Measuring communication as a core outcome in aphasia trials: Results of the ROMA-2 international core outcome set development meeting

BACKGROUND: Evidence-based recommendations for a core outcome set (COS; minimum set of outcomes) for aphasia treatment research have been developed (the Research Outcome Measurement in Aphasia-ROMA, COS). Five recommended core outcome constructs: communication, language, quality of life, emotional well-being and patient-reported satisfaction/impact of treatment, were identified through three international consensus studies. Constructs were paired with outcome measurement instruments (OMIs) during an international consensus meeting (ROMA-1). Before the current study (ROMA-2), agreement had not been reached on OMIs for the constructs of communication or patient-reported satisfaction/impact of treatment. AIM: To establish consensus on a communication OMI for inclusion in the ROMA COS. METHODS & PROCEDURES: Research methods were based on recommendations from the Core Outcome Measures in Effectiveness Trials (COMET) Initiative. Participants with expertise in design and conduct of aphasia trials, measurement instrument development/testing and/or communication outcome measurement were recruited through an open call. Before the consensus meeting, participants agreed on a definition of communication, identified appropriate OMIs, extracted their measurement properties and established criteria for their quality assessment. During the consensus meeting they short-listed OMIs and participants without conflicts of interest voted on the two most highly ranked instruments. Consensus was defined a priori as agreement by ≥ 70% of participants. OUTCOMES & RESULTS: In total, 40 researchers from nine countries participated in ROMA-2 (including four facilitators and three-panel members who participated in pre-meeting activities only). A total of 20 OMIs were identified and evaluated. Eight short-listed communication measures were further evaluated for their measurement properties and ranked. Participants in the consensus meeting (n = 33) who did not have conflicts of interest (n = 29) voted on the top two ranked OMIs: The Scenario Test (TST) and the Communication Activities of Daily Living-3 (CADL-3). TST received 72% (n = 21) of 'yes' votes and the CADL-3 received 28% (n = 8) of 'yes' votes. CONCLUSIONS & IMPLICATIONS: Consensus was achieved that TST was the preferred communication OMI for inclusion in the ROMA COS. It is currently available in the original Dutch version and has been adapted into English, German and Greek. Further consideration must be given to the best way to measure communication in people with mild aphasia. Development of a patient-reported measure for satisfaction with/impact of treatment and multilingual versions of all OMIs of the COS is still required. Implementation of the ROMA COS would improve research outcome measurement and the quality, relevance, transparency, replicability and efficiency of aphasia treatment research. WHAT THIS PAPER ADDS: What is already known on this subject International consensus has been reached on five core constructs to be routinely measured in aphasia treatment studies. International consensus has also been established for OMIs for the three constructs of language, quality of life and emotional well-being. Before this study, OMIs for the constructs of communication and patient-reported satisfaction/impact of treatment were not established. What this paper adds to existing knowledge We gained international consensus on an OMI for the construct of communication. TST is recommended for inclusion in the ROMA COS for routine use in aphasia treatment research. What are the potential or actual clinical implications of this work? The ROMA COS recommends OMIs for a minimum set of outcomes for adults with post-stroke aphasia within phases I-IV aphasia treatment research. Although not intended for clinical use, clinicians may employ the instruments of the ROMA COS, considering the quality of their measurement properties. The systematic inclusion of a measure of communication, such as TST, in clinical practice could ultimately support the implementation of research evidence and best practices

Albumin-derived perfluorocarbon-based artificial oxygen carriers can avoid hypoxic tissue damage in massive hemodilution

AbstractArtificial blood for clinical use is not yet available therefore, we previously developed artificial oxygen carriers (capsules) and showed their functionality in vitro and biocompatibility in vivo. Herein, we assessed the functionality of the capsules in vivo in a normovolemic hemodilution rat-model. We stepwise exchanged the blood of male Wistar-rats with medium either in the presence of capsules (treatment) or in their absence (control). We investigated tissue hypoxia thoroughly through online biomonitoring, determination of enzyme activity and pancreatic hormones in plasma, histochemical and immunohistochemical staining of small intestine, heart, liver and spleen as well as in situ hybridization of kidneys. After hemodilution, treated animals show higher arterial blood pressure and have a stable body temperature. Additionally, they show a more stable pH, a higher oxygen partial pressure (pO2), and a lower carbon dioxide partial pressure (pCO2). Interestingly, blood-glucose-levels drop severely in treated animals, presumably due to glucose consumption. Creatine kinase values in these animals are increased and isoenzyme analysis indicates the spleen as origin. Moreover, the small intestine of treated animals show reduced hypoxic injury compared to controls and the kidneys have reduced expression of the hypoxia-inducible erythropoietin mRNA. In conclusion, our capsules can prevent hypoxic tissue damage. The results provide a proof of concept for capsules as adequate erythrocyte substitute.</jats:p

Use of the chromosomal class A β-lactamase of Mycobacterium fortuitum D316 to study potentially poor substrates and inhibitory β-lactam compounds

Sixteen different compounds usually considered β-lactamase stable or representing potential β-lactam inhibitors and inactivators were tested against the β-lactamase produced by Mycobacterium fortuitum. The compounds exhibiting the most interesting properties were BRL42715, which was by far the best inactivator, and CGP31608 and ceftazidime, which were not recognized by the enzyme. These compounds thus exhibited adequate properties for fighting mycobacterial infections. Although cloxacillin, dicloxacillin, cefoxitin, and CP65207-2 exhibited poor inhibitory efficiency against the enzyme, they were also rather poor substrates and might be considered potential antimycobacterial agents. By contrast, CGP31523A and ceftamet were good substrates

Use of the chromosomal class A beta-lactamase of Mycobacterium fortuitum D316 to study potentially poor substrates and inhibitory beta-lactam compounds.

Sixteen different compounds usually considered beta-lactamase stable or representing potential beta-lactam inhibitors and inactivators were tested against the beta-lactamase produced by Mycobacterium fortuitum. The compounds exhibiting the most interesting properties were BRL42715, which was by far the best inactivator, and CGP31608 and ceftazidime, which were not recognized by the enzyme. These compounds thus exhibited adequate properties for fighting mycobacterial infections. Although cloxacillin, dicloxacillin, cefoxitin, and CP65207-2 exhibited poor inhibitory efficiency against the enzyme, they were also rather poor substrates and might be considered potential antimycobacterial agents. By contrast, CGP31523A and ceftamet were good substrates

TEEMLEAP : A New Testbed for Exploring Machine Learning in Atmospheric Prediction for Research and Education

In the past 5 years, data‐driven prediction models and Machine Learning (ML) techniques have revolutionized weather forecasting. Meteorological services around the world are now developing ML components to enhance (or even replace) their numerical weather prediction systems. This shift creates new challenges and opportunities for universities and research centers, calling for a much closer cooperation of meteorology with mathematics and computer sciences, updates of teaching curricula, and new research infrastructures and strategies. To address these challenges, an interdisciplinary team of scientists from the Karlsruhe Institute of Technology (KIT) and the German Meteorological Service (DWD) created the TEstbed for Exploring Machine LEarning in Atmospheric Prediction (TEEMLEAP). Implemented on KIT\u27s supercomputer HoreKa, the TEEMLEAP testbed simulates the entire operational weather forecasting chain using ERA5 reanalysis data as pseudo‐observations and DWD\u27s Basic Cycling environment for conducting assimilation‐prediction‐cycling experiments. Moreover, first steps are taken toward the integration of new datadriven components like FourCastNet and ML‐based post‐processing methods. The TEEMLEAP testbed allows systematic investigation of a wide range of issues related to weather forecasting such as optimizing the observational system, uncertainty quantification, and developing hybrid systems that integrate ML with physics‐based models. This document outlines the testbed\u27s setup, demonstrates its functionality with a pilot experiment, and discusses examples of potential applications. Future plans include creating educational modules and developing a higher‐resolution regional version of the testbed that could be used for assimilating field campaign observations