Cadherin-5: a biomarker for metastatic breast cancer with optimum efficacy in oestrogen receptor-positive breast cancers with vascular invasion

Background: A glycoproteomic study has previously shown cadherin-5 (CDH5) to be a serological marker of metastatic breast cancer when both protein levels and glycosylation status were assessed. In this study we aimed to further validate the utility of CDH5 as a biomarker for breast cancer progression. Methods: A nested case–control study of serum samples from breast cancer patients, of which n=52 had developed a distant metastatic recurrence within 5 years post-diagnosis and n=60 had remained recurrence-free. ELISAs were used to quantify patient serum CDH5 levels and assess glycosylation by Helix pomatia agglutinin (HPA) binding. Clinicopathological, treatment and lifestyle factors associated with metastasis and elevated biomarker levels were identified. Results: Elevated CDH5 levels (P=0.028) and ratios of CDH5:HPA binding (P=0.007) distinguished patients with metastatic disease from those that remained metastasis-free. Multivariate analysis showed that the association between CDH5:HPA ratio and the formation of distant metastases was driven by patients with oestrogen receptor (ER+) positive cancer with vascular invasion (VI+). Conclusions: CDH5 levels and the CDH5 glycosylation represent biomarker tests that distinguish patients with metastatic breast cancer from those that remain metastasis-free. The test reached optimal sensitivity and specificity in ER-positive cancers with vascular invasion.

Abstract thinking: a predictor of modelling ability?

In this paper, we describe a study of the abstract thinking skills of a group of students studying object-oriented modelling as part of a Masters course. Abstract thinking has long been considered a core skill for computer scientists. This study is part of attempts to gather evidence about the link between abstract thinking skills and success in the Computer Science discipline. The results of this study show a positive correlation between the scores of the students in the abstract thinking test with the marks achieved in the module. However, the small numbers in the study mean that wider research is needed.

The N-terminal domain modulates α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor desensitization

AMPA receptors are tetrameric glutamate-gated ion channels that mediate fast synaptic neurotransmission in mammalian brain. Their subunits contain a two-lobed N-terminal domain (NTD) that comprises over 40% of the mature polypeptide. The NTD is not obligatory for the assembly of tetrameric receptors, and its functional role is still unclear. By analyzing full-length and NTD-deleted GluA1-4 AMPA receptors expressed in HEK 293 cells, we found that the removal of the NTD leads to a significant reduction in receptor transport to the plasma membrane, a higher steady state-to-peak current ratio of glutamate responses, and strongly increased sensitivity to glutamate toxicity in cell culture. Further analyses showed that NTD-deleted receptors display both a slower onset of desensitization and a faster recovery from desensitization of agonist responses. Our results indicate that the NTD promotes the biosynthetic maturation of AMPA receptors and, for membrane-expressed channels, enhances the stability of the desensitized state. Moreover, these findings suggest that interactions of the NTD with extracellular/synaptic ligands may be able to fine-tune AMPA receptor-mediated responses, in analogy with the allosteric regulatory role demonstrated for the NTD of NMDA receptors.

Risk Modelling Framework for Emergency Hospital Readmission, Using Hospital Episode Statistics Inpatient Data

The objective of this study was to develop, test and benchmark a framework and a predictive risk model for hospital emergency readmission within 12 months. We performed the development using routinely collected Hospital Episode Statistics data covering inpatient hospital admissions in England. Three different timeframes were used for training, testing and benchmarking: 1999 to 2004, 2000 to 2005 and 2004 to 2009 financial years. Each timeframe includes 20% of all inpatients admitted within the trigger year. The comparisons were made using positive predictive value, sensitivity and specificity for different risk cut-offs, risk bands and top risk segments, together with the receiver operating characteristic curve. The constructed Bayes Point Machine using this feature selection framework produces a risk probability for each admitted patient, and it was validated for different timeframes, sub-populations and cut-off points. At risk cut-off of 50%, the positive predictive value was 69.3% to 73.7%, the specificity was 88.0% to 88.9% and sensitivity was 44.5% to 46.3% across different timeframes. Also, the area under the receiver operating characteristic curve was 73.0% to 74.3%. The developed framework and model performed considerably better than existing modelling approaches with high precision and moderate sensitivity.