Protein tyrosine phosphatases in glioma biology

Gliomas are a diverse group of brain tumors of glial origin. Most are characterized by diffuse infiltrative growth in the surrounding brain. In combination with their refractive nature to chemotherapy this makes it almost impossible to cure patients using combinations of conventional therapeutic strategies. The drastically increased knowledge about the molecular underpinnings of gliomas during the last decade has elicited high expectations for a more rational and effective therapy for these tumors. Most studies on the molecular pathways involved in glioma biology thus far had a strong focus on growth factor receptor protein tyrosine kinase (PTK) and phosphatidylinositol phosphatase signaling pathways. Except for the tumor suppressor PTEN, much less attention has been paid to the PTK counterparts, the protein tyrosine phosphatase (PTP) superfamily, in gliomas. PTPs are instrumental in the reversible phosphorylation of tyrosine residues and have emerged as important regulators of signaling pathways that are linked to various developmental and disease-related processes. Here, we provide an overview of the current knowledge on PTP involvement in gliomagenesis. So far, the data point to the potential implication of receptor-type (RPTPδ, DEP1, RPTPμ, RPTPζ) and intracellular (PTP1B, TCPTP, SHP2, PTPN13) classical PTPs, dual-specific PTPs (MKP-1, VHP, PRL-3, KAP, PTEN) and the CDC25B and CDC25C PTPs in glioma biology. Like PTKs, these PTPs may represent promising targets for the development of novel diagnostic and therapeutic strategies in the treatment of high-grade gliomas

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Learning from 25 years of the extensible N-Dimensional Data Format

The extensible N-Dimensional Data Format (NDF) was designed and developed in the late 1980s to provide a data model suitable for use in a variety of astronomy data processing applications supported by the UK Starlink Project. Starlink applications were used extensively, primarily in the UK astronomical community, and form the basis of a number of advanced data reduction pipelines today. This paper provides an overview of the historical drivers for the development of NDF and the lessons learned from using a defined hierarchical data model for many years in data reduction software, data pipelines and in data acquisition systems