Objective Randomized controlled trials (RCTs) are the gold standard method for evaluating whether a treatment works in health care but can be difficult to find and make use of. We describe the development and evaluation of a system to automatically find and categorize all new RCT reports. Materials and Methods Trialstreamer continuously monitors PubMed and the World Health Organization International Clinical Trials Registry Platform, looking for new RCTs in humans using a validated classifier. We combine machine learning and rule-based methods to extract information from the RCT abstracts, including free-text descriptions of trial PICO (populations, interventions/comparators, and outcomes) elements and map these snippets to normalized MeSH (Medical Subject Headings) vocabulary terms. We additionally identify sample sizes, predict the risk of bias, and extract text conveying key findings. We store all extracted data in a database, which we make freely available for download, and via a search portal, which allows users to enter structured clinical queries. Results are ranked automatically to prioritize larger and higher-quality studies. Results As of early June 2020, we have indexed 673 191 publications of RCTs, of which 22 363 were published in the first 5 months of 2020 (142 per day). We additionally include 304 111 trial registrations from the International Clinical Trials Registry Platform. The median trial sample size was 66. Conclusions We present an automated system for finding and categorizing RCTs. This yields a novel resource: a database of structured information automatically extracted for all published RCTs in humans. We make daily updates of this database available on our website (https://trialstreamer.robotreviewer.net).

Among the 15 extracellular domains of the mannose 6-phosphate/ insulin-like growth factor-2 receptor (M6P/IGF2R), domain 11 has evolved a binding site for IGF2 to negatively regulate ligand bioavailability and mammalian growth. Despite the highly evolved structural loops of the IGF2:domain 11 binding site, affinity-enhancing AB loop mutations suggest that binding is modifiable. Here we examine the extent to which IGF2:domain 11 affinity, and its specificity over IGF1, can be enhanced, and we examine the structural basis of the mechanistic and functional consequences. Domain 11 binding loop mutants were selected by yeast surface display combined with high-resolution structure-based predictions, and validated by surface plasmon resonance. We discovered previously unidentified mutations in the ligand-interacting surface binding loops (AB, CD, FG, and HI). Five combined mutations increased rigidity of the AB loop, as confirmed by NMR. When added to three independently identified CD and FG loop mutations that reduced the k off value by twofold, these mutations resulted in an overall selective 100-fold improvement in affinity. The structural basis of the evolved affinity was improved shape complementarity established by interloop (AB-CD) and intraloop (FG-FG) side chain interactions. The high affinity of the combinatorial domain 11 Fc fusion proteins functioned as ligand-soluble antagonists or traps that depleted pathological IGF2 isoforms from serum and abrogated IGF2-dependent signaling in vivo. An evolved and reengineered high-specificity M6P/IGF2R domain 11 binding site for IGF2 may improve therapeutic targeting of the frequent IGF2 gain of function observed in human cancer.growth factor receptor | protein evolution | insulin-like growth factor 2 | binding kinetics | biological therapy T he functional evolution of proteins is largely considered to occur by chance, frequently because of unpredictable and specific events that confer a structure-based change in function sufficient for subsequent selection or "gain of fitness" (1). One such evolutionary biochemical example is the initial acquisition and subsequent gain of affinity between the insulin-like growth factor 2 (IGF2) ligand and a single domain of a nonsignaling mannose 6-phosphate (M6P)/IGF2 receptor (IGF2R) (domain 11). The structural and functional basis of this evolutionary path, which has occurred over 150 million years of mammalian evolution, has been reported previously (2). The questions that we address in the present work are whether the IGF2:domain 11 interaction has reached an optimal state in the context of IGF2 activation of signaling receptors and in the ligand clearance function of M6P/IGF2R, and how far can we extend the binding interaction in terms of structural, biophysical, and functional properties.Functionally, and unlike products of other mammalian imprinted genes, domain 11 is unusual because it specifically evolved to bind to an evolutionary conserved IGF2 ligand with high affinity (3-5). After binding, clearance of extracellular IGF2...

Aims/hypothesis Problematic hypoglycaemia still complicates insulin therapy for some with type 1 diabetes. This study describes baseline emotional, cognitive and behavioural characteristics in participants in the HARPdoc trial, which evaluates a novel intervention for treatment-resistant problematic hypoglycaemia. Methods We documented a cross-sectional baseline description of 99 adults with type 1 diabetes and problematic hypoglycaemia despite structured education in flexible insulin therapy. The following measures were included: Hypoglycaemia Fear Survey II (HFS-II); Attitudes to Awareness of Hypoglycaemia questionnaire (A2A); Hospital Anxiety and Depression Index; and Problem Areas In Diabetes. k-mean cluster analysis was applied to HFS-II and A2A factors. Data were compared with a peer group without problematic hypoglycaemia, propensity-matched for age, sex and diabetes duration (n = 81). Results The HARPdoc cohort had long-duration diabetes (mean ± SD 35.8 ± 15.4 years), mean ± SD Gold score 5.3 ± 1.2 and a median (IQR) of 5.0 (2.0–12.0) severe hypoglycaemia episodes in the previous year. Most individuals had been offered technology and 49.5% screened positive for anxiety (35.0% for depression and 31.3% for high diabetes distress). The cohort segregated into two clusters: in one (n = 68), people endorsed A2A cognitive barriers to hypoglycaemia avoidance, with low fear on HFS-II factors; in the other (n = 29), A2A factor scores were low and HFS-II high. Anxiety and depression scores were significantly lower in the comparator group. Conclusions/interpretation The HARPdoc protocol successfully recruited people with treatment-resistant problematic hypoglycaemia. The participants had high anxiety and depression. Most of the cohort endorsed unhelpful health beliefs around hypoglycaemia, with low fear of hypoglycaemia, a combination that may contribute to persistence of problematic hypoglycaemia and may be a target for adjunctive psychological therapies. Graphical abstract

Objective Randomized controlled trials (RCTs) are the gold standard method for evaluating whether a treatment works in healthcare, but can be difficult to find and make use of. We describe the development and evaluation of a system to automatically find and categorize all new RCT reports. Materials and Methods Trialstreamer, continuously monitors PubMed and the WHO International Clinical Trials Registry Platform (ICTRP), looking for new RCTs in humans using a validated classifier. We combine machine learning and rule-based methods to extract information from the RCT abstracts, including free-text descriptions of trial populations, interventions and outcomes (the 'PICO') and map these snippets to normalised MeSH vocabulary terms. We additionally identify sample sizes, predict the risk of bias, and extract text conveying key findings. We store all extracted data in a database which we make freely available for download, and via a search portal, which allows users to enter structured clinical queries. Results are ranked automatically to prioritize larger and higher-quality studies. Results As of May 2020, we have indexed 669,895 publications of RCTs, of which 18,485 were published in the first four months of 2020 (144/day). We additionally include 303,319 trial registrations from ICTRP. The median trial sample size in the RCTs was 66. Conclusions We present an automated system for finding and categorising RCTs. This yields a novel resource: A database of structured information automatically extracted for all published RCTs in humans. We make daily updates of this database available on our website (trialstreamer.robotreviewer.net).

OBJECTIVE The Hypoglycemia Fear Survey-II (HFS-II) is a well-validated measure of fear of hypoglycemia in people with type 1 diabetes. The aim of this study was to explore the relationships between hypoglycemia worries, behaviors, and cognitive barriers to hypoglycemia avoidance and hypoglycemia awareness status, severe hypoglycemia, and HbA1c. RESEARCH DESIGN AND METHODS Participants with type 1 diabetes (n = 178), with the study population enriched for people at risk for severe hypoglycemia (49%), completed questionnaires for assessing hypoglycemia fear (HFS-II), hyperglycemia avoidance (Hyperglycemia Avoidance Scale [HAS]), diabetes distress (Problem Areas In Diabetes [PAID]), and cognitive barriers to hypoglycemia avoidance (Attitudes to Awareness of Hypoglycemia [A2A]). Exploratory factor analysis was applied to the HFS-II. We sought to establish clusters based on HFS-II, A2A, Gold, HAS, and PAID using k-means clustering. RESULTS Four HFS-II factors were identified: Sought Safety, Restricted Activity, Ran High, and Worry. While Sought Safety, Restricted Activity, and Worry increased with progressively impaired awareness and recurrent severe hypoglycemia, Ran High did not. With cluster analysis we outlined four clusters: two clusters with preserved hypoglycemia awareness were differentiated by low fear/low cognitive barriers to hypoglycemia avoidance (cluster 1) versus high fear and distress and increased Ran High behaviors (cluster 2). Two clusters with impaired hypoglycemia awareness were differentiated by low fear/high cognitive barriers (cluster 3) as well as high fear/low cognitive barriers (cluster 4). CONCLUSIONS This is the first study to define clusters of hypoglycemia experience by worry, behaviors, and cognitive barriers to hypoglycemia avoidance. The resulting subtypes may be important in understanding and treating problematic hypoglycemia.