Background Trace amine-associated receptor 1 (TAAR1) agonism shows promise for treating psychosis, prompting us to synthesise data from human and non-human studies. Methods We co-produced a living systematic review of controlled studies examining TAAR1 agonists in individuals (with or without psychosis/schizophrenia) and relevant animal models. Two independent reviewers identified studies in multiple electronic databases (until 17.11.2023), extracted data, and assessed risk of bias. Primary outcomes were standardised mean differences (SMD) for overall symptoms in human studies and hyperlocomotion in animal models. We also examined adverse events and neurotransmitter signalling. We synthesised data with random-effects meta-analyses. Results Nine randomised trials provided data for two TAAR1 agonists (ulotaront and ralmitaront), and 15 animal studies for 10 TAAR1 agonists. Ulotaront and ralmitaront demonstrated few differences compared to placebo in improving overall symptoms in adults with acute schizophrenia (N=4 studies, n=1291 participants; SMD=0.15, 95%CI: -0.05, 0.34), and ralmitaront was less efficacious than risperidone (N=1, n=156, SMD=-0.53, 95%CI: -0.86, -0.20). Large placebo response was observed in ulotaront phase-III trials. Limited evidence suggested a relatively benign side-effect profile for TAAR1 agonists, although nausea and sedation were common after a single dose of ulotaront. In animal studies, TAAR1 agonists improved hyperlocomotion compared to control (N=13 studies, k=41 experiments, SMD=1.01, 95%CI: 0.74, 1.27), but seemed less efficacious compared to dopamine D2 receptor antagonists (N=4, k=7, SMD=-0.62, 95%CI: -1.32, 0.08). Limited human and animal data indicated that TAAR1 agonists may regulate presynaptic dopaminergic signalling. Conclusions TAAR1 agonists may be less efficacious than dopamine D2 receptor antagonists already licensed for schizophrenia. The results are preliminary due to the limited number of drugs examined, lack of longer-term data, publication bias, and assay sensitivity concerns in trials associated with large placebo response. Considering their unique mechanism of action, relatively benign side-effect profile and ongoing drug development, further research is warranted. Registration PROSPERO-ID:CRD42023451628.

Background Trace amine-associated receptor 1 (TAAR1) agonism shows promise for treating psychosis, prompting us to synthesise data from human and non-human studies. Methods We co-produced a living systematic review of controlled studies examining TAAR1 agonists in individuals (with or without psychosis/schizophrenia) and relevant animal models. Two independent reviewers identified studies in multiple electronic databases (until 17.11.2023), extracted data, and assessed risk of bias. Primary outcomes were standardised mean differences (SMD) for overall symptoms in human studies and hyperlocomotion in animal models. We also examined adverse events and neurotransmitter signalling. We synthesised data with random-effects meta-analyses. Results Nine randomised trials provided data for two TAAR1 agonists (ulotaront and ralmitaront), and 15 animal studies for 10 TAAR1 agonists. Ulotaront and ralmitaront demonstrated few differences compared to placebo in improving overall symptoms in adults with acute schizophrenia (N=4 studies, n=1291 participants; SMD=0.15, 95%CI: -0.05, 0.34), and ralmitaront was less efficacious than risperidone (N=1, n=156, SMD=-0.53, 95%CI: -0.86, -0.20). Large placebo response was observed in ulotaront phase-III trials. Limited evidence suggested a relatively benign side-effect profile for TAAR1 agonists, although nausea and sedation were common after a single dose of ulotaront. In animal studies, TAAR1 agonists improved hyperlocomotion compared to control (N=13 studies, k=41 experiments, SMD=1.01, 95%CI: 0.74, 1.27), but seemed less efficacious compared to dopamine D2 receptor antagonists (N=4, k=7, SMD=-0.62, 95%CI: -1.32, 0.08). Limited human and animal data indicated that TAAR1 agonists may regulate presynaptic dopaminergic signalling. Conclusions TAAR1 agonists may be less efficacious than dopamine D2 receptor antagonists already licensed for schizophrenia. The results are preliminary due to the limited number of drugs examined, lack of longer-term data, publication bias, and assay sensitivity concerns in trials associated with large placebo response. Considering their unique mechanism of action, relatively benign side-effect profile and ongoing drug development, further research is warranted. Registration PROSPERO-ID:CRD42023451628.

Abstract Objective Software developed using artificial intelligence may automatically identify arterial occlusion and provide collateral vessel scoring on CT angiography (CTA) performed acutely for ischemic stroke. We aimed to assess the diagnostic accuracy of e‐CTA by Brainomix™ Ltd by large‐scale independent testing using expert reading as the reference standard. Methods We identified a large clinically representative sample of baseline CTA from 6 studies that recruited patients with acute stroke symptoms involving any arterial territory. We compared e‐CTA results with masked expert interpretation of the same scans for the presence and location of laterality‐matched arterial occlusion and/or abnormal collateral score combined into a single measure of arterial abnormality. We tested the diagnostic accuracy of e‐CTA for identifying any arterial abnormality (and in a sensitivity analysis compliant with the manufacturer's guidance that software only be used to assess the anterior circulation). Results We include CTA from 668 patients (50% female; median: age 71 years, NIHSS 9, 2.3 h from stroke onset). Experts identified arterial occlusion in 365 patients (55%); most (343, 94%) involved the anterior circulation. Software successfully processed 545/668 (82%) CTAs. The sensitivity, specificity and diagnostic accuracy of e‐CTA for detecting arterial abnormality were each 72% (95% CI = 66–77%). Diagnostic accuracy was non‐significantly improved in a sensitivity analysis excluding occlusions from outside the anterior circulation (76%, 95% CI = 72–80%). Interpretation Compared to experts, the diagnostic accuracy of e‐CTA for identifying acute arterial abnormality was 72–76%. Users of e‐CTA should be competent in CTA interpretation to ensure all potential thrombectomy candidates are identified.

This report presents the activities and outcomes related to the five use cases (UCs) identified by EFSA within the Specific Contract No 1 implementing the Framework contract No OC/EFSA/AMU/2021/03. UC1 aims to assess the usability and effectiveness of text summarisation tools applied to the context of Public consultations (PCs) with the objective to use AI-based automatic text summarisation (ATS) techniques to generate a summary for each attachment to the comments received for a PC. UC2 aims at automating the keywords identification step within the systematic review (SR) process, to explore the current capabilities of the existing tools to enhance the keywords identification process to facilitate the subsequent retrieval of potentially relevant studies with the help of machine learning (ML) and artificial intelligence (AI). UC3 assesses the capabilities of deduplication within DistillerSR with the objective to evaluate the effectiveness of different user-defined deduplication settings available to DistillerSR users to correctly identify and remove duplicates while highlighting the limitations of the tool. The objective of UC4 is to develop a tailored manual for the use of Distiller SR within EFSA's SR framework to facilitate the relevance screening step, replacing human-based relevance screening of titles and abstract with an AI-assisted process. UC5 was designed to exploit the capabilities of the DistillerSR Artificial Intelligence SYstem (DAISY) in DistillerSR to build and use classifiers for semi-automated AI-assisted characterisation or classification of studies within the SR process. Analogous to UC4, also the UC5 aimed at creating a manual helping the users to benefit from this recently introduced functionality within DistillerSR.

Systematic reviews and meta-analysis are the cornerstones of evidence-based decision making and priority setting. However, traditional systematic reviews are time and labour intensive, limiting their feasibility to comprehensively evaluate the latest evidence in research-intensive areas. Recent developments in automation, machine learning and systematic review technologies have enabled efficiency gains. Building upon these advances, we developed Systematic Online Living Evidence Summaries (SOLES) to accelerate evidence synthesis. In this approach, we integrate automated processes to continuously gather, synthesise and summarise all existing evidence from a research domain, and report the resulting current curated content as interrogatable databases via interactive web applications. SOLES can benefit various stakeholders by (i) providing a systematic overview of current evidence to identify knowledge gaps, (ii) providing an accelerated starting point for a more detailed systematic review, and (iii) facilitating collaboration and coordination in evidence synthesis.

OBJECTIVE:The purpose of this study was to test e-ASPECTS software in patients with stroke. Marketed as a decision-support tool, e-ASPECTS may detect features of ischemia or hemorrhage on computed tomography (CT) imaging and quantify ischemic extent using Alberta Stroke Program Early CT Score (ASPECTS). METHODS:Using CT from 9 stroke studies, we compared software with masked experts. As per indications for software use, we assessed e-ASPECTS results for patients with/without middle cerebral artery (MCA) ischemia but no other cause of stroke. In an analysis outside the intended use of the software, we enriched our dataset with non-MCA ischemia, hemorrhage, and mimics to simulate a representative "front door" hospital population. With final diagnosis as the reference standard, we tested the diagnostic accuracy of e-ASPECTS for identifying stroke features (ischemia, hyperattenuated arteries, and hemorrhage) in the representative population. RESULTS:We included 4,100 patients (51% women, median age = 78 years, National Institutes of Health Stroke Scale [NIHSS] = 10, onset to scan = 2.5 hours). Final diagnosis was ischemia (78%), hemorrhage (14%), or mimic (8%). From 3,035 CTs with expert-rated ASPECTS, most (2084/3035, 69%) e-ASPECTS results were within one point of experts. In the representative population, the diagnostic accuracy of e-ASPECTS was 71% (95% confidence interval [CI] = 70-72%) for detecting ischemic features, 85% (83-86%) for hemorrhage. Software identified more false positive ischemia (12% vs 2%) and hemorrhage (14% vs <1%) than experts. INTERPRETATION:On independent testing, e-ASPECTS provided moderate agreement with experts and overcalled stroke features. Therefore, future prospective trials testing impacts of artificial intelligence (AI) software on patient care and outcome are required before widespread implementation of stroke decision-support software. ANN NEUROL 2022;92:943-957.

Animal models are primary tools for understanding the development of human disease and testing treatment interventions before they are escalated to clinical trials. There is a rapidly growing literature utilising models of neurological disease, with many novel drug candidates demonstrating efficacy in vivo. However, the translational success of interventions which emerge from this pipeline remains extremely low – with an estimated failure rate of 99.6% for Alzheimer’s disease clinical trials. Systematic reviews and meta-analyses of preclinical data provide us with an overview of the available evidence, allow us to assess how generalisable the findings may be to other species and other environments (external validity), and the likelihood that the evidence is unbiased and trustworthy (internal validity). Findings from systematic reviews can inform guidance to improve the reporting quality and rigor of future research, generate hypothesis, and most importantly form part of a framework to deliver evidence-based medicine. For this reason, systematic reviews are used regularly to appraise the evidence from clinical studies – however are not yet common practice for the larger body of preclinical literature that underpins clinical trial design. Preclinical systematic reviews are resource-intensive and often out of date by the time they are complete. To enable preclinical systematic reviews to form part of a translationally relevant evidence framework, we have developed and integrated a series of automation tools and methodologies for the continual synthesis and quality assessment of in vivo experiments. To obtain relevant records as they are published, we have utilised the PubMed API to fetch new records based on predetermined search strategies. For study selection, we have trained machine learning algorithms (based at the EPPI-Centre, UCL) to include studies which meet our inclusion criteria. To assess reporting quality, we have used text-mining techniques on the full-text publications. Regular expressions have been developed and validated within our group for the reporting of randomisation to experimental groups, blinded assessment of outcome, sample size calculations and conflict of interest statements. We have also built regular expression dictionaries to categorise studies by disease model(s), treatment(s), and outcome measure(s) reported. Using these categorised datasets, we have built interactive web applications or ‘living’ evidence summaries using the R programming language. These web applications visualise the literature, allow users to interrogate the dataset and to download the relevant citations for a model(s), intervention(s) and outcome measure(s) of interest. So far, we have used this approach using evidence in preclinical models of depression (https://camarades.shinyapps.io/Preclinical-Models-of-Depression/) and Alzheimer’s disease (https://camarades.shinyapps.io/LivingEvidence_AD/). We aim to curate and improve upon these applications and expand this methodology to other disease areas. This work forms the basis of a ‘living’ framework to synthesise preclinical evidence as it emerges and track research trends and reporting quality over time. We anticipate that this framework can enhance the speed at which systematic reviews of preclinical research can be performed and provide an important resource for all stakeholders in AD research.

BackgroundAmyotrophic lateral sclerosis (ALS) is a rapidly fatal neurodegenerative disease. Despite decades of clinical trials, there remains a pressing unmet need for effective treatments. We reviewed past and present ALS clinical trials to understand the methodological challenges in trial design and delivery.MethodsTrial registry databases including clinicaltrials. gov, International Clinical Trials Registry Platform, European Union Clinical Trials Register, and PubMed were systematically searched to identify Phase II, Phase II/III and Phase III Clinical Trials of Investigational Medicinal Products (CTIMPs) assessing potential disease modifying treatments in ALS. Trials registered, completed or published during 2008–2019 were included.Results125 CTIMPs, evaluating 76 drugs, involving 15647 people with ALS (pwALS) were reviewed. Ten drugs were tested in three or more trials. Trials employed predominantly traditional two-arm designs; only 12 used novel designs. Median number of participants was 86. 40% of trials had an attrition rate ≥ 20%. There was a wide variation of primary outcome measures and primary endpoints used.ConclusionHistorically, limited participation of pwALS in trials, resources and outcome measures hindered definitive and timely evaluation of drugs in two-arm trials. We propose that future trials will need to be more flexible, scalable and acceptable to all stakeholders.charis.wong@ed.ac.uk

There are many factors that contribute to the reproducibility and replicability of scientific research. There is a need to understand the research ecosystem, and improvements will require combined efforts across all parts of this ecosystem. National structures can play an important role in coordinating these efforts, working collaboratively with researchers, institutions, funders, publishers, learned societies and other sectoral organisations, and providing a monitoring and reporting function. Whilst many new ways of working and emerging innovations hold a great deal of promise, it will be important to invest in meta-research activity to ensure that these approaches are evidence based, work as intended, and do not have unintended consequences. Addressing reproducibility will require working collaboratively across the research ecosystem to share best practice and to make the most effective use of resources. The UK Reproducibility Network (UKRN) brings together Local Networks of researchers, Institutions, and External Stakeholders (funders, publishers, learned societies and other sectoral organisations), to coordinate action on reproducibility and work to ensure the UK retains its place as a centre for world-leading research. This activity is coordinated by the UKRN Steering Group. We consider this structure as valuable, bringing together a range of voices at a range of levels to support the combined efforts required to enact change.

In this paper we present a metric to assess the smoothness of a trigonometric interpolation through an incomplete set of sample points.We measure smoothness as the power of a particular derivative of a 2π-periodic Dirichlet interpolant through some sample points.We show that we do not need to explicitly complete the sample set or perform the interpolation, but can simply work with the available sample points, under the assumption that any missing points are chosen to minimise the metric, and present a simple and robust approach to the computation of this metric.We assess the accuracy and computational complexity of this approach, and compare it to benchmarks.

Background: Time-to-event data is frequently reported in both clinical and preclinical research spheres. Systematic review and meta-analysis is a tool that can help to identify pitfalls in preclinical research conduct and reporting that can help to improve translational efficacy. However, pooling of studies using hazard ratios (HRs) is cumbersome especially in preclinical meta-analyses including large numbers of small studies. Median survival is a much simpler metric although because of some limitations, which may not apply to preclinical data, it is generally not used in survival meta-analysis. We aimed to appraise its performance when compared with hazard ratio-based meta-analysis when pooling large numbers of small, imprecise studies. Methods: We simulated a survival dataset with features representative of a typical preclinical survival meta-analysis, including with influence of a treatment and a number of covariates. We calculated individual patient data-based hazard ratios and median survival ratios (MSRs), comparing the summary statistics directly and their performance at random-effects meta-analysis. Finally, we compared their sensitivity to detect associations between treatment and influential covariates at meta-regression. Results: There was an imperfect correlation between MSR and HR, although the opposing direction of treatment effects between summary statistics appeared not to be a major issue. Precision was more conservative for HR than MSR, meaning that estimates of heterogeneity were lower. There was a slight sensitivity advantage for MSR at meta-analysis and meta-regression, although power was low in all circumstances. Conclusions: We believe we have validated MSR as a summary statistic for use in a meta-analysis of small, imprecise experimental survival studies-helping to increase confidence and efficiency in future reviews in this area. While assessment of study precision and therefore weighting is less reliable, MSR appears to perform favourably during meta-analysis. Sensitivity of meta-regression was low for this set of parameters, so pooling of treatments to increase sample size may be required to ensure confidence in preclinical survival meta-regressions.

We report 3 empirical studies that represent the first systematic attempt to explore the relationship between emotional and decisional forgiveness and intentional forgetting. On this basis, we propose a model that provides a credible explanation for the relationship between forgiveness and forgetting. Specifically, we propose that engaging in emotional forgiveness promotes the psychological distancing of an offense, such that victims construe the offense at a higher and more abstract level. This high-level construal, in turn, promotes larger intentional forgetting effects, which, in turn, promote increased emotional forgiveness. Our studies found that participants in an emotional forgiveness manipulation reported increased psychological distance and recalled more high-level construals than did participants in either a decisional or no-forgiveness manipulation (Study 1). Using the list-method directed forgetting paradigm. we found that participants in an emotional forgiveness manipulation showed larger forgetting effects for both offense-relevant and -irrelevant information using both hypothetical (Study 2) and real-life (Study 3) moral transgressions compared with participants in either decisional or no-forgiveness manipulations. The potential implications of these findings for coping with unpleasant episodes in our lives are considered.

Background: The process of translating preclinical findings into a clinical setting takes decades. Previous studies have suggested that only 5-10% of the most promising preclinical studies are successfully translated into viable clinical applications. The underlying determinants of this low success rate (e.g. poor experimental design, suboptimal animal models, poor reporting) have not been examined in an empirical manner. Our study aims to determine the contemporary success rate of preclinical-to-clinical translation, and subsequently determine if an association between preclinical study design and translational success/failure exists.Methods: Established systematic review methodology will be used with regards to the literature search, article screening and study selection process. Preclinical, basic science studies published in high impact basic science journals between 1995 and 2015 will be included. Included studies will focus on publicly available interventions with potential clinical promise. The primary outcome will be successful clinical translation of promising therapies - defined as the conduct of at least one Phase II trial (or greater) with a positive finding. A case-control study will then be performed to evaluate the association between elements of preclinical study design and reporting and the likelihood of successful translation.Discussion: This study will provide a comprehensive analysis of the therapeutic translation from the laboratory bench to the bedside. Importantly, any association between factors of study design and the success of translation will be identified. These findings may inform future research teams attempting preclinical-to-clinical translation. Results will be disseminated to identified knowledge users that fund/support preclinical research.

In the context of extracting analytic eigen- or singular values from a polynomial matrix, a suitable cost function is the smoothness of continuous, real, and potentially symmetric periodic functions. This smoothness can be measured as the power of the derivatives of that function, and can be tied to a set of sample points on the unit circle that may be incomplete. We have previously explored the utility of this cost function, and here provide refinements by (i) analysing properties of the cost function and (ii) imposing additional constraints on its evaluation.

Pulsed wave (PW) Doppler ultrasound systems are commonly used to examine blood flow dynamics and the technique plays a very important role in numerous diagnostic applications. Commonly, narrow-band PW systems estimate the blood velocity using an autocorrelation-based estimator. Herein, we examine a recently proposed hybrid frequency estimator, and via extensive numerical simulations using simulated blood scatterers show the achievable performance gain of this method as compared to the traditional approach.

Gravitational orbits are simulated from the time-averaged sum of particle-particle orbital pairs at the Planck scale. Every particle is connected to every other particle in the simulation by a circular `gravitational' orbital (unit of momentum) forming an n-body universe wide lattice of discrete rotating particle-particle orbitals with particles at the orbital poles, each orbital barycenter as the orbital center. Each particle is assigned point co-ordinates in an expanding 4-axis hyper-sphere array (the simulation universe). In Planck unit terms, each particle-particle orbital rotates 1 unit of Planck length per unit of Planck time ($v$ = $c$) in hyper-sphere coordinates, all particle co-ordinates are then summed and averaged before the next unit of Planck time, thus this approach can be run on a serial processor. As this is a dimensionless geometrical model, a barycenter between orbiting objects, dimension-ed constants $G, h, c, ...$ and forces are not required although measurements can be converted to Planck units.

This paper develops first-order complex adaptive notch filters (CANFs) which are computationally simple, in order to minimise resource usage in an ASIC firmware implementation, but when connected in cascade can achieve substantial attenuation of multiple interference tones with slowly varying frequencies. When existing first-order CANFs are used in this way the adaptation is biased due to the presence of multiple tones at the filter's input; some algorithms are also un-normalised, which is unsatisfactory when the interfering tones may have a wide range of amplitudes. This paper describes a normalised algorithm for first order adaptive filters which is computationally simple and offers significantly reduced bias when used in a multi-notch cascade.

This paper introduces a cost function for the smoothness of a continuous periodic function, of which only some samples are given. This cost function is important e.g. when associating samples in frequency bins for problems such as analytic singular or eigenvalue decompositions. We demonstrate the utility of the cost function, and study some of its complexity and conditioning issues.

Despite its potential to accelerate academic progress in psychological science, public data sharing remains relatively uncommon. In order to discover the perceived barriers to public data sharing and possible means for lowering them, we conducted a survey, which elicited responses from 600 authors of articles in psychology. The results confirmed that data are shared only infrequently. Perceived barriers included respondents' belief that sharing is not a common practice in their fields, their preference to share data only upon request, their perception that sharing requires extra work, and their lack of training in sharing data. Our survey suggests that strong encouragement from institutions, journals, and funders will be particularly effective in overcoming these barriers, in combination with educational materials that demonstrate where and how data can be shared effectively.