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At least 73 records · Page 4Linked to original sources
PubMed2006

Linking the PANSS, BPRS, and CGI: clinical implications.

To understand what given scores of the Brief Psychiatric Rating Scale (BPRS) and the Positive and Negative Syndrome Scale (PANSS) mean from a clinical point of view is important for the translation of research results into practice. We therefore (a) compared the absolute change of the BPRS/PANSS with the Clinical Global Impressions Ratings (CGI) -improvement score and the change of the CGI severity score, (b) analyzed whether the severity of illness at baseline had an impact on the latter association, and (c) attempted to replicate previous BPRS findings using a completely different data set based upon the PANSS-derived BPRS. The method used was equipercentile linking of BPRS and CGI ratings from 14 drug trials in acutely ill patients with schizophrenia (n=5970). An absolute reduction of the BPRS/PANSS by approximately 10/15 points corresponded to a CGI change of 'minimally improved' and to a change of the CGI severity score by one severity step. However, the latter associations depended on the severity of symptoms at baseline. Less severely ill patients required less BPRS/PANSS total score reduction to achieve the same CGI-improvement score than more severely ill patients. This effect of initial severity was attenuated using percentage rather than absolute BPRS/PANSS reduction scores. The linking analysis between the absolute BPRS/PANSS reduction and the CGI may have an implication for the interpretation of efficacy differences found in clinical trials, and for sample size estimations. Clinicians seem to base CGI ratings on relative change rather than on absolute change of symptoms.

Adult↗
PubMed2003

Drug prescriptions after acute myocardial infarction: dosage, compliance, and persistence.

BACKGROUND: Although it has been well documented that aspirin, beta-blockers, angiotensin-converting enzyme (ACE) inhibitors, and lipid-lowering drugs are under-prescribed for patients with acute myocardial infarction (Am Heart J 2003;145:438-44.), few studies have examined dosage and long-term compliance and persistence patterns for the use of these drugs after AMI. METHODS: Using Quebec administrative data on all elderly (aged > or =65 years) survivors of hospital admissions for AMI between 1996 and 1998 (n = 14,057), we studied the discharge prescriptions, dosages, patient compliance, and persistence during this period for aspirin, beta-blockers, ACE inhibitors, and lipid-lowering drugs. RESULTS: Rates of discharge medications were suboptimal (aspirin 65%, beta-blockers 54%, ACE inhibitors 45%, lipid-lowering drugs 21%). Most patients with prescriptions for aspirin and ACE inhibitors were prescribed dosages equivalent to those administered in clinical trials (99% and 88%, respectively). In contrast, only 20% of patients with beta-blocker prescriptions and 48% of patients with lipid-lowering drug prescriptions were prescribed clinical trial doses. For patients with discharge prescriptions, 1-year compliance rates were high (aspirin 74%, beta-blockers 74%, ACE inhibitors 70%, lipid-lowering drugs 84%), as were the 1-year persistence rates (aspirin 71%, beta-blockers 72%, ACE inhibitors 69%, lipid-lowering drugs 80%). CONCLUSION: Although cardiac drugs are under-prescribed to patients with AMI, once prescribed, patients are likely to adhere to these prescriptions, with high rates of compliance and persistence.

Acute Disease↗
PubMed2005

Identification and characterization of GIV, a novel Galpha i/s-interacting protein found on COPI, endoplasmic reticulum-Golgi transport vesicles.

In this report, we characterize GIV (Galpha-interacting vesicle-associated protein), a novel protein that binds members of the Galpha(i) and Galpha subfamilies of heterotrimeric G proteins. The Galpha(s) interaction site was mapped to an 83-amino acid region of GIV that is enriched in highly charged amino acids. BLAST searches revealed two additional mammalian family members, Daple and an uncharacterized protein, FLJ00354. These family members share the highest homology at the Galpha binding domain, are homologous at the N terminus and central coiled coil domain but diverge at the C terminus. Using affinity-purified IgG made against two different regions of the protein, we localized GIV to COPI, endoplasmic reticulum (ER)-Golgi transport vesicles concentrated in the Golgi region in GH3 pituitary cells and COS7 cells. Identification as COPI vesicles was based on colocalization with beta-COP, a marker for these vesicles. GIV also codistributes in the Golgi region with endogenous calnuc and the KDEL receptor, which are cis Golgi markers and with Galpha(i3)-yellow fluorescent protein expressed in COS7 cells. By immunoelectron microscopy, GIV colocalizes with beta-COP and Galpha(i3) on vesicles found in close proximity to ER exit sites and to cis Golgi cisternae. In cell fractions prepared from rat liver, GIV is concentrated in a carrier vesicle fraction (CV2) enriched in ER-Golgi transport vesicles. beta-COP and several Galpha subunits (Galpha(i1-3), Galpha(s)) are also most enriched in CV2. Our results demonstrate the existence of a novel Galpha-interacting protein associated with COPI transport vesicles that may play a role in Galpha-mediated effects on vesicle trafficking within the Golgi and/or between the ER and the Golgi.

Amino Acid Sequence↗
PubMed2003

Decision tree SAR models for developmental toxicity based on an FDA/TERIS database.

Humans are exposed to thousands of environmental chemicals for which no developmental toxicity information is available. Structure-activity relationships (SARs) are models that could be used to efficiently predict the biological activity of potential developmental toxicants. However, at this time, no adequate SAR models of developmental toxicity are available for risk assessment. In the present study, a new developmental database was compiled by combining toxicity information from the Teratogen Information System (TERIS) and the Food and Drug Administration (FDA) guidelines. We implemented a decision tree modeling procedure, using Classification and Regression Tree software and a model ensemble approach termed bagging. We then assessed the empirical distributions of the prediction accuracy measures of the single and ensemble-based models, achieved by repeating our modeling experiment many times by repeated random partitioning of the working database. The decision tree developmental SAR models exhibited modest prediction accuracy. Bagging tended to enhance the accuracy of prediction. Also, the model ensemble approach reduced the variability of prediction measures compared to the single model approach. Further research with data derived from animal species- and endpoint-specific components of an extended and refined FDA/TERIS database has the potential to derive SAR models that would be useful in the developmental risk assessment of the thousands of untested chemicals.

Abnormalities, Drug-Induced↗
PubMed2004

Genome-scale gene function prediction using multiple sources of high-throughput data in yeast Saccharomyces cerevisiae.

Characterizing gene function is one of the major challenging tasks in the post-genomic era. To address this challenge, we have developed GeneFAS (Gene Function Annotation System), a new integrated probabilistic method for cellular function prediction by combining information from protein-protein interactions, protein complexes, microarray gene expression profiles, and annotations of known proteins through an integrative statistical model. Our approach is based on a novel assessment for the relationship between (1) the interaction/correlation of two proteins' high-throughput data and (2) their functional relationship in terms of their Gene Ontology (GO) hierarchy. We have developed a Web server for the predictions. We have applied our method to yeast Saccharomyces cerevisiae and predicted functions for 1548 out of 2472 unannotated proteins.

Automation↗
PubMed2004

LIP index for peptide classification using MS/MS and SEQUEST search via logistic regression.

This study addresses the issue of peptide identification resulting from tandem mass spectrometry proteomics analysis followed by database search. This work shows that the Logistic Identification of Peptides (LIP) Index achieves high sensitivity and specificity for peptide classification relative to a manually verified "gold" standard and also accurately estimates the probability of a correct peptide match. The LIP Index is a weighted average of SEQUEST output variables based on logistic regression models and is a transparent, easy to use, inclusive, extendable, and statistically sound approach to classify correct peptide identifications. Modifications, such as normalizing cross-correlations (Xcorr) for peptide length, adjusting for charge state, and the number of tryptic termini, significantly improve the fit the logistic regression models, as well as increase sensitivity and specificity. The LIP Index also incorporates earlier developed statistical models on spectral quality assessment and peptide identification, which further improves sensitivity and specificity.

Algorithms↗
PubMed2004

WHEATSHEAF: an algorithm to average protein structure ensembles.

A new algorithm is described that forms a single structure representative of ensembles of structures from files in the format used by the Protein Data Bank. A first attempt is made by averaging in the space spanned by bond lengths, inter-bond rotations and symmetry-multiplied dihedral rotations. This normally produces well formed regular secondary-structure elements, but the intervening less well ordered regions are often distorted because of the invalidity of averaging large rotations about divergent axes. For this reason, the algorithm includes a second stage that pulls the interatomic distances towards more fully representative values. Results produced by this method have proved better as judged by conventional quality checks than any input structure in nearly all cases tested so far, especially for the backbone, and much better than those produced by commonly used alternative methods.

Algorithms↗
PubMed2005

Spectral magnitude effects on the analyses of secondary structure from circular dichroism spectroscopic data.

The effects of spectral magnitude on the calculated secondary structures derived from circular dichroism (CD) spectra were examined for a number of the most commonly used algorithms and reference databases. Proteins with different secondary structures, ranging from mostly helical to mostly beta-sheet, but which were not components of existing reference databases, were used as test systems. These proteins had known crystal structures, so it was possible to ascertain the effects of magnitude on both the accuracy of determining the secondary structure and the goodness-of-fit of the calculated structures to the experimental data. It was found that most algorithms are highly sensitive to spectral magnitude, and that the goodness-of-fit parameter may be a useful tool in assessing the correct scaling of the data. This means that parameters that affect magnitude, including calibration of the instrument, the spectral cell pathlength, and the protein concentration, must be accurately determined to obtain correct secondary structural analyses of proteins from CD data using empirical methods.

Albumins↗
PubMed2004

Benchmarking B cell epitope prediction: underperformance of existing methods.

Sequence profiling is used routinely to predict the location of B-cell epitopes. In the postgenomic era, the need for reliable epitope prediction is clear. We assessed 484 amino acid propensity scales in combination with ranges of plotting parameters to examine exhaustively the correlation of peaks and epitope location within 50 proteins mapped for polyclonal responses. After examining more than 10(6) combinations, we found that even the best set of scales and parameters performed only marginally better than random. Our results confirm the null hypothesis: Single-scale amino acid propensity profiles cannot be used to predict epitope location reliably. The implication for studies using such methods is obvious.

Amino Acids↗