Bayesian classification with Gaussian processes

We consider the problem of assigning an input vector class='mathrm'>bfx to one of class='mathrm'>m classes by predicting class='mathrm'>P(c|bfx) for class='mathrm'>c = 1, ldots, m. For a two-class problem, the probability of class 1 given class='mathrm'>bfx is estimated by class='mathrm'>s(y(bfx)), where class='mathrm'>s(y) = 1/(1 + e^-y). A Gaussian process prior is placed on class='mathrm'>y(bfx), and is combined with the training data to obtain predictions for new class='mathrm'>bfx points. We provide a Bayesian treatment, integrating over uncertainty in class='mathrm'>y and in the parameters that control the Gaussian process prior; the necessary integration over class='mathrm'>y is carried out using Laplace's approximation. The method is generalized to multi-class problems class='mathrm'>(m >2) using the softmax function. We demonstrate the effectiveness of the method on a number of datasets.

The clinical significance of medicines reconciliation in children admitted to hospital

Aims and Objectives: The NICE/NPSA guidance on Medicines Reconciliation in adults upon hospital admission excludes children under the age of 16.1 Hence the primary aim and objective of this study was to use medicines reconciliation to primarily identify if discrepancies occur upon hospital admission. Secondary objectives were to clinically assess for harm discrepancies that were identified in paediatric patients on long term medications at four hospitals across the UK. Method: Medicines reconciliation is a procedure where the current medication history of a patient prior to hospital admission would be taken and verifying the medication orders made at hospital admission against this history, addressing any discrepancies identified. Medicines reconciliation was carried out prospectively for 244 paediatric patients on chronic medication across four UK hospitals (Birmingham, London, Leeds and North Staffordshire) between January – May 2011. Medicines reconciliation was conducted by a clinical pharmacist using the following sources of information: 1) the patient's Pre-Admission Medication (PAM) from the patient's general practitioner 2) examination of the Patient's Own Medications brought into hospital, 3) a semi-structured interview with the parent-carers and 4) identification of admission medication orders written on the drug chart prior to clinical pharmacy input (Drug Chart). Discrepancies between the PAM and Drug Chart were documented and classified as intentional or unintentional. Intentional discrepancies were defined as changes that were made knowingly by the prescriber and confirmed. Unintentional discrepancies were assessed for clinical significance by an expert panel and assigned a significance score based on the likelihood of causing potential discomfort or clinical deterioration: class 1 unlikely, class 2 moderate and class 3 severe.2 Results: 1004 medication regimens were included from the 244 patients across the four sites. 588 of the 1004 (59%) medicines, had discrepancies between the PAM and Drug Chart; of these 36% (n = 209) were unintentional and included for clinically assessment. 189 drug discrepancies 30% were classified as class 1, 47% were class 2 and 23% were class 3 discrepancies. The remaining 20 discrepancies were cases where deviating from the PAM would have been the right thing to do, which might suggest that an intentional but undocumented discrepancy by the prescriber writing up the admission order may have occurred. Conclusion: The results suggest that medication discrepancies in paediatric patients do occur upon hospital admission, which do have a potential to cause harm and that medicines reconciliation is a potential solution to preventing such discrepancies. References: 1. National Institute for Health and Clinical Excellence. National Patient Safety Agency. PSG001. Technical patient safety solutions for medicines reconciliation on admission of adults to hospital. London: NICE; 2007. 2. Cornish, P. L., Knowles, S. R., Marchesano, et al. Unintended Medication Discrepancies at the Time of Hospital Admission. Archives of Internal Medicine 2005; 165:424–429

A hydrogen-bonded polar network in the core of the glucagon-like peptide-1 receptor is a fulcrum for biased agonism:lessons from class B crystal structuress

The glucagon-like peptide 1 (GLP-1) receptor is a class B G protein-coupled receptor (GPCR) that is a key target for treatments for type II diabetes and obesity. This receptor, like other class B GPCRs, displays biased agonism, though the physiologic significance of this is yet to be elucidated. Previous work has implicated R2.60190 , N3.43240 , Q7.49394 , and H6.52363 as key residues involved in peptide-mediated biased agonism, with R2.60190 , N3.43240 , and Q7.49394 predicted to form a polar interaction network. In this study, we used novel insight gained from recent crystal structures of the transmembrane domains of the glucagon and corticotropin releasing factor 1 (CRF1) receptors to develop improved models of the GLP-1 receptor that predict additional key molecular interactions with these amino acids. We have introduced E6.53364 A, N3.43240 Q, Q7.49493N, and N3.43240 Q/Q7.49 Q/Q7.49493N mutations to probe the role of predicted H-bonding and charge-charge interactions in driving cAMP, calcium, or extracellular signal-regulated kinase (ERK) signaling. A polar interaction between E6.53364 and R2.60190 was predicted to be important for GLP-1- and exendin-4-, but not oxyntomodulin-mediated cAMP formation and also ERK1/2 phosphorylation. In contrast, Q7.49394 , but not R2.60190 /E6.53364 was critical for calcium mobilization for all three peptides. Mutation of N3.43240 and Q7.49394 had differential effects on individual peptides, providing evidence for molecular differences in activation transition. Collectively, this work expands our understanding of peptide-mediated signaling from the GLP-1 receptor and the key role that the central polar network plays in these events.