P2X receptor trafficking in neurons is subunit specific

P2X receptors within the CNS mediate excitatory synaptic transmission and also act presynaptically to modulate neurotransmitter release. We have studied the targeting and trafficking of P2X4 and P2X2 receptors heterologously expressed in cultured olfactory bulb neurons. Homomeric P2X4 receptors had a punctate distribution, and many of the puncta colocalized with early endosomes. In contrast, P2X2 receptors were primarily localized at the plasma membrane. By antibody-labeling of surface receptors in living neurons, we showed that P2X4 receptors undergo rapid constitutive internalization and subsequent reinsertion into the plasma membrane, whereas P2X2 receptors were not regulated in such a way. The internalization of P2X4 receptors was dynamin-dependent, and the binding of ATP enhanced the basal rate of retrieval in a Ca2+-independent manner. The presence of the P2X4 subunit in a P2X4/6 heteromer governed the trafficking properties of the receptor. P2X receptors acted presynaptically to enhance the release of glutamate, suggesting that the regulated cycling of P2X4-containing receptors might provide a mechanism for modulation of synaptic transmission

paRTner: UK-Africa partnership for radiotherapy

Kate Ricketts and Gary Royle reflect on the first year of the paRTner project, a global health partnership for radiotherapy between the UK and Ghana

Multiresolution Models for Nonstationary Spatial Covariance Functions

This is the pre-print version of the article found in Statistical Modelling (http://smj.sagepub.com/).Many geophysical and environmental problems depend on estimating a spatial process that has nonstationary structure. A nonstationary model is proposed based on the spatial field being a linear combination of a multiresolution (wavelet) basis functions and random coefficients. The key is to allow for a limited some number of correlations among coefficients and also to use a wavelet basis that is smooth. When approximately 6 % nonzero correlations are enforced, this representation gives a good approximation to a family of Matern covariance functions. This sparseness is important not only for model parsimony but also has implications for the efficient analysis of large spatial data sets. The covariance model is successfully applied to ozone model output and results in a nonstationary but smooth estimate.This work was supported by National Science Foundation grants DMS-93122686 and DMS-9815344.This work was supported by National Science Foundation grants DMS-93122686 and DMS-9815344

A methodology to extract outcomes from routine healthcare data for patients with locally advanced non-small cell lung cancer

BACKGROUND: Outcomes for patients in UK with locally advanced non-small cell lung cancer (LA NSCLC) are amongst the worst in Europe. Assessing outcomes is important for analysing the effectiveness of current practice. However, data quality is inconsistent and regular large scale analysis is challenging. This project investigates the use of routine healthcare datasets to determine progression free survival (PFS) and overall survival (OS) of patients treated with primary radical radiotherapy for LA NSCLC. METHODS: All LA NSCLC patients treated with primary radical radiotherapy in a 2 year period were identified and paired manual and routine data generated for an initial pilot study. Manual data was extracted information from hospital records and considered the gold standard. Key time points were date of diagnosis, recurrence, death or last clinical encounter. Routine data was collected from various data sources including, Hospital Episode Statistics, Personal Demographic Service, chemotherapy data, and radiotherapy datasets. Relevant event dates were defined by proxy time points and refined using backdating and time interval optimization. Dataset correlations were then tested on key clinical outcome indicators to establish if routine data could be used as a reliable proxy measure for manual data. RESULTS: Forty-three patients were identified for the pilot study. The manual data showed a median age of 67 years (range 46- 89 years) and all patients had stage IIIA/B disease. Using the manual data, the median PFS was 10.78 months (range 1.58-37.49 months) and median OS was 16.36 months (range 2.69-37.49 months). Based on routine data, using proxy measures, the estimated median PFS was 10.68 months (range 1.61-31.93 months) and estimated median OS was 15.38 months (range 2.14-33.71 months). Overall, the routine data underestimated the PFS and OS of the manual data but there was good correlation with a Pearson correlation coefficient of 0.94 for PFS and 0.97 for OS. CONCLUSIONS: This is a novel approach to use routine datasets to determine outcome indicators in patients with LA NSCLC that will be a surrogate to analysing manual data. The ability to enable efficient and large scale analysis of current lung cancer strategies has a huge potential impact on the healthcare system

A methodology to extract outcomes from routine healthcare data for patients with locally advanced non-small cell lung cancer

BACKGROUND: Outcomes for patients in UK with locally advanced non-small cell lung cancer (LA NSCLC) are amongst the worst in Europe. Assessing outcomes is important for analysing the effectiveness of current practice. However, data quality is inconsistent and regular large scale analysis is challenging. This project investigates the use of routine healthcare datasets to determine progression free survival (PFS) and overall survival (OS) of patients treated with primary radical radiotherapy for LA NSCLC. METHODS: All LA NSCLC patients treated with primary radical radiotherapy in a 2 year period were identified and paired manual and routine data generated for an initial pilot study. Manual data was extracted information from hospital records and considered the gold standard. Key time points were date of diagnosis, recurrence, death or last clinical encounter. Routine data was collected from various data sources including, Hospital Episode Statistics, Personal Demographic Service, chemotherapy data, and radiotherapy datasets. Relevant event dates were defined by proxy time points and refined using backdating and time interval optimization. Dataset correlations were then tested on key clinical outcome indicators to establish if routine data could be used as a reliable proxy measure for manual data. RESULTS: Forty-three patients were identified for the pilot study. The manual data showed a median age of 67 years (range 46- 89 years) and all patients had stage IIIA/B disease. Using the manual data, the median PFS was 10.78 months (range 1.58-37.49 months) and median OS was 16.36 months (range 2.69-37.49 months). Based on routine data, using proxy measures, the estimated median PFS was 10.68 months (range 1.61-31.93 months) and estimated median OS was 15.38 months (range 2.14-33.71 months). Overall, the routine data underestimated the PFS and OS of the manual data but there was good correlation with a Pearson correlation coefficient of 0.94 for PFS and 0.97 for OS. CONCLUSIONS: This is a novel approach to use routine datasets to determine outcome indicators in patients with LA NSCLC that will be a surrogate to analysing manual data. The ability to enable efficient and large scale analysis of current lung cancer strategies has a huge potential impact on the healthcare system

Population Influences on Tornado Reports in the United States

The number of tornadoes reported in the United States is believed to be less than the actual incidence of tornadoes, especially prior to the 1990s, because tornadoes may be undetectable by human witnesses in sparsely populated areas. We use a hierarchical Bayesian model to simultaneously correct for population-based sampling bias and estimate tornado density using historical tornado report data. The expected result is that F2-F5 compared to F0-F1 tornado reports would vary less with population density. The results agree with this hypothesis for the following population centers: Atlanta, GA; Champaign, IL; Des Moines, IA. However, the results indicated just the opposite in Oklahoma. We speculate the result is explained by misclassification of tornadoes that were worthy of F2-F5 Fujita scale rating but were classified as F0-F1 tornadoes, thereby artificially decreasing the number of F2-F5 and increasing the number of F0-F1 reports in rural Oklahoma.Wikle and Zhou acknowledge the support of NSF grant DMS 0139903. Anderson acknowledges the support of NSF grant ATM-9911417