Discrepancies in autologous bone marrow stem cell trials and enhancement of ejection fraction (DAMASCENE): weighted regression and meta-analysis

Objective To investigate whether discrepancies in trials of use of bone marrow stem cells in patients with heart disease account for the variation in reported effect size in improvement of left ventricular function. Design Identification and counting of factual discrepancies in trial reports, and sample size weighted regression against therapeutic effect size. Meta-analysis of trials that provided sufficient information. Data sources PubMed and Embase from inception to April 2013. Eligibility for selecting studies Randomised controlled trials evaluating the effect of autologous bone marrow stem cells for heart disease on mean left ventricular ejection fraction. Results There were over 600 discrepancies in 133 reports from 49 trials. There was a significant association between the number of discrepancies and the reported increment in EF with bone marrow stem cell therapy (Spearman’s r=0.4, P=0.005). Trials with no discrepancies were a small minority (five trials) and showed a mean EF effect size of −0.4%. The 24 trials with 1-10 discrepancies showed a mean effect size of 2.1%. The 12 with 11-20 discrepancies showed a mean effect of size 3.0%. The three with 21-30 discrepancies showed a mean effect size of 5.7%. The high discrepancy group, comprising five trials with over 30 discrepancies each, showed a mean effect size of 7.7%. Conclusions Avoiding discrepancies is difficult but is important because discrepancy count is related to effect size. The mechanism is unknown but should be explored in the design of future trials because in the five trials without discrepancies the effect of bone marrow stem cell therapy on ejection fraction is zero

Effectiveness of eHealth weight management interventions in overweight and obese adults from low socioeconomic groups: a systematic review.

BACKGROUND: Low socioeconomic status (SES) is associated with increased rates of overweight and obesity. Proponents of electronic health (eHealth) hypothesise that its inclusion in weight management interventions can improve efficacy by mitigating typical barriers associated with low SES. OBJECTIVES: To establish the scope of eHealth weight management interventions for people with overweight and obesity from a low SES. Secondary objectives were to determine the efficacy of eHealth interventions in facilitating weight loss, physical activity and fitness improvements. METHODS: Four databases and grey literature were systematically searched to identify eligible studies published in English from inception to May 2021. Studies examining an eHealth intervention with low SES participants were included. Outcomes included temporal change in weight and BMI, anthropometry, physiological measures and physical activity levels. The number and heterogeneity of studies precluded any meta-analyses; thus, a narrative review was undertaken. RESULTS: Four experimental studies with low risk of bias were reviewed. There was variance in how SES was defined. Study aims and eHealth media also varied and included reducing/maintaining weight or increasing physical activity using interactive websites or voice responses, periodic communication and discourse via telephone, social media, text messaging or eNewsletters. Irrespectively, all studies reported short-term weight loss. eHealth interventions also increased short-term physical activity levels where it was assessed, but did not change anthropometry or physiological measures. None reported any effect on physical fitness. CONCLUSIONS: This review revealed short-term effects of eHealth interventions on weight loss and increased physical activity levels for low SES participants. Evidence was limited to a small number of studies, with small to moderate sample sizes. Inter-study comparison is challenging because of considerable variability. Future work should prioritise how to utilise eHealth in the longer term either as a supportive public health measure or by determining its long-term efficacy in engendering volitional health behaviour changes. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42021243973

Outcomes Following eHealth Weight Management Interventions in Adults With Overweight and Obesity From Low Socioeconomic Groups: Protocol for a Systematic Review.

BACKGROUND: Obesity is a complex health condition with multiple associated comorbidities and increased economic costs. People from low socioeconomic status (SES) backgrounds are more likely to be overweight and obese and are less successful in traditional weight management programs. It is possible that eHealth interventions may be more successful in reaching people from low SES groups than traditional face-to-face models, by overcoming certain barriers associated with traditional interventions. It is not yet known, however, if eHealth weight management interventions are effective in people living with overweight and obesity from a low SES background. OBJECTIVE: The primary aim of this study is to evaluate the efficacy of eHealth weight management interventions for people with overweight and obesity from low SES groups. METHODS: A systematic review on relevant electronic databases (MEDLINE, Embase, Emcare, and CINAHL) will be undertaken to identify eligible studies published in English up until May 2021. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement to guide the systematic review, two reviewers will independently screen, select, and extract data and complete a risk of bias assessment of search results according to predefined criteria. Studies that have investigated an eHealth weight management intervention within a low SES population will be included. Primary outcomes include weight, BMI, and percentage weight change compared at baseline and at least one other time point. Secondary outcomes may include a range of anthropometric and physical fitness and activity measures. If sufficient studies are homogeneous, then we will pool results of individual outcomes using meta-analysis. RESULTS: Searches have been completed, resulting in 2256 studies identified. Once duplicates were removed, 1545 studies remained for title and abstract review. CONCLUSIONS: The use of eHealth in weight management programs has increased significantly in recent years and will continue to do so; however, it is uncertain if eHealth weight management programs are effective in a low SES population. The results of this systematic review will therefore provide a summary of the evidence for interventions using eHealth for people living with overweight and obesity and from a low SES background. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42021243973; https://tinyurl.com/2p8fxtnw. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34546

Efficient labelling for efficient deep learning: the benefit of a multiple-image-ranking method to generate high volume training data applied to ventricular slice level classification in cardiac MRI

BACKGROUND: Getting the most value from expert clinicians' limited labelling time is a major challenge for artificial intelligence (AI) development in clinical imaging. We present a novel method for ground-truth labelling of cardiac magnetic resonance imaging (CMR) image data by leveraging multiple clinician experts ranking multiple images on a single ordinal axis, rather than manual labelling of one image at a time. We apply this strategy to train a deep learning (DL) model to classify the anatomical position of CMR images. This allows the automated removal of slices that do not contain the left ventricular (LV) myocardium. METHODS: Anonymised LV short-axis slices from 300 random scans (3,552 individual images) were extracted. Each image's anatomical position relative to the LV was labelled using two different strategies performed for 5 hours each: (I) 'one-image-at-a-time': each image labelled according to its position: 'too basal', 'LV', or 'too apical' individually by one of three experts; and (II) 'multiple-image-ranking': three independent experts ordered slices according to their relative position from 'most-basal' to 'most apical' in batches of eight until each image had been viewed at least 3 times. Two convolutional neural networks were trained for a three-way classification task (each model using data from one labelling strategy). The models' performance was evaluated by accuracy, F1-score, and area under the receiver operating characteristics curve (ROC AUC). RESULTS: After excluding images with artefact, 3,323 images were labelled by both strategies. The model trained using labels from the 'multiple-image-ranking strategy' performed better than the model using the 'one-image-at-a-time' labelling strategy (accuracy 86% vs. 72%, P=0.02; F1-score 0.86 vs. 0.75; ROC AUC 0.95 vs. 0.86). For expert clinicians performing this task manually the intra-observer variability was low (Cohen's κ=0.90), but the inter-observer variability was higher (Cohen's κ=0.77). CONCLUSIONS: We present proof of concept that, given the same clinician labelling effort, comparing multiple images side-by-side using a 'multiple-image-ranking' strategy achieves ground truth labels for DL more accurately than by classifying images individually. We demonstrate a potential clinical application: the automatic removal of unrequired CMR images. This leads to increased efficiency by focussing human and machine attention on images which are needed to answer clinical questions

Minimization of phonon-tunneling dissipation in mechanical resonators

Micro- and nanoscale mechanical resonators have recently emerged as ubiquitous devices for use in advanced technological applications, for example in mobile communications and inertial sensors, and as novel tools for fundamental scientific endeavors. Their performance is in many cases limited by the deleterious effects of mechanical damping. Here, we report a significant advancement towards understanding and controlling support-induced losses in generic mechanical resonators. We begin by introducing an efficient numerical solver, based on the "phonon-tunneling" approach, capable of predicting the design-limited damping of high-quality mechanical resonators. Further, through careful device engineering, we isolate support-induced losses and perform the first rigorous experimental test of the strong geometric dependence of this loss mechanism. Our results are in excellent agreement with theory, demonstrating the predictive power of our approach. In combination with recent progress on complementary dissipation mechanisms, our phonon-tunneling solver represents a major step towards accurate prediction of the mechanical quality factor.Comment: 12 pages, 4 figure

Difficulty in detecting discrepancies in a clinical trial report: 260-reader evaluation

Background: Scientific literature can contain errors. Discrepancies, defined as two or more statements or results that cannot both be true, may be a signal of problems with a trial report. In this study, we report how many discrepancies are detected by a large panel of readers examining a trial report containing a large number of discrepancies. Methods: We approached a convenience sample of 343 journal readers in seven countries, and invited them in person to participate in a study. They were asked to examine the tables and figures of one published article for discrepancies. 260 participants agreed, ranging from medical students to professors. The discrepancies they identified were tabulated and counted. There were 39 different discrepancies identified. We evaluated the probability of discrepancy identification, and whether more time spent or greater participant experience as academic authors improved the ability to detect discrepancies. Results: Overall, 95.3% of discrepancies were missed. Most participants (62%) were unable to find any discrepancies. Only 11.5% noticed more than 10% of the discrepancies. More discrepancies were noted by participants who spent more time on the task (Spearman’s ρ = 0.22, P < 0.01), and those with more experience of publishing papers (Spearman’s ρ = 0.13 with number of publications, P = 0.04). Conclusions: Noticing discrepancies is difficult. Most readers miss most discrepancies even when asked specifically to look for them. The probability of a discrepancy evading an individual sensitized reader is 95%, making it important that, when problems are identified after publication, readers are able to communicate with each other. When made aware of discrepancies, the majority of readers support editorial action to correct the scientific record

Clinical and genetic characterisation of dystrophin-deficient muscular dystrophy in a family of Miniature Poodle dogs

Four full-sibling intact male Miniature Poodles were evaluated at 4–19 months of age. One was clinically normal and three were affected. All affected dogs were reluctant to exercise and had generalised muscle atrophy, a stiff gait and a markedly elevated serum creatine kinase activity. Two affected dogs also showed poor development, learning difficulties and episodes of abnormal behaviour. In these two dogs, investigations into forebrain structural and metabolic diseases were unremarkable; electromyography demonstrated fibrillation potentials and complex repetitive discharges in the infraspinatus, supraspinatus and epaxial muscles. Histopathological, immunohistochemical and immunoblotting analyses of muscle biopsies were consistent with dystrophin-deficient muscular dystrophy. DNA samples were obtained from all four full-sibling male Poodles, a healthy female littermate and the dam, which was clinically normal. Whole genome sequencing of one affected dog revealed a >5 Mb deletion on the X chromosome, encompassing the entire DMD gene. The exact deletion breakpoints could not be experimentally ascertained, but we confirmed that this region was deleted in all affected males, but not in the unaffected dogs. Quantitative polymerase chain reaction confirmed all three affected males were hemizygous for the mutant X chromosome, while the wildtype chromosome was observed in the unaffected male littermate. The female littermate and the dam were both heterozygous for the mutant chromosome. Forty-four Miniature Poodles from the general population were screened for the mutation and were homozygous for the wildtype chromosome. The finding represents a naturally-occurring mutation causing dystrophin-deficient muscular dystrophy in the dog

Smartphone-based remote monitoring for chronic heart failure: mixed methods analysis of user experience from patient and nurse perspectives

BACKGROUND: Community-based management by heart failure specialist nurses (HFSNs) is key to improving self-care in heart failure with reduced ejection fraction. Remote monitoring (RM) can aid nurse-led management, but in the literature, user feedback evaluation is skewed in favor of the patient rather than nursing user experience. Furthermore, the ways in which different groups use the same RM platform at the same time are rarely directly compared in the literature. We present a balanced semantic analysis of user feedback from patient and nurse perspectives of Luscii, a smartphone-based RM strategy combining self-measurement of vital signs, instant messaging, and e-learning. OBJECTIVE: This study aims to (1) evaluate how patients and nurses use this type of RM (usage type), (2) evaluate patients' and nurses' user feedback on this type of RM (user experience), and (3) directly compare the usage type and user experience of patients and nurses using the same type of RM platform at the same time. METHODS: We performed a retrospective usage type and user experience evaluation of the RM platform from the perspective of both patients with heart failure with reduced ejection fraction and the HFSNs using the platform to manage them. We conducted semantic analysis of written patient feedback provided via the platform and a focus group of 6 HFSNs. Additionally, as an indirect measure of tablet adherence, self-measured vital signs (blood pressure, heart rate, and body mass) were extracted from the RM platform at onboarding and 3 months later. Paired 2-tailed t tests were used to evaluate differences between mean scores across the 2 timepoints. RESULTS: A total of 79 patients (mean age 62 years; 35%, 28/79 female) were included. Semantic analysis of usage type revealed extensive, bidirectional information exchange between patients and HFSNs using the platform. Semantic analysis of user experience demonstrates a range of positive and negative perspectives. Positive impacts included increased patient engagement, convenience for both user groups, and continuity of care. Negative impacts included information overload for patients and increased workload for nurses. After the patients used the platform for 3 months, they showed significant reductions in heart rate (P=.004) and blood pressure (P=.008) but not body mass (P=.97) compared with onboarding. CONCLUSIONS: Smartphone-based RM with messaging and e-learning facilitates bilateral information sharing between patients and nurses on a range of topics. Patient and nurse user experience is largely positive and symmetrical, but there are possible negative impacts on patient attention and nurse workload. We recommend RM providers involve patient and nurse users in platform development, including recognition of RM usage in nursing job plans