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

A guide to integrating immunohistochemistry and chemical imaging

© 2018 The Royal Society of Chemistry. Chemical imaging provides new insight into the fundamental atomic, molecular, and biochemical composition of tissue and how they are interrelated in normal physiology. Visualising and quantifying products of pathogenic reactions long before structural changes become apparent also adds a new dimension to understanding disease pathogenesis. While chemical imaging in isolation is somewhat limited by the nature of information it can provide (e.g. peptides, metals, lipids, or functional groups), integrating immunohistochemistry allows simultaneous, targeted imaging of biomolecules while also mapping tissue composition. Together, this approach can provide invaluable information on the inner workings of the cell and the molecular basis of diseases

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

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

Quantitative immuno-mass spectrometry imaging of skeletal muscle dystrophin

Emerging and promising therapeutic interventions for Duchenne muscular dystrophy (DMD) are confounded by the challenges of quantifying dystrophin. Current approaches have poor precision, require large amounts of tissue, and are difficult to standardize. This paper presents an immuno-mass spectrometry imaging method using gadolinium (Gd)-labeled anti-dystrophin antibodies and laser ablation-inductively coupled plasma-mass spectrometry to simultaneously quantify and localize dystrophin in muscle sections. Gd is quantified as a proxy for the relative expression of dystrophin and was validated in murine and human skeletal muscle sections following k-means clustering segmentation, before application to DMD patients with different gene mutations where dystrophin expression was measured up to 100 µg kg−1 Gd. These results demonstrate that immuno-mass spectrometry imaging is a viable approach for pre-clinical to clinical research in DMD. It rapidly quantified relative dystrophin in single tissue sections, efficiently used valuable patient resources, and may provide information on drug efficacy for clinical translation

Methodological criteria for the assessment of moderators in systematic reviews of randomised controlled trials : a consensus study

Background: Current methodological guidelines provide advice about the assessment of sub-group analysis within RCTs, but do not specify explicit criteria for assessment. Our objective was to provide researchers with a set of criteria that will facilitate the grading of evidence for moderators, in systematic reviews. Method: We developed a set of criteria from methodological manuscripts (n = 18) using snowballing technique, and electronic database searches. Criteria were reviewed by an international Delphi panel (n = 21), comprising authors who have published methodological papers in this area, and researchers who have been active in the study of sub-group analysis in RCTs. We used the Research ANd Development/University of California Los Angeles appropriateness method to assess consensus on the quantitative data. Free responses were coded for consensus and disagreement. In a subsequent round additional criteria were extracted from the Cochrane Reviewers’ Handbook, and the process was repeated. Results: The recommendations are that meta-analysts report both confirmatory and exploratory findings for subgroups analysis. Confirmatory findings must only come from studies in which a specific theory/evidence based apriori statement is made. Exploratory findings may be used to inform future/subsequent trials. However, for inclusion in the meta-analysis of moderators, the following additional criteria should be applied to each study: Baseline factors should be measured prior to randomisation, measurement of baseline factors should be of adequate reliability and validity, and a specific test of the interaction between baseline factors and interventions must be presented. Conclusions: There is consensus from a group of 21 international experts that methodological criteria to assess moderators within systematic reviews of RCTs is both timely and necessary. The consensus from the experts resulted in five criteria divided into two groups when synthesising evidence: confirmatory findings to support hypotheses about moderators and exploratory findings to inform future research. These recommendations are discussed in reference to previous recommendations for evaluating and reporting moderator studies

The conceptualisation and measurement of DSM-5 Internet Gaming Disorder: the development of the IGD-20 Test

Background: Over the last decade, there has been growing concern about ‘gaming addiction’ and its widely documented detrimental impacts on a minority of individuals that play excessively. The latest (fifth) edition of the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders (DSM-5) included nine criteria for the potential diagnosis of Internet Gaming Disorder (IGD) and noted that it was a condition that warranted further empirical study. Aim: The main aim of this study was to develop a valid and reliable standardised psychometrically robust tool in addition to providing empirically supported cut-off points. Methods: A sample of 1003 gamers (85.2% males; mean age 26 years) from 57 different countries were recruited via online gaming forums. Validity was assessed by confirmatory factor analysis (CFA), criterion-related validity, and concurrent validity. Latent profile analysis was also carried to distinguish disordered gamers from non-disordered gamers. Sensitivity and specificity analyses were performed to determine an empirical cut-off for the test. Results: The CFA confirmed the viability of IGD-20 Test with a six-factor structure (salience, mood modification, tolerance, withdrawal, conflict and relapse) for the assessment of IGD according to the nine criteria from DSM-5. The IGD-20 Test proved to be valid and reliable. According to the latent profile analysis, 5.3% of the total participants were classed as disordered gamers. Additionally, an optimal empirical cut-off of 71 points (out of 100) seemed to be adequate according to the sensitivity and specificity analyses carried

Effects of percutaneous coronary intervention on death and myocardial infarction stratified by stable and unstable coronary artery disease: a meta-analysis of randomized controlled trials

Background: In patients presenting with ST-segment–elevation myocardial infarction, percutaneous coronary intervention (PCI) reduces mortality when compared with fibrinolysis. In other forms of coronary artery disease (CAD), however, it has been controversial whether PCI reduces mortality. In this meta-analysis, we examine the benefits of PCI in (1) patients post–myocardial infarction (MI) who did not receive immediate revascularization; (2) patients who have undergone primary PCI for ST-segment–elevation myocardial infarction but have residual coronary lesions; (3) patients who have suffered a non–ST-segment–elevation acute coronary syndrome; and (4) patients with truly stable CAD with no recent infarct. This analysis includes data from the recently presented International Study of Comparative Health Effectiveness with Medical and Invasive Approaches (ISCHEMIA) and Complete versus Culprit-Only Revascularization Strategies to Treat Multivessel Disease after Early PCI for STEMI (COMPLETE) trials. Methods and Results: We systematically identified all randomized trials of PCI on a background of medical therapy for the treatment of CAD. The ISCHEMIA trial, presented in November 2019, was eligible for inclusion. Data were combined using a random-effects meta-analysis. The primary end point was all-cause mortality. Forty-six trials, including 37 757 patients, were eligible. In the 3 unstable scenarios, PCI had the following effects on mortality: unrevascularized post-MI relative risk (RR) 0.68 (95% CI, 0.45–1.03); P=0.07; multivessel disease following ST-segment–elevation myocardial infarction (RR, 0.84 [95% CI, 0.69–1.04]; P=0.11); non–ST-segment–elevation acute coronary syndrome (RR, 0.84 [95% CI, 0.72–0.97]; P=0.02). Overall, in these unstable scenarios PCI was associated with a significant reduction in mortality (RR, 0.84 [95% CI, 0.75–0.93]; P=0.02). In unstable CAD, PCI also reduced cardiac death (RR, 0.69 [95% CI, 0.53–0.90]; P=0.007) and MI (RR, 0.74 [95% CI, 0.62–0.90]; P=0.002). For stable CAD, PCI did not reduce mortality (RR, 0.98 [95% CI, 0.87–1.11]), cardiac death (RR, 0.89 [95% CI, 0.71–1.12]; P=0.33), or MI (RR, 0.96 [95% CI, 0.86–1.08]; P=0.54). Conclusions: PCI prevents death, cardiac death, and MI in patients with unstable CAD. For patients with stable CAD, PCI shows no evidence of an effect on any of these outcomes

Transfer RNA-derived small RNAs in the cancer transcriptome

The cellular lifetime includes stages such as differentiation, proliferation, division, senescence and apoptosis.These stages are driven by a strictly ordered process of transcription dynamics. Molecular disruption to RNA polymerase assembly, chromatin remodelling and transcription factor binding through to RNA editing, splicing, post-transcriptional regulation and ribosome scanning can result in significant costs arising from genome instability. Cancer development is one example of when such disruption takes place. RNA silencing is a term used to describe the effects of post-transcriptional gene silencing mediated by a diverse set of small RNA molecules. Small RNAs are crucial for regulating gene expression and microguarding genome integrity.RNA silencing studies predominantly focus on small RNAs such as microRNAs, short-interfering RNAs and piwi-interacting RNAs. We describe an emerging renewal of inter-est in a‘larger’small RNA, the transfer RNA (tRNA).Precisely generated tRNA-derived small RNAs, named tRNA halves (tiRNAs) and tRNA fragments (tRFs), have been reported to be abundant with dysregulation associated with cancer. Transfection of tiRNAs inhibits protein translation by displacing eukaryotic initiation factors from messenger RNA (mRNA) and inaugurating stress granule formation.Knockdown of an overexpressed tRF inhibits cancer cell proliferation. Recovery of lacking tRFs prevents cancer metastasis. The dual oncogenic and tumour-suppressive role is typical of functional small RNAs. We review recent reports on tiRNA and tRF discovery and biogenesis, identification and analysis from next-generation sequencing data and a mechanistic animal study to demonstrate their physiological role in cancer biology. We propose tRNA-derived small RNA-mediated RNA silencing is an innate defence mechanism to prevent oncogenic translation. We expect that cancer cells are percipient to their ablated control of transcription and attempt to prevent loss of genome control through RNA silencing