The function of a novel gene product is typically predicted by transitive assignment of annotation from similar sequences. We describe a novel method, GOtcha, for predicting gene product function by annotation with Gene Ontology (GO) terms. GOtcha predicts GO term associations with term-specific probability (P-score) measures of confidence. Term-specific probabilities are a novel feature of GOtcha and allow the identification of conflicts or uncertainty in annotation.Plasmodium falciparum and six other genomes. GOtcha was compared quantitatively for retrieval of assigned GO terms against direct transitive assignment from the highest scoring annotated BLAST search hit (TOPBLAST). GOtcha exploits information deep into the 'twilight zone' of similarity search matches, making use of much information that is otherwise discarded by more simplistic approaches.The GOtcha method was applied to the recently sequenced genome for E-value cutoff of 10-4.At a P-score cutoff of 50%, GOtcha provided 60% better recovery of annotation terms and 20% higher selectivity than annotation with TOPBLAST at an Plasmodium falciparum annotation and is being adopted by many other genome sequencing projects.The GOtcha method is a useful tool for genome annotators. It has identified both errors and omissions in the original  It is now often possible to obtain the complete genome sequence of an organism in a few months, but without a directed approach, determining the function of potential gene products through biological experimentation is inefficient. Accordingly, methods for function prediction are required to direct experiments in function verification. In the context of this paper the term function is used to refer to all aspects of a gene product's behaviour. This includes the concepts described by the Gene Ontology classifications for Molecular Function, Biological Process and Cellular Component. It is explicitly stated in the text when a more specific interpretation of function is intended.A powerful tool in the annotation of novel genomes is the prediction of function by similarity to a sequence of known function. Such 'transitive function assignment' can work very well where there is a clear match to a homologue with a well established function. However, accurate functional assignment is difficult in cases where the match is less well defined, either due to lower sequence similarity or the presence of many candidates with differing functions. Gerlt and Babbitt  reviewedKeywords and restricted vocabularies do not solve the problem of conflicting assignments. Unless some computable form of relationship is present between terms, it is not possible to provide any automated form of conflict resolution between terms or to identify computationally where one term is a more specific descriptor than another.An ontology represented as a graph can provide a solution to this problem. Ontologies are restricted vocabularies, or sets of terms where each term is explicitly related to parent terms and child terms (and hence to sibling terms). The Gene Ontology (GO)  is a desThe availability of the Gene Ontology has provided for the first time, a broadly accepted classification system for function assignment that can be analysed computationally. Previous work using other classification schemes, such as restricted vocabularies based on SwissProt keywords, suffered because of the lack of a distinct relationship between terms and/or due to typographical differences ,7. SinceXie and coworkers  have comE-value of the pairwise match. GOblet [Two tools based on BLAST searches have recently been presented in the literature. OntoBlast  provided. GOblet  also appPlasmodium falciparum) [In this paper we present a novel method, GOtcha, that can be applied to any database search technique that returns scored matches. We have initially implemented this with BLAST searches and extend the analysis from the similarity match scores for a search in order to provide an empirical estimate of the confidence in each predicted function. We have applied this method to Malaria (ciparum)  and six The assessment of the global accuracy of a particular annotation method is extremely problematic in the absence of a computable annotation scheme. Gene Ontology provides such a computable scheme and we present here a quantitative measure for comparison of function annotations based on assignment to GO terms. This provides a metric for direct objective comparison of annotation methods that is independent of arbitrary cut off values. The new accuracy measure encompasses true positives, false positives and false negatives, so combining sensitivity and selectivity in one value.Two sets of annotation predictions were determined for each data set in the study. One was based on all available GO annotations and the other on a reduced set of GO annotations that excluded gene-associations with the evidence code IEA (Inferred by Electronic Annotation). IEA annotations are usually considered to be less reliable as they have not been assessed by a human curator. In contrast, ISS annotations (Inferred from Sequence Similarity) are annotations which, whilst being derived electronically, have been assessed by a human curator and can be considered sufficiently reliable. IEA annotations may however give a broader coverage than non-IEA annotations. On average, each dataset contained slightly more than 50% IEA annotations, though the vast majority of the sequences had some non-IEA annotation. The number of sequences for each dataset is listed in Table y-axis indicates the proportion of annotations provided by the genome project (given annotations) that were annotated to some degree by either GOtcha . This E-value cutoff is at the top end of the E-values between 10-4 and 10-20 typically used as a threshold for confident function assignment [E-values.Figure a Figure  or TOPBLa Figure . At a P-signment -20. The x-axis representing the minimum P-score. A low P-score represents low confidence in the annotation. A high P-score represents high confidence in the annotation. Figures x-axis representing the maximum E-value. A low E-value represents high confidence in the annotation. A high E-value represents low confidence in the annotation. In Figure x-axis approaches the maximum relatively quickly when moving from high P-score to low P-score, typically coming very close to the total number of sequences annotated well before the P-score has dropped to 50%. This represents a broad coverage of sequence space, assigning annotation at a relatively nonspecific level to most sequences. In terms of the total number of annotations, these rise steadily as the P-score cut off drops. At very low P-scores (below 10%) the total number of annotations increases rapidly, indicating an increase in the spectrum of functions matched with only weak similarity. The number of annotations per sequence increases gradually as the P-score drops until a rapid rise at low P-scores . Figure E-value below the cutoff on the x-axis. At an E-value of 10-4 TOPBLAST shows a selectivity of 53.4% (43–60% s.d. 5.7). Accordingly, GOtcha outperforms TOPBLAST with improved coverage and better selectivity for each genome examined. Both the GOtcha and the TOPBLAST analyses include gene associations that are children of obsolete (GO:0008369) and the three 'unknowns' . The obsolete terms comprise a very small proportion (1.5% mean 0 – 3.1% s.d. 1.1) of the total number of annotations  of the number of annotations recovered by GOtcha.Function assignment was repeated using the same BLAST search results but excluding the IEA coded gene-associations. Figure The number of annotations per sequence was reduced by comparison to the data shown in Figure x-axis. Figure E-value below the cutoff on the x-axis. GOtcha with a P-value cutoff of 50% shows a selectivity of 60% (35–79% s.d. 14). TOPBLAST with an E-value of 10-4 shows a selectivity of 49% (25–59% s.d. 11). In all cases except that of Arabidopsis GOtcha shows a clear improvement over TOPBLAST with a mean improvement in selectivity of 1.2 fold (0.85 – 1.4 s.d. 0.17).Figure One issue with excluding IEA annotations is that the coverage of functions in the genome is lowered. This inevitably will lead to a higher number of positives that have incorrectly been assigned as false as a result of the incomplete sequence annotations. Despite excluding terms for which there is no annotation to the ontology under examination, the results are skewed by assigning a proportion of true positives as false positives. This indicates that the method is performing more poorly than is in fact the case. We have examined the nature of the false positives in more detail below.Comparing function assignment methods is difficult. Typically the standard against which they are assessed is an incompletely annotated dataset. Both a lack of experminental data confirming potential functions and a lack of knowledge about potential functions can lead to the standard data being less perfectly annotated that would be desired. It is not realistically possible in an automated analysis to cope with unrecorded true positives that are registered in the analysis as false positives. It is therefore the case that any analysis of accuracy can only give an estimate of minimum accuracy.Accuracy can also be difficult to compare between two methods that annotate to different subsets of GO. One method may only annotate to relatively general terms, allowing for a better claimed specificity than a method that attempts to annotate to a more specific level. GOtcha predicts at all levels of the GO hierarchy. It assigns a probability to every combination of GO term – sequence association and should be compared to other function assignment algorithms using a global metric, one which can account for over-specificity and under-specificity in a set of predictions as well as incorrect assignment.Ouzounis and Karp  describeWhen applied to annotation using a DAG such as GO the number of potential categories is reduced from the eight described in TABS to three. TABS was developed to compare annotations where the terms used are not implicitly related through a computable structure such as a DAG. As we are using a DAG where ancestor nodes are implicitly associated with the gene through direct association of a child node, the prediction for a particular sequence becomes a set of GO terms (the nodeset) comprising all nodes that match the prediction rather than just the most specific terms. The accuracy of a prediction can then be assessed by observing the presence of nodes in both the node sets for annotations and for the predictions rather than assigning qualitative values. The more distant a given prediction node set is from the annotation node set, the smaller a proportion of nodes (GO terms) they will have in common.The effect of a quantitative approach on the TABS categories is as follows: TABS category 0 is unchanged. This is an exact match and is represented by the presence of the term in the node sets for both original annotation and current prediction. TABS category 1 is no longer relevant. A controlled vocabulary is being used so there is no scope for typographical errors of the type described by Ouzounis and Karp or by Tsoka  or Iliopn ∈ A ∩ B), false positives (term associations in B but not in A, n ∈ B, n ∉ A) and the false negatives (term associations in A but not in B, n ∈ A, n ∉ B). The aim of any prediction method is to maximise the number of matches (true positives) whilst minimising the errors . The number of true negatives does not need to be considered as this number is very large and essentially constant over the analysis. We can use the following relation as an error quotient to assess prediction methods.Given two sets A and B corresponding to a given annotation set and a predicted set (each node in the set comprising a sequence – GO term association) we are interested in the true matches  has been determined for both GOtcha and top BLAST hit annotation sets, both with and without the use of automated annotations (IEA evidence code) for transitive function assignment . It may be that the annotation set used as the reference in comparing these results was incomplete. This would result in some true positives being incorrectly assigned as false positives with a corresponding increase in REQ. However, this would apply similarly to GOtcha and to the top BLAST hit analysis.Minimum nt Table . When aud Figure  and 0.12P. falciparum chromosomes  were assessed by hand to give an indication of the completeness of the curated annotations. Results for selected sequences in this set are shown in additional file Samples of the false positive function predictions by GOtcha with the highest P-scores from three P. falciparum genome project if no function had been identified during the first-pass automatic annotation. However, the addition of GO terms to sequences by GOtcha prompted the original annotation to be re-evaluated. For example, PFL1875w shows a hit to the Pfam K+ tetramerisation domain (Pfam:0224, E = 10-9) supports the GOtcha annotation although it is at a level that genome annotators may feel is marginal. In PFL1780w, stronger supporting evidence (a hit with E = 10-12 to Pfam:04140, isoprenylcysteine carboxyl methyl transferase domain) indicates again that GOtcha can suggest GO annotations that have been previously overlooked.In some examples, genes that were annotated as encoding hypothetical proteins could be re-annotated based on GOtcha predictions. GO terms had not been assigned during the manual curation phase of the In several examples, GOtcha predicted either additional functions or more specific GO terms to describe previously annotated functions. PFC0495w encodes a putative aspartyl protease. When all evidence codes were included, a molecular function of pepsin A activity is predicted. This protein matches pepsin A domains defined by the InterPro entry IPR001461 ('Peptidase_A1 pepsin A'), thus the term from GOtcha is likely to be correct.PFL2465 encodes a thymidylate kinase, which was correctly annotated by GOtcha as being involved in dTTP biosynthesis. GOtcha also indicates 'dTDP biosynthesis' as a suitable GO process term. Thymidylate kinase catalyses the synthesis of dTDP, a necessary step in dTTP biosynthesis. However, the human annotator missed the fact that dTDP biosynthesis is not a 'part of' dTTP biosynthesis within the ontology structure and in such cases, terms describing both processes must be employed.P. falciparum genome, many of which have arisen from retrospective corrections and amendments to gene models. For instance, GOtcha provides detailed annotation for a putative ATPase synthase F1 alpha subunit (PFB0795w) almost completely lacking useful GO terms. GOtcha also suggested GO terms relating to translation elongation for PFL1710c. A highly significant hit to Pfam:00009 (Elongation factor Tu GTP binding domain, E = 10-46) indicates that this GOtcha prediction may well be more accurate then the original genome annotation.Sometimes, GOtcha highlighted erroneous omissions in the GO annotation of the Annotations performed with IEA terms appeared to be more specific than those where IEA terms were excluded. In many cases, such as PFC0495w, the difference was quite pronounced. Here the protein was implicated in 'proteolysis and peptidolysis' when all annotations were included but filtering out IEA annotations resulted in the more general, and less useful, description of 'metabolism'.Out of the 20 genes inspected, PFL1825w was the only example where GO terms were incorrectly suggested for the biological process, molecular function and cellular component aspects of GO. In other cases, mis-annotations often had low I scores (predictions made with P-scores > 50% but very low associated I-scores ≪ 0.1) or were due to terms taken from slightly too far down a branch in the ontology structure. For example 'ATP-binding and phosphorylation-dependent chloride channel' was predicted for PFB0795w, an ATP synthase.The cellular component of gene products are hard to annotate – often BLAST is insufficient to recognise the targeting information encoded in signal and transit peptides and specific signal sequence detection methods such as PSORT II  must be It is hard to measure what proportion of the calculated false positives does in fact represent serious mis-annotation. Although the hand analysis may provide representative examples, it is too small to be of statistical significance. Genuine false positives (with high P- and I-scores) were fewer than would be expected from the P-score. Despite the small sample size, these results show that GOtcha performs well as a guide to the manual assignment of GO terms. Not only can it provide suggestions for more granular annotation but it can highlight terms that would otherwise be missed by a human annotator.REQ goes up. This may well indicate a degree of inaccuracy in the IEA based annotations, or incomplete coverage by the human curated annotations. GOtcha, however, makes a significantly better use of the BLAST search result in the quality and coverage of the annotation.One of the major problems facing assessment of function assignment is the separation of annotation and test datasets. In this analysis we have tackled this issue by taking individual genome datasets as the test sets and using other genome datasets for the annotation source from which to transitively assign function. The scoring mechanism used for estimating accuracy values is independent of both test and annotation datasets, since it makes use of sequences that are found in neither. Whilst the sequences are independent, the annotations associated with these sequences may not be. Many of the computationally assigned annotations are derived from analyses involving the 'independent' datasets and can therefore not be regarded as entirely independent. IEA annotations are primarily obtained from sequence similarity searches. As a consequence it is not surprising that the results obtained for both GOtcha and TOPBLAST when IEA annotations are included are so similar. Interestingly, when IEA based annotations are excluded from the TOPBLAST analysis, the REQ, the GOtcha predictions for the human genome were compared to the genome consortium annotations with weights ranging from 0.5 to 15  equally to over prediction errors . In order to examine the effect of the weighting on 5 Figure . As expeREQ as described in this paper, each term in the nodeset is weighted equally. This may not be the most appropriate measure. The granularity of terms in Gene Ontology is not constant across the ontologies, nor is it readily quantifiable. This may lead to bias in the metric, where differences in the presence or absence of closely related terms is weighted equally to presence or absence of more distantly related terms even though they have the same graph path distance between them. There is also the issue of prevalence. Some terms occur in almost every nodeset, others are less prevalent. The most appropriate form for a quantitative metric will need to be examined in future work.The metric presented here is an objective measure of method performance but has some drawbacks. Using the Transitive function assignment is limited by the sensitivity of the underlying search method and the scope of the dataset being searched. The GOtcha method of preparing a weighted composite view of the functions from a complete set of search results provides a significant improvement in the annotation of sequences when compared to a method that selects the most significant annotated hit. GOtcha also provides a confidence measure for the putative function assignments, allowing for the determination of an appropriate level of specificity for the annotation set. Hennig and co-workers examined the ability of BLAST analysis to transitively assign function from distant taxa, concluding that for the majority of cases, GO-based annotation would give a good result . In thisThe GOtcha method has several significant advantages over the transitive assignment of function by TOPBLAST. Firstly each function assignment has a directly understandable accuracy estimate that can be interpreted without any knowledge of the prediction methodology. This accuracy estimate is function-specific, unlike general rules of thumb that are applied to interpretation of BLAST search results. Secondly, the GOtcha method provides much greater coverage than a top annotated match approach, annotating more sequences with reasonable confidence. In many cases it provides annotations for sequences that otherwise would have no annotations. Finally, it provides term specific annotation accuracy estimates. This is a significant advantage over TOPBLAST where every term in the set predicted for an individual sequence has the same value and a biologist interpreting the results is given little indication of which terms can reasonably be accepted. In contrast, GOtcha provides individual P-scores for each term. This allows a rapid visual examination of the prediction as a graph or a list, indicating appropriate points at which experimental verification may best be directed.In order to assess the accuracy of annotations to tree-like ontologies we have developed an objective flexible scoring metric that provides a global analysis, including assessment of both false positives and false negatives. This metric also provides a means for comparison of methods that is not dependent on the selection of any particular parameter threshold or cutoff in the scoring method used.The underlying mapping methodology applied in GOtcha can readily incorporate other search methods that provide a more sensitive similarity search. Combining search methods should also provide a better coverage of sequence space occupied by distant homologues , and sucAll data were obtained in the same week (week 9, 2003) to provide a consistent time point at which to perform the analysis.Plasmodium falciparum) sequence data for the recently determined genomic sequence [Malaria (sequence  were obtDrosophila melanogaster) data were obtained from Flybase [Fruit fly ( Flybase  as releaSacchyromyces cerevisiae) data were obtained from the Sacchyromyces Genome Database [Yeast (Database . The setVibrio cholerae data were obtained from The Institute for Genomic Research [Cholera Research . The datHomo sapiens) data were obtained from Swiss-Prot using the conceptual complete human proteome from the Swiss-Prot/EnsEMBL collaboration dated 6 March 2003 [Human (rch 2003 . The datCaenorhabditis elegans) data were obtained from Wormbase release 97 [Worm  data were obtained from The Arabidopsis Information Resource [Thale cress . Results were stored in a relational database (PostgreSQL version 7.3) or as flat files where appropriate. BLAST result parsing was performed with the BioPerl toolkit (release 0.7) . SequencD. melanogaster was the query/subject set, blastp otherwise). Default parameters were used . Each sequence database search produces a ranked set of sequences similar to the query sequence. The search result for each genome database search is parsed and a list of pairwise matches between the query sequence and the subject database sequences obtained.The GOtcha method is illustrated by a cartoon in Figure 10(E), 0 } where E is the expectancy score for that pairwise match. In this way the whole subtree to the root node is assigned the R-score. The GOtcha method allows mappings obtained from many sequence matches to be combined. For each node , R-scores for all pairwise matches which contain annotation to that node are summed and normalised to the total R-score for the root node of that ontology . This normalisation gives an internal relative score (the I-score), producing a weighted composite subgraph of the GO. This normalisation effectively removes bias in the E-value due to database size or search program used. A confidence measure is calculated as loge of the root node score (the C-score). Accordingly, this provides two measures for an individual predicted gene-association; A score relative to the other predicted gene-associations in the node set (the I-score) and a score for the function prediction as a whole (the C-score).For each similarity match between the query sequence and a database sequence, a set of GO terms corresponding to the gene-associations for the database sequence is retrieved from the appropriate gene-association dataset. The set of GO terms and all ancestral terms (the nodeset) are assigned a score R = max { -logEach genome was searched individually and I-score and C-score for each GO term association were averaged across all genome searches that provide at least one annotated pairwise match. Averaging across genomes in this way provides some correction for individual genes with exceptionally high copy numbers in certain genomes. In this paper the term 'function prediction' relating to an individual sequence refers to a prediction of a set of GO term – sequence associations . Averaging of the individual search results avoids the over-representation of large genomes in the final annotation set and allows the final result to be weighted towards a particular taxonomic grouping should that be desired. Each gene association represents a function assignment of a gene product with a GO term and is annotated with an evidence code providing an indication of the reliability of a particular annotation. The GOtcha method allows specific classes of annotation, such as those derived exclusively from computational analyses, to be excluded from the analysis if required.Although higher C-score and I-score values correspond to greater confidence in the transitive assignment of function than lower C-score or I-score values, it is not immediately apparent how these values should be interpreted. Examination of preliminary results indicated that there was considerable variation between GO terms in the confidence that can be placed in a prediction with a given I-score and C-score (data not shown). Accordingly we have created an empirically based estimate of accuracy (the P-score, expressed as a percentage) that can be used to indicate confidence in the prediction of association between a GO term and a gene product.A background set of 518226 annotated sequences from the SwissProt gene associations were included in the accuracy estimate after excluding taxa corresponding to the search databases and their subspecies. All background sequences were subject to a search against all 7 species specific datasets and a set of function predictions obtained as described above. A scoring table for each GO term was prepared by segregating all predictions for that GO term on I-score and C-score. I-scores were divided into ten rows by dividing the range (0 – 1) evenly. C-scores were divided into columns by unit ranges (0–1, 1–2, 2–3 and so on). This gave rise to approximately one hundred cells for each GO term table. Each prediction was assigned to a cell based upon its I-score and C-score. The overall accuracy of each cell was determined by comparison of the predicted associations in that cell to the annotations provided by the GO Annotation project (GOA) and calculated as the proportion of true positives to the sum of true and false positives. The table for a specific GO term was then used to deliver the P-score based on any given I-score and C-score pair for a predicted association between that GO term and the query sequence. A similar set of tables was constructed from background analyses from which terms with IEA associations were excluded. For GO terms where there are few datapoints with which to estimate accuracy reliably, accuracy estimation falls back to a scoring table that combines results over all GO terms from that ontology with the same number of ancestors.E-value for that hit.The same BLAST searches used for function assignment with the GOtcha method were analysed. Function assignments for the nodeset corresponding to the top annotated BLAST match (TOPBLAST) for each genomic dataset were transferred to the query sequence with a score corresponding to the DAG, Directed Acyclic Graph. URI, Uniform Resource Identifier. BLAST, Basic Local Alignment Search Tool. TOPBLAST, Top annotated BLAST match. Perl, Practical Extraction and Report Language. GO, Gene Ontology. TABS, Transitive Annotation Based Score. NCBI, National Centre for Biological Information. s.d., Standard deviation.The GOtcha method was devised and implemented by DMAM who also prepared the manuscript. MB performed the manual assessment of false positives and provided feedback on the presentation of results. GJB provided essential guidance for the performance assessment and revision of the manuscript.The supplementary data contains representative examples from the manual assessment of false positives. It is portrayed in tabular format and indicates the benchmark annotation, the highest scoring predicted incorrect annotation by GOtcha and the lowest scoring predicted annotation by GOtcha.Click here for file


Detection of DNA-binding sites in proteins is of enormous interest for technologies targeting gene regulation and manipulation. We have previously shown that a residue and its sequence neighbor information can be used to predict DNA-binding candidates in a protein sequence. This sequence-based prediction method is applicable even if no sequence homology with a previously known DNA-binding protein is observed. Here we implement a neural network based algorithm to utilize evolutionary information of amino acid sequences in terms of their position specific scoring matrices (PSSMs) for a better prediction of DNA-binding sites.An average of sensitivity and specificity using PSSMs is up to 8.7% better than the prediction with sequence information only. Much smaller data sets could be used to generate PSSM with minimal loss of prediction accuracy.One problem in using PSSM-derived prediction is obtaining lengthy and time-consuming alignments against large sequence databases. In order to speed up the process of generating PSSMs, we tried to use different reference data sets (sequence space) against which a target protein is scanned for PSI-BLAST iterations. We find that a very small set of proteins can actually be used as such a reference data without losing much of the prediction value. This makes the process of generating PSSMs very rapid and even amenable to be used at a genome level. A web server has been developed to provide these predictions of DNA-binding sites for any new protein from its amino acid sequence.Online predictions based on this method are available at  There has been a growing interest in the prediction of DNA-binding sites in proteins which play crucial roles in gene regulation -4. We haHere we report the use of such evolutionary information in improving the prediction of DNA-binding sites of proteins. We note that one of the major problems in applying evolutionary information by way of position specific scoring matrices (PSSMs) for sequence based prediction is that such matrices are generated over large data sets and take a long time to complete. Thus large scale predictions remain inaccessible to moderately capable computers. This is a serious limitation in the portability of neural network based predictions using PSSMs . In thisPosition Specific Iterative BLAST (PSI BLAST) is a strong measure of residue conservation in a given location. In the absence of any alignments, PSI BLAST simply returns a 20-dimensional vector representing probabilities of conservation against mutations to 20 different amino acids including itself. A matrix consisting of such vector representations for all residues in a given sequence is called Position Specific Scoring Matrix or PSSM. When a residue is conserved through cycles of PSI BLAST, it is likely to be due to a purpose i.e. biological function. It has been established by several authors cited in the introduction that the prediction of structural properties is significantly enhanced by the use of PSSMs compared to predictions based on unique representations of amino acid sequence and its environment. Protein structure universe is vast and a prediction of structural properties should span the entire range of this diversity. However, the question of predicting DNA-binding sites is much narrower and hence the significance of conservation of residues at specific locations may be limited to a subset of this protein space. Such reduction in the protein search space or the reference data sets against which PSSM-based predictions should be attempted is desired for a rapid prediction of binding sites as well as portability of prediction methods. Compact reference data size can not only answer these questions of speed and portability but also try to minimize noise in information contents and improve prediction quality.Table In terms of CPU time, it may be noted that the time taken by 62 protein sequences used here is about one hour for the best (PIR) data sets. These times are prohibitively large for making predictions at a genomic scale or for providing rapid web services. A compromise could be obtained by using PDNA-RDN instead, which reduces the CPU time by a factor more than 8. The loss of net prediction for this compromise is about 1.9%, which is still 6.8% better than the predictions obtained from sequence information only. PSSMs against this data set for a typical protein of 500 residues can be generated in about 1 s, making it possible to run large scale predictions. A smaller size of reference data and high speed of PSSMs also make this method portable and light weight with a strong predictive ability.Binary decision function of the neural network (see Methods) assigns a value of zero (not binding) or 1 (binding) based on a threshold on the real value output received at the output node. Most of the accuracy scores presented here have been obtained by using 0.5 as the cutoff (mid point of the transfer function range). By changing this threshold from 0.5 to higher and lower values, the balance between sensitivity and specificity can be adjusted. In our online prediction we also present the scores obtained for a ROC analysis of such adjustments Figure . ROC forWe have provided online predictions based on the above method at our web site . The rawA PSSM-based neural network method for predicting DNA-binding sites in proteins has been developed. PSSMs were developed against different data sets and it was observed that significant computer time can be saved by replacing the reference data sets with much smaller reference data sets without loss of much prediction ability. Redundant reference data sets show a better prediction than the non-redundant data sets. A web server was developed to provide prediction of DNA-binding sites based on this method. In addition, the web server provides BLAST alignments against a reference data set of known DNA-binding proteins.This is the (non redundant) target data set of 62 DNA-binding proteins from Protein Data Bank (PDB) . The samformatdb program of the BLAST package [This is a new data set, developed for this work. We have selected all Protein-DNA complexes from PDB and separated their chains. 1386 protein chains were obtained in this way. FASTA formatted sequences were subsequently formatted using PDNA-RDN), obtained from the procedure mentioned above was filtered to remove redundancy at 90% sequence identity level by using sequence clustering program BLASTCLUST [formatdb. This data set is called PDNA-NR90.The data set (ASTCLUST . ResultiPDB-ALL (47,189 sequences) is a data set of all protein sequences obtained from NCBI. PIR is the sequence data set (283,177 sequences) of Protein Information Resource at Georgetown University [iversity . NCBI-NRTarget sequences are scanned against the reference data sets to compile a set of alignment profiles or position specific scoring matrices (PSSMs) using Position Specific Iterative BLAST (PSI BLAST) program . Three cConservation scores in 20 amino acid positions for every residues form 20 columns (column 3 onwards) of corresponding row in a PSI-BLAST PSSM. For every residue, we make a binary or real-value (interpreted as probability) prediction of that residue being a binding site or not. Input for every prediction is the PSSM score on the row corresponding to this target residue and two more rows on either side, totaling 20 × 5 = 100 inputs  in addition to the input layer described above and a single node output layer. Large number of units in the hidden layer and additional layers were not tried because the data size does not justify an unreasonably large neural network. Network signal is transferred to subsequent layers by an algebraic summation of inputs from the previous layer. Total signal in the last unit is transformed to a real output by a binary decision function much in the same way as in our previous work except that the input to the network is now replaced by PSSM scores rather than 20 bit binary coding .A six-fold cross-validation has been used in this work. Out of 62 proteins, 10 were removed at one time and the remaining 52 were trained until the accuracy on the left-out 10 also improved. Six random sets are created in this way and the figures in Table Data imbalance in the two binary categories for this neural network makes the choice of error function particularly important. We have used an accuracy score called Net Prediction, which is the average of sensitivity and specificity values defined below. Neural network learns to maximize this accuracy score rather than minimizing an error function.Sensitivity is defined as the number of correct prediction in the binding category relative to total number of such items in the original data and specificity is the number of correctly rejected residues in this category relative to the total number of non-binding residues in the original data.Sensitivity (S1) = 100 * TP/(TP+FN)     (1)Specificity (S2) = 100 * TN/(TN+FP)     (2)where TP: True Positive; TN: True Negative; FP: False Positive; FN: False NegativeRelative number of true positive (TP) values in the prediction was termed as accuracy in our previous work. We have avoided using that term here, as we prefer a more operational definition of accuracy measures here. The imbalance of sensitivity (S1) and specificity (S2) is taken care of by comparing the Net Prediction of the models which gives a better comparison when S1 and S2 vary from one sample to the other. Thus,Net Prediction (NP) = (S1+S2)/2     (3)PSSM: Position Specific Substitution MatrixPSI BLAST: Position Specific Iterative Basic Local Alignment Search ToolSA conceived and implemented this project. AS contributed in manuscript preparation, results analysis and discussions.


Clitoromegaly is a frequent congenital malformation, but acquired clitoral enlargement is relatively rare.Two acquired clitoromegaly cases treated in Atatürk Training Hospital, Izmir, Turkey are presented.History from both patients revealed clitoromegaly over the last three years. Neither gynecological nor systemic abnormalities were detected in either patient. Karyotype analyses and hormonal tests were normal. Abdominal and gynaecological ultrasound did not show any cystic lesion or other abnormal finding. Computerized tomography scan of the adrenal glands was normal. Clitoroplasty with preservation of neurovascular pedicles was performed for the treatment of clitoromegaly.The patients were diagnosed as "idiopathic isolated" clitoromegaly. To the best of our knowledge, there has been no detailed report about idiopathic clitoromegaly in the literature. Two cases with clitoromegaly were treated in Atatürk Training Hospital, Izmir, Turkey.A 22-year-old gravida 0 case 1) and 19-year-old gravida 0 (case 2) presented with acquired clitoromegaly, leading to psychological distress. Histories taken from both patients revealed a gradually growing clitoris in the last three years, no history of drug abuse or family history of clitoromegaly and no clitoral irritation secondary to masturbation or other sexual functions. Case one had a phallus 20 mm in length that increased to 30 mm on arousal , testosterone, androstenedione, dehydroepiandrosterone sulphate (DHEA-S), follicule stimulating hormone (FSH), luteinizing hormone (LH), 17 hydroxy progesterone (17-OH-P), prolactin, adrenocorticotropic hormone (ACTH), cortisol, placental lactogen (PL), deoxycorticosterone, deoxycortisol 11, triiodothyronine (T3), thyroxine (T4), thyroid stimulating hormone (TSH), beta-human chorionic gonadotrophin (β-hCG), carcinoembryonic antigen (CEA) were measured before the operations and the results were normal. 17-ketosteroid output in 24-hour-urine specimen was normal in both patients. Abdominal and gynecological ultrasound did not show any cystic lesion or abnormal finding. Computed tomography scan of the adrenal glands was normal.No abnormality suggestive of a possible relation to clitoromegaly was found in all laboratory and radiological tests.Both patients underwent clitoroplasty with preservation of the neurovascular pedicles under general anesthesia. A traction suture of 3/0 nylon was placed in the glans of clitoris  or adrenogenital syndrome, caused by an enzyme defect in the normal pathway of steroid biosynthesis . VirilizBilateral hilus cell tumors of the ovary, steroid producing gonadal tumors, adrenal androgen-secreting carcinoma, Leydig cell tumor of the ovaries and metastatic carcinosarcoma of the urinary bladder have been reported to cause clitoromegaly -9.Fetal exposure to danazol has been described as cause for clitoromegaly . An inteAmong the non-hormonal conditions are neurofibromatosis (NF), epidermClitoral cysts arise from epidermis displaced into the dermis or the subcutaneous tissue either during the prenatal period or after a trauma.Various syndromes resulting from non-hormonal conditions may cause clitoromegaly. Kazlauskaite et al reported a case diagnosed as congenital generalized lipodystrophy (CGL) presenting with generalized body-fat loss, prominent musculature, hepatomegaly, clitoromegaly and mild hirsutism . CGL is Androgen insensitivity syndrome is a heterogeneous disorder with a wide spectrum of phenotypic abnormalities, ranging from a complete female phenotype to ambiguous forms that more closely resemble males. The primary abnormality is a defective androgen receptor protein due to mutation of the androgen receptor gene.Nevus lipomatous cutaneous superficialis (NLCS) is a relatively rare condition characterized by groups of ectopic fat cells dispersed in various parts of the body  that mayPseudohypertrophy of the clitoris has been reported in small girls due to masturbation: manipulations of the skin of prepuce leads to repeated mechanical trauma, which expands the prepuce and labia minora, thus imitating true clitoral enlargement .The objectives of clitoroplasty are preservation of sexual arousal function and sensation, and cosmetic. Historically, until 1960s, clitoral hypertrophy was treated surgically by amputation (clitoridectomy). SurgicaThe authors declare that they have no competing interests.EC conceived the study and prepared the manuscript draft for submission. AA, NS, OC and YO did the literature search and participated in the preparation of the manuscript.All authors read and approved the final manuscript.


The intent of this study was to examine the recovery of individuals who had been hospitalized for severe acute respiratory syndrome (SARS) in the year following their discharge from the hospital. Parameters studied included serum levels of SARS coronavirus (SARS-CoV) IgG antibody, tests of lung function, and imaging data to evaluate changes in lung fibrosis. In addition, we explored the incidence of femoral head necrosis in some of the individuals recovering from SARS.LCO] < 80% of predicted value [pred]) received regular lung function tests and HRCT examinations in the follow-up phase in order to document the changes in their lung condition. Some patients who complained of joint pain were given magnetic resonance imaging (MRI) examinations of their femoral heads.The subjects of this study were 383 clinically diagnosed SARS patients in Beijing, China. They were tested regularly for serum levels of SARS-CoV IgG antibody and lung function and were given chest X-rays and/or high resolution computerized tomography (HRCT) examinations at the Chinese PLA General Hospital during the 12 months that followed their release from the hospital. Those individuals who were found to have lung diffusion abnormities (transfer coefficient for carbon monoxide [DLCO < 80% pred) and 21.5% (67 of 311 patients) exhibited lung fibrotic changes. In the 12 month duration of this study, all of the 40 patients with lung diffusion abnormities who were examined exhibited some improvement of lung function and fibrosis detected by radiography. Of the individuals receiving MRI examinations, 23.1% (18 of 78 patients) showed signs of femoral head necrosis.Of all the subjects, 81.2% (311 of 383 patients) tested positive for serum SARS-CoV IgG. Of those testing positive, 27.3% (85 of 311 patients) were suffering from lung diffusion abnormities (DThe lack of sero-positive SARS-CoV in some individuals suggests that there may have been some misdiagnosed cases among the subjects included in this study. Of those testing positive, the serum levels of SARS-CoV IgG antibody decreased significantly during the 12 months after hospital discharge. Additionally, we found that the individuals who had lung fibrosis showed some spontaneous recovery. Finally, some of the subjects developed femoral head necrosis. Severe acute respiratory syndrome (SARS) is a new infectious disease in humans. The first victim of SARS to be diagnosed was a businessman from the city of Foshan in Guangdong Province, China. SARS patients may present with a spectrum of symptoms and signs, ranging from relatively asymptomatic to fulminant pneumonitis and death . Lung inAll of the subjects of this study were discharged from Beijing Xiaotangshan Hospital, Beijing Armed Police Hospital, and Chinese 309 PLA Hospital, and all gave their informed consent.The subjects of our investigation were 383 clinically diagnosed SARS patients in the convalescent phase  undergoing testing from May, 2003 to June, 2004. Each clinical diagnosis was based on the Clinical Diagnosis Standard for SARS Patients issued by the Ministry of Chinese Public Health [Each patient returned a month after the first visit followed by one visit every 3 months. Serum SARS-CoV IgG was tested at each return visit. If negative results were obtained twice consecutively, the case was regarded as a misdiagnosis and the patient did not undergo a follow-up survey. Patients with positive SARS-CoV IgG and abnormal pulmonary diffusion received regular pulmonary function tests and those showing pulmonary fibrosis in imaging examinations received further regular HRCT examinations. Some individuals observed to have avascular necrosis of the femoral head received MRI examinations 3–6 months later.(1.1) The individual has a history of close contact with SARS patients or is part of a cluster of cases of SARS or there is clinical evidence of having infected other patients.(1.2) The individual has a history of recent travel to an area where SARS cases have been reported within 2 weeks and secondary infected SARS cases have been found.Acute onset of SARS generally begins with a prodrome of fever with a temperature >38°C, sometimes accompanied by chills, myalgia and anthralgia, headaches, and fatigue. Upper respiratory tract symptoms of catarrh are not prominent, although cough may be present. If present, it is mainly a dry cough, occasionally with blood streak sputum. Some individuals have chest discomfort, and severe cases may present with tachypnea, panting, and even respiratory distress.Generally, there are no obvious pulmonary signs among SARS patients. Wet rales and signs of lung consolidation, as well as decreased respiration and other signs of pleural effusion can occasionally be found.Note: Some patients do not show initial symptoms of fever, especially those who have had recent surgery or those having chronic diseases.White blood cell counts are generally normal or below normal, with decreased absolute lymphocyte counts.The typical imaging profile of SARS is of multiple patchy opacities with bilateral distribution. The opacities are usually ground-glass in appearance, sometimes with air space consolidation, evolving progressively over the course of the disease. The evolution is very rapid in some cases, resulting in the confluence of lesions and large areas of opacification in a short time. If the chest radiological examination is negative, reexamination after 1 to 2 days should be done.Suspected cases: In accordance with 1.1+2+3, 1.2+2+4 or 2+3+4.Clinically diagnosed cases: In accordance with 1.1+2+4, 1.2+2+4+5, or 1.2+2+3+4.The SARS-CoV IgG antibody in serum specimens from recovering SARS patients was assayed by the BGI-GBI Biotech Company with an enzyme-linked immunosorbent assay (ELISA) kit (No. S20030003, BGI-GBI Biotech Company, Beijing, P.R. China). The wells containing polystyrene microplate strips were coated with two recombinant SARS-CoV antigens that are well-characterized. Recovering SARS patients' serum samples in the diluent buffer (1:10) were incubated in the coated wells for 30 min at 37°C and then the wells were washed 5 times with the washing buffer. The dilutedenzyme-labeled anti-human IgG (100 μl) was added to the wells and incubated for 20 min at 37°C. The wells were washed 5 times with the washing buffer. A tetramethyl-benzidine substrate was then added to each well. The presence of specific antibodies was indicated by a yellow color developing after substrate addition. The reaction was terminated by addition of hydrochloric acid. The intensity of the color was measured spectrophotometrically at 450 nm to quantify the amount of antibody in the specimen. The optical density (OD) measured was compared with a standard calibration curve constructed for each lot, yielding concentration values for the samples. The OD values of both the positive and negative controls were determined. The threshold value for IgG was 0.18 OD units, calculated as the mean + 2 standard deviation (SD) levels of the readings given by 1000 control blood donor sera samples. If the OD was above the threshold value, the sample was considered to be positive for SARS-CoV IgG [1), vital capacity (VC), forced vital capacity (FVC), total lung capacity (TLC), transfer coefficient for carbon monoxide (DLCO), and carbon monoxide diffusion constant (DLCO/VA) measured by means of the single-breath test. The hemoglobin level was also measured to adjust the DLCO value. The results were compared with those of age- and sex-matched controls and expressed as a percentage of predicted values. Pulmonary function was regarded as abnormal when the DLCO was less than 80% of predicted values (pred). This was considered a diffusion deficit [Each recovering SARS patient underwent a standard pulmonary function test (SensorMedics 2200, Yorba Linda, U.S.A) for forced expiratory volume in 1 second . All CXR and HRCT images were assessed by three radiologists via a viewing console. The three radiologists were aware of the patients' clinical diagnosis at the time of their review of the radiographs. The final conclusions were established by consensus. Each segment of the lung was reviewed for ground-glass opacification, interstitial thickening, bronchiectasis, and architectural distortion. Abnormalities were magnified by means of a zoom function and were examined for intralobular interstitial, interlobular septal, or peribronchovascular interstitial thickening. Attention was also paid to the presence or absence of nodules or masses, cavitation or calcification, and emphysema. The presence of parenchymal bands, irregular interfaces , thickened interstitium, and traction bronchiectasis were considered as evidence of fibrotic changes [Frontal chest X-ray radiographs (CXR) were obtained at the first follow-up visit for each recovering SARS patient. If abnormities were found in the CXR or if the D changes .1-weighted  11–14] scans of the hips were done. If there were any abnormalities noted in the T1-weighted images, further T1-weighted sagittal images and coronal short tau inversion recovery (inversion time 145, TR 3500–5000, TE 80–120) or turbo-spin-echo T2-weighted images with fat suppression (TR 2500–3000, TE 80–120) were obtained. Images 3 mm thick were obtained for the coronal studies, and 4 mm thick images were obtained for the sagittal studies. Osteonecrosis was diagnosed by the presence of a band of low signal intensity in T1-weighted images [All MRI examinations were done using a 1.5 T Signa CVi imager . For the patients who complained of chronic bone and joint pain, coronal T ± SD unless otherwise indicated. Statistical analyses were done by one-way analysis of variance (ANOVA), Student-Newman-Keuls, and Chi-square test for multiple comparisons. We used the STATA™ 7.0 statistical analysis software for Windows  for evaluating the results of our study. With each statistical test, the criterion for significance was a p value of less than 0.05.All data were expressed as the The interval from hospital discharge to the first follow-up visit was 45.0 ± 20.7 days (Range: 11–104 days). Of the 383 individuals participating in our study, 311 patients (81.2%) tested positive for SARS-CoV IgG and 72 (18.8%) tested negative. (All patientswere tested twice for SARS-CoV IgG.) Of these, 33 patients  with positive SARS-CoV IgG and abnormal pulmonary diffusion received regular follow-up examinations each month, from June to December in 2003, and every two months, from January to June in 2004. Tables LCO among the 383 patients participating in our study. Of the 311 individuals testing positive for SARS-CoV IgG, there were 85 with abnormal DLCO (27.3%, 85/311), in contrast to just 3 cases with abnormal DLCO among the 72 subjects testing negative for SARS-CoV IgG (4.2%, 3/72). There was a statistically significant difference between positive and negative SARS-CoV IgG groups in DLCO values  with abnormal D.0% with LCO and positive SARS-CoV IgG, 40 individuals received pulmonary function tests 4 times within the year at 42.0 ± 10.4, 70.0 ± 11.8 and 155.1 ± 42.9 day intervals. Among these 40 patients, there were 23 who exhibited abnormal DLCO at their second pulmonary function examination, 23 at the third examination, and 20 at the fourth examination  with abnormal D.Pulmonary fibrosis was detected by CXR and confirmed by HRCT examination in 72 SARS patients in the convalescent phase. Among these, there were 4 individuals with negative and 67 with positive SARS-CoV IgG. Of the 40 patients who received HRCT examinations at least 4 times, all showed improvement in the fibrotic condition Figure .Of the 311 convalescent SARS patients with sero-positive SARS-CoV IgG, 78 received femoral head MRI examinations. The Imaging showed that 18 of these patients (23.1%, 18/78) had avascular necrosis of the femoral head. Of these 18 individuals, 8 had avascular necrosis of both femoral heads and 10 had avascular necrosis of one femoral head. Ten of the 18 patients showed first stage changes and 8 had secondary changes. During the 3–6 month follow-up visits for these individuals, there were no obvious changes in the avascular necrosis for these patient.Since the outbreak of SARS at the end of 2002, despite the great efforts that have been extended, the mechanisms, clinical characteristics, prognosis and effective therapeutics for this disease have not been adequately clarified. Both the SARS virus itself and the anti-viral therapy (such as high-dose glucocorticoids) used in treatment can cause various degrees of toxicity and side effects, including pulmonary fibrosis and avascular necrosis of the femoral head, even in the convalescent phase. Follow-up surveys of SARS patients in the convalescent phase are needed for recognizing the clinical characteristics of this disease and reevaluation of the therapeutic treatments .In our study, 72 individuals 18.8%) showed negative results in the SARS-CoV IgG antibody test for at least two tests, suggesting that there may have been misdiagnosis of some clinically diagnosed SARS patients. Comparison of the Chinese clinical diagnosis standard (published April, 2003) , indicat.8% showeBy regular examination of pulmonary function and CXR, we found that those with pulmonary fibrotic changes were able to heal on their own. The fibrotic tissue was absorbed and pulmonary diffusion and VC improved with time, suggesting that the mechanism of lung injury and lung fibrosis caused by the SARS-CoV may have a different pathophysiological process compared to other lung diseases, such as idiopathic pulmonary fibrosis or pulmonary fibrosis secondary to adult respiratory stress syndrome. The reason is not clear. However, in our follow-up study, we found some ground-glass-like changes in the HRCT images from SARS patients one year after discharge. This result shows that changes in the lung can still be observed in convalescents ,9.Recent concern has focused on a complication of SARS in the convalescent phase, when avascular necrosis develops on the femoral head. The morbidity of this condition is reported to be 15% to 30% in some SARS patients in Mainland China ,10. AmonIn conclusion, SARS, as a new disease, remains unfamiliar to mankind. It has high rates of morbidity and mortality in the acute phase. A significant proportion of patients surviving the acute illness have impairment in their overall functional capacity and health status in the convalescent phase after discharge from the hospital. Follow-up surveys of SARS patients in the convalescent phase are needed to understand the clinical characteristics of this disease. Our findings suggest that follow up studies of these patients are required for a longer duration, including comprehensive assessments for detection and appropriate management of any persistent or emerging sequelae. These types of investigation may facilitate the search for effective therapeutics and aid in ultimately conquering this disease.


Locally advanced colorectal cancers form a distinct subgroup where contiguous organs could be involved without distant metastases and so may be amenable to curative surgical resection. It was our objective to report our experience in treating six such patients with operable locally advanced colorectal carcinomas.We retrospectively reviewed the case notes of 47 patients who were diagnosed with colorectal cancers at M S Ramaiah Medical Teaching Hospital between the years 1996 – 2001. Six patients were identified with T4 lesions, adjacent organ involvement and with no nodal involvement. The treatments and outcomes for these patients were then reviewed.Two of three patients with rectal malignancies who underwent pelvic exenteration succumbed to disease recurrence within the first 18 months. One of the three patients with colonic cancers died of non malignant causes. The other two are disease free till date.Aggressive multivisceral resections for locally advanced colonic cancers might be appropriate. Rectal cancers when locally advanced may be considered for pelvic exenteration, but a more guarded prognosis may apply. Locally advanced colorectal tumors constitutes to about 5 – 22% of all colorectal cancers at the time of presentation . This ty2 and that of leucovorin 20 mg/m2. This combination was administered as infusion on day 1 to 5, and six such cycles every 28 days were instituted as part of chemotherapy regimen. The therapy in colonic carcinoma was sequenced as 1 cycle of chemotherapy followed by radiation and the remaining 5 cycles after completion of radiation. In case of rectal cancers, the chemotherapy was administered concurrently with radiation. The administration of leucovorin was with-held during radiation and instead, the dose of 5-FU was increased to 500 mg/m2. Follow up was by clinical examination every 3 months for first two years, and six monthly until 5 years. The evaluation included alternate year CT scan, annual US abdomen, chest X-ray, colonoscopy and CEA levels every six months.During the years 1996–2001, 47 patients with colorectal cancers were treated at our institute. All patients underwent a rectal exam, punch biopsy if it were an accessible rectal lesion or colonoscopy and biopsy if it were a colonic lesion. Screening colonoscopy for a synchronous lesion was however done for all patients. Patients in whom a biopsy confirmed a malignant lesion underwent an ultrasound (US) study of the abdomen, chest X-ray and Carcino-embryonic antigen (CEA) estimation. Patients with doubtful involvement of adjacent organs or structures underwent CT scan. Cystoscopy was performed when urinary bladder involvement was suspected on CT scan. Patients were diagnosed as locally advanced when the staging evaluation showed involvement of adjacent organ or structure. Though nodal evaluation is suboptimal with US or CT scan, all patients with 'N0' status on these investigations were included as locally advanced while those with 'N+' on evaluation were excluded. Patients with metastasis to any organ were excluded. The preoperative staging of the tumor was T4, N0, M0. Elective surgery was performed on all cases. No pre-operative therapy was administered. All patients were administered adjuvant radiation and chemotherapy which was instituted immediately after wound healing (between 16 and 29 days post surgery). A total dose of 5000 centi-Gray of external beam radiation in combination with chemotherapy consisting of 5-Fluoro-Uracil (5-FU) and leucovorin was administered. The dose of 5-FU was 425 mg/mThe two live patients have given consent for publication. Consents have been obtained from the legal heirs of the remaining four patients.The study group consisted of six patients (29 to 70 years), three of whom had locally advanced colonic disease and three of whom had locally advanced rectal disease. Pre operative CEA levels were surprisingly within normal limits in all six patients. The pre operative colonoscopic or rectal biopsy had determined adenocarcinoma in all six patients . About 6 months prior to presentation at our institute, he was diagnosed as rectal cancer and was advised abdomino-perineal resection. Patient was scared of surgery and waited without any therapy. Rest of the large bowel was normal on colonoscopy. A CT scan showed involvement of the urinary bladder and prostate by a rectal mass while bony pelvis appeared free of tumor involvement. The patient underwent a total pelvic exenteration with formation of a urinary diversion by ileal conduit. After a lower midline laparotomy, abdomen was evaluated for any ascites or liver metastasis. Para-aortic area was palpated and rest of peritoneal cavity was evaluated for any metastatic deposit. Inferior mesenteric artery was ligated beyond the origin of the left colic artery. Bilateral pelvic nodal dissection was completed. Sigmoid colon and rectum was mobilized as in abdominoperineal resection over the sacral hollow. Anteriorly the dissection was carried out in the retropubic space to access the urethra beyond the prostate. Lateral dissection was carried out which included the ligation of the superior vesical vessels. Both the ureters were ligated below the pelvic brim in the true pelvis at least 3 cm proximal to palpable disease. Isolated loop of ileum was mobilized based on two vessels. One end of the loop was closed and both ureters were anastomosed separately to the loop. Intestinal continuity was obtained with ileo-ileal end to end anastomosis. This ileal loop was brought out as a urinary stoma on the right side and the cut colon was brought out as colostomy on the left side. The patient's postoperative recovery was unremarkable except for a uereteric leak which settled by 10Patient had bleeding per rectum and constipation for 4 months. Patient had significant weight loss over previous 2 months. Patient had circumferential rectal carcinoma involving uterus and upper part of vagina. Rectal carcinoma was proved by biopsy. Rest of the colon was normal on colonoscopy. Upper vaginal mucosa was intact but indurated underneath. CT scan showed infiltration of lower part of uterus and upper vagina. Patient underwent posterior pelvic exenteration. Uterus, including both the fallopian tubes and ovaries were removed en-bloc along with vagina and rectum. Surgery was followed by adjuvant radiation and chemotherapy. Patient succumbed to the disease 1 year later with multiple metastases in liver, brain and malignant ascites.th post operative day, patient developed vomiting and had raised serum creatinine values. Values of fibrin degradation products were also raised and a diagnosis of disseminated intravascular coagulation and renal failure was entertained. Patient had prolonged prothrombin time but did not have any clinical bleed. He died on 30th day post operative while on recovery from the same. The post surgical pathology reports are summarized in Table Patient had bleeding per rectum and constipation for 3 months. A preoperative CT scan with rectal and intravenous contrast revealed involvement of prostate and possibly bladder. Colonoscopy did not reveal any other lesion. He underwent a total pelvic exenteration  declare that they have no competing interests.KH: Was the principal treating surgeon apart from designing and conceptualizing the article.YVN: Was the assistant in the procedures and made the preliminary draft of the article.SN: Planned and administered adjuvant radiation.


Concern over bio-terrorism has led to recognition that traditional public health surveillance for specific conditions is unlikely to provide timely indication of some disease outbreaks, either naturally occurring or induced by a bioweapon. In non-traditional surveillance, the use of health care resources are monitored in "near real" time for the first signs of an outbreak, such as increases in emergency department (ED) visits for respiratory, gastrointestinal or neurological chief complaints (CC).We collected ED CCs from 2/1/94 – 5/31/02 as a training set. A first-order model was developed for each of seven CC categories by accounting for long-term, day-of-week, and seasonal effects. We assessed predictive performance on subsequent data from 6/1/02 – 5/31/03, compared CC counts to predictions and confidence limits, and identified anomalies .Each CC category exhibited significant day-of-week differences. For most categories, counts peaked on Monday. There were seasonal cycles in both respiratory and undifferentiated infection complaints and the season-to-season variability in peak date was summarized using a hierarchical model. For example, the average peak date for respiratory complaints was January 22, with a season-to-season standard deviation of 12 days. This season-to-season variation makes it challenging to predict respiratory CCs so we focused our effort and discussion on prediction performance for this difficult category. Total ED visits increased over the study period by 4%, but respiratory complaints decreased by roughly 20%, illustrating that long-term averages in the data set need not reflect future behavior in data subsets.We found that ED CCs provided timely indicators for outbreaks. Our approach led to successful identification of a respiratory outbreak one-to-two weeks in advance of reports from the state-wide sentinel flu surveillance and of a reported increase in positive laboratory test results. Traditional public health surveillance for specific conditions is unlikely to quickly identify a disease outbreak. Emergency department (ED) data appears to have the potential for more timely disease surveillance -6. In noCrucial to any surveillance is an understanding of normal patterns in the data. Utilization patterns in the ED are thought to be difficult to predict, due to large variability that arises in part because EDs are required to medically evaluate and stabilize everyone who requests care. Therefore, ED visit rates cannot be controlled by insurers, institutions or policies.Many "drop in" surveillance systems have been hampered by a lack of knowledge of baseline activity . Other sSome "active" ED surveillance systems provide on-going data collection. The EMERGEncy ID NET  and RSVPThis paper reviews our experience with an operational near-real-time surveillance system, the Bio-Surveillance Analysis, Feedback, Evaluation and Response (B-SAFER) system. B-SAFER is the result of collaboration between the Los Alamos National Laboratory, the University of New Mexico Health Sciences Center, and the New Mexico Department of Health. Medical surveillance systems such as B-SAFER require considerable expertise in computer science and systems integration for their design and architecture, to comply with security and privacy issues, and to ensure timely flow of information ,12. ThesThis observational study uses data from the Emergency Center of the University Hospital, Albuquerque NM (UH), a tertiary-care county-university health sciences center. The Emergency Center includes pediatric and adult emergency departments, an urgent care center, a trauma center and an observation unit. There are roughly 200 patient visits per day, or 73,000 per year, representing 36 % of Emergency Department visits in Albuquerque. This study was approved by the Institutional Review Boards of the University of New Mexico Health Sciences Center and Los Alamos National Laboratory.The data is from the computerized ED patient tracking system in place since 1994. Data in the system includes: date and time of arrival and discharge, age, sex, chief complaints, discharge diagnoses and disposition. CCs are recorded by the nurse at the time of triage and entered into the system by a clerk. The clerk may select from a drop-down menu of complaints or may enter the complaints as free text. The menu option is rarely used because clerks find free-text entry more flexible and convenient.We group daily CC counts into seven categories: respiratory, gastrointestinal (GI), undifferentiated infection (UDI), lymphatic, skin, neurological, and "other" Table . The "otTo obtain this scheme, we reviewed a frequency table of all CCs which occurred at least 5 times over nine years and assigned relevant CCs to groups, as was done for example in  and 16]16]. Key Each CC is assigned to a group when the first match was made between a word in the CC and a word in our CC dictionary. There were no examples of negative chief complaints such as "no cough", in our nine-year database, so we did not develop a system for handling these. Any negative complaint would have been classified into the category of the key word.The training set for model development was retrospective cohort data from 2/1/94 through 5/31/02. We used least squares regression in started log scale to fit trends, seasonal effects, and day-of-week effects. The "started log" is the logarithm of one plus the number of daily CCs. We add one before taking the logarithm to avoid problems with taking the logarithm of zero counts. The started log scale results in more symmetrically distributed forecast errors with variance that is much less dependent on the mean count. Results are back-transformed to natural scale for display. Error bars behave as desired (widening when the CC count increases) and skewing is commensurate with the magnitudes of the baseline values.A model that incorporates the above effects isS(d) = [Σi ci × Ii(d)] + [c8 + c9 × d] + [c10 × cos(2πd / 365.25) + c11 × sin(2πd / 365.25)]whereS(d) denotes the started log of the number of chief complaints) for day d, where "Day 1" is taken as February 1, 1994,a) i ci × Ii(d)] captures the day-of-the-week effect, where the sum is over the indices i = 1 to 7 and Ii(d) denotes the indicator function for day d, i.e., Ii(d) equals 1 when day d is the i-th day of the week and equals zero otherwise, and the seven model coefficients {ci} are constrained to sum to zero,b) [Σc8 + c9 × d] captures a long term linear effect,c) [c10 × cos(2πd / 365.25) + c11 × sin(2πd / 365.25)] captures the seasonal component, where the average number of days per year is 365.25, with the model coefficients c10 and c11 dictating the time and amplitude of the seasonal effect.d) [c9 is zero for complaint categories that do not exhibit linear long term trends, and the coefficients c10 and c11 are zero for categories which do not exhibit seasonality. A plot of the predicted respiratory complaints and corresponding upper confidence limits from this model is given in Figure This model arises from transforming the counts to started log scale and adding day-of-the-week effects to the regressor variables in a cyclical regression model ,17. ApplAs with all statistical models, it is important to assess goodness of fit. A careful residual analysis reveals trends in the forecast errors, the most important of which follow from the one-size-fits-all character of the model. That is, the first order model postulates that complaint activity peaks with the same magnitude and at exactly the same time of year from season to season. Such postulated behavior is only approximately true, and the season-to-season differences lead to the trends in residuals from the model.For example, if a peak of respiratory complaints occurs later than average in the year, then the baseline will initially over-predict (in anticipation of an average peak time) and then under-predict . Similarly, if the amplitude of a season's peak is higher or lower than the historical average amplitude, predictions will be consistently too low or too high near the peak. We return to this subject in the section on hierarchical modeling.By comparing CC counts to predictions and confidence limits, anomalies can be identified ,19. ExtrA particular test, based on Page's statistic, is optimal for detecting a constant excess above baseline when the start time and duration of the excess is unknown . This teP(d). On each day d, the forecast error εd between the started log of the observed number of complaints and the started log of the baseline prediction is computed for each complaint category. The standard deviation sd of εd is computed as well. Then Page's statistic is calculated for day d asPage's statistic is a type of cumulative sum, which we denote as P(d) = maximum of 0 and [P(d-1) + εd/sd - 1/2].P(d) becomes too large, then the observed complaint levels are significantly greater than the baseline predictions and complaint levels are deemed anomalously high. Here, the phrase "too large" is formally defined in terms of the desired false positive rate for monitoring, and the threshold value for P(d) is calibrated using empirical data to account for model prediction errors.If A comprehensive comparison to other data streams is beyond our scope. Briefly, we compared our respiratory CC counts to existing influenza data for the 2002–2003 influenza season. We evaluated New Mexico (NM) Department of Health sentinel influenza surveillance data reported weekly by approximately 20 clinics. When the week-long monitoring period is combined with the time needed for compilation and dissemination, notification of an outbreak early in one week is often not formally received until two weeks after it occurred. We obtained virology laboratory data from routine clinical and surveillance testing of respiratory specimens reported by three laboratories that conduct at least 75% of the clinical virology testing for NM. Such data streams have their own timeliness issues, due in part to culturing of samples.ED data from 2/1/94 – 5/31/02 is used as training data for least squares fitting to establish control limits; data from 6/1/02 – 53/31/03 is then used in near real time surveillance. Approximately, 17% of the complaints fall into the respiratory category, 10% gastrointestinal, 6% undifferentiated infection, 3% skin, 3% neurological, 1% lymphatic and 60% "other."For all systemic complaint categories there are day-of-week differences. See Figure For respiratory and UDI complaints, there is an average difference of 7 cases per week between Monday and Friday, and the weekly differences conform to a bell-shaped statistical distribution. While day-of-week effects are statistically significant in all categories owing to the size of the data set, in some categories there are so few complaints that the difference is of no practical consequence. For example, in the lymphatic and neurologic categories, the average difference between the weekly peak (Monday) and weekly minimum (Saturday) is less than one case per day. Certain other daily effects may exist, e.g. holiday effects , but samAs is well known, there are annual cycles of respiratory complaints with peaks in January or February. Figure c9 component would have been modified periodically, or a nonlinear model would have been used.Total ED visits increased over the study period by 4%, in part reflecting the population increase of about 1.5 % per year for the metropolitan area. Rates for most of the complaint categories have changed over the eight years that data has been collected. Respiratory complaints show a decrease of roughly 20% over the monitoring period . These plots illustrate how a misfit to the one-size-fits-all model can produce systematic trends in surveillance data. This subject is revisited in the section on hierarchical modeling.We focus on respiratory CCs because they have the strongest season-to-season variation, which makes them the most challenging to predict, and because respiratory is thought to be one of the most important bioterrorist categories. Overlaying the data streams on the baseline plot in real time allows for visual inspection of the results, similar to that for a standard control chart. Figure P(d) preceded a rise in reports from the state-wide influenza sentinel surveillance system. A similar pattern, delayed by several weeks, was found in the rise of requests for laboratory tests for influenza. ED CCs also preceded New Mexico reporting of deaths from pneumonia and influenza. We conclude that surveillance using the first order model is sufficiently sensitive to mild departures from baseline activity and that it can provide timely notification relative to traditional surveillance.The peak in respiratory CCs and the elevated K extra respiratory CC counts beginning at random days during the test year, with the simulated outbreak lasting from 1 to 10 days, from 2 to 10 days, and exactly 1 day. Generally, departures of approximately 3 or more standard deviations from the baseline model should be detected with high probability. The simulated per-day shift above the baseline prediction ranged from 1 to 5 standard deviations in our simulations, so testing one day at a time could fail to detect those outbreak having small per-day shifts. Also, because of the pattern in the residuals near each seasonal peak, we considered EWMA  as one way to modify the current forecast on the basis of errors in the recent past.In a simulation study we injected In Table Long-term trends can occur in surveillance data for multiple reasons. Changes may occur in: local resources , access and reimbursement practices (facilities change which insurance plans with which they are associated, major shifts in insurers drives patients to other facilities), changes in the underlying population (shifts in population size or age), and changes in the local economy. Moving averages were not used because although they generate visually pleasing curves they smooth over day-of-week and seasonal effects that are important for developing baselines.Concerning model quality, one useful test is whether the forecast error variance in the testing data is approximately the same as that in the training data. Upon dividing the forecast errors in the test data by their standard deviations in the training data, the scaled forecast error variances range from 0.87 to 1.07 for the seven CC categories . Further, the fraction of scaled forecast errors that exceed 1.96 ranged from 0.0 to 0.033 . Thus, departures from stationarity in the time series are mild enough that the forecast errors show that future complaints can be reasonably well predicted using a single baseline model for each category.When monitoring complaint levels over multi-year time frames, it is necessary to periodically update baseline model coefficients in order to minimize the extrapolation in forecasting. One approach to choosing an update frequency is to do a planned update every year, but also monitor residuals for patterns, including shifting variance, that have not been observed previously to check whether additional updates are needed.The first-order model is useful for routine monitoring. It has the obvious shortcoming, however, of describing each season in a one-size-fits-all fashion. As noted above in evaluation of the model's goodness-of-fit, forecast errors reflect modelling imperfections as well as random variability, limiting somewhat the sensitivity of surveillance to detect smaller anomalies. Improving on this situation requires more refined baselining.Hierarchical methods  can overApplying the hierarchical model to respiratory CC data illustrates the season-specific nature of chief complaints. On the average, our respiratory complaints peak on January 22, with a season-to-season standard deviation in the day of the peak of 12 days. The durations of individual seasons, defined in terms of the standard deviations for the Gaussian-shaped peaks, vary by factor of two over the monitoring period. And there is no apparent relation between the time that the peak occurs and the magnitude of the flu season.Use of hierarchical models for real time syndromic monitoring could be considered, but at a significant computational cost. In order to capture the peak time and magnitude of an ongoing season, the model must be updated on a frequent (e.g., weekly) basis, involving lengthy runs of Markov chain Monte Carlo software. Because the first order cyclical regression model fits the data sufficiently well to detect anomalies of interest, we have used the first order model for routine monitoring. A similar first order cyclical regression model is used by the Centers for Disease Control to monitor pneumonia and influenza related mortality data , also wiInfluenza surveillance basing alerts on comparison to historical data were described by Irvine . Daily cLazarus et. al.  use a geReis and Mandl  used CCsThe EWMA also adjusts to multi-day outbreaks and therefore suffers from signal loss if the outbreak persists for multiple days  are more common in the warm months from outdoor cooking and recreational water exposures.Seasonal effects in infectious diseases are best known for respiratory infections . This seasonality is in large part due to the yearly winter influenza epidemics. Our data is quite consistent with these findings. However, we provide an analysis of the pattern of respiratory related chief complaints based on the longest (8.6 years) historical data base. Furthermore, the seasonality of infectious disease complaints for other body systems, or for non-ID related complaints has not previously been reported. Seasonality in specific GI infections has been noted in other settings. Some gastrointestinal infections are more common in the winter (e.g. rotavirus) while others  declare that they have no competing interests.JB and EU provided medical advice from the ED and public health perspectives. They were responsible for choice and groupings of chief complaints. JB obtained IRB approvals and recruited sites. TB and RP are responsible for choice and implementation of statistical tests. DF provided computer systems, interface and architecture. EJ provided technical input, review and project direction. JB, EU, TB and RP collaboratively wrote the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Recent studies suggest that hypercholesterolemia, an established risk factor for atherosclerosis, is also a risk factor for Alzheimer's disease. The myeloid scavenger receptor CD36 binds oxidized lipoproteins that accumulate with hypercholesterolemia and mediates their clearance from the circulation and peripheral tissues. Recently, we demonstrated that CD36 also binds fibrillar β-amyloid and initiates a signaling cascade that regulates microglial recruitment and activation. As increased lipoprotein oxidation and accumulation of lipid peroxidation products have been reported in Alzheimer's disease, we investigated whether β-amyloid altered oxidized lipoprotein clearance via CD36.125I-labeled oxidized lipoproteins via CD36, SR-A and CD36/SR-A-independent pathways, (c) expression of SR-A and CD36. In addition, using mice with targeted deletions in essential kinases in the CD36-signaling cascade, we investigated whether Aβ-CD36 signaling altered metabolism of oxidized lipoproteins.The availability of mice genetically deficient in class A (SRAI & II) and class B (CD36) scavenger receptors has facilitated studies to discriminate their individual actions. Using primary microglia and macrophages, we assessed the impact of Aβ on: (a) cholesterol ester accumulation by GC-MS and neutral lipid staining, (b) binding, uptake and degradation of In primary microglia and macrophages, Aβ inhibited binding, uptake and degradation of oxidized low density lipoprotein (oxLDL) in a dose-dependent manner. While untreated cells accumulated abundant cholesterol ester in the presence of oxLDL, cells treated with Aβ were devoid of cholesterol ester. Pretreatment of cells with Aβ did not affect subsequent degradation of oxidized lipoproteins, indicating that lysosomal accumulation of Aβ did not disrupt this degradation pathway. Using mice with targeted deletions of the scavenger receptors, we demonstrated that Aβ inhibited oxidized lipoprotein binding and its subsequent degradation via CD36, but not SRA, and this was independent of Aβ-CD36-signaling. Furthermore, Aβ treatment decreased CD36, but not SRA, mRNA and protein, thereby reducing cell surface expression of this oxLDL receptor.Together, these data demonstrate that in the presence of β-amyloid, CD36-mediated clearance of oxidized lipoproteins is abrogated, which would promote the extracellular accumulation of these pro-inflammatory lipids and perpetuate lipid peroxidation. Hypercholesterolemia is an established risk factor for atherosclerosis and a number of recent epidemiological studies have suggested a link between increased circulating cholesterol levels and Alzheimer's disease (AD) . LipoproSra null mice have failed to show a role for this receptor in the pathogenesis of AD [in vivo [Phagocyte expressed pattern recognition receptors (PRR) are the first line of defense of the innate immune system against foreign or modified proteins and lipids. Scavenger receptors are pattern recognition receptors that bind and internalize a wide range of ligands, including certain polyanions, modified forms of LDL, advanced glycation endproducts and apoptotic cells . These ris of AD , it has is of AD -19. In A[in vivo . SimilarSra and Cd36 has facilitated studies to discriminate the actions of these individual scavenger receptors. We show that Aβ dose-dependently inhibits oxLDL binding, lysosomal degradation and cholesterol ester accumulation in macrophages and microglia. This inhibitory effect was mediated specifically via CD36 and could be reversed by removal of extracellular Aβ, indicating that the lysosomal degradation pathway was not directly impaired. Furthermore, activation of CD36-signaling by Aβ did not mediate this inhibitory effect, as targeted inactivation of essential downstream kinases did not restore oxLDL degradation. Together, these data demonstrate that Aβ impairs the ability of CD36 to scavenge oxidized lipids by competing for receptor binding. This suggests that accumulation of Aβ in the brain and vessel wall in AD would inhibit the clearance of pro-inflammatory oxidized phospholipids and oxidized-phospholipid-containing particles such as lipoproteins, thereby promoting lipid peroxidation.Aβ has previously been shown to reduce uptake of LDL modified by acetylation, in microglia and SRA- or SR-B1-transfected cells . We have1-42 and reverse Aβ42-1 (revAβ) peptides were obtained from Biosource International . To induce fibril formation, Aβ1-42 was resuspended in H2O at 1 mg/ml and incubated for 1 week (37°C) and fibril formation was confirmed by thioflavine S (Sigma-Aldrich Co., St. Louis, Missouri) fluorescent staining as we previously described [Aβescribed ,18.Cd36-/- mice were generated in our laboratory as previously described [Sra-/-) mice were generously provided from Dr. T. Kodama (University of Tokyo, Japan) [Sra and Cd36. Double knockout mice (Sra-/-/Cd36-/-) were generated from heterozygote intercrosses at the expected ration of 1:16. Wild type age-matched C57BL/6 mice (The Jackson Laboratory, Bar Harbor, Maine) were used as controls for these three lines. Lyn-/- and Fyn-/- mice were obtained from The Jackson Laboratory and Lyn-/-, Fyn-/- and wild type littermate control mice were generated from heterozygote intercrosses. All mice were maintained in a pathogen-free facility with free access to rodent chow and water. All experimental procedures were carried out in accordance with Massachusetts General Hospital's institutional guidelines for use of laboratory animals.The escribed  and SraI, Japan) . Both moi.p. injection with 3% thioglycollate as we previously described [+ and F4/80+ as determined by flow cytometric analysis. Primary microglia were prepared from mixed brain cultures of neonatal mice as we previously described [+ and express SR-A and CD36 [Macrophages were collected from 6–8 week old mice by peritoneal lavage 4 days after escribed ,25. Cellescribed . Brieflyand CD36 ,17,18.125I-LDL and LDL (d = 1.019 - 1.063) were purchased from Biomedical Technologies (Stoughton, Massachusetts) and oxidized as we previously described [4 in PBS at 37°C for 6 or 10 h. Oxidation was terminated by the addition of 50 μM butylated hydroxytoluene and 200 μM EDTA and oxLDL was used within 2 days of preparation. Moderately oxidized LDL (6 h oxidation) had a relative electrophoretic mobility of approximately 2.5–3 times that of native, unmodified LDL, whereas extensively oxidized LDL (10 h oxidation) had a relative mobility four times that of native LDL.Human escribed . LDL was125I-oxLDL binding, degradation and uptake was performed on confluent monolayers of peritoneal macrophages (7 × 105) and microglia (5 × 105) in 24 well plates as we previously described [125I-oxLDL was added to cells in the presence or absence of 30-fold excess unlabeled oxLDL, native LDL, Aβ1-42, or revAβ peptide for 5 h at 37°C. To measure 125I-oxLDL degradation, media were removed and assayed for TCA-soluble non-iodide degradation products. To measure 125I-oxLDL binding in the presence Aβ1-42 or revAβ, cells were washed 3x with 50 mM Tris pH 7.4, 0.15 N NaCl and 2 mg/ml BSA, 1x with 50 mM Tris pH 7.4 and 0.15 N NaCl and treated with 0.4% dextran sulfate to release surface bound 125I-oxLDL [125I-oxLDL uptake, cells were washed 3x in 50 mM Tris pH 7.4 and 0.15 N NaCl, lysed in 0.1 N NaOH and assayed for 125I and cellular protein content. In some experiments, cell-association of oxLDL (cell-surface bound and endocytosed oxLDL) was measured by omitting the dextran sulfate treatment. Cellular protein content was measured by BCA assay (Pierce, Rockford, IL) and degradation, binding and uptake activity are expressed as ng 125I-oxLDL/mg protein. Specific degradation was calculated as the difference of total cellular degradation of 125I-oxLDL in the presence and absence of 30-fold excess unlabelled oxLDL competitor. All measurements were performed in triplicate and are representative of at least 3 experiments.Measurement of escribed . Briefly5I-oxLDL . To meas1-42 or revAβ. Cholesterol ester accumulation was assessed by gas chromatography-mass spectrometry (GC-MS) and oil red O staining as we previously described [Macrophages and microglia were cultured with 40 μg/ml of oxLDL for 48 h in the presence or absence of Aβescribed ,26. For HotStarTaq polymerase. PCR was performed using a BioRad iCycler under the following conditions: 15 min at 95°C, followed by 30 cycles of 30 sec at 95°C, 30 sec at 55°C and 30 sec at 72°C. Each sample was analyzed in triplicate and the amount of CD36, SRA and GAPDH mRNA in each sample was calculated from a standard curve of known template. Data are expressed as the mean number of CD36 and SRA molecules normalized to GAPDH.Total RNA was extracted using Trizol B reagent and real-time quantitative RT-PCR (QRT-PCR) was performed using the QuantiTect SYBR Green PCR kit  as we previously described ,18. EachCells were washed in ice-cold PBS and lysed in radioimmune precipitation buffer containing protease and phosphatase inhibitors. For detection of CD36, 30 μg of protein was run on an 8% denaturing SDS-polyacrylamide gel, transferred to nitrocellulose and blocked overnight in 5% nonfat dry milk and 3% BSA in Tris-buffered saline containing 0.1% Tween 20 (TBS-T) as we previously described ,26. Memb1-42, but not revAβ, dose-dependently inhibited lysosomal degradation of 125I-oxLDL  to cholesterol ester for storage. This neutral lipid is retained in cytoplasmic lipid droplets for storage and/or efflux from the cell. Using gas chromatograpy-mass spectrometry, we quantified the cholesterol ester content of macrophages treated with oxLDL in the presence and absence of Aβ1-42. As expected, untreated cells did not contain measurable cholesterol ester, while macrophages treated with 40 μg/ml oxLDL for 48 h accumulated approximately 80 μg cholesterol ester/mg cellular protein  of 125I-oxLDL by greater than 50%, indicating that this scavenger receptor is not essential for the inhibitory action of Aβ  direct competition for CD36 binding, or (2) inhibition of oxLDL metabolism as a result of Aβ/CD36 signal transduction. To address whether CD36 signaling inhibits cellular oxLDL degradation, we used macrophages with targeted deletions in two kinases in this pathway, Lyn and Fyn, which have previously been shown to be required for CD36-mediated p44/42 activation, MCP-1 secretion and ROS production [1-42 effectively inhibited 125I-oxLDL degradation in Lyn-/- and Fyn-/- macrophages, suggesting that this signaling pathway does not inhibit oxLDL metabolism . Interestingly, in untreated Fyn-/- macrophages 125I-oxLDL degradation was increased 2-fold , suggesting that the CD36-dependent contribution to oxLDL binding was totally inhibited. To confirm that other myeloid scavenger receptors were not inhibited by Aβ, assesed 125I-oxLDL binding in Sra-/-Cd36-/- macrophages. No effect of Aβ was observed in these cells, demonstrating the specificity of Aβ inhibition of oxLDL binding to CD36.The finding that CD36 is required for Aβoduction . Howeverism Fig. . Furtherold Fig.  indicatiNumerous studies have demonstrated elevated markers of lipid peroxidation in the brains, CSF and plasma of Alzheimer's disease patients, including thiobarbituric acid-reactive substances, 4-hydroxy-2-nonenal (HNE), acrolein and F2-isoprostanes, which are suggestive of a persistent pro-oxidant environment ,29,30. L-/- mice, there is a 60% impairment in microglial binding of Aβ and reactive oxygen production, however, AD-associated brain pathology is not reduced [-/- cells. By contrast, the effect of Aβ was abolished in the absence of CD36, indicating that this receptor is the target of Aβ action.In addition to CD36, two other scavenger receptor family members have been shown to be expressed in the brain and to bind Aβ. The Class A scavenger receptors, SRA I and II, and the class B SR-BI are expressed by neonatal microglia, but unlike CD36, these receptors are not expressed by microglia in the normal adult brain ,15. Howe reduced ,33. SRA  reduced . Convers reduced ,21,35. W1-42 would be predicted to foster inflammation and tissue injury. While we have shown that Aβ blocks CD36 binding of oxLDL, and its subsequent degradation, we would predict that similar results would be found with oxidized lipoproteins isolated from the CNS, astrocytes or microglia. Although serum and brain lipoprotein particles differ in their apolipoprotein composition [The difficulty in isolating human lipoproteins from the CNS has limited their experimental use, however, several groups have shown that oxidized serum lipoproteins, including LDL, HDL and VLDL, are toxic to neurons -39, and position ,41-44, tposition ,41. The position ,6. InhibDisruption of oxidized lipoprotein metabolism by Aβ may also be relevant in the context of atherosclerosis. Cholesterol oxidation products generated during the inflammatory component of atherosclerosis have been shown to accelerate β-amyloid fibril formation ,45. FurtrevAβ, reverse β-amyloid peptide 42-1; SRA, scavenger receptor A; SR-BI, scavenger receptor B I.Aβ, β-amyloid peptide 1–42; ACAT, acyl-coenzyme A:cholesterol acyltransferase; AD, Alzheimer's disease; CSF, cerebral spinal fluid; DMEM, Dubelcco's modified Eagle medium; FCS, fetal calf serum; fAβ, fibrillar Aβ; GC-MS, gas chromatography-mass spectrometry HNE, 4-hydroxy-2-nonenal; oxLDL, oxidized low density lipoprotein; The authors declare that they have no competing interests.125I-oxLDL binding, uptake and degradation, and participated in the design of the study and analysis of results. LAM and TK isolated the primary microglia and macrophages, performed western blots, quantitative RT-PCR, and measurements of 125I-oxLDL binding, uptake and degradation. AAT performed measurements of 125I-oxLDL binding, uptake and degradation. KJM conceived of the study, participated in its design and wrote the manuscript. All authors read and approved the final manuscript.VVK performed the measurements of