Objective To examine the hypothesis that risk of oesophageal, but not of gastric or colorectal, cancer is increased in users of oral bisphosphonates. Design Nested case-control analysis within a primary care cohort of about 6 million people in the UK, with prospectively recorded information on prescribing of bisphosphonates.

No abstract

activity from a specific brain region across multiple trials in response to the same stimulus or execution of the same behavioral task is a common neurophysiology protocol. The raster plots of the spike trains often show strong between-trial and within-trial dynamics, yet the standard analysis of these data with the peristimulus time histogram (PSTH) and ANOVA do not consider between-trial dynamics. By itself, the PSTH does not provide a framework for statistical inference. We present a state-space generalized linear model (SS-GLM) to formulate a point process representation of between-trial and within-trial neural spiking dynamics. Our model has the PSTH as a special case. We provide a framework for model estimation, model selection, goodness-of-fit analysis, and inference. In an analysis of hippocampal neural activity recorded from a monkey performing a location-scene association task, we demonstrate how the SS-GLM may be used to answer frequently posed neurophysiological questions including, What is the nature of the between-trial and within-trial task-specific modulation of the neural spiking activity? How can we characterize learning-related neural dynamics? What are the timescales and characteristics of the neuron's biophysical properties? Our results demonstrate that the SS-GLM is a more informative tool than the PSTH and ANOVA for analysis of multiple trial neural responses and that it provides a quantitative characterization of the between-trial and withintrial neural dynamics readily visible in raster plots, as well as the less apparent fast (1-10 ms), intermediate (11-20 ms), and longer (Ͼ20 ms) timescale features of the neuron's biophysical properties.

In primates, little is known about intrinsic electrophysiological properties of neocortical neurons and their morphological correlates. To classify inhibitory cells (interneurons) in layers 2–3 of monkey dorsolateral prefrontal cortex we used whole cell voltage recordings and intracellular labeling in slice preparation with subsequent morphological reconstructions. Regular spiking pyramidal cells have been also included in the sample. Neurons were successfully segregated into three physiological clusters: regular-, intermediate-, and fast-spiking cells using cluster analysis as a multivariate exploratory technique. When morphological types of neurons were mapped on the physiological clusters, the cluster of regular spiking cells contained all pyramidal cells, whereas the intermediate- and fast-spiking clusters consisted exclusively of interneurons. The cluster of fast-spiking cells contained all of the chandelier cells and the majority of local, medium, and wide arbor (basket) interneurons. The cluster of intermediate spiking cells predominantly consisted of cells with the morphology of neurogliaform or vertically oriented (double-bouquet) interneurons. Thus a quantitative approach enabled us to demonstrate that intrinsic electrophysiological properties of neurons in the monkey prefrontal cortex define distinct cell types, which also display distinct morphologies.

No abstract

Use of menopausal hormone therapy (HT) has been associated with reduced risk of colorectal cancer; evidence for its effect on other gastrointestinal cancers is limited. We conducted a nested case-control study within a UK cohort, and meta-analyses combining our results with those from published studies. Our study included women aged 501 in the UK General Practice Research Database (GPRD): 1,054 with oesophageal, 750 with gastric and 4,708 with colorectal cancer, and 5 age-and practice-matched controls per case. Relative risks (RRs) and 95% confidence intervals (CIs) for cancer in relation to prospectively-recorded HT prescriptions were estimated by conditional logistic regression. Women prescribed HT had a reduced risk of oesophageal cancer (adjusted RR for 11 vs. no HT prescriptions, 0.68, 95% CI 0.53-0.88; p 5 0.004), gastric cancer (0.75, 0.54-1.05; p 5 0.1) and colorectal cancer (0.81, 0.73-0.90; p < 0.001). There were no significant differences in cancer risk by HT type, estimated duration of HT use or between past and current users. In meta-analyses, risks for ever vs. never use of HT were significantly reduced for all three cancers (summary RR for oesophageal cancer, 0.68, 0.55-0.84, p < 0.001; for gastric cancer, 0.78, 0.65-0.94, p 5 0.008; for colorectal cancer, 0.84, 0.81-0.88, p < 0.001). In high-income countries, estimated incidence over 5 years of these three cancers combined in women aged 50-64 was 2.9/1,000 in HT users and 3.6/1,000 in never users. The absolute reduction in risk of these cancers in HT users is small compared to the HTassociated increased risk of breast cancer.Although the use of certain types of hormone therapy (HT) for the menopause has been associated with increased risk of cancers of the reproductive tract, including breast, ovary and endometrium, 1,2 there is also evidence for a reduced risk of colorectal cancer in HT users. [3][4][5][6][7] However it is unclear whether the association with colorectal cancer differs by HT type or pattern of use, or by cancer site. [8][9][10] Epidemiological evidence for an association between HT use and risk of the less common cancers of the gastrointestinal tract is limited, with most published results showing non-significant reductions in risk in HT users both for oesophageal cancer [11][12][13][14] and for gastric cancer. 12,13,[15][16][17] We report here on the relation between prospectively recorded prescribing information for HT and the subsequent incidence of cancers of the oesophagus, stomach and colorectum in the UK General Practice Research Database (GPRD) cohort. We also report the results of meta-analyses combining our findings with other published data on the relation between HT use and the risk of each of the three gastrointestinal cancers.

Osteoporosis is the most common age-related bone disease worldwide and is usually clinically asymptomatic until the first fracture happens. MicroRNAs are critical molecular regulators in bone remodelling processes and are stabilised in the blood. The aim of this project was to identify circulatory microRNAs associated with osteoporosis using advanced PCR arrays initially and the identified differentially-expressed microRNAs were validated in clinical samples using RT-qPCR. A total of 161 participants were recruited and 139 participants were included in this study with local ethical approvals prior to recruitment. RNAs were extracted, purified, quantified and analysed from all serum and plasma samples. Differentially-expressed miRNAs were identified using miRNA PCR arrays initially and validated in 139 serum and 134 plasma clinical samples using RT-qPCR. Following validation of identified miRNAs in individual clinical samples using RT-qPCR, circulating miRNAs, hsa-miR-122-5p and hsa-miR-4516 were statistically significantly differentially-expressed between non-osteoporotic controls, osteopaenia and osteoporosis patients. Further analysis showed that the levels of these microRNAs were associated with fragility fracture and correlated with the low bone mineral density in osteoporosis patients. The results show that circulating hsa-miR-122-5p and hsa-miR-4516 could be potential diagnostic biomarkers for osteoporosis in the future.

There is an emerging need in clinical research to accurately predict patients’ disease status and disease progression by optimally integrating multivariate clinical information. Clinical data are often collected over time for multiple biomarkers of different types (e.g. continuous, binary and counts). In this paper, we present a flexible and dynamic (time-dependent) discriminant analysis approach in which multiple biomarkers of various types are jointly modelled for classification purposes by the multivariate generalized linear mixed model. We propose a mixture of normal distributions for the random effects to allow additional flexibility when modelling the complex correlation between longitudinal biomarkers and to robustify the model and the classification procedure against misspecification of the random effects distribution. These longitudinal models are subsequently used in a multivariate time-dependent discriminant scheme to predict, at any time point, the probability of belonging to a particular risk group. The methodology is illustrated using clinical data from patients with epilepsy, where the aim is to identify patients who will not achieve remission of seizures within a five-year follow-up period.