be entered and suggesting doses that might exceed the maximum safe dose. We describe the development of LoAD Calc, a mobile health (mHealth) app developed using a modified version of the Information Systems Research framework, which adds design thinking modes to the original framework. The app was enhanced through multiple iterations and developed with the aid of contextual observations and interviews, brainswarming sessions, prototyping, and continuous feedback. The design process led to the creation of two prototypes which underwent thorough testing by a sample of eight anesthesiologists. The final version of the app, LoAD Calc, was deployed on Apple and Android mobile test platforms and tested again by the same sample until deemed fit for release