Visualizing levels of osteoblast differentiation by a two-color promoter-GFP strategy: Type I collagen-GFPcyan and osteocalcin-GFPtpz

A 3.9 kb DNA fragment of human osteocalcin promoter and 3.6 kb DNA fragment of the rat collagen type1a1 promoter linked with visually distinguishable GFP isomers, topaz and cyan, were used for multiplex analysis of osteoblast lineage progression. Three patterns of dual transgene, expression can be appreciated in primary bone cell cultures derived from the transgenic mice and by histology of their corresponding bones. Our data support the interpretation that strong pOBCol3.6GFPcyan alone is found in newly formed osteoblasts, while strong pOBCol3.6GFPcyan and hOC-GFPtpz are present in osteoblasts actively making a new matrix. Osteoblasts expressing strong hOC-GFPtpz and weak pOBCol3.6GF-Pcyan are also present and may or may not be producing mineralized matrix. This multiplex approach reveals the heterogeneity within the mature osteoblast population that cannot be appreciated by current histological methods. It should be useful to identify and isolate populations of cells within an osteoblast lineage as they progress through stages of differentiation.

Teleneurology

Teleneurology enables neurology to be practised when the doctor and patient are not present in the same place, and possibly not at the same time. The two main techniques are: (1) videoconferencing, which enables communication between a doctor and a patient who are in different places at the same time (often called real-time or synchronous), and (2) email, where the consultation is carried out without the patient being present, at a time convenient to the doctors involved (asynchronous or store-and-forward teleneurology). Some problems that can be solved by teleneurology include: (1) patients admitted to hospital with acute neurological symptoms rarely see a neurologist; (2) delayed treatment for acute stroke; (3) non-optimum management of epilepsy; (4) unproductive travel time for neurologists; (5) extremely poor access to a neurologist for doctors in the developing world; (6) long waiting times to see a neurologist. Neurology is a specialty that, because of the emphasis on accurate interpretation of a history, does lend itself to telemedicine. It has been a late starter in realizing the benefits of telemedicine and most of the publications on teleneurology have been in the last five years. Its uptake within the neurological community is low but increasing. Telemedicine requires a significant change in how neurologists practise. The evidence to date is that teleneurology can narrow the gap between patients with neurological disease and the doctors who are trained to look after them.

Genomewide linkage study in 1,176 affected sister pair families identifies a significant susceptibility locus for endometriosis on chromosome 10q26

Endometriosis is a common gynecological disease that affects up to 10% of women in their reproductive years. It causes pelvic pain, severe dysmenorrhea, and subfertility. The disease is defined as the presence of tissue resembling endometrium in sites outside the uterus. Its cause remains uncertain despite 150 years of hypothesis-driven research, and thus the therapeutic options are limited. Disease predisposition is inherited as a complex genetic trait, which provides an alternative route to understanding the disease. We seek to identify susceptibility loci, using a positional-cloning approach that starts with linkage analysis to identify genomic regions likely to harbor these genes. We conducted a linkage study of 1,176 families ( 931 from an Australian group and 245 from a U. K. group), each with at least two members-mainly affected sister pairs-with surgically diagnosed disease. We have identified a region of significant linkage on chromosome 10q26 ( maximum LOD score [MLS] of 3.09; genomewide P = .047) and another region of suggestive linkage on chromosome 20p13 MLS p 2.09). Minor peaks with MLS > 1.0) were found on chromosomes 2, 6, 7, 8, 12, 14, 15, and 17. This is the first report of linkage to a major locus for endometriosis. The findings will facilitate discovery of novel positional genetic variants that influence the risk of developing this debilitating disease. Greater understanding of the aberrant cellular and molecular mechanisms involved in the etiology and pathophysiology of endometriosis should lead to better diagnostic methods and targeted treatments.

Australian and New Zealand practice in intensive care (ANZPIC II: The spectrum of practice in the diagnosis and management of pneumonia in patients requiring mechanical ventilation

This study of ventilated patients investigated current clinical practice in 476 episodes of pneumonia (48% community-acquired pneumonia, 24% hospital-acquired pneumonia, 28% ventilator-associated pneumonia) using a prospective survey in 14 intensive care units (ICUs) within Australia and New Zealand. Diagnostic methods and confidence, disease severity, microbiology and antibiotic use were assessed. All pneumonia types had similar mortality (community-acquired pneumonia 33%, hospital-acquired pneumonia 37% and ventilator-associated pneumonia 24%, P = 0.15) with no inter-hospital differences (P = 0.08-0.91). Bronchoscopy was performed in 26%, its use predicted by admission hospital (one tertiary: OR 9.98, CI 95% 5.11-19.49, P < 0.001; one regional: OR 629, CI 95% 3.24-12.20, P < 0.001), clinical signs of consolidation (OR 3.72, CI 95% 2.09-662, P < 0.001) and diagnostic confidence (OR 2.19, CI 95% 1.29-3.72, P = 0.004). Bronchoscopy did not predict outcome (P = 0.11) or appropriate antibiotic selection (P = 0.69). Inappropriate antibiotic prescription was similar for all pneumonia types (11-13%, P = 0.12) and hospitals (0-16%, P = 0.25). Blood cultures were taken in 51% of cases. For community-acquired pneumonia, 70% received a third generation cephalosporin and 65% a macrolide. Third generation cephalosporins were less frequently used for mild infections (OR 0.38, CI 95% 0.16-0.90, P = 0.03), hospital-acquired pneumonia (OR 0.40, CI 95% 0.23-0.72, P < 0.01), ventilator-associated pneumonia (OR 0.04, CI 95% 0.02-0.13, P < 0.001), suspected aspiration (OR 0.20, CI 95% 0.04-0.92, P = 0.04), in one regional (OR 0.26, CI 95% 0.07-0.97, P = 0.05) and one tertiary hospital (OR 0.14, CI 95% 0.03-0.73, P = 0.02) but were more commonly used in older patients (OR 1.02, CI 95% 1.01-1.03, P = 0.01). There is practice variability in bronchoscopy and antibiotic use for pneumonia in Australian and New Zealand ICUs without significant impact on patient outcome, as the prevalence of inappropriate antibiotic prescription is low. There are opportunities for improving microbiological diagnostic work-up for isolation of aetiological pathogens.

Matrix effects: The Achilles heel of quantitative high-performance liquid chromatography-electrospray-tandem mass spectrometry

High-performance liquid chromatography coupled by an electrospray ion source to a tandem mass spectrometer (HPLC-EST-MS/ MS) is the current analytical method of choice for quantitation of analytes in biological matrices. With HPLC-ESI-MS/MS having the characteristics of high selectivity, sensitivity, and throughput, this technology is being increasingly used in the clinical laboratory. An important issue to be addressed in method development, validation, and routine use of HPLC-ESI-MS/MS is matrix effects. Matrix effects are the alteration of ionization efficiency by the presence of coeluting substances. These effects are unseen in the chromatograrn but have deleterious impact on methods accuracy and sensitivity. The two common ways to assess matrix effects are either by the postextraction addition method or the postcolumn infusion method. To remove or minimize matrix effects, modification to the sample extraction methodology and improved chromatographic separation must be performed. These two parameters are linked together and form the basis of developing a successful and robust quantitative HPLC-EST-MS/MS method. Due to the heterogenous nature of the population being studied, the variability of a method must be assessed in samples taken from a variety of subjects. In this paper, the major aspects of matrix effects are discussed with an approach to address matrix effects during method validation proposed. (c) 2004 The Canadian Society of Clinical Chemists. All rights reserved.

A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays

Motivation: An important problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. We provide a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. Current methods of implementing this approach either have some limitations due to the minimal assumptions made or with more specific assumptions are computationally intensive. Results: By converting to a z-score the value of the test statistic used to test the significance of each gene, we propose a simple two-component normal mixture that models adequately the distribution of this score. The usefulness of our approach is demonstrated on three real datasets.

A wave equation technique for designing compact gradient coils

A primary purpose of this research is to design a gradient coil that is planar in construction and can be inserted within existing infrastructure. The proposed wave equation method for the design of gradient coils is novel within the field. it is comprehensively shown how this method can be used to design the planar x-, y-, and z-gradient wire windings to produce the required magnetic fields within a certain domain. The solution for the cylindrical gradient coil set is also elucidated. The wave equation technique is compared with the well-known target held method to gauge the quality of resultant design. In the case of the planar gradient coil design, it is shown that using the new method, a set of compact gradient coils with large field of view can be produced. The final design is considerably smaller in dimension when compared with the design obtained using the target field method, and therefore the manufacturing costs and materials required are somewhat reduced.

A Mixture model with random-effects components for clustering correlated gene-expression profiles

Motivation: The clustering of gene profiles across some experimental conditions of interest contributes significantly to the elucidation of unknown gene function, the validation of gene discoveries and the interpretation of biological processes. However, this clustering problem is not straightforward as the profiles of the genes are not all independently distributed and the expression levels may have been obtained from an experimental design involving replicated arrays. Ignoring the dependence between the gene profiles and the structure of the replicated data can result in important sources of variability in the experiments being overlooked in the analysis, with the consequent possibility of misleading inferences being made. We propose a random-effects model that provides a unified approach to the clustering of genes with correlated expression levels measured in a wide variety of experimental situations. Our model is an extension of the normal mixture model to account for the correlations between the gene profiles and to enable covariate information to be incorporated into the clustering process. Hence the model is applicable to longitudinal studies with or without replication, for example, time-course experiments by using time as a covariate, and to cross-sectional experiments by using categorical covariates to represent the different experimental classes. Results: We show that our random-effects model can be fitted by maximum likelihood via the EM algorithm for which the E(expectation) and M(maximization) steps can be implemented in closed form. Hence our model can be fitted deterministically without the need for time-consuming Monte Carlo approximations. The effectiveness of our model-based procedure for the clustering of correlated gene profiles is demonstrated on three real datasets, representing typical microarray experimental designs, covering time-course, repeated-measurement and cross-sectional data. In these examples, relevant clusters of the genes are obtained, which are supported by existing gene-function annotation. A synthetic dataset is considered too.

Epitope-blocking enzyme-linked immunosorbent assay for detection of antibodies to Ross River virus in vertebrate sera

We describe the development of an epitope-blocking enzyme-linked immunosorbent assay (ELISA) for the sensitive and rapid detection of antibodies to Ross River virus (RRV) in human sera and known vertebrate host species. This ELISA provides an alternative method for the serodiagnosis of RRV infections.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

Comparison of commercially available and novel West Nile virus immunoassays for detection of seroconversion in pig-tailed macaques (Macaca nemestrina)

We report the assessment and validation of an NS1 epitope-blocking enzyme-linked immunosorbent assay (ELISA) for detection of antibodies to West Nile virus (WNV) in macaques. Sera from naturally infected Macaca nemestrina were tested by ELISA and plaque reduction neutralization test (PRNT). Results were correlated with hemagglutination inhibition (HAI) data. Our results demonstrate that the blocking ELISA rapidly and specifically detects WNV infection in M. nemestrina. In addition, the diagnostic value of 7 commercially available immunoassays (PanBio immunoglobulin [Ig] M ELISA, PanBio IgG ELISA, PanBio immunofluorescence assay (IFA), InBios IgG ELISA, InBios IgM ELISA, Focus Diagnostics IgG ELISA, and Focus Diagnostics IgM ELISA) in M. nemestrina was evaluated and compared with that of the epitope-blocking ELISA. The PanBio IgG ELISA was found to effectively diagnose WNV exposure in M. nemestrina. Further, PanBio IFA slides are fast and reliable screening tools for diagnosing flaviviral exposure in M. nemestrina.

Multiagent realization of prediction-based diagnosis and loss prevention

A multiagent diagnostic system implemented in a Protege-JADE-JESS environment interfaced with a dynamic simulator and database services is described in this paper. The proposed system architecture enables the use of a combination of diagnostic methods from heterogeneous knowledge sources. The process ontology and the process agents are designed based on the structure of the process system, while the diagnostic agents implement the applied diagnostic methods. A specific completeness coordinator agent is implemented to coordinate the diagnostic agents based on different methods. The system is demonstrated on a case study for diagnosis of faults in a granulation process based on HAZOP and FMEA analysis.