Assessing length of stay and associated characteristics of geriatric patients in Northern Ireland

The effective provision of care for the elderly is becoming increasingly more difficult. This is due to the rising proportion of elderly in the population, increasing demands placed on the health services and the financial strain placed on an already stretched economy. The research presented in this paper uses three different models to represent the length of stay distribution of geriatric patients admitted to one of the six key acute hospitals in Northern Ireland and various patient characteristics associated with their respective length of stay. The accurate modelling of bed usage within wards would enable hospital managers to prepare patient discharge packages and rehabilitation services in advance. The models presented within the paper include a Cox proportional hazards model, a Bayesian network with a discrete variable to represent length of stay and a special conditional phase-type model (C-Ph) with a connecting outcome node. This research demonstrates the new efficient fitting algorithm employed for Coxian phase-type distributions while updating C-Ph models for recent elderly patient data.

Population-based study reveals new risk-stratification biomarker panel for Barrett's esophagus

BACKGROUND & AIMS: The risk of progression of Barrett's esophagus (BE) to esophageal adenocarcinoma (EAC) is low and difficult to calculate. Accurate tools to determine risk are needed to optimize surveillance and intervention. We assessed the ability of candidate biomarkers to predict which cases of BE will progress to EAC or high-grade dysplasia and identified those that can be measured in formalin-fixed tissues. METHODS: We analyzed data from a nested case-control study performed using the population-based Northern Ireland BE Register (1993-2005). Cases who progressed to EAC (n = 89) or high-grade dysplasia =6 months after diagnosis with BE were matched to controls (nonprogressors, n = 291), for age, sex, and year of BE diagnosis. Established biomarkers (abnormal DNA content, p53, and cyclin A expression) and new biomarkers (levels of sialyl Lewis(a), Lewis(x), and Aspergillus oryzae lectin [AOL] and binding of wheat germ agglutinin) were assessed in paraffin-embedded tissue samples from patients with a first diagnosis of BE. Conditional logistic regression analysis was applied to assess odds of progression for patients with dysplastic and nondysplastic BE, based on biomarker status. RESULTS: Low-grade dysplasia and all biomarkers tested, other than Lewis(x), were associated with risk of EAC or high-grade dysplasia. In backward selection, a panel comprising low-grade dysplasia, abnormal DNA ploidy, and AOL most accurately identified progressors and nonprogressors. The adjusted odds ratio for progression of patients with BE with low-grade dysplasia was 3.74 (95% confidence interval, 2.43-5.79) for each additional biomarker and the risk increased by 2.99 for each additional factor (95% confidence interval, 1.72-5.20) in patients without dysplasia. CONCLUSIONS: Low-grade dysplasia, abnormal DNA ploidy, and AOL can be used to identify patients with BE most likely to develop EAC or high-grade dysplasia.

Contributions of two UDP-glucose dehydrogenases to viability and polymyxin B resistance of Burkholderia cenocepacia

Burkholderia cenocepacia is highly resistant to antimicrobial peptides and we hypothesized that the conversion of UDP-glucose to UDP-glucuronic acid, a reaction catalysed by the enzyme UDP-glucose dehydrogenase (Ugd) would be important for this resistance. The genome of B. cenocepacia contains three predicted ugd genes: ugd(BCAL2946), ugd(BCAM0855) and ugd(BCAM2034), all of which were individually inactivated. Only inactivation of ugd(BCAL2946) resulted in increased sensitivity to polymyxin B and this sensitivity could be overcome when either ugd(BCAL2946) or ugd(BCAM0855) but not ugd(BCAM2034) was expressed from plasmids. The growth of a conditional ugd(BCAL2946) mutant, created in the Deltaugd(BCAM0855) background, was significantly impaired under non-permissive conditions. Growth could be rescued by either ugd(BCAL2946) or ugd(BCAM0855) expressed in trans, but not by ugd(BCAM2034). Biochemical analysis of the purified, recombinant forms of Ugd(BCAL2946) and Ugd(BCAM0855) revealed that they are soluble homodimers with similar in vitro Ugd activity and comparable kinetic constants for their substrates UDP-glucose and NAD(+). Purified Ugd(BCAM2034) showed no in vitro Ugd activity. Real-time PCR analysis showed that the expression of ugd(BCAL2946) was 5.4- and 135-fold greater than that of ugd(BCAM0855) and ugd(BCAM2034), respectively. Together, these data indicate that the combined activity of Ugd(BCAL2946) and Ugd(BCAM0855) is essential for the survival of B. cenocepacia but only the most highly expressed ugd gene, ugd(BCAL2946), is required for polymyxin B resistance.

A putative gene cluster for aminoarabinose biosynthesis is essential for Burkholderia cenocepacia viability

Using a conditional mutagenesis strategy we demonstrate here that a gene cluster encoding putative aminoarabinose (Ara4N) biosynthesis enzymes is essential for the viability of Burkholderia cenocepacia. Loss of viability is associated with dramatic changes in bacterial cell morphology and ultrastructure, increased permeability to propidium iodide, and sensitivity to sodium dodecyl sulfate, suggesting a general cell envelope defect caused by the lack of Ara4N.

Regression analysis of spatial data

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.

A test for psychometric function shift

A nonparametric, small-sample-size test for the homogeneity of two psychometric functions against the left- and right-shift alternatives has been developed. The test is designed to determine whether it is safe to amalgamate psychometric functions obtained in different experimental sessions. The sum of the lower and upper p-values of the exact (conditional) Fisher test for several 2 × 2 contingency tables (one for each point of the psychometric function) is employed as the test statistic. The probability distribution of the statistic under the null (homogeneity) hypothesis is evaluated to obtain corresponding p-values. Power functions of the test have been computed by randomly generating samples from Weibull psychometric functions. The test is free of any assumptions about the shape of the psychometric function; it requires only that all observations are statistically independent. © 2011 Psychonomic Society, Inc.

Breast Cancer Risk in Relation to Occupations with Exposure to Carcinogens and Endocrine Disruptors:a Canadian Case-Control Study

Background

Endocrine disrupting chemicals and carcinogens, some of which may not yet have been classified as such, are present in many occupational environments and could increase breast cancer risk. Prior research has identified associations with breast cancer and work in agricultural and industrial settings. The purpose of this study was to further characterize possible links between breast cancer risk and occupation, particularly in farming and manufacturing, as well as to examine the impacts of early agricultural exposures, and exposure effects that are specific to the endocrine receptor status of tumours.

Methods

1005 breast cancer cases referred by a regional cancer center and 1147 randomly-selected community controls provided detailed data including occupational and reproductive histories. All reported jobs were industry- and occupation-coded for the construction of cumulative exposure metrics representing likely exposure to carcinogens and endocrine disruptors. In a frequency-matched case?control design, exposure effects were estimated using conditional logistic regression.

Results

Across all sectors, women in jobs with potentially high exposures to carcinogens and endocrine disruptors had elevated breast cancer risk (OR = 1.42; 95% CI, 1.18-1.73, for 10 years exposure duration). Specific sectors with elevated risk included: agriculture (OR = 1.36; 95% CI, 1.01-1.82); bars-gambling (OR = 2.28; 95% CI, 0.94-5.53); automotive plastics manufacturing (OR = 2.68; 95% CI, 1.47-4.88), food canning (OR = 2.35; 95% CI, 1.00-5.53), and metalworking (OR = 1.73; 95% CI, 1.02-2.92). Estrogen receptor status of tumors with elevated risk differed by occupational grouping. Premenopausal breast cancer risk was highest for automotive plastics (OR = 4.76; 95% CI, 1.58-14.4) and food canning (OR = 5.70; 95% CI, 1.03-31.5).

Conclusions

These observations support hypotheses linking breast cancer risk and exposures likely to include carcinogens and endocrine disruptors, and demonstrate the value of detailed work histories in environmental and occupational epidemiology.

Episodic memory and learning in patients with chronic schizophrenia

This study explored the pattern of memory functioning in 58 patients with chronic schizophrenia and compared their performance with 53 normal controls. Multiple domains of memory were assessed, including verbal and nonverbal memory span, verbal and non-verbal paired associate learning, verbal and visual long-term memory, spatial and non-spatial conditional associative learning, recognition memory and memory for temporal order. Consistent with previous studies, substantial deficits in long-term memory were observed, with relative preservation of memory span. Memory for temporal order and recognition memory was intact, although significant deficits were observed on the conditional associative learning tasks. There was no evidence of lateralized memory impairment. In these respects, the pattern of memory impairment in schizophrenia is more similar in nature to that found in patients with memory dysfunction following mesiotemporal lobe lesions, rather than that associated with focal frontal lobe damage. (C) 1999 Elsevier Science B.V. All rights reserved.

Sequencing and its consequences:Path dependence and the relationships between genetics and medicalization

Both advocacy for and critiques of the Human Genome Project assume a self-sustaining relationship between genetics and. medicalization. However, this assumption ignores the ways in which the meanings of genetic research are conditional on its position in sequences of events. Based, on analyses of three conditions for which at least one putative gene or genetic marker has been identified, this article argues that critical junctures in the institutional stabilization of phenotypes and the mechanisms that sustain such classifications over time configure the practices and meanings of genetic research. Path dependence is critical to understanding the lack of consistent fit between genetics and medlcalization.

XBP1 mRNA splicing triggers an autophagic response in endothelial cells through BECLIN-1 transcriptional activation

Sustained activation of X-box-binding protein 1 (XBP1) results in endothelial cell (EC) apoptosis and atherosclerosis development. The present study provides evidence that XBP1 mRNA splicing triggered an autophagic response in ECs by inducing autophagic vesicle formation and markers of autophagy BECLIN-1 and microtubule-associated protein 1 light chain 3ß (LC3-ßII). Endostatin activated autophagic gene expression through XBP1 mRNA splicing in an inositol-requiring enzyme 1a (IRE1a)-dependent manner. Knockdown of XBP1 or IRE1a by shRNA in ECs ablated endostatin-induced autophagosome formation. Importantly, data from arterial vessels from XBP1 EC conditional knock-out (XBP1eko) mice demonstrated that XBP1 deficiency in ECs reduced the basal level of LC3ß expression and ablated response to endostatin. Chromatin immunoprecipitation assays further revealed that the spliced XBP1 isoform bound directly to the BECLIN-1 promoter at the region from nt -537 to -755. BECLIN-1 deficiency in ECs abolished the XBP1-induced autophagy response, whereas spliced XBP1 did not induce transcriptional activation of a truncated BECLIN-1 promoter. These results suggest that XBP1 mRNA splicing triggers an autophagic signal pathway through transcriptional regulation of BECLIN-1.

Entinostat prevents leukemia maintenance in a collaborating oncogene-dependent model of CN-AML.

The incidence of refractory acute myeloid leukemia (AML) is on the increase due in part to an aging population that fails to respond to traditional therapies. High throughput genomic analysis promises better diagnosis, prognosis and therapeutic intervention based on improved patient stratification. Relevant pre-clinical models are urgently required to advance drug development in this area. The collaborating oncogenes, HOXA9 and MEIS1, are frequently co-overexpressed in cytogenetically normal AML (CN-AML) and a conditional transplantation mouse model was developed that demonstrated oncogene-dependency and expression levels comparable to CN-AML patients. Integration of gene signatures obtained from the mouse model and a cohort of CN-AML patients using statistically significant connectivity Map (sscMap) analysis identified Entinostat as a drug with the potential to alter the leukemic condition towards the normal state. Ex vivo treatment of leukemic cells, but not age-matched normal bone marrow controls, with Entinostat validated the gene signature and resulted in reduced viability in liquid culture, impaired colony formation and loss of the leukemia initiating cell. Furthermore, in vivo treatment with Entinostat resulted in prolonged survival of leukemic mice. This study demonstrates that the HDAC inhibitor Entinostat inhibits disease maintenance and prolongs survival in a clinically relevant murine model of cytogenetically normal AML. © 2013 AlphaMed Press

Physicians' and Nurses' Perceived Usefulness and Acceptability of a Family Information Booklet about Comfort Care in Advanced Dementia

Background: Families of patients with advanced dementia need to be informed about the course of the dementia and comfort care. Conditional for health care providers educating families is their knowledge and comfort in family education. Methods: Perceived usefulness and acceptability of a Canadian family booklet explaining possible complications and comfort care in dementia was assessed by physicians and nurses caring for dementia patients in 14 nursing homes in Lombardy, Italy and 21 in the Netherlands. The practitioners received a questionnaire and translated versions adapted to local practice where needed. In 10 of 21 Dutch homes, physicians evaluated only the original Canadian version in English. A 15-item scale assessed the booklet's acceptability, for example, to inform families, or for educational purposes. Perceived usefulness referred to proportion of families of dementia patients for whom the booklet would be useful. A total of 168 evaluations were available for multivariable regression analyses. Results: The practitioners anticipated that the booklet would be useful for most families. Evaluation of the Dutch translation of the booklet was similar to the English version. Country (Netherlands) and profession (nurses) were independently associated with better acceptability. Usefulness was perceived as better by Italian respondents and nurses, but only in analyses unadjusted for the higher educational needs of these respondents. Conclusion: Overall, the concept of written information on comfort care was appreciated by practitioners of European countries differing in attitudes toward end-of-life care. A booklet may help practitioners, and in particular nurses, in providing comfort care for dementia patients and their families. © Copyright 2011, Mary Ann Liebert, Inc. 2011.

PROSTATIC INTRAEPITHELIAL NEOPLASIA (PIN) - PERFORMANCE OF BAYESIAN BELIEF NETWORK FOR DIAGNOSIS AND GRADING

Prostatic intraepithelial neoplasia (PIN) diagnosis and grading are affected by uncertainties which arise from the fact that almost all knowledge of PIN histopathology is expressed in concepts, descriptive linguistic terms, and words. A Bayesian belief network (BBN) was therefore used to reduce the problem of uncertainty in diagnostic clue assessment, while still considering the dependences between elements in the reasoning sequence. A shallow network was used with an open-tree topology, with eight first-level descendant nodes for the diagnostic clues (evidence nodes), each independently linked by a conditional probability matrix to a root node containing the diagnostic alternatives (decision node). One of the evidence nodes was based on the tissue architecture and the others were based on cell features. The system was designed to be interactive, in that the histopathologist entered evidence into the network in the form of likelihood ratios for outcomes at each evidence node. The efficiency of the network was tested on a series of 110 prostate specimens, subdivided as follows: 22 cases of non-neoplastic prostate or benign prostatic tissue (NP), 22 PINs of low grade (PINlow), 22 PINs of high grade (PINhigh), 22 prostatic adenocarcinomas with cribriform pattern (PACcri), and 22 prostatic adenocarcinomas with large acinar pattern (PAClgac). The results obtained in the benign and malignant categories showed that the belief for the diagnostic alternatives is very high, the values being in general more than 0.8 and often close to 1.0. When considering the PIN lesions, the network classified and graded most of the cases with high certainty. However, there were some cases which showed values less than 0.8 (13 cases out of 44), thus indicating that there are situations in which the feature changes are intermediate between contiguous categories or grades. Discrepancy between morphological grading and the BBN results was observed in four out of 44 PIN cases: one PINlow was classified as PINhigh and three PINhigh were classified as PINlow. In conclusion, the network can grade PlN lesions and differentiate them from other prostate lesions with certainty. In particular, it offers a descriptive classifier which is readily implemented and which allows the use of linguistic, fuzzy variables.

EXPERT-SYSTEM SUPPORT USING BAYESIAN BELIEF NETWORKS IN THE DIAGNOSIS OF FINE-NEEDLE ASPIRATION BIOPSY SPECIMENS OF THE BREAST

Aim-To develop an expert system model for the diagnosis of fine needle aspiration cytology (FNAC) of the breast.

Methods-Knowledge and uncertainty were represented in the form of a Bayesian belief network which permitted the combination of diagnostic evidence in a cumulative manner and provided a final probability for the possible diagnostic outcomes. The network comprised 10 cytological features (evidence nodes), each independently linked to the diagnosis (decision node) by a conditional probability matrix. The system was designed to be interactive in that the cytopathologist entered evidence into the network in the form of likelihood ratios for the outcomes at each evidence node.

Results-The efficiency of the network was tested on a series of 40 breast FNAC specimens. The highest diagnostic probability provided by the network agreed with the cytopathologists' diagnosis in 100% of cases for the assessment of discrete, benign, and malignant aspirates. A typical probably benign cases were given probabilities in favour of a benign diagnosis. Suspicious cases tended to have similar probabilities for both diagnostic outcomes and so, correctly, could not be assigned as benign or malignant. A closer examination of cumulative belief graphs for the diagnostic sequence of each case provided insight into the diagnostic process, and quantitative data which improved the identification of suspicious cases.

Conclusion-The further development of such a system will have three important roles in breast cytodiagnosis: (1) to aid the cytologist in making a more consistent and objective diagnosis; (2) to provide a teaching tool on breast cytological diagnosis for the non-expert; and (3) it is the first stage in the development of a system capable of automated diagnosis through the use of expert system machine vision.

An Examination of Transformation Techniques to Investigate and Interpret Multivariate Geochemical Data Analysis: Tellus Case Study

This research aims to use the multivariate geochemical dataset, generated by the Tellus project, to investigate the appropriate use of transformation methods to maintain the integrity of geochemical data and inherent constrained behaviour in multivariate relationships. The widely used normal score transform is compared with the use of a stepwise conditional transform technique. The Tellus Project, managed by GSNI and funded by the Department of Enterprise Trade and Development and the EU’s Building Sustainable Prosperity Fund, involves the most comprehensive geological mapping project ever undertaken in Northern Ireland. Previous study has demonstrated spatial variability in the Tellus data but geostatistical analysis and interpretation of the datasets requires use of an appropriate methodology that reproduces the inherently complex multivariate relations. Previous investigation of the Tellus geochemical data has included use of Gaussian-based techniques. However, earth science variables are rarely Gaussian, hence transformation of data is integral to the approach. The multivariate geochemical dataset generated by the Tellus project provides an opportunity to investigate the appropriate use of transformation methods, as required for Gaussian-based geostatistical analysis. In particular, the stepwise conditional transform is investigated and developed for the geochemical datasets obtained as part of the Tellus project. The transform is applied to four variables in a bivariate nested fashion due to the limited availability of data. Simulation of these transformed variables is then carried out, along with a corresponding back transformation to original units. Results show that the stepwise transform is successful in reproducing both univariate statistics and the complex bivariate relations exhibited by the data. Greater fidelity to multivariate relationships will improve uncertainty models, which are required for consequent geological, environmental and economic inferences.