Activity

com/psipred/s4pred), along with documentation. It will also be provided as a part of the PSIPRED web service (http//bioinf.cs.ucl.ac.uk/psipred/).

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

Cases of cerebral venous sinus thrombosis in combination with thrombocytopenia have recently been reported within 4 to 28 days of vaccination with the ChAdOx1 nCov-19 (AstraZeneca/Oxford) and Ad.26.COV2.S (Janssen/Johnson & Johnson) COVID-19 vaccines. An immune-mediated response associated with platelet factor 4/heparin antibodies has been proposed as the underlying pathomechanism.

To determine the frequencies of admission thrombocytopenia, heparin-induced thrombocytopenia, and presence of platelet factor 4/heparin antibodies in patients diagnosed with cerebral venous sinus thrombosis prior to the COVID-19 pandemic.

This was a descriptive analysis of a retrospective sample of consecutive patients diagnosed with cerebral venous sinus thrombosis between January 1987 and March 2018 from 7 hospitals participating in the International Cerebral Venous Sinus Thrombosis Consortium from Finland, the Netherlands, Switzerland, Sweden, Mexico, Iran, and Costa Rica. Of 952 patients, 865 with available baseline pral venous sinus thrombosis included in the laboratory analysis, 8 (9%) had thrombocytopenia, and none (95% CI, 0%-4%) had platelet factor 4/heparin antibodies.

In patients with cerebral venous sinus thrombosis prior to the COVID-19 pandemic, baseline thrombocytopenia was uncommon, and heparin-induced thrombocytopenia and platelet factor 4/heparin antibodies were rare. These findings may inform investigations of the possible association between the ChAdOx1 nCoV-19 and Ad26.COV2.S COVID-19 vaccines and cerebral venous sinus thrombosis with thrombocytopenia.

In patients with cerebral venous sinus thrombosis prior to the COVID-19 pandemic, baseline thrombocytopenia was uncommon, and heparin-induced thrombocytopenia and platelet factor 4/heparin antibodies were rare. These findings may inform investigations of the possible association between the ChAdOx1 nCoV-19 and Ad26.COV2.S COVID-19 vaccines and cerebral venous sinus thrombosis with thrombocytopenia.

Clinical trials are the essential stage of every drug development program for the treatment to become available to patients. Despite the importance of well-structured clinical trial databases and their tremendous value for drug discovery and development such instances are very rare. Presently large-scale information on clinical trials is stored in clinical trial registers which are relatively structured, but the mappings to external databases of drugs and diseases are increasingly lacking. The precise production of such links would enable us to interrogate richer harmonized datasets for invaluable insights.

We present a neural approach for medical concept normalization of diseases and drugs. Our two-stage approach is based on Bidirectional Encoder Representations from Transformers (BERT). In the training stage, we optimize the relative similarity of mentions and concept names from a terminology via triplet loss. In the inference stage, we obtain the closest concept name representation in a common embedding space to a given mention representation. We performed a set of experiments on a dataset of abstracts and a real-world dataset of trial records with interventions and conditions mapped to drug and disease terminologies. The latter includes mentions associated with one or more concepts (in-KB) or zero (out-of-KB, nil prediction). Experiments show that our approach significantly outperforms baseline and state-of-the-art architectures. Moreover, we demonstrate that our approach is effective in knowledge transfer from the scientific literature to clinical trial data.

We make code and data freely available at hidden\_during\_review\_process.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.Identifying the frequencies of the drug-side effects is a very important issue in pharmacological studies and drug risk-benefit. However, designing clinical trials to determine the frequencies is usually time consuming and expensive, and most existing methods can only predict the drug-side effect existence or associations, not their frequencies. Inspired by the recent progress of graph neural networks in the recommended system, we develop a novel prediction model for drug-side effect frequencies, using a graph attention network to integrate three different types of features, including the similarity information, known drug-side effect frequency information and word embeddings. In comparison, the few available studies focusing on frequency prediction use only the known drug-side effect frequency scores. One novel approach used in this work first decomposes the feature types in drug-side effect graph to extract different view representation vectors based on three different type features, and then recombines these latent view vectors automatically to obtain unified embeddings for prediction. The proposed method demonstrates high effectiveness in 10-fold cross-validation. The computational results show that the proposed method achieves the best performance in the benchmark dataset, outperforming the state-of-the-art matrix decomposition model. In addition, some ablation experiments and visual analyses are also supplied to illustrate the usefulness of our method for the prediction of the drug-side effect frequencies. The codes of MGPred are available at https//github.com/zhc940702/MGPred and https//zenodo.org/record/4449613.

Glucocorticosteroids (GCs) are recommended to suppress inflammation in people with active RA. This systematic review and meta-analysis aimed to quantify the effects of systemic GCs on RA pain.

A systematic literature review of randomised controlled trials (RCTs) in RA comparing systemic GCs to inactive treatment. Three databases were and spontaneous pain and evoked pain outcomes were extracted. Standardized mean differences (SMDs) and mean differences (MDs) were meta-analysed. Heterogeneity (I2, tau statistics) and bias (funnel plot, Eggers test) were assessed. Subgroup analyses investigated sources of variation. This study was pre-registered (PROSPERO CRD42019111562).

18903 titles, 880 abstracts and 226 full texts were assessed. PTC596 Thirty three RCTs suitable for the meta-analysis included 2658 participants. Pain scores (spontaneous pain) decreased in participants treated with oral GCs; SMD= -0.65 (15 studies, 95% CI, -0.82, -0.49, p< 0.001) with significant heterogeneity (I2=56%, p= 0.0002). Efficacy displayed time-related decreases after GC initiation.