The association between metacognitive abilities and outcome measures among people with schizophrenia: A meta-analysis.

BACKGROUND: Deficits in metacognition are one of the major causes of the difficulties experienced by individuals with schizophrenia. Studies have linked these deficits to symptom exacerbation and deterioration in psychosocial functioning. The aim of the present meta-analysis was to examine the extensive existing literature regarding metacognitive deficits among persons with schizophrenia; a further aim was to assess the extent to which metacognitive abilities are linked to outcome measures of symptoms and psychosocial functioning. METHOD: We conducted a systematic literature search of studies examining the relationship between metacognitive abilities and outcome measures among people with schizophrenia. We then analyzed the data using a random-effects meta-analytic model with Cohen's d standardized mean effect size. RESULTS: Heterogeneity analyses (k=32, Cohen's d=-.12, 95% CI.-1.92 to 1.7) produced a significant Q-statistic (Q=456.89) and a high amount of heterogeneity, as indicated by the I2 statistic (93.04%), suggesting that moderator analyses were appropriate. As hypothesized, measure type moderated the metacognitive deficit with homogenous effect for psychosocial functioning measures (Q=9.81, I2=19.47%, d=.94. 95% CI .58 to 1.2) and symptoms (Q=19.87, I2=0%, d=-1.07, 95% CI -1.18 to -.75). Further analysis found homogenous effects for MAS-A subscales as well as PANSS factors of symptoms. CONCLUSION: Our meta-analysis results illustrated a significant association between metacognitive deficits and both symptomatic and psychosocial functioning measures. These links suggest that the associations between metacognitive abilities and symptomatic outcomes are different from those between metacognitive abilities and psychosocial functioning measures. Intriguing hypotheses are raised regarding the role that metacognitive abilities play in both symptoms and psychosocial functioning measures of people diagnosed with schizophrenia spectrum disorders.

Evaluating between-pathway models with expression data.

Between-pathway models (BPMs) are network motifs consisting of pairs of putative redundant pathways. In this article, we show how adding another source of high-throughput data--microarray gene expression data from knockout experiments--allows us to identify a compensatory functional relationship between genes from the two BPM pathways. We evaluate the quality of the BPMs from four different studies, and we describe how our methods might be extended to refine pathways.

High throughput interaction data reveals degree conservation of hub proteins.

Research in model organisms relies on unspoken assumptions about the conservation of protein-protein interactions across species, yet several analyses suggest such conservation is limited. Fortunately, for many purposes the crucial issue is not global conservation of interactions, but preferential conservation of functionally important ones. An observed bias towards essentiality in highly-connected proteins implies the functional importance of such "hubs". We therefore define the notion of degree-conservation and demonstrate that hubs are preferentially degree-conserved. We show that a protein is more likely to be a hub if it has a high-degree ortholog, and that once a protein becomes a hub, it tends to remain so. We also identify a positive correlation between the average degree of a protein and the conservation of its interaction partners, and we find that the conservation of individual hub interactions is surprisingly high. Our work has important implications for prediction of protein function, computational inference of PPIs, and interpretation of data from model organisms.

[Global eradication of poliomyelitis. On the 80th anniversary of the founding of the National Institute of Health]. / Svetová eradikace poliomyelitidy. k 80. výrocí zalození Státního zdravotního ustavu.

Global eradication of poliomyelitis launched and coordinated by the World Health Organization since 1988 is close to being achieved: the main objective is to discontinue the circulation of the wild, neurovirulent, endemic virus of poliomyelitis. Although this objective has been within the reach, there are two other risks, lower but existing and posing possible threat to the eradication, that need to be controlled: 1) wild polioviruses stored in laboratories should be destroyed or reliably contained and 2) emergence and epidemic role of neurovirulent polioviruses potentially derived from attenuated oral polio vaccine should be prevented. After all these global eradication objectives are met and three years relapse from its certification, under intensive and sensitive surveillance, it will be possible to consider whether or not to stop vaccination against poliomyelitis in the world. The global eradication of poliomyelitis is a complex issue that will require further efforts in the field of both the biomedical research and organizational, economic and political approaches. In the Czech Republic, poliomyelitis has been eradicated since 1960. The former Czechoslovakia was the first country in the world to achieve and to scientifically demonstrate nationwide eradication of poliomyelitis. The current post-eradication surveillance of poliomyelitis in the Czech Republic is managed by the Centre of Epidemiology and Microbiology of the National Institute of Public Health in cooperation with the Ministry of Health of the Czech Republic, WHO Regional Office for Europe in Copenhagen and National Certification Committee for Poliomyelitis Eradication.

Detection of antibodies to polyomavirus SV40 in two central European countries.

Simian virus 40 (SV40) is significantly associated with some human cancers. However, the frequency of tumor-associated virus detection differs by geographic regions, so it is important to understand the status of SV40 infections in different populations. Poliovaccines potentially containing live SV40 were used in well-documented nationwide vaccination programs in Hungary and the Czech Republic that are reported here. We analyzed serum samples from periodic surveillance programs in those two countries for antibodies to SV40 using a specific plaque reduction neutralization assay. The prevalence of antibodies was between 1.3 and 8.7% in Hungary and from 1.0 to 4.0% in the Czech Republic. Females had a higher rate of antibodies than males, reaching in certain age groups 15.6% in Hungary and 8.3% in the Czech Republic. Antibodies to SV40 were found in similar proportions in both countries among persons not directly exposed to poliovaccines and subjects vaccinated in the era of SV40-free vaccines. Complexities and limitations of current serological approaches to epidemiological studies of SV40 in humans are discussed. These data suggest that SV40 may be present in these populations and emphasize the importance of follow-up studies to determine the pathogenesis of infections by this emerging human agent.

Chemosensitivity prediction by transcriptional profiling.

In an effort to develop a genomics-based approach to the prediction of drug response, we have developed an algorithm for classification of cell line chemosensitivity based on gene expression profiles alone. Using oligonucleotide microarrays, the expression levels of 6,817 genes were measured in a panel of 60 human cancer cell lines (the NCI-60) for which the chemosensitivity profiles of thousands of chemical compounds have been determined. We sought to determine whether the gene expression signatures of untreated cells were sufficient for the prediction of chemosensitivity. Gene expression-based classifiers of sensitivity or resistance for 232 compounds were generated and then evaluated on independent sets of data. The classifiers were designed to be independent of the cells' tissue of origin. The accuracy of chemosensitivity prediction was considerably better than would be expected by chance. Eighty-eight of 232 expression-based classifiers performed accurately (with P < 0.05) on an independent test set, whereas only 12 of the 232 would be expected to do so by chance. These results suggest that at least for a subset of compounds genomic approaches to chemosensitivity prediction are feasible.

Transcriptional profiling in cancer: the path to clinical pharmacogenomics.

Cancer is a major source of morbidity and mortality, second only to heart disease as the leading cause of death in most developed countries. Our ability to treat the disease depends heavily on our understanding its aetiology. Different types of cancer often respond best to different courses of treatment, yet our understanding of cancer classification remains imperfect. Novel expression-monitoring technology provides unique opportunities to learn more about cancer at the molecular level, to improve classification methods, and to progress towards personalised cancer treatment. This review describes the techniques that make such advances possible, summarises recent work in the area, and discusses future developments needed to realise the potential of a thorough molecular classification of cancer.

Evaluation of normalization procedures for oligonucleotide array data based on spiked cRNA controls.

BACKGROUND: Affymetrix oligonucleotide arrays simultaneously measure the abundances of thousands of mRNAs in biological samples. Comparability of array results is necessary for the creation of large-scale gene expression databases. The standard strategy for normalizing oligonucleotide array readouts has practical drawbacks. We describe alternative normalization procedures for oligonucleotide arrays based on a common pool of known biotin-labeled cRNAs spiked into each hybridization. RESULTS: We first explore the conditions for validity of the 'constant mean assumption', the key assumption underlying current normalization methods. We introduce 'frequency normalization', a 'spike-in'-based normalization method which estimates array sensitivity, reduces background noise and allows comparison between array designs. This approach does not rely on the constant mean assumption and so can be effective in conditions where standard procedures fail. We also define 'scaled frequency', a hybrid normalization method relying on both spiked transcripts and the constant mean assumption while maintaining all other advantages of frequency normalization. We compare these two procedures to a standard global normalization method using experimental data. We also use simulated data to estimate accuracy and investigate the effects of noise. We find that scaled frequency is as reproducible and accurate as global normalization while offering several practical advantages. CONCLUSIONS: Scaled frequency quantitation is a convenient, reproducible technique that performs as well as global normalization on serial experiments with the same array design, while offering several additional features. Specifically, the scaled-frequency method enables the comparison of expression measurements across different array designs, yields estimates of absolute message abundance in cRNA and determines the sensitivity of individual arrays.

Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks.

In an effort to find gene regulatory networks and clusters of genes that affect cancer susceptibility to anticancer agents, we joined a database with baseline expression levels of 7,245 genes measured by using microarrays in 60 cancer cell lines, to a database with the amounts of 5,084 anticancer agents needed to inhibit growth of those same cell lines. Comprehensive pair-wise correlations were calculated between gene expression and measures of agent susceptibility. Associations weaker than a threshold strength were removed, leaving networks of highly correlated genes and agents called relevance networks. Hypotheses for potential single-gene determinants of anticancer agent susceptibility were constructed. The effect of random chance in the large number of calculations performed was empirically determined by repeated random permutation testing; only associations stronger than those seen in multiply permuted data were used in clustering. We discuss the advantages of this methodology over alternative approaches, such as phylogenetic-type tree clustering and self-organizing maps.

Evaluation of predictive factors for neurocognitive dysfunction in patients with inactive systemic lupus erythematosus.

OBJECTIVE: To determine predictive factors associated with the cognitive dysfunction in patients with inactive systemic lupus erythematosus (SLE). METHODS: Consecutive patients followed at the Lupus Clinic with inactive SLE (SLE Disease Activity Index, SLEDAI, = 0) underwent a battery of neuropsychological tests; Beck Depression Inventory and psychiatric assessment were also performed. Neurocognitive dysfunction was defined as 3 abnormal scores. Data were analyzed using chi-square tests, ANOVA tests, and logistic regression. RESULTS: Twenty-five of the 58 patients with SLE (43%) versus 9 of 47 healthy controls (19%) demonstrated neurocognitive dysfunction (p < 0.01). Neurocognitive dysfunction was not associated with depression or a psychiatric diagnosis, use of steroids, or previous or current evidence for fibromyalgia. SLEDAI > 10 at first presentation to the Lupus Clinic and previous vasculitis were associated with neurocognitive dysfunction, but previous central nervous system disease, renal disease, renal damage, or atherosclerotic complications were not. Neurophysiologic studies at the time of the assessment were not predictive of neurocognitive dysfunction. CONCLUSION: Patients with inactive SLE demonstrate neurocognitive dysfunction. This is associated with more disease activity at presentation, but is not associated with specific organ involvement or organ damage.

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.

Radiation hybrid map of the mouse genome.

Radiation hybrid (RH) maps are a useful tool for genome analysis, providing a direct method for localizing genes and anchoring physical maps and genomic sequence along chromosomes. The construction of a comprehensive RH map for the human genome has resulted in gene maps reflecting the location of more than 30,000 human genes. Here we report the first comprehensive RH map of the mouse genome. The map contains 2,486 loci screened against an RH panel of 93 cell lines. Most loci (93%) are simple sequence length polymorphisms (SSLPs) taken from the mouse genetic map, thereby providing direct integration between these two key maps. We performed RH mapping by a new and efficient approach in which we replaced traditional gel- or hybridization-based assays by a homogeneous 5'-nuclease assays involving a single common probe for all genetic markers. The map provides essentially complete connectivity and coverage across the genome, and good resolution for ordering loci, with 1 centiRay (cR) corresponding to an average of approximately 100 kb. The RH map, together with an accompanying World-Wide Web server, makes it possible for any investigator to rapidly localize sequences in the mouse genome. Together with the previously constructed genetic map and a YAC-based physical map reported in a companion paper, the fundamental maps required for mouse genomics are now available.

A YAC-based physical map of the mouse genome.

A physical map of the mouse genome is an essential tool for both positional cloning and genomic sequencing in this key model system for biomedical research. Indeed, the construction of a mouse physical map with markers spaced at an average interval of 300 kb is one of the stated goals of the Human Genome Project. Here we report the results of a project at the Whitehead Institute/MIT Center for Genome Research to construct such a physical map of the mouse. We built the map by screening sequenced-tagged sites (STSs) against a large-insert yeast artificial chromosome (YAC) library and then integrating the STS-content information with a dense genetic map. The integrated map shows the location of 9,787 loci, providing landmarks with an average spacing of approximately 300 kb and affording YAC coverage of approximately 92% of the mouse genome. We also report the results of a project at the MRC UK Mouse Genome Centre targeted at chromosome X. The project produced a YAC-based map containing 619 loci (with 121 loci in common with the Whitehead map and 498 additional loci), providing especially dense coverage of this sex chromosome. The YAC-based physical map directly facilitates positional cloning of mouse mutations by providing ready access to most of the genome. More generally, use of this map in addition to a newly constructed radiation hybrid (RH) map provides a comprehensive framework for mouse genomic studies.

A high-density integrated genetic linkage and radiation hybrid map of the laboratory rat.

The laboratory rat (Rattus norvegicus) is a key animal model for biomedical research. However, the genetic infrastructure required for connecting phenotype and genotype in the rat is currently incomplete. Here, we report the construction and integration of two genomic maps: a dense genetic linkage map of the rat and the first radiation hybrid (RH) map of the rat. The genetic map was constructed in two F2 intercrosses (SHRSP x BN and FHH x ACI), containing a total of 4736 simple sequence length polymorphism (SSLP) markers. Allele sizes for 4328 of the genetic markers were characterized in 48 of the most commonly used inbred strains. The RH map is a lod >/= 3 framework map, including 983 SSLPs, thereby allowing integration with markers on various genetic maps and with markers mapped on the RH panel. Together, the maps provide an integrated reference to >3000 genes and ESTs and >8500 genetic markers (5211 of our SSLPs and >3500 SSLPs developed by other groups). [Bihoreau et al. (1997); James and Tanigami, RHdb (http:www.ebi.ac.uk/RHdb/index.html); Wilder (http://www.nih.gov/niams/scientific/ratgbase); Serikawa et al. (1992); RATMAP server (http://ratmap.gen.gu.se)] RH maps (v. 2.0) have been posted on our web sites at http://goliath.ifrc.mcw.edu/LGR/index.html or http://curatools.curagen.com/ratmap. Both web sites provide an RH mapping server where investigators can localize their own RH vectors relative to this map. The raw data have been deposited in the RHdb database. Taken together, these maps provide the basic tools for rat genomics. The RH map provides the means to rapidly localize genetic markers, genes, and ESTs within the rat genome. These maps provide the basic tools for rat genomics. They will facilitate studies of multifactorial disease and functional genomics, allow construction of physical maps, and provide a scaffold for both directed and large-scale sequencing efforts and comparative genomics in this important experimental organism.

Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation.

Array technologies have made it straightforward to monitor simultaneously the expression pattern of thousands of genes. The challenge now is to interpret such massive data sets. The first step is to extract the fundamental patterns of gene expression inherent in the data. This paper describes the application of self-organizing maps, a type of mathematical cluster analysis that is particularly well suited for recognizing and classifying features in complex, multidimensional data. The method has been implemented in a publicly available computer package, GENECLUSTER, that performs the analytical calculations and provides easy data visualization. To illustrate the value of such analysis, the approach is applied to hematopoietic differentiation in four well studied models (HL-60, U937, Jurkat, and NB4 cells). Expression patterns of some 6,000 human genes were assayed, and an online database was created. GENECLUSTER was used to organize the genes into biologically relevant clusters that suggest novel hypotheses about hematopoietic differentiation-for example, highlighting certain genes and pathways involved in "differentiation therapy" used in the treatment of acute promyelocytic leukemia.

A physical map of 30,000 human genes.

A map of 30,181 human gene-based markers was assembled and integrated with the current genetic map by radiation hybrid mapping. The new gene map contains nearly twice as many genes as the previous release, includes most genes that encode proteins of known function, and is twofold to threefold more accurate than the previous version. A redesigned, more informative and functional World Wide Web site (www.ncbi.nlm.nih.gov/genemap) provides the mapping information and associated data and annotations. This resource constitutes an important infrastructure and tool for the study of complex genetic traits, the positional cloning of disease genes, the cross-referencing of mammalian genomes, and validated human transcribed sequences for large-scale studies of gene expression.

Pattern of neuropsychologic dysfunction in inactive systemic lupus erythematosus.

The pattern of neuropsychological dysfunction in patients with inactive systemic lupus erythematosus (SLE) was examined. Fifty-eight subjects with inactive SLE and 47 healthy controls were administered a standardized neuropsychological test battery. Summary scores reflecting 18 different cognitive processes were derived. Subjects were designated cognitively impaired if three or more summary scores differed significantly from premorbid estimates of cognitive functioning. Cognitive impairment was identified in 43% of subjects with inactive SLE and 19% of healthy controls. Subjects with inactive SLE, as a group, performed significantly worse than healthy controls on measures of auditory verbal memory, visual spatial memory, psychomotor speed, and motor functioning. A significantly greater proportion of subjects with inactive SLE than healthy controls was impaired only on a measure of visual spatial memory. Cognitive impairment in subjects with inactive SLE was associated with increasing age. There were no associations between cognitive impairment and current depressive symptoms or current corticosteroid use. These findings suggest that cognitive dysfunction occurs frequently in inactive SLE. The variability of performance of subjects with inactive SLE is consistent with the heterogeneity of CNS involvement in the disease.

Building human genome maps with radiation hybrids.

Genome maps are crucial tools in human genetic research, providing known landmarks for locating disease genes and frameworks for large-scale sequencing. Radiation hybrid mapping is one technique for building genome maps. In this paper, we describe the methods used to build radiation hybrid maps of the entire human genome. We present the hidden Markov model that we employ to estimate the likelihood of a map despite uncertainty about the data, and we discuss the problem of searching for maximum-likelihood maps. We describe the graph algorithms used to find sparse but reliable initial maps and our methods of extending them. Finally, we show results validating our software on simulated data, and we describe our genome-wide human radiation hybrid maps and the evidence supporting them.

An STS-based map of the human genome.

A physical map has been constructed of the human genome containing 15,086 sequence-tagged sites (STSs), with an average spacing of 199 kilobases. The project involved assembly of a radiation hybrid map of the human genome containing 6193 loci and incorporated a genetic linkage map of the human genome containing 5264 loci. This information was combined with the results of STS-content screening of 10,850 loci against a yeast artificial chromosome library to produce an integrated map, anchored by the radiation hybrid and genetic maps. The map provides radiation hybrid coverage of 99 percent and physical coverage of 94 percent of the human genome. The map also represents an early step in an international project to generate a transcript map of the human genome, with more than 3235 expressed sequences localized. The STSs in the map provide a scaffold for initiating large-scale sequencing of the human genome.