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High fructose consumption is associated with an increased risk of cardiometabolic disease, and fructose feeding dose-dependently induces markers reflective of poor metabolic health. However, unlike glucose, surprisingly little is known about person-to-person differences in postprandial plasma fructose patterns. Herein, we performed post hoc analyses of two published studies to address this question. In the first cohort, 16 participants' all-day plasma fructose concentration patterns (08:00-23:30) were determined (8 women and 8 men) while consuming mixed meals (breakfast, lunch, and dinner) with a fructose-sweetened beverage at each meal (30% of calories). Individually plotted results demonstrate remarkably disparate fructose patterns with respect to peak concentration and timing. A secondary study confirmed substantial interindividual variability in plasma fructose patterns over 240 min in 16 adults consuming Ensure®, a commercially available mixed macronutrient drink containing a low dose of fructose. The health ramifications of interindividual variations in postprandial fructose metabolism and the underlying physiological mechanisms driving differences in post-meal blood patterns remain to be explored. Future research is warranted to determine if interindividual variability in fructose digestion, metabolism, and postprandial blood concentration patterns is associated with cardiometabolic health phenotypes and disease risk.

Trees growing in natural and managed environments have the capacity to act as conduits for the transport of greenhouse gases produced belowground to the atmosphere. Nitrous oxide (N2O) emissions have been observed from tree stems in natural ecosystems but have not yet been measured in the context of forested former landfill sites. This research gap was addressed by an investigation quantifying stem and soil N2O emissions from a closed UK landfill and a comparable natural site. Measurements were made by using flux chambers and gas chromatography over a four-month period. Analyses showed that the average N2O stem fluxes from the landfill and non-landfill sites were 0.63 ± 0.06 μg m–2 h–1 and 0.26 ± 0.05 μg m–2 h–1, respectively. The former landfill site showed seasonal patterns in N2O stem emissions and decreasing N2O fluxes with increased stem sampling position above the forest floor. Tree stem emissions accounted for 1% of the total landfill N2O surface flux, which is lower than the contribution of stem fluxes to the total surface flux in dry and flooded boreal forests.

Research Data Management (RDM) is an inherently complex area that presents a challenge for institutions to effectively upskill people in RDM and get them to enact RDM best practices. This systematic review aims to provide a reference point for institutions and academic developers to design and deliver effective training for their students and staff to navigate the RDM landscape. After narrowing down from a pool of 1256 articles across 7 databases to 28 articles for analysis, this systematic review provides an overview of available RDM training and identifies three main themes in relation to key features of this RDM training.

Our modern environment is said to be obesogenic, promoting the consumption of calorically dense foods and reducing energy expenditure. One factor thought to drive excess energy intake is the abundance of cues signaling the availability of highly palatable foods. Indeed, these cues exert powerful influences over food-related decision-making. Although obesity is associated with changes to several cognitive domains, the specific role of cues in producing this shift and on decision-making more generally, remains poorly understood. Here we review the literature examining how obesity and palatable diets affect the ability of Pavlovian cues to influence instrumental food-seeking behaviors by examining rodent and human studies incorporating Pavlovian-instrumental transfer (PIT) protocols. There are two types of PIT: (a) general PIT that tests whether cues can energize actions elicited in the pursuit of food generally, and (b) specific PIT which tests whether cues can elicit an action that earns a specific food outcome when faced with a choice. Both types of PIT have been shown to be vulnerable to alterations as a result of changes to diet and obesity. However, effects appear to be driven less by increases in body fat and more by palatable diet exposure per se. We discuss the limitations and implications of the current findings. The challenges for future research are to uncover the mechanisms underlying these alterations to PIT, which appear unrelated to excess weight itself, and to better model the complex determinants of food choice in humans.

Students must actively engage in problem solving to effectively learn in introductory physics courses. However, students often get stuck and are not able to make progress when solving problems outside of their current ability, particularly when one-on-one tutoring and instructor office hours are a limited resource. One effective technique consists of providing students with worked examples during the problem-solving process. While the benefits of worked examples are well established, less is known about how the format of the worked example affects student learning, or the effect of solution videos on student metacognition. This study presents three experiments investigating how the format of animated worked examples affects student learning and metacognition. The results indicate that students learn equally well from different styles of solution videos that follow multimedia learning principles. In addition, attempting to solve problems before viewing the solution videos facilitates learning for problems just outside a student's current ability, but not for more difficult problems. Further, attempting to solve very difficult problems before viewing animated solution videos can potentially lead to overconfidence, where students believe that they learned more from the solutions than they have actually learned.

IntroductionTwo-dimensional strain analysis is a powerful analysis modality, however, clinical utilization has been limited by variability between different analysis systems and operators. We compared strain in adults and children using vendor-specific and vendor-independent software to evaluate variability.MethodsOne hundred and ten subjects (50/110 pediatric, 80/110 normal left ventricular function) had echocardiograms with a General Electric ultrasound scanner between September 2010 and January 2012. Left ventricular longitudinal strain was derived with EchoPAC (General Electric, v10.8.1), a vendor-specific software, and Velocity Vector Imaging (Siemens, v3.5), which is vendor-independent. Three independent readers analyzed all the echocardiograms yielding 330 datasets.ResultsMean left ventricular global longitudinal Lagrangian strain was -18.1 ± SD 4.4% for EchoPAC and -15.3 ± SD 4.1% for Velocity Vector Imaging. Velocity Vector Imaging yielded lower absolute global longitudinal Lagrangian strain by mean 2.9 (±SD 2.7, p 3-5 strain points on repeat measurements. Strain values between the systems are not interchangeable.

Background. Each year, approximately 5000 New Zealanders are admitted to hospital with first-time acute coronary syndrome (ACS). The Multi-Ethnic New Zealand Study of Acute Coronary Syndromes (MENZACS) is a prospective longitudinal cohort study embedded within the All New Zealand Acute Coronary Syndrome Quality Improvement (ANZACS-QI) registry in six hospitals. The objective of MENZACS is to examine the relationship between clinical, genomic, and cardiometabolic markers in relation to presentation and outcomes post-ACS. Methods. Patients with first-time ACS are enrolled and study-specific research data is collected alongside the ANZACS-QI registry. The research blood samples are stored for future genetic/biomarker assays. Dietary information is collected with a food frequency questionnaire and information about physical activity, smoking, and stress is also collected via questionnaire. Detailed family history, ancestry, and ethnicity data are recorded on all participants. Results. During the period between 2015 and 2019, there were 2015 patients enrolled. The mean age was 61 years, with 60% of patients aged Conclusion. MENZACS represents a cohort with optimal contemporary management and will be a significant epidemiological bioresource for the study of environmental and genetic factors contributing to ACS in New Zealand’s multi-ethnic environment. The study will utilise clinical, nutritional, lifestyle, genomic, and biomarker analyses to explore factors influencing the progression of coronary disease and develop risk prediction models for health outcomes.

Abstract Background Cyclooxygenase-2 (COX-2), which is rapidly upregulated by inflammation, is a key enzyme catalyzing the rate-limiting step in the synthesis of several inflammatory prostanoids. Successful positron emission tomography (PET) radioligand imaging of COX-2 in vivo could be a potentially powerful tool for assessing inflammatory response in the brain and periphery. To date, however, the development of PET radioligands for COX-2 has had limited success. Methods The novel PET tracer [11C]MC1 was used to examine COX-2 expression [1] in the brains of four rhesus macaques at baseline and after injection of the inflammogen lipopolysaccharide (LPS) into the right putamen, and [2] in the joints of two human participants with rheumatoid arthritis and two healthy individuals. In the primate study, two monkeys had one LPS injection, and two monkeys had a second injection 33 and 44 days, respectively, after the first LPS injection. As a comparator, COX-1 expression was measured using [11C]PS13. Results COX-2 binding, expressed as the ratio of specific to nondisplaceable uptake (BP ND) of [11C]MC1, increased on day 1 post-LPS injection; no such increase in COX-1 expression, measured using [11C]PS13, was observed. The day after the second LPS injection, a brain lesion (~ 0.5 cm in diameter) with high COX-2 density and high BP ND (1.8) was observed. Postmortem brain analysis at the gene transcript or protein level confirmed in vivo PET results. An incidental finding in an unrelated monkey found a line of COX-2 positivity along an incision in skull muscle, demonstrating that [11C]MC1 can localize inflammation peripheral to the brain. In patients with rheumatoid arthritis, [11C]MC1 successfully imaged upregulated COX-2 in the arthritic hand and shoulder and apparently in the brain. Uptake was blocked by celecoxib, a COX-2 preferential inhibitor. Conclusions Taken together, these results indicate that [11C]MC1 can image and quantify COX-2 upregulation in both monkey brain after LPS-induced neuroinflammation and in human peripheral tissue with inflammation. Trial registration ClinicalTrials.gov NCT03912428. Registered April 11, 2019.

ICMA, a software framework to create 3D finite element models of the left ventricle from cardiac ultrasound or magnetic resonance imaging (MRI) data, has been made available as an open-source code. The framework is hardware vendor independent and uses spec

Encouraged by positive clinical results at the University of Illinois, mastery-style homework was integrated into a large semester-long preparatory physics course via an online homework system that used narrated animated video solutions as correctives. This paper discusses the impact and evolution of the homework in its first two years. The first iteration revealed that students were frustrated and did not engage with the system in an effective way. Intending to reduce that frustration and quell negative behavior, the mastery requirement was relaxed, transfer between versions was reduced, and the addition of a direct discussion with students about the homework were implemented in its second year. The results showed that details of implementation can substantially affect students' behavior; large and statistically significant effects were observed as a reduction in frustration (with self-identified "frustrated" students dropping from 60% in 2014 to 30% in 2015) and improvement in performance (average student mastery rate of 59% to 69%).

Background: Multi-omics delivers more biological insight than targeted investigations. We applied multi-omics to patients with heart failure with reduced ejection fraction (HFrEF). Methods: 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography mass spectrometry (LC-MS/GC-MS) and solid-phase microextraction (SPME) volatilomics in plasma and urine. HFrEF was defined using left ventricular global longitudinal strain, ejection fraction and NTproBNP. A consumer breath acetone (BrACE) sensor validated results in n = 73. Results: 28 metabolites were identified by GCMS, 35 by LCMS and 4 volatiles by SPME in plasma and urine. Alanine, aspartate and glutamate, citric acid cycle, arginine biosynthesis, glyoxylate and dicarboxylate metabolism were altered in HFrEF. Plasma acetone correlated with NT-proBNP (r = 0.59, 95% CI 0.4 to 0.7), 2-oxovaleric and cis-aconitic acid, involved with ketone metabolism and mitochondrial energetics. BrACE > 1.5 ppm discriminated HF from other cardiac pathology (AUC 0.8, 95% CI 0.61 to 0.92, p < 0.0001). Conclusion: Breath acetone discriminated HFrEF from other cardiac pathology using a consumer sensor, but was not cardiac specific.

Aim: We propose a method for screening full blood count metadata for evidence of communicable and noncommunicable diseases using machine learning (ML). Materials & methods: High dimensional hematology metadata was extracted over an 11-month period from Sysmex hematology analyzers from 43,761 patients. Predictive models for age, sex and individuality were developed to demonstrate the personalized nature of hematology data. Both numeric and raw flow cytometry data were used for both supervised and unsupervised ML to predict the presence of pneumonia, urinary tract infection and COVID-19. Heart failure was used as an objective to prove method generalizability. Results: Chronological age was predicted by a deep neural network with R2: 0.59; mean absolute error: 12; sex with AUROC: 0.83, phi: 0.47; individuality with 99.7% accuracy, phi: 0.97; pneumonia with AUROC: 0.74, sensitivity 58%, specificity 79%, 95% CI: 0.73–0.75, p < 0.0001; urinary tract infection AUROC: 0.68, sensitivity 52%, specificity 79%, 95% CI: 0.67–0.68, p < 0.0001; COVID-19 AUROC: 0.8, sensitivity 82%, specificity 75%, 95% CI: 0.79–0.8, p = 0.0006; and heart failure area under the receiver operator curve (AUROC): 0.78, sensitivity 72%, specificity 72%, 95% CI: 0.77–0.78; p < 0.0001. Conclusion: ML applied to hematology data could predict communicable and noncommunicable diseases, both at local and global levels.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is a major cause of stroke and morbidity. Recent genome-wide association studies have shown that paired-like homeodomain transcription factor 2 (Pitx2) to be strongly associated with AF. However, the mechanisms underlying Pitx2 modulated arrhythmogenesis and variable effectiveness of antiarrhythmic drugs (AADs) in patients in the presence or absence of impaired Pitx2 expression remain unclear. We have developed multi-scale computer models, ranging from a single cell to tissue level, to mimic control and Pitx2-knockout atria by incorporating recent experimental data on Pitx2-induced electrical and structural remodeling in humans, as well as the effects of AADs. The key findings of this study are twofold. We have demonstrated that shortened action potential duration, slow conduction and triggered activity occur due to electrical and structural remodelling under Pitx2 deficiency conditions. Notably, the elevated function of calcium transport ATPase increases sarcoplasmic reticulum Ca2+ concentration, thereby enhancing susceptibility to triggered activity. Furthermore, heterogeneity is further elevated due to Pitx2 deficiency: 1) Electrical heterogeneity between left and right atria increases; and 2) Increased fibrosis and decreased cell-cell coupling due to structural remodelling slow electrical propagation and provide obstacles to attract re-entry, facilitating the initiation of re-entrant circuits. Secondly, our study suggests that flecainide has antiarrhythmic effects on AF due to impaired Pitx2 by preventing spontaneous calcium release and increasing wavelength. Furthermore, our study suggests that Na+ channel effects alone are insufficient to explain the efficacy of flecainide. Our study may provide the mechanisms underlying Pitx2-induced AF and possible explanation behind the AAD effects of flecainide in patients with Pitx2 deficiency.

Insulin is known to act in the central nervous system to regulate several physiological and behavioural outcomes, including energy balance, glucose homeostasis and cognitive functioning. However, the neuronal populations through which insulin enhances cognitive performance remain unidentified. Insulin receptors are found in neuropeptide-Y (NPY) expressing neurons, which are abundant in the hypothalamus and hippocampus; regions involved in feeding behaviour and spatial memory, respectively. Here we show that mice with a tissue specific knockout of insulin receptors in NPY expressing neurons (IRlox/lox; NPYCre/+) display an impaired performance in the probe trial of the Morris Water Maze compared with control mice at both the 6 and the 12, but not at the 24 months time point, consistent with a crucial role of insulin and NPY in cognitive functioning. By 24 months of age all groups demonstrated similar reductions in spatial memory performance. Together, these data suggest that the mechanisms through which insulin influences cognitive functioning are, at least in part, via insulin receptor signaling in NPY expressing neurons. These results also highlight that cognitive impairments observed in aging may be due to impaired insulin signaling.

Abstract Background The aim of mentorship is to build the mentees capacity, enhance their skills and improve their ability to produce desired outcomes. However, the mentoring relationship is vulnerable to a number of challenges that may undermine its effectiveness and sustainability. We aimed to explore the experiences and perceptions of student and junior faculty mentees and senior faculty mentors at the Makerere University College of Health Sciences and identify the key factors defined by mentees and mentors as necessary for a successful mentorship program. Methods A qualitative design involving focus group discussions (FGDs) and key informant interviews (KII) was used. A total of eight KII and four FGDs were conducted, audio recorded and transcribed verbatim. Open coding of the transcripts was performed, and major themes were identified through multiple readings based on thematic analysis. Results Six key themes were shared by the mentees and mentors including: 1) defining the role of the mentor; 2) desired characteristics of a mentor and a mentoring relationship, with an emphasis on mutual trust and respect; 3) overlapping roles of mentors and supervisors; 4) issues with the process for identifying mentors, including the benefits and drawbacks of the mentee selecting mentor vs. being assigned a mentor; 5) current barriers to mentoring, including lack of knowledge about current program, lack of formal structure, uncertainly about who should initiate relationship, and unclear roles and expectations and 6) recommendations for the future development of mentoring programme, including the need for a formalized programme, and training adapted to the local context. Conclusions The mentees and mentors described the role of the mentor and desired characteristics of mentors and a mentoring relationship similarly. Most concerns about mentoring occurred when current mentoring programmes and practices were not well aligned with these desired characteristics. Recommendations for future development of mentoring included greater formalization of mentoring with mentoring programmes based on shared expectations and adapted to the local context.

Narrated animated solution videos were implemented in a clinical study that compared a mastery setting that employed repeated cycles of testing with instructional support to a group that had a single opportunity to experience the materials. The mastery setting students attempted sequential questions sets on a topic, with animated solutions between each set, until mastery was achieved, combining formative assessment with worked examples. Students showed significant improvement from their first to second tries on similar sets of problems, attributable to the feedback and solutions they were given after the first try. These improvements were shown in two topics, superposition and electric potential. The single try group was given one version of the questions and solutions, and while they were not required to watch the solutions to move forward, they chose to. On a post-test including near and far transfer questions, no significant difference was seen between the mastery group and the single try group, but both significantly outperformed a control group that received no instructional support, indicating that students successfully transferred the skills from the solutions to the post-test.

This paper is part of a series of studies to improve the efficacy of online physics homework activities by integrating narrated animated solutions with mastery inspired exercises. In a clinical study using first- and second-year university students, the mastery group attempted question sets in four levels, with animated solutions between each attempt, until mastery was achieved on each level. This combined elements of formative assessment, the worked example effect, and mastery learning. The homework group attempted questions with immediate feedback and unlimited tries. The two groups took a similar amount of time to complete the activity. The mastery group significantly outperformed the homework group on a free response post-test that required students to show their work in solving near and far transfer problems.

Raman spectroscopy has proven valuable for determining the composition of manufactured drug products, as well as identifying counterfeit drugs. Here we present a simple method to determine the active pharmaceutical ingredient (API) mass percent in a sample that does not require knowledge of the identities or relative mass percents of the inactive pharmaceutical ingredients (excipients). And further, we demonstrated the ability of the method to pass or fail a manufactured drug product batch based on a calculated acceptance value in accordance with the US Pharmacopeia method for content uniformity. The method was developed by fitting the Raman spectra of 30 Claritin® tablets with weighted percentages of the Raman spectrum of its API, loratadine, and a composite spectrum of the known excipients. The mean loratadine mass of 9.79 ± 40 mg per 100 mg tablet compared favorably to the 10.21 ± 0.63 mg per 100 mg tablet determined using high-performance liquid chromatography, both of which met the acceptance value to pass the 10 mg API product as labelled. The method was then applied to a generic version of the Claritin product that employed different excipients of unknown mass percents. A Raman spectrum representative of all excipients was created by subtracting the API Raman spectrum from the product spectrum. The Raman spectra of the 30 generic tablets were then fit with weighted percents of the pure loratadine spectrum and the created excipient spectrum, and used to determine a mean API mass for the tablets of 10.12 ± 40 mg, again meeting the acceptance value for the 10 mg API product. The data suggest that this simple method could be used to pass or fail manufactured drug product batches in accordance with the US Pharmacopeia method for content uniformity, without knowledge of the excipients.

Students must actively engage in problem solving to effectively learn in introductory physics courses. However, students often get stuck and are not able to make progress when solving problems outside of their current ability, particularly when one-on-one tutoring and instructor office hours are a limited resource. One effective technique consists of providing students with worked examples during the problem-solving process. While the benefits of worked examples are well established, less is known about how the format of the worked example affects student learning, or the effect of solution videos on student metacognition. This study presents three experiments investigating how the format of animated worked examples affects student learning and metacognition. The results indicate that students learn equally well from different styles of solution videos that follow multimedia learning principles. In addition, attempting to solve problems before viewing the solution videos facilitates learning for problems just outside a student’s current ability, but not for more difficult problems. Further, attempting to solve very difficult problems before viewing animated solution videos can potentially lead to overconfidence, where students believe that they learned more from the solutions than they have actually learned.

Visual representations play a critical role in teaching physics. However, since we do not have a satisfactory understanding of how visual perception impacts the construction of abstract knowledge, most visual representations used in instructions are either created based on existing conventions or designed according to the instructor's intuition, which leads to a significant variance in their effectiveness. In this paper we propose a cognitive mechanism based on grounded cognition, suggesting that visual perception affects understanding by activating "perceptual symbols": the basic cognitive unit used by the brain to construct a concept. A good visual representation activates perceptual symbols that are essential for the construction of the represented concept, whereas a bad representation does the opposite. As a proof of concept, we conducted a clinical experiment in which participants received three different versions of a multimedia tutorial teaching the integral expression of electric potential. The three versions were only different by the details of the visual representation design, only one of which contained perceptual features that activate perceptual symbols essential for constructing the idea of "accumulation." On a following post-test, participants receiving this version of tutorial significantly outperformed those who received the other two versions of tutorials designed to mimic conventional visual representations used in classrooms.

Obesity and high fat diet consumption contribute to the development of metabolic disorders, insulin resistance, neuroinflammation, and cognitive impairments. CNS administration of insulin into the brain can attenuate these cognitive impairments. The present study investigated whether hippocampal-dependent spatial memory impairments in a dietary induced mouse model of obesity could be improved by the direct administration of insulin into the hippocampus and whether this was associated with markers of hippocampal inflammation. C57Bl/6J mice consumed a low fat or high fat diet for 16 weeks and continuous intrahippocampal saline or insulin infusion for the final 4 weeks, during a period of behavioral testing, before gene expression analysis was performed. The high fat diet group demonstrated poorer spatial memory performance in the Morris water maze and Y-maze, supporting the hypothesis that high fat diet leads to hippocampal dependent cognitive impairment. Insulin infusion into the hippocampus reversed the deficit of high fat diet consumption on both of the tasks. Increased expression of inflammatory markers was detected in the hippocampus in the high fat diet group and expression of these markers was ameliorated in insulin infused mice. This demonstrates that CNS insulin can improve hippocampal-dependent memory and that hippocampal inflammation may be a factor in the development of cognitive deficits associated with diet-induced obesity. Furthermore, these data suggest that insulin may act to attenuate high fat diet induced cognitive deficits by reducing neuroinflammation.

Background: Meta-analysis is a widely used tool in which weighted information from multiple similar studies is aggregated to increase statistical power. However, the exponential growth of publications in key areas of medical science has rendered manual identification of relevant studies increasingly time-consuming. The aim of this work was to develop a machine learning technique capable of robust automatic study selection for meta-analysis. We have validated this approach with an up-to-date meta-analysis to investigate the association between diabetes mellitus (DM) and new-onset atrial fibrillation (AF).Methods: The PubMed online database was searched from 1960 to September 2017 where 4,177 publications that mentioned both DM and AF were identified. Relevant studies were selected as follows. First, publications were clustered based on common text features using an unsupervised K-means algorithm. Clusters that best matched the selected set of potentially relevant studies (a “training” set of 139 articles) were then identified by using maximum entropy classification. The 139 articles selected automatically on this basis were screened manually to identify potentially relevant studies. To determine the validity of the automated process, a parallel set of studies was also assembled by manually screening all initially searched publications. Finally, detailed manual selection was performed on the full texts of the studies in both sets using standard criteria. Quality assessment, meta-regression random-effects models, sensitivity analysis and publication bias assessment were then conducted.Results: Machine learning-assisted screening identified the same 29 studies for meta-analysis as those identified by using manual screening alone. Machine learning enabled more robust and efficient study selection, reducing the number of studies needed for manual screening from 4,177 to 556 articles. A pooled analysis using the most conservative estimates indicated that patients with DM had ~49% greater risk of developing AF compared with individuals without DM. After adjusting for three additional risk factors i.e., hypertension, obesity and heart disease, the relative risk was 23%. Using multivariate adjusted models, the risk for developing AF in patients with DM was similar for all DM subtypes. Women with DM were 24% more likely to develop AF than men with DM. The risk for new-onset AF in patients with DM has also increased over the years.Conclusions: We have developed a novel machine learning method to identify publications suitable for inclusion in meta-analysis.This approach has the capacity to provide for a more efficient and more objective study selection process for future such studies. We have used it to demonstrate that DM is a strong, independent risk factor for AF, particularly for women.

Electrocardiogram (ECG)-based detection of left ventricular systolic dysfunction (LVSD) has poor specificity and positive predictive value, even when including major ECG abnormalities, such as left bundle branch block (LBBB) within the criteria for diagnosis. Although machine-read ECG algorithms do not provide information on LVSD, advanced ECG (A-ECG), using multiparameter scores, has superior diagnostic utility to strictly conventional ECG for identifying various cardiac pathologies, including LVSD. Methods: We evaluated the diagnostic utility of A-ECG in a case-control study of 40 patients with LVSD (LV ejection fraction < 50% by echocardiography), due to non-ischemic cardiomyopathy (NICM), and 39 other patients without LVSD. Diagnostic sensitivity and specificity for LVSD were determined after applying a previously validated probabilistic A-ECG score for LVSD to stored standard (10 s) clinical 12L ECGs. In 25 of the NICM patients who had serial ECGs and echocardiograms, changes in the A-ECG score versus in echocardiographic LV ejection fraction were also studied to determine the level of agreement between the two tests. Results: Analyses by A-ECG had a sensitivity of 95% for LVSD (93% if excluding N = 11 patients with LBBB) and specificity of 95%. In the 29 NICM patients without LBBB who had serial ECGs, sensitivity improved to 97% when all ECGs were considered. By comparison, human readers in a busy clinical environment had a sensitivity of 90% and specificity of 63%. A-ECG score trajectories demonstrated improvement, deterioration or no change in LVSD, which agreed with echocardiography, in 76% of cases (n = 25). Conclusion: A-ECG scoring detects LVSD due to NICM with high sensitivity and specificity. Serial A-ECG score trajectories also represent a method for inexpensively demonstrating changes in LVSD. A-ECG scoring may be of particular value in areas where echocardiography is unavailable, or as a gatekeeper for echocardiography.

BACKGROUND:Increased spatial QRS-T angle has been shown to predict appropriate implantable cardioverter defibrilIator (ICD) therapy in patients with left ventricular systolic dysfunction (LVSD). We performed a retrospective cohort study in patients with left ventricular ejection fraction (LVEF) 31-40% to assess the relationship between the spatial QRS-T angle and other advanced ECG (A-ECG) as well as echocardiographic metadata, with all-cause mortality or ICD implantation for secondary prevention. METHODS:534 patients ≤75 years of age with LVEF 31-40% were identified through an echocardiography reporting database. Digital 12-lead ECGs were retrospectively matched to 295 of these patients, for whom echocardiographic and A-ECG metadata were then generated. Data mining was applied to discover novel ECG and echocardiographic markers of risk. Machine learning was used to develop a model to predict possible outcomes. RESULTS:49 patients (17%) had events, defined as either mortality (n = 16) or ICD implantation for secondary prevention (n = 33). 72 parameters (58 A-ECG, 14 echocardiographic) were univariately different (p110° had an adjusted HR of 3.4 (95% CI 1.6 to 7.4) for secondary ICD implantation or all-cause death and adjusted HR of 4.1 (95% CI 1.2 to 13.9) for future heart failure admission. There was a loss of complexity between A-ECG and echocardiographic variables with an increasing degree of disease. CONCLUSION:Spatial QRS-T angle >110° was strongly associated with arrhythmic events and all-cause death. Deep analysis of global ECG and echocardiographic metadata revealed underlying relationships, which otherwise would not have been appreciated. Delivered at scale such techniques may prove useful in clinical decision making in the future.

In the 20th century, a group of talented individuals with the surname Rogers made creative contributions to American society. This article examines the most noted of these personalities and their effect on the mental health of a nation. Although it is unlikely that a group with the same last name will be as prominent again, it is crucial for counselors to be mindful of the therapeutic impact of entertainers, as well as counselors, to wellness.