P. aeruginosa is a pathogen that causes acute and chronic lung infections by interacting with the pulmonary epithelial cells. We use this example for examining the ways of triggering the response of the eukaryotic cell(s), leading us to a better understanding of the details of the inflammatory process in general.Binding of a bacteria to a eukaryotic cell triggers a complex network of interactions in and between both cells. Considering a set of genes co-expressed during the antibacterial response of human lung epithelial cells, we constructed a promoter model for the search of additional target genes potentially involved in the same cell response. The model construction is based on the consideration of pair-wise combinations of transcription factor binding sites (TFBS).It has been shown that the antibacterial response of human epithelial cells is triggered by at least two distinct pathways. We therefore supposed that there are two subsets of promoters activated by each of them. Optimally, they should be "complementary" in the sense of appearing in complementary subsets of the (+)-training set. We developed the concept of complementary pairs, i.e., two mutually exclusive pairs of TFBS, each of which should be found in one of the two complementary subsets.We suggest a simple, but exhaustive method for searching for TFBS pairs which characterize the whole (+)-training set, as well as for complementary pairs. Applying this method, we came up with a promoter model of antibacterial response genes that consists of one TFBS pair which should be found in the whole training set and four complementary pairs.We applied this model to screening of 13,000 upstream regions of human genes and identified 430 new target genes which are potentially involved in antibacterial defense mechanisms. In spite of numerous sophisticated approaches devoted to this subject [Promoter model construction is a way to utilize information about coexpressed genes; this kind of information becomes more and more available with the advent of gene expression mass data, mainly from microarray experiments. Having a promoter model at hand, one has (i) an explanatory model that and how the coexpressed gene may be coregulated, and (ii) a means to scan the whole genome for additional genes that may belong to the same "regulon". The field of searching for regulatory elements  subject -9, we stPseudomonas aeruginosa. Binding of bacteria to a eukaryotic cell triggers a complex network of interactions within and between both cells. P. aeruginosa is a pathogen that causes acute and chronic lung infections affecting pulmonary epithelial cells [The biological system we consider in this work is the transcriptional regulation of the response of lung epithelial cells to infection with al cells ,11. We uP. aeruginosa to the epithelial cells, the response of these cells is triggered by at least two distinct agents: bacterial lipopolysaccharides [After adhesion of charides  and/or bcharides . Both pacharides ,15; proncharides  have beecharides ,3,8,9.We use this consideration as a basis for constructing a predictive promoter model. We searched for combinations of potential transcription factor binding sites (TFBS), considering those transcription factors (TFs) that are known to be involved in antibacterial responses. Some of the found combinations could be predicted from the fact that they may constitute well-known composite elements, like those containing NF-κB and C/EBP or NF-κB and Sp1 binding sites [TRANSCompel, ]. We staWe suggest a simple, but exhaustive method for searching for TFBS pairs which characterize the whole training set, and combinations of mutually exclusive pairs (complementary pairs). The idea of starting the analysis with a "seed" of sequences allows a very biology-driven way of initial filtering of information.To enhance the statistical reliability and to get additional evidence in TFBS combination search, we applied the principal idea of phylogenetic footprinting (using orthologous mouse promoters), yet proposing a different view on applicability of this approach.Finally we came up with a promoter model which we applied to screening of 13,000 upstream regions human genes. We identified 430 new target genes which are potentially involved in antibacterial defense mechanisms.In every step of our investigations we tried to combine purely computational approaches with the preexisting experiment-based knowledge, as it is represented in corresponding databases and literature, and with our own biological expertise. To develop a promoter model, the first task is to select those transcription factors, the binding sites of which shall consitute the model. The overwhelming majority of methods and tools estimating the relevance of predicted TF binding sites in promoter regions are based on their over- and underrepresentation in a positive (+) training set in comparison with some negative (-) training set. If, however, a binding site is ubiquitous, or very degenerate, so that it can be found frequently in any sequence, the comparison with basically any (-)-training would not reveal any significance for its occurrence. That tells nothing about their functionality in any specific case, which may be dependent on some additional factors and/or other conditions. Therefore, basing the decision about the relevance of a transcription factor for a certain cellular response solely on whether its predicted binding sites are overrepresented in the responding promoters may lead to a loss of important information. Thus, we did not rely on this kind of evidence but rather chose the candidate transcription factors according to available experimental data. We found 5 factors reported in literature as taking part in anti-bacterial or similar responses and selected them as candidate TFs ,15,18-29On the other hand, some of the factors, which have also been mentioned in literature as potentially relevant (e.g., SRF ) or mighFinally, we constructed our promoter model of binding sites of 5 TFs (NF-κB, C/EBP, AP-1, Elk-1, Sp1), considering their pairwise combinations and some combinations of higher order (complementary pairs, see below).predictive model, since it is also important to have minimal occurrence of a discriminating feature in the (-)-training set. In the further work we considered all pairs with p < 0.005, but as this did not reasonably restrict the list of considered pairs, we had to apply an additional filtering approach. For this purpose we used a simple characteristic such as the percentage of sequences in (+)- and (-)-training sets. By operating directly with percentages we could easily filter out those pairs which would identify too many false positive sequences, thus getting rid of a substantial part of useless information. This procedure allows to estimate immediately the applicability of the model to identify further candidate genes that may be involved in the cellular response under consideration (see Methods).In several steps of the model construction we had to estimate overrepresentation of a feature in the (+)-training set compared with the (-)-training set. We operated with the number of sequences that possess the considered feature, in our case a pair of TFBS, at least once. Otherwise, mere enrichment of a feature in the (+)-training set may be due to strong clustering in a few members of that set which would not lead to a useful prediction model. At the first step the T-test has been performed ), but it appeared to be a weak filter: for example, we could find several pairs which showed, if estimated with T-test, a remarkable overrepresentation (p < 0.001), but with a difference of 97% in the (+)-training set versus 85% in the (-)-training set, which is of no practical use to construct a The main problem of promoter model construction are the numerous false positives. Developing our approaches we applied some anti-false-positives measures :• distance assumptions• identification of "seed" sequences• phylogenetic conservation• subclassification into complementary sequence sets.In the following, we will comment on each item in more details.The commonly accepted view that functionally cooperating transcription factors may physically interact with each other triggered us to introduce certain assumptions concerning the distances between the considered TFBS. Transcription factors can interact either immediately with each other or through some  mediator proteins (co-factors). Principally there can be many ways of taking this into account, since our knowledge about the mechanisms of interaction is limited. In this work we used two different approaches to consider distances in the promoter model development.at a certain distance, but within a certain distance range, we considered the pairs occurring in segments of a certain length.In the first case we based our assumptions on the structure of known composite elements. We assumed that the binding sites of interacting TFs should occur in a distance of not more than 150 bp to each other  Directly interacting factors should have the binding sites at a close distance.(b) The factors interacting through some co-factor may have binding sites on some medium distance, depending on the size and other properties of the co-factor (and the factors themselves).(c) We can also expect direct interaction of another type, when the two factors are not located in the nearest neighborhood, but their interaction requires the DNA to bend or even to loop. This means that the distance is no longer a close one, although we cannot estimate the distance range for this case; thus, we allowed different ranges of distances, excluding only the closest ones.We searched for pairs in three distance ranges, roughly called "close", "middle" and "far", all with adjustable borders, so that moving them we could get the best proportion of percentages in (+)- and (-)-training sets. We used the search in the distance ranges as a starting point, but some of the found pairs required optimization of the borders, so that they finally did not fit into any of the predefined ranges. The initial "close" range was taken as 5–20 bp, to exclude the overlapping of the sites, but to allow close interaction; however, the border had to be shifted in many cases up to 50 bp. The initial "middle" range was chosen from 21 to 140 bp (the number of nucleotides wrapping around the core particle of the nucleosome); the "long" range had its upper border at 250 bp.Initially the idea of "seed" sequences was exploited because of the desire to make use of preexisting biological knowledge about the expressed genes and also because of doubts in the reliability of the available data set. Different experimental approaches differ in their reliability. The microarray analysis is not absolutely reliable ,34-36, sTherefore, we started our analysis with a group of "seed" sequences, which we considered for distinct reasons more reliable and preferable. Choosing a seed group, we took into consideration two kinds of evidence; the first was the source of information, i. e. the methods with which the gene has been shown to participate in the response. We took the promoter sequences of those genes which have been reported by other methods but microarray analysis ,38-47,47The second kind of evidence was whether we could find any additional biological reasoning for the gene to participate in this kind of reply. For instance, a well-known participant of the NF-κB-activating pathway such as IκBα, or participants of different pathways which are likely to be triggered here as well, like c-Jun or PKC, were estimated as the first candidates for the "seed" group.Methods) -training set was reduced by at least two orders of magnitude: depending on the "seed" the number of considered pairs varied from 50 to 400. In the next steps this number was reduced by another order of magnitude Table .Each "seed" is characterized by its own set of pairs. To ensure the robustness of the obtained results, we undertook the "leave-one-out" test, removing consecutively one sequence of the "seed" set (for the combined "seed" sets which included human and mouse orthologs we excluded simultaneously both orthologous sequences). This has been repeated for each sequence (or ortholog pair). Only the robust pairs have been taken into further consideration.Evolutionary conservation of a  TFBS is generally accepted as an additional criterion for a predicted site to be functional . HowThe observed fact is that functional features are not necessarily bound to conserved regions, as long as we speak about primary sequence conservation. Dealing with such degenerate objects as TF binding sites, one should not expect an absolute conservation of their binding sequences. From the functional point of view, it seems to be more reasonable to expect that not the sequences, but the mere occurrence of binding sites and/or their combinations as well as (perhaps) their spatial arrangement would be preserved among evolutionarily related genomes. That is the approach that we use in the present work, completely refraining from sequence alignments. We search for those pairs of TFBS which can be found in human and corresponding mouse orthologous promoter regions, considering the promoter as a metastring of TFBS. We took a feature (the pair of TFBS) into account only if we could identify it in both orthologous promoters, not taking into consideration in what region of the promoter it appeared; we also did not try to align metastrings of TFBS symbols, since they may be interrupted by many additional predicted TFBS . While this work was in progress, we found a very similar approach in the work of Eisen and coworkers ,55, who The idea that combinations or clusters of regulatory sites in upstream regions provide specific transcriptional control is not new ,8,56. NeIn the following, we will formalize our approach and describe the logics of our investigation.All procedures are described for the example of pairwise combinations, but principally all of them can be applied to combinations of higher orders. We restricted our attempt to pairs for sake of computational feasibility.Methods. A pair is taken into account if it has been found in a sequence at least once.We consider all possible pairwise combinations of TFBS in each sequence, as described in m and n located in a distance range from r1 to r2 (where r1 ≤ r2) on either strand of DNA (+ or -). We can denote the sets of sequences containing pairs in different relative orientation as, .Let us consider two TFBS To allow inversions of DNA segments containing pairs, we consider three classes of combinations Fig. :i = 1,...3  represents the set of sequences with a pair of i-th class m, ni) ((r1, r2).In more general form for  be a fraction of the sequences  in the (+)-training set, and  the fraction of sequences  in the (-)-training (control) set.Let  by choosing appropriate values for m, n, i and r1, r2. Also, we are interested only in pairs, which are present in at least a minimum fraction of (+)training sequences (C1) and in a defined maximum fraction of (-)-training sequences (C2). They can be filtered in advance.We have to solve now the optimization problem to maximize the difference  for whichThus, we search for such C1,2 ≤ 1 are adjustable parameters.where 0 ≤ C1 = 0.8 and C2 = 0.4. We could not find pairs which would satisfy more stringent parameters, i. e. either higher C1 or lower C2; on the other hand, requirement (1) was found to be satisfied by a lot of different combinations which gave rise to the same Pt and Pc.For single pairs we chose r1, r2) from the expression  ). Each possible type of pair is determined by values of m, n and i. We can list all types of pairs and assign a number j to each pair in this list. Then each type of pair is characterized by mj, nj, ij:To make the analysis more specific, we can consider combinations of pairs instead of single pairs. For sake of simplicity, we will omit furtheron  we can identify  and , which appear in the (+)training set simultaneously:For two different A triple or a combination of a higher order can be represented in the same way.The antibacterial response of the cell is triggered by at least two distinct pathways, and it may be therefore supposed that there are subsets of promoters activated by each of them. Optimally, they should be "complementary" in the sense of appearing in complementary subsets of the (+)-training set Fig .Seed sequences; Table Complementary pairs were searched first in a "seed" subset of the (+)-training set of sequences Fig , step 1.C1 is therefore always set to 1, );(a) they together cover the whole subset (C3 and C4, see below), with an allowed overlap (defined by the parameter C5).(b) each of them can be found in not more and not less than a certain number of sequences -training set of 33 sequences, looking only for the combinations found in the second step (i.e., in the 12 "seed" and their orthologous sequences) Fig. , step 4.The percentage of the pair occurrence in the (-)-training set has been counted on the first step with the subsequent filtering of pairs.A rather large number of combinations satisfied the requirements described in the previous section. However, when we selected those that were robust in a "leave-one-out" test for the "seed" sets, the final list of potential model constituents was shortened down to only 2 ubiquitous and 12 complementary pairs.We found one satisfactory pair which should be found in all promoters of target genes:AP - 1, NF - κB(1)(10,93)AP-1, NF - κB, class 1, distance from 10 to 93 bp; see Fig. -training set 52%) -training set revealed the presence of all pairs under consideration). But the simultaneous usage of all the pairs could overfit the model, so we did not apply them all, sacrificing a bit of specificity for sake of a higher sensitivity.Finally, we came up with 4 complementary pairs Fig.  composedC1, C2 (0.7<C1<0.8, 0.4<C2<0.5) for common pairs; for complementary pairs we considered the case with C3 = 0.3 C4 = 0.7 C5 = 0.2. The probability to find by chance a "seed" of 12 sequences which would produce at least one pair common for the random selection of 33 sequences (including the "seed") depends on the chosen C1, C2 and is found to vary between p < 0.0005 (C1 = 0.8, C2 = 0.4, the parameters used for our model construction) and p = 0.02 (C1 = 0.7, C2 = 0.4). We failed to find any complementary pairs after 1000 iterations of the permutation test with the parameters used for the "real" (not permuted) model construction. These results suggest that the success of the model construction based on the search for combinations of TFBS is strictly dependent on the selected training set  and that the significance of the findings, depending on the correct choice of the adjustable parameters, is high enough to claim their non-randomness. Thus, we can say that in the described case the pairs found in the given (+)-training set with the given parameters are the real characteristics of this set.In order to avoid the overfitting of the model and to demonstrate the significance of our results, we performed a permutation test. For that, we conducted 2000 iterations of random permutation of (+) and (-) labels in the training sets and tried to rebuild the model using the procedure described above. The rate of correct classification on this random selection was estimated. The cases of common and complementary pairs were considered separately. The analysis was made for different The model consists of two kinds of combinations of pairs: ubiquitous pairs , and complementary pairs. We can divide the model into two modules, one for each kind of combination.M1 and M2 be modules comprising ubiquitous pairs and complementary pairs, respectively.Let M1 comprises the pair AP-1, NF-κB(1)(10,93).Module M2 comprises all complementary combinations listed in the Fig. m) in M2.Module S(M) the set of sequences which possess the whole model M; then we can also consider S(M1) and S(M2) (the sets possesing the modules M1 and M2, respectively), and S(m) – the set with a submodule m.To apply the model means to search for sequences containing all these combinations. Let us call Thenm); in this case we consider four submodules, so the sequences containing M2 can be found as:Module M2 consists of submodules (S(M2) = S(m1) ∩ S(m2) ∩ S(m3) ∩ S(m4),where the set with each submodule we must consider as a union of sequence sets containing the complementary pairs:The final result of application of the model M can be presented asS(M) = S(M1) ∩ S(M2)The model gives 3.4% of false positives and re-identifies 52% of the whole (+)-training set, but these 52% comprise all most reliable sequences of the set .Applying our promoter model to screening of 13000 upstream regions from a collection of human 5'-flanking sequences , we idenP. aeruginosa binding. One of the results of our work is a list of potential target genes, enriched with different regulatory proteins, including transcription factors and known participants of the ascending pathways. This theoretical result must have two practical consequences: first, it allows to restrict further experimental research to a manageable number of candidate genes; second, it enables to understand or to clarify some uncertain details concerning the triggering pathways, and thus to make some new predictions based on this information. There is a number of published tools for searching for regulatory modules  [7,5nd Noble ,59-63. Tnd Noble . Any of nd Noble , and theIn the present work we tried to address the listed problems. We could not, of course, improve the situation with the paucity of experimental data, only endeavored to make our data searches as accurate and exhaustive as possible. In principle we developed our approaches basing them, whenever possible, on biological reasoning. We find it extremely important to use as much experimental evidence as it is available at the moment. In our approach we alternated two different kinds of steps – expanding the data and restricting it: exhaustive data search – "seed" and distance constraints – exhaustive enumeration of all possible pairs – complementary pair constraints.To avoid the problem of low confidence in the (+)-training set (which may occur not only in our specific case), we developed the approach of "seed" sequences. The difference from the "seeds" used in cluster analysis is that in our approach the choice of the "seed" is biologically based. Although the "seed" approach is, obviously, a restrictive measure, moreover, a pre-process restriction, which may result in missing potentially relevant additional sequence features, we find it useful and appropriate when the choice of the "seed" is made on a solid biological basis. After having applied the restrictive "seed" technique and distance assumptions, we undertake an exhaustive, complete enumeration of all possible pairs of potential TF binding sites that can be found in the (+)-training set, which in turn reveals a large number of combinations. This list of all found pairs is processed under a new kind of constraints imposed by the search for complementary pairs.The search for complementary pairs is a completely new approach, which supplies us with a new kind of information. It enables to identify subsets of the (+)-training set which possess different regulatory modules, thus suggesting their triggering by different regulatory pathways. This kind of information becomes extremely important in two cases: (i) when two or more pathways are presupposed to be triggered in the cellular response, like in the case considered in this work; (ii) when the (+)-training set consists of not really co-regulated, but of co-expressed genes, without precise information about which of them are regulated by the same mechanism. The identification of complementary pairs and, consequently, groups of sequences enables to better define the co-regulated genes thus providing a partial, although only predicted, confirmation of the co-regulation, and at the same time to better understand the ascending pathways.P. aeruginosa this pathway is not the only one [The final result of our search supported the idea of complementary pairs. There is a lot of evidence in literature that interleukin 8, β-defensin, monocyte chemoattractant protein and different mucins are regulated through LPS-triggered pathway(s) ,15,38. Oonly one , but we Our approach, as any other, has its limits. It has been shown for the genuine composite elements of certain types (for instance, NF-AT and AP-1)  that oneThe next source of limitations we see in the preselection of factors according to published data. Obviously, we can not expect that the experimental data is exhaustive; some of the transcription factors may be not reported just because their participance in a certain process has not yet been investigated. On the other hand, statistical overrepresentation, as it has already been mentioned before, can not be taken by itself as proof of biological functionality or its lack; some TFBS cannot be overrepresented due to their degenerate nature. We had no other idea of how to take into account those TFBS which are not overrepresented, but to rely on published experimental data. We find that the usual methods based on statistical overrepresentaion are even more restrictive, but maybe the best solution could be found in merging both approaches – i.e., using the experimental evidence along with statistical ones, for instance using Bayesian techniques.P. aeruginosa binding, and further development of the methodological approaches, making them more flexible and applicable to any similar task. The list of predicted target genes has to be evaluated experimentally, but may have its value for further research already on the present step. The future work on reconstructing the intracellular pathways triggering the genetic program of the antibacterial cell response will be well supported with the information picked up from this list. It may give some hints for the next steps of experimental research, for instance providing information about the first candidates to be checked. The information about the complementary subsets of regulated genes helps to better understand the triggering pathways, and the complementarity of their function is a subject for further consideration.We see the perspectives of this work in two different fields: further investigation of regulatory networks triggered by The methodological approaches presented in this paper can be, of course, applied to other objects. In this work we focused on the experimentally proven basis for the initial choice of transcription factors. This kind of evidence is stronger than any prediction, but it can work only when this information is available, which may be not the case for some other sets of genes or cellular situations. In the next step of development we would like to allow also an exhaustive computational search through the whole list of known TFs for potential constituents of the models. The usage of Bayesian techniques, as mentioned in the previous paragraph, would be also appropriate for this kind of predictions.We suggest a methodology for promoter model construction based on the search of TFBS pairs and show how it works in the particular case of antibacterial response of human lung epithelial cells. We show that the method allows to identify and predict subsets of target genes potentially triggered by different regulatory pathways and thus possessing different regulatory modules. The methodology is easily applicable to any similar task and does not depend on the number of included TFs and/or number of investigated sequences, which only should not be too low for statistical reasons., release 77-1.Eukaryotic Promoter Database , release 3.0DBTSS, the database of transcription start sites ® Professional release 7.1 TRANSCompel® Professional release 7.1 TRANSFAC® Professional release 4.1 TRANSPATHThe positive (+) training set comprises:P. aeruginosa by means of:1. Promoters of human genes shown to be expressed in epithelial cells after interaction with a. microarray analysis ,b. other methods ,28,37,382. Orthologous mouse promoters., or from DBTSS, the database of proven transcription start sites . The length of the sequences was 600 bp (-500/+100). This region comprises most of then known upstream elements and corresponds to the upstream region used by Davuluri et al. as "proximal promoters" for promoter recognition [The sequences were derived either from Eukaryotic Promoter Database ognition , plus a ognition .The "seed" set is a subset of the positive training set selected for highest experimental reliability (see Table The negative (-) training set was composed of randomly chosen 5'-upstream sequences derived from the TRANSGENOME information resource of annotated human genome features . The setP. aeruginosa binding or in the pathways triggered during similar responses. The search revealed 5 candidate factors: NF-κB [We based our selection of TFs on experimental evidence. For that we undertook an extended literature search, looking for the TFs which have been shown to take part either directly in the response of epithelial cells to s: NF-κB ,23,24,26s: NF-κB ,24,25,27s: NF-κB ,24 and Ss: NF-κB ,29,48.® database [Including C/EBP and Sp1 in the list was additionally reasoned by the fact that these factors are known to be second constituents in the most frequent NF-κB-containing composite elements as they are compiled in the TRANSCompeldatabase . Moreove® [®); (ii) at least one hit for every searched transcription factor could be found in every sequence of the (+)-training set. The lower border for the thresholds was predefined as 0.80/0.79 (core similarity/ matrix similarity).We made this search with the weight matrix approach using the Match™ tool ; the mat® . For theWe considered all the coordinates (with strand information) of all potential TF binding sites found by Match™ for each transcription factor. Further on, we examined all possible combinations of the coordinates, thus revealing all possible pairs in the sequence.Formalization of the approach, choosing the most promising results achieved with either of them. We considered all pairs of TFs within these segments. All the pairs of one type found within one distance range were merged. We considered a pair only if it appeared in the sequence at least once (within a certain distance), not taking in account the number of pairs in each sequence.We worked under two different kinds of distance assumptions as described in ES developed the methodological approaches as well as statistical analysis and conducted the data analysis. EW conceived the study and participated in its design and coordination. Both authors drafted the manuscript. Both authors read and approved the manuscript.The question is, if we choose by chance a subset of sequences, will our algorithm be able to define a model, specific to such a random subset? In other words, will this algorithm allow to make a model of anything, without dependence on the preselection of the sets ((+)-training set and/or the "seed" set)? We tried to prove the validity of the algorithm theoretically.k sequences, 100% of which possess the required common feature: a pair, a combination of pairs or complementary pairs, just by chance. Note that this estimation is written not for the whole model construction process, but only for the first step of it, where we consider only the "seed" sequences.Our algorithm is based on the definition of biologically relevant "seed" sets, in which we search for the candidate pairs . Therefore, in order to answer the question, it is reasonable to estimate the probability to come across a "seed" set of f) of the TFs included in the model and the frequencies of all possible pairs (F), constructed of these sites. If the frequencies of single sites and the pairs of them satisfy the equationLet us consider the frequencies of predicted single sites (Fij = fi fj,     (1)Fij as the probabilities of independent events, which is a prerequisite for the following formalism.we can interpret Methods). We did not take into consideration distances and orientations; the probability estimated for the general case will decrease further with the addition of new constraints.We measured the frequencies of predicted single sites and the frequencies of all possible pairs in the (-)-training set -training set, mi is the number of sequences possessing the i-th site, and Mij is the number of sequences possessing pairs of the i-th and j-th sites. Fij was then calculated as (1) and compared with the measured value.where For all cases investigated in this work, the difference between the calculated and measured values did not exceed standard deviation (σ), only in one case getting to 1,5 σ (data not shown). This confirms the correctness of using pair frequencies as probabilities in this case.Ppair to find a set of k sequences in N with any (at least one) pair, same in all k. We can enumerate all possible pairs of sites of the considered TFs, considering only the cases of the independent sites (i<j). Let U be the number of all possible pairs, then we can callLet us estimate the probability Fij = Fu,u ∈ {1,..., U}Ppair can be calculated as:It is easy to show, that the probability Ppairs 2to find k sequences with any common pairwise combination of pairs (pair of pairs). The pairs of pairs may consist either of 3 (when one site is shared) or of 4 different sites ; their probabilities therefore are:Let us estimate the probability andfi, fj, fl, fo are the frequencies of the single sites of the considered TFs, i<j<l<o.where We can enumerate all possible pairs of pairs (notating them as Q):V be the number of all possible pairs of pairs, V = t + s.Let k sequences each possessing a pair of pairs of one type, is:Analogously to (2), the probability to find v ∈ {1,..., V}.where k sequences with any complementary pair (complementary combinations). We consider pairs as complementary, if two of them are found in the seed set in not more than 60% of the sequences and not less than 40 %, the allowed overlap being 20%. The two complementary pairs together must cover the whole seed set. In the case studied here, comprising the 12 sequences of the seed set, we fixed that each of the pairs should be present in at least 5, but not more than 7 sequences, and they are allowed to co-occur in 0–2 sequences.Let us estimate the probability to find The probability that we choose 12 sequences, possessing any one pair of complementary pairs in accordance with these requirements can be calculated as:u, w ∈ {1,..., U}, and  are the binomial coefficients (note that this formula implies that Pcompl reaches the maximum when the frequencies of both pairs are 0.5).where Methods). The results are:All the probabilities were calculated for the (-)training set of 2040 5'-upstream sequences and for the set of 5 selected transcription factors (see Ppair = 0.44 ± 0.02Ppairs 2= 0.13 ± 0.01Pcompl = 0.013± 0.003We have estimated the simplest variant, considering each time only one feature (1 pair, 2 pairs, or complementary pairs). In this case it can be seen that the simultaneous occurrence of 1 or 2 pairs in 12 randomly chosen sequences has a rather high probability, and thus we can not base our model construction on the search of only these features.  describes the probability to find any combination of 3 or 4 sites, therefore, up to 6 pairs;obviously, the simultaneous search of more than 6 pairs will definitely overfit the model, so we do not consider this case). The probability to find 12 sequences sharing complementary pairs is much lower, so the consideration of a complementary combination makes the model much more specific, and the probability of finding a model with a complementary pair "by chance" is sufficiently low for us to claim that the proposed algorithm is valid. Note that this is a very rough estimation, considering only the upper borders; we would like to emphasize once more, that the probabilities were calculated without considering orientation and distance constraints, and that this is the estimation made for only the very first step of analysis: choosing of a seed set with needed properties. Obviously, this value depends on the number of the sequences in the "seed". Note that when we spread our requirements for simultaneous search on the whole (+)-training set (which is the next step of the model construction) the probability of constructing a model "by chance" will drop dramatically.The whole list of genes found with the promoter model when applying it to the collection of 13000 human 5'-upstream sequences. This list is not cleaned from hypothetical genes.Click here for fileThe list of genes (found with the promoter model when applying it to the collection of 13000 human 5'-upstream sequences) cleaned from hypothetical genes.Click here for file


Gene expression patterns from peripheral blood cells may be useful as biomarkers for monitoring MS progression and response to therapy, argue Kaminski and Achiron Despite the significant progress in increasing our understanding of the immune mechanisms of multiple sclerosis (MS), in improving clinical classification and brain imaging, and in developing new treatments, the factors that determine the course of the disease are mostly unknown . CurrentThe most commonly used disease-modifying therapies are interferon β (IFNβ)  and glatThe diagnosis and management of disease could be transformed thanks to the completion of the human genome project, the availability of sequence information for nearly every gene, and the advent of novel high throughput technologies  a biological state.Glatiramer acetate: A synthetic protein made of four amino acids found in myelin. It is used as an immunomodulator drug in treating MS.IFNβ: A cytokine that is secreted from fibroblasts in response to stimulation by a live or inactivated virus or by double-stranded RNA. It is used as an immunomodulator drug in treating MS.Microarray: A technology that allows the simultaneous profiling of the expression of thousands of genes (even whole genomes). Multiple gene detectors (oligonucleotides or cDNAs) are deposited on a slide that is hybridized with fluorescently labeled samples.PCR (polymerase chain reaction): The exponential amplification of a DNA fragment using repeated activation of a heat-stable DNA polymerase.Real-time PCR : A method in which the quantitation of the products of PCR is made by measuring fluorescent emission. It is used for accurate quantitation of mRNA.RT-PCR (reverse transcription–polymerase chain reaction): PCR that is performed on cDNA generated from RNA. It is used for mRNA detection and quantitation.Supervised classification: A process in which classifiers are learned from user-defined groups (classes).Unsupervised classification: A process in which classifiers are learned without user-defined groups (classes), i.e., without a predefined training set.In diseases that do not require tissue resection for diagnosis or therapy, it is rare to obtain tissues for analysis. This problem is even more pronounced in diseases like MS, in which the target organ is the very inaccessible brain and spinal cord. Despite these limitations, several groups used microarrays to analyze brain tissues obtained posthumously from patients who had MS and identified genes that characterized either acute or chronic lesions ,10,11. HIn MS, looking for markers of disease activity in the much more accessible peripheral blood does not require a significant leap of faith. MS is an autoimmune disease, and it is possible that some of the cells involved in the pathogenesis of the disease will be found in the bloodstream. Abnormal T cell populations have repeatedly been observed in the peripheral blood of patients with MS ,13,14. WFortunately, recent observations suggest that these doubts are unfounded. Bomprezzi et al.  determinWeinstock-Guttman et al.  analyzedPLoS Biology, Baranzini et al. [And now, in a new study published in last month's i et al.  provide Interestingly, individual and pairs of genes did not perform that well, and all three genes in a triplet were required for the highest accuracy (about 80%–90%). The minimal combinatorial number of genes that contains the most predictive information is not available since combinations of more than three genes were not performed. Although the results were not tested on an independent dataset, as is frequently requested , the autWhat could Baranzini and colleagues' findings mean? Clearly, the most obvious conclusion is that the lack of response did not result from the deactivation of IFNβ. The effect of IFNβ on MX1, IFNAr1, and STAT2 was observed for two years in all patients, suggesting that the response did not depend on IFNβ bioavailability. Considering that PBMCs represent an admixture of multiple cell types, the most plausible explanation is a simple lack of shift in subcellular populations.However, the importance of Baranzini and colleagues' study lies not in its mechanistic insights, but in its clinical relevance. The careful design of the experiment, the use of reproducible real-time PCR instead of microarrays, the meticulous analysis, and the previous observations ,17,19,20


Malignant small bowel tumors are very rare and leiomyosarcoma accounts for less than 15% of the cases. Management of these tumors is challenging in view of nonspecific symptoms, unusual presentation and high incidence of metastasis. In this case report, an unusual presentation of jejunal sarcoma and management of liver metastasis with radiofrequency ablation (RFA) is discussed.A 45-year-old male presented with anemia and features of small bowel obstruction. Operative findings revealed a mass lesion in jejunum with intussusception of proximal loop. Resection of bowel mass was performed. Histopathological findings were suggestive of leiomyosarcoma. After 3-years of follow-up, the patient developed recurrence in infracolic omentum and a liver metastasis. The omental mass was resected and liver lesion was managed with radiofrequency ablation.Jejunal leiomyosarcoma is a rare variety of malignant small bowel tumor and a clinical presentation with intussusception is unusual. We suggest that an aggressive management approach using a combination of surgery and a newer technique like RFA can be attempted in patients with limited metastatic spread to liver to prolong the long-term survival in a subset of patients. Malignant tumors of the small bowel are rare and accounts for the <2 % of total gastrointestinal (GI) malignancy ,2. The aA 45-year-old male was referred to our center with the diagnosis of suspected non-Hodgkin's lymphoma (NHL) of the bowel in December 1999. He had generalized weakness for 2-years along with recurrent vomiting, occasional constipation and melaena for last 2 months. The diagnosis was considered after an ultrasound (USG) guided fine needle aspiration cytology (FNAC) from the intra-abdominal mass done elsewhere and showed features suspicious of NHL.At presentation, patient's general condition was poor and he was dehydrated and pale. There was no peripheral lymphaedenopathy. Abdominal examination revealed an ill-defined, mobile, nontendor lump in left paraumblical region extending up to left lumber region. There was no hepato-splenomegaly. Examination of chest and cardiovascular system was normal. After initial resuscitation with crystalloids and blood transfusion, patient was further investigated.At presentation his hemoglobin was low (6.4 gm%) but rest of the routine haematological investigations were within normal limit. The chest X-ray was normal. Abdominal ultrasound (USG) showed a left upper abdominal mass lesion suggestive of bowel mass. The upper GI endoscopy was normal. The barium meal follow through examination was suggestive of intussusception of proximal jejunum and a suspected mass lesion at the leading edge of intussusceptum. An USG guided core needle biopsy of mass was performed because of earlier suspicion of NHL, which showed smooth muscle bundles with areas of necrosis. Based on the above findings and biopsy report, patient was taken up for exploratory laparotomy 3 weeks after initial presentation.Operative findings revealed an intussuscepted proximal jejunum loop 12 cm distal to duodeno-jejunal flexure and a vascular polypoidal growth measuring 6.5 × 5 × 3.5 cms on serosal surface of jejunum. Liver and spleen were normal. There was no evidence of mesenteric or retroperitoneal lymphaedenopathy, ascites or peritoneal disease. The resection of involved segment of jejunum with 5 cm margins and an end-to-end anastomosis was performed. The postoperative course was uneventful.Pathological examination of the specimen revealed two mass lesions measuring 4.5 cm & 3 cm in continuity  located on mucosal and serosal surface of jejunum respectively. Microscopically it showed spindle cell tumor with a mitotic rate of 5/10 high power fields (HPF). The tumor was negative for desmin, S-100, CD 34 and c-kit (CD117) but focally positive for actin on immunohistochemistry (IHC). The resection margins were free. The final diagnosis of malignant spindle cell tumor – leiomyosarcoma of jejunum was made. Due to negative margins and no evidence of disease elsewhere, no adjuvant therapy was planned and patient was kept on regular follow up. He was assessed clinically at three months interval and an USG of abdomen was performed six monthly. Patient remained disease free for 39 months. After that a follow-up USG showed a space-occupying lesion (SOL) in segment VII of liver and a mass lesion in left upper abdomen. The patient was asymptomatic and abdominal examination revealed no abnormality. A computerized tomographic (CT) scan of abdomen was performed which showed a 2 × 2 cms, brightly enhancing SOL in segment VII of liver  declare that they have no competing interests.SVSD, NKS: Surgical management and review of manuscript.AS, SH, SK: Review of literature and preparation of manuscript.ST, DKP: Radiofrequency ablation.All authors have read and approved the contents of manuscript.


The delivery of implantable cardioverter defibrillator (ICD) therapy is sophisticated and requires the programming of over 100 settings. Physicians tailor these settings with the intention of optimizing ICD therapeutic efficacy, but the usefulness of this approach has not been studied and is unknown. Empiric programming of settings such as anti-tachycardia pacing (ATP) has been demonstrated to be effective, but an empiric approach to programming all VT/VF detection and therapy settings has not been studied. A single standardized empiric programming regimen was developed based on key strategies with the intention of restricting shock delivery to circumstances when it is the only effective and appropriate therapy. The EMPIRIC trial is a worldwide, multi-center, prospective, one-to-one randomized comparison of empiric to physician tailored programming for VT/VF detection and therapy in a broad group of about 900 dual chamber ICD patients. The trial will provide a better understanding of how particular programming strategies impact the quantity of shocks delivered and facilitate optimization of complex ICD programming. Over the past decade ICD implantation has become increasingly straightforward, yet ICD programming and follow up has become more complex due to device feature and capability enhancements. While sophisticated algorithms provide high sensitivity and improved specificity of arrhythmia detection, allowing delivery of necessary effective therapy with minimization of inappropriate defibrillation shocks, detection and therapy of ventricular tachycardia (VT) / ventricular fibrillation (VF) still requires programming about 100 settings .Good programming choices are crucial as they relate to patient acceptance of ICD therapy. It has been found that patients who receive multiple shocks have greater difficulty adjusting to the ICD implant. These patients may become anxious or depressed, especially if a prior history of these ailments exists . ReducinTo date, there is no proven consensus on how to use information about the patient's complex diseases to program the ICD, and usually little is known about the patient's spontaneous VT rates, their risk of syncope, or therapies to effectively terminate spontaneous ventricular arrhythmias. Furthermore, ICD indications have dramatically changed within the last five years. Physicians may retain old programming habits even with enhanced devices or expanding patient indications, which may result in sub-optimal detection and therapy, such as unnecessary shocks for faster VT, supraventricular tachycardia (SVT), and non-sustained VT. Physicians often adjust many programmable settings that may benefit the patient. For example, physicians may prescribe patient-specific regimens for anti-tachycardia pacing (ATP) or shock energies based on lab testing. While one would expect this tailoring of programming to improve outcomes, it has never been studied.Empiric programming has been shown to be effective for subsets of ICD settings, including subsets of dual chamber detection and ATP therapies ,5-10. WhA proven optimal programming approach would be useful for simplifying therapy prescription, improving therapy outcomes, reducing inadvertent programming errors, and overall reducing shock-related morbidity. The EMPIRIC trial has been designed to evaluate a standardized empiric programming regimen by testing the hypothesis stated below. The EMPIRIC trial outcome will provide an understanding of how programming strategies impact defibrillation shock delivery in ICD therapy.This trial tests the hypothesis that the shock related morbidity of ICD therapy is similar whether patients are treated with a standardized empiric programming regimen for VT/VF detection and therapy or with a patient-specific physician tailored approach.Only sustained VT/VF that cannot be painlessly terminated should result in shock therapy and it is unusual for supraventricular arrhythmias (SVT) to require shock therapy. Shock morbidity is related to the number and frequency of shocks that patients receive and therefore morbidity is reduced if shocks are delivered only when necessary for effective arrhythmia termination. Thus, indices that address shock morbidity should reflect both the frequency and appropriateness of shocks for VT/VF and SVT.Shock morbidity is quantifiable by determination of the following:true VT/VF episodes that are shocked♦ proportion of true SVT episodes that are shocked♦ proportion of ♦ time to first shock (VT/VF or SVT)♦ time to first VT/VF shock♦ time to first SVT shockThese parameters are used to define the Empiric Trial's main objectives.and the proportion of shocked SVT episodes in a population whose ICDs are programmed using a standardized regimen for VT/VF detection and therapy, is either similar to or less than the same proportion in a similar population whose ICDs are programmed using a physician-tailored approach. This primary objective was chosen to independently evaluate the effects of programming on both appropriate and inappropriate ICD shocks (which are likely to have different implications for patient management). The advantage of this approach is that it focuses on frequency of shock delivery while also allowing an assessment of their appropriateness. However, this assessment could be confounded by a disproportionate number of SVT events in the two study groups. For example, an abundance of non-shocked SVT events in the physician-tailored arm, despite a greater incidence of inappropriate SVT shock therapies in that arm, nevertheless would result in the proportion of SVT episodes shocked being similar in the two arms. The analysis is also heavily dependent on the electrogram data stored in the ICDs. Given the electrogram storage capability of ICDs, differing rates of electrogram storage might occur between study arms or between VT/VF and SVT episodes that may skew the amount of data available for analysis. Therefore, the key secondary endpoint in this study is considered to be the time to delivery of first shock therapy in any given patient. This endpoint offers the advantage that it enables patient cross over to occur between the study arms without endpoint compromise and it is a clinically robust indicator of patient shock-related morbidity. Furthermore, its analysis is not influenced by the appropriateness or otherwise of a shock therapy and therefore cannot be confounded by differential occurrence of non-shocked SVT events in the study arms.The primary objective is to demonstrate that the proportion of shocked VT/VF episodes Other secondary endpoints will further evaluate the impact of the standardized programming regimen on patients by an assessment of detection performance, health care utilization, shock impact on device longevity, and "true VT/VF" episode durations.The EMPIRIC trial is a worldwide, multi-center, prospective, one-to-one randomized comparison of empiric to physician tailored programming. About 900 patients were enrolled worldwide at 52 centers from August 2002 to October 2003. Each patient will be followed for approximately one year.The inclusion criteria require patients to meet all of the following conditions:1. Indicated for an ICD according to internationally accepted criteria.2. Willing to sign informed consent or offer a legal representative who can provide consent.3. Achieved a 10 Joule safety margin at implant.Patients are excluded if they:1. Have permanent atrial fibrillation (AF).2. Had a previous ICD.3. Have a medical condition that precludes the testing required by the protocol or limited trial participation.4. Have a life expectancy less than one year.5. Are unable to complete follow-ups at the trial center.6. Are enrolled or participating in another clinical trial.Patients receiving a Marquis DR ICD are randomized to one of the two programming approaches after meeting a 10 J safety margin. In order to control for physician practice between the two treatment arms, randomization is stratified by treatment center. Further, since the incidence and prevalence of spontaneous VT/VF and SVT among primary prevention patients is not well known, randomization is also stratified by ICD indication (secondary vs. primary). A secondary indication includes patients with a history of spontaneous sustained VT/VF or syncope with suspected VT. A primary prevention indication includes all other patients.The physician tailored approach is based on the standard practice of each physician. All VT/VF programming may be tailored to the patient except that VT detection must be turned to 'On' or 'Monitor' to record episodes of slower VT.The empiric standardized regimen is based on various programming strategies to reduce shocks. In this arm, initial device settings are fixed . In order to protect protocol design integrity, reprogramming will be encouraged for non-justified programming deviations. In this manner the initial treatment strategies are tested using an intention-to-treat analysis with characterization of programming changes.The empiric arm standardized programming regimen is based on the following key strategies to reduce shocks.Multiple ATP attempts for VT≤ 200 bpm: Three sequences of ATP will be attempted for rhythms with ventricular rates ≤ 200 bpm. Empiric ATP has been shown to terminate ≥ 90% of VTs in the VT zone [•  VT zone -10. Furt VT zone . Three s VT zone . ATP wil VT zone ,12-14.ATP for VTs 201 – 250 bpm: One sequence of ATP will be delivered for fast VTs (FVT) using the FVT via VF zone, which maintains sensitivity to polymorphic VT (PVT) and VF and delivers ATP if the 8 beats prior to FVT detection are ≤ 250 bpm. Approximately 81% of ICD detected VF is monomorphic VT (MVT). MVT can be pace-terminated approximately 75% of the time with one sequence of ATP, without increased risk of syncope or acceleration [• leration ,7,15.Longer detection duration: The VF initial beats to detect will be set to 18 of 24. Shorter beats to detect are often programmed by physicians, but may increase the unnecessary shocks for non-sustained VT and for SVTs. At least 25% of ICD-detected VF is non-sustained VT/VF [• ed VT/VF -17.st VF and FVT Shock: High Output 1A 30 Joule energy will be used for the first VF and FVT shock. This will allow additional time for spontaneous conversions that frequently occur. A higher shock energy may also improve 1st shock success and therefore reduce the need for multiple shocks within an episode. The LESS study found no difference in 1st shock success with 31 J versus DFT++, however it analyzed all VT/VF faster than 200 bpm [st shocks is due to concerns about syncope. Several recent studies have shown very low syncope rates [•  200 bpm  ATP shoupe rates ,19 FurthEmpiric SVT Criteria: The PR logic criteria of AF/A. Flutter and Sinus Tach will be programmed 'On' in all patients. These criteria have been shown to have a relative VT/VF sensitivity of 100% and a positive predictive value to 88.4% [• to 88.4% .SVT Criteria applied to faster rates: The SVT limit and VF rate cut-off will be increased to 200 bpm in all patients to provide SVT discrimination at faster rates. Two of the top five reasons for inappropriate detections in the GEM DR Study (933 patients) were a ventricular rate during AF in VF zone and a SVT cycle length faster than programmed SVT limit [• Avoid detecting 1:1 SVTs with Long PRs as VT: 1:1 SVTs with long PR intervals accounted for 38% of inappropriate detections in the Gem DR (7271) Clinical Study [• al Study .Longer detection duration: VF initial beats to detect will be set to 18 of 24. Shorter beats to detect may result in more unnecessary shocks for SVTs or ventricular over-sensing.• ATP attempts: In addition to terminating ventricular arrhythmias without shocks, ATP should eliminate some inappropriate shocks when inappropriate detections occur by terminating SVTs or slowing conduction.• VT rate cut-off is one of the most important ICD settings because it can result in untreated symptomatic VT if set too fast, however it can result in unnecessary therapies for non-sustained VT, SVTs, or sensing issues, if set too slow. Reports have shown that some secondary prevention patients have significant symptoms for VTs outside treated zones [The ed zones . The VT both the proportion of shocked VT/VF episodes and the proportion of shocked SVT episodes are no more than 10 percentage points greater in the empiric arm than the physician tailored arm. The chosen margin 10 percent is considered clinically important.The primary endpoint is the proportion of true episodes that are shocked during the 12-month follow-up period. The standardized empiric programming regimen will be considered non-inferior to the physician tailored programming approach if It is assumed that 24% of patients will have at least one true VT/VF episode and 33% of patients will have at least one true SVT episode during the 12-month follow-up period. Based on unpublished data from other Medtronic trials, the within-patient correlation coefficient for multiple episodes is assumed to be 0.3. Assuming a similar distribution of episode counts per patient as observed in these previous trials and a shock rate of 30% and 14% for VT/VF and SVT episodes respectively, a total of 900 patients (450 in each arm) will give at least 80% power for the VT/VF hypothesis and 90% power for the SVT hypothesis, each tested at the significance level 0.05.The critical secondary endpoint, time to first shock therapy, will be analyzed using the Cox proportional hazards model for 1) any VT/VF or SVT, 2) true VT/VF only and 3) true SVT only. The empiric programming approach will be considered non-inferior if the upper confidence limit for the hazard ratio is less than 1.5.To better understand the changing ICD patient populations, we will investigate whether or not the proportion of appropriate and inappropriate shocks delivered is related to the following baseline characteristics: main indication for implant , left ventricular ejection fraction, CAD status, history of Atrial Tach/Atrial Fib/Atrial Flutter, NYHA classification, use of amiodarone, sotalol, or beta-blockers, and inducibility for VT/VF. In addition, to facilitate understanding of the optimal programmable settings for various patient sub-groups, we will consider the impact of programmable settings on outcomes. In particular, we will examine the "treated cut-off" (TC), which is the VT detection cut-off if VT detection is 'On' or the VF detection cut-off if VT detection is 'Off' or 'Monitor'. Outcomes in patients with a faster TC (physician tailored arm) will be compared to patients with slower TC (either physician tailored arm or empiric arm). Other programmable settings that will be investigated include the number of beats to detect VF and the number of ATP attempts based at various rates (e.g., <175 bpm, 175–200 bpm, >200 bpm). The types of arrhythmias, median ventricular cycle length, and therapies delivered will also be characterized relative to the patient's conditions and programming. Furthermore, the incidence of slower VTs in patients without a history of spontaneous, sustained monomorphic VT will be characterized.The EMPIRIC trial is a worldwide, multi-center, prospective, one-to-one randomized comparison of shock- related morbidity in a population of about 900 ICD patients whose ICD therapy is determined either by a standardized programming regimen or by physician tailored programming of VT/VF detection and therapy. Shock-related morbidity is assessed by a primary objective that compares between study arms the proportion of VT/VF episodes that are shocked and the proportion of SVT episodes that are shocked, and by a key secondary endpoint that compares to time to first shock therapy.ICD patient populations have rapidly changed within the last five years but little has been published on optimal programming for the emerging patient subsets (e.g., primary prevention). Therefore a standardized regimen of parameters is used in this trial for all patient populations. Today's patient population is quite diverse, so a slightly more sophisticated programming approach may be necessary (e.g. change VT cut-off based on main ICD indication) or perhaps complex physician tailoring is critical to reducing shocks.The EMPIRIC trial will characterize the shock morbidity of a single empiric programming approach compared to patient-specific, physician tailored programming. Empiric programming may be an acceptable strategy if it achieves equivalence with physician tailored programming. The EMPIRIC trial results will also provide a better understanding of how particular programming strategies impact the frequency of shocks delivered and will facilitate a way to optimize complex ICD programming.1. Have you received reimbursements, fees, funding, or salary from an organization that may in any way gain or lose financially from the publication of this paper in the past five years, or is such an organization financing the article-processing charge for this article?Dr. Morgan: Yes, Medtronic has paid me honoraria.Dr. Sterns: Yes, I am a paid investigator in several Medtronic clinical trials and key investigator in the present trial. I understand that Medtronic is paying for the processing fee for this article.Dr. Wilkoff: Yes, Medtronic, Guidant, St. Jude MedicalHanson, Ousdigian, and Otterness: Yes, Employees of Medtronic.2. Have you held any stocks or shares in an organization that may in any way gain or lose financially from the publication of this paper?Dr. Morgan and Dr. Sterns and Dr. Wilkoff: NoHanson, Ousdigian, and Otterness: Yes, own Medtronic stock.3. Do you have any other financial competing interests?Dr. Morgan and Dr. Sterns and Dr. Wilkoff: and Hanson and Ousdigian and Otterness: No.4. Are there any non-financial competing interests you would like to declare in relation to this paper?Dr. Morgan and Dr. Sterns and Dr. Wilkoff: and Hanson and Ousdigian and Otterness: No.All 6 authors contributed to the study design and writing of this manuscript.


This was particularly disturbing in that we are in the midst of a growing epidemic of obesity and diabetes with very alarming figures and projections from all over the world. Any intervention that has the potential for helping curb this dangerous epidemic which claims thousands of lives every day should be looked at with a great deal of objectivity.Before beginning the review of this book, we had no particular opinion about the role of low carbohydrate diets in diabetes. In order to write a fair and unbiased review, we have done a rather extensive search on the subject. One of the most disturbing findings of our search is the amount of hostility towards low carbohydrate diets that is on the web and in the scientific literature. We found several sites that present no scientific arguments but are, rather, full of Letter on Corpulence Addressed to the Public [The low carbohydrate approach, in fact, is not new and was used in England more than a century ago, made popular by William Harvey , an ENT e Public , the firThe Atkins Diabetes Revolution  plan is The Atkins Diabetes Revolution book is an attempt by the authors to present the low carbohydrate diet as a preventive and treatment strategy for patients with type 2 diabetes and those with the metabolic syndrome, who are at high risk for developing diabetes and cardiovascular disease. In doing so, the book, which is very well written, and which clearly presents illustrative cases, explains very complex metabolic concept in a very easy to read and understandable format. The first nine chapters explain the different concepts involved in glucose and lipid metabolism and the interplay of the various cardiovascular risk factors that culminate in cardiovascular disease the number one killer of Americans today. Definitions of metabolic syndrome, pre-diabetes, body mass index, waist to hip ratio, central obesity and their relationship to diabetes, heart attacks and strokes, are eloquently presented with a great deal of accuracy yet in a simple format. Most impressive were the case presentations, especially that of reactive hypoglycemia and carbohydrate craving. This response is associated with hyperinsulinemia in the pre-diabetic phase and sometimes puzzles clinicians unless they know to look for it.The second section of the book is devoted to an in-depth discussion of the various macro and micronutrients and their role in diabetes and obesity. Concepts such as the glycemic index and glycemic load are very well illustrated. The last section consists of meal plans and menus of low carbohydrate diet that the book is advocating.1c, something that has been clearly shown to reduce microvascular disease in both type 1 and type 2 diabetes [The concept of low carbohydrate diet and glycemic control certainly has a pathophysiological merit. First, dietary carbohydrates are the principal source for the initial rise of glucose in the diabetic populations, who generally have a defect in the first phase insulin secretion that is responsible for handling the glucose load . There idiabetes ,8. Secondiabetes ,10. So, diabetes ,10. Alsodiabetes . This fadiabetes . This ladiabetes . The facdiabetes -15.In two recent randomized controlled trials published in the New England Journal of Medicine ,17, the 2subfractions [Despite the evidence from these randomized controlled trials, published in the prestigious New England Journal of Medicine, there is a significant amount of reluctance in the scientific community to acknowledge the beneficial effects of low carbohydrate diets. These studies, in fact, provide a striking example of this resistance. A commentary in the same issue of the New England Journal of Medicine  states tractions ,22.On the other hand, the American Diabetes Association, despite recommending the traditional low fat diet, has recently reduced the recommended carbohydrate contents in the diet, perhaps reflecting a trend towards a reduced carbohydrate diet to follow .Atkins Diabetes Revolution has a list price of $25.95 and is available at Amazon.com and presumably other sites for half that price. Possibly, a shorter and still more affordable version of the book would be helpful for diabetic patients, their families and for the general reader, to help identify their risk for the disease.Returning to the Atkins book, despite the fact that the book is very well referenced, certain statements such as "high carbohydrate diet leads to diabetes" are not well substantiated, unless of course such a diet leads to weight gain, which it may. Furthermore, the book does not devote a sufficient amount of space discussing the side effects associated with dieting in general and low carbohydrate diet in particular. This is of concern, since it leaves the reader with the impression that the low carbohydrate diet or dieting, in general, has no negative consequences. Nonetheless, the amount of information the book provides in a simple, yet accurate format will benefit patients with diabetes and their families as well as those who are at risk for developing diabetes and the metabolic syndrome. If, after reading this book, the reader is able to identify that he or she is at risk for diabetes and the metabolic syndrome and takes action that could potentially save his or her life the book will be a valuable contribution. Atkins Diabetes Revolution, however, is sufficiently convincing to make us believe that some form of low carbohydrate intervention is worth investigating and should be considered by practitioners. The highly negative un-scientific response of critics, if anything, encourages us in this direction.As clinicians, we would not be comfortable recommending any diet without first hand experience. The


Health disparities are a growing concern. Recently, we conducted a practice-based trial to help primary care physicians improve adherence with 21 quality indicators relevant to the primary and secondary prevention of cardiovascular disease and stroke. Although the primary concern in that study was whether patients in intervention practices outperformed those in control practices, we were also interested in determining whether minority patients were more, less, or just as likely to benefit from the intervention as non-minorities.Baseline (fourth quarter 2000) and follow-up (fourth quarter 2002) data were obtained from 3 intervention practices believed to have at least 10% minority representation. Two practices had a black (non-Hispanic) population sufficient for analysis, while the other had a sufficient Hispanic population. Within each practice, changes in the 21 indicators were compared between the minority patient population and the entire patient population. The proportion of measures in which minority patients exhibited greater improvement was calculated for each practice and for all 3 practices combined, and comparisons were made using non-parametric methods.For all black patients, the observed improvement in 50% of 22 eligible study indicators was better than that observed among all white patients in the same practices. The average changes in the study indicators observed among the black and white patients were not significantly different (p = 0.300) from one another. Likewise for all minority patients in all 3 practices combined, the observed improvement in 14 of 29 (43.3%) eligible study indicators was better than that observed among all white patients. The average changes in the study indicators among all minority patients were not significantly different from the changes observed among the white patients (p = 0.272).Among 3 intervention practices involved in a quality improvement project, there did not appear to be any significant disparity between minority and non-minority patients in the improvement in study indicators. In 2002, the Institute of Medicine (IOM) issued a report suggesting that minorities are more likely than non-minorities to receive a lower quality of healthcare . BecauseDisparities are particularly evident in the area of chronic diseases. Although blacks are more likely than whites to have blood pressure monitoring, cholesterol screening, and smoking counseling, coronary heart disease is more prevalent among blacks than among whites . Additioth report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure outlines specific guidelines for preventing and managing hypertension, hyperlipidemia, and coronary heart disease [In hopes of improving health outcomes and prevention practices for all patients, much focus has recently been given towards the improvement in quality of healthcare. For example, the 7 disease . The 2nd disease . Other qRecently, there have also been a number of practice-based interventions aimed at improving the quality of healthcare for patients. For example, researchers have shown that a practice-based intervention  can enhance the quality of care for families of young children . AdditioWhat these earlier interventions have lacked, however, are analyses examining whether the interventions have improved the quality of care for all patients, regardless of ethnicity. Because these types of interventions are heavily dependent on physician and/or clinical staff interaction with patients, because ethnic minorities may have less trust in their healthcare providers , and becThe aim of this study was to examine whether or not a multi-method quality improvement (QI) intervention was equally successful among patients of different ethnicities. Some of the findings from this QI intervention have been previously published ,12, and The multi-method QI intervention added practice site visits (for academic detailing and QI facilitation) and network meetings (for sharing of best practices) to the approach of guideline dissemination and audit and feedback, employed in a less intensive intervention. Ten sites received the intensive multi-method QI intervention, and ten sites received the less intensive intervention. The study was conducted in a practice-based research network (PPRNet) among users of a common electronic medical record (Practice Partner Patient Records, Seattle WA), which historically provided audit and feedback to its practice members.As a supplement to the original study, we were also interested in whether minority patients were more, less, or just as likely to benefit from the intervention as non-minorities. The study presented here focused on outcome and process measures for minorities within 3 primary care practices, all of which received the intensive intervention. These 3 practices (labeled A, B, and C) were selected because they each had a significant (i.e. > 10%) proportion of minority patients and had recorded patient ethnicity in their electronic medical record. Practice A is an urban internal medicine practice in the Midwestern U.S. with 5 healthcare providers. Practice B is a rural family medicine practice in the Northeastern U.S. with 8 healthcare providers. Practice C is an urban family medicine practice in the Southeastern U.S.A total of 21 study indicators  data sets on standard microcomputers for analyses.To determine practice performance on the study indicators, participating practices ran a computer program to extract patient activity during the previous quarter from their electronic medical record. To protect patient confidentiality, the extract program assigned an anonymous numerical identifier unique to each patient. The extract program obtained demographic information such as age, ethnicity, and gender, and diagnoses, medications, laboratory data, and vital signs. Text of consultation reports, progress notes, and discharge summaries were not extracted. The data were copied to diskettes and mailed to PPRNet or sent electronically via a secure server. In the PPRNet offices, data were bridged to standard data dictionaries and converted to SASIn each patient's electronic medical record, ethnicity was recorded as white, black/African American, American Indian/Alaskan native, Asian, native Hawaiian/other Pacific islander, and "some other ethnicity", while ethnicity was recorded as Hispanic/Latino and non-Hispanic/Latino, all in concordance with the 2000 U.S. Census ethnicity categories. Currently, these physician practices allow the patient to designate their ethnicity categorization. However, because this process for collecting ethnicity data began in the middle of our study, some ethnicity categorizations were made by the office staff within each of the practices. Ethnicity data was only available on approximately 42% of patients, due to the fact that the electronic medical record software program did not require physicians to enter patients' ethnicity data until its most recent version was released, which occurred during the study time frame. Improvements in process and outcome measures were compared between minority and non-minority patients. Minority was defined as any ethnic designation other than white non-Hispanic.Changes in the process and outcome measures were of primary interest in this study. Within each practice, these changes were compared between the minority patient population and the white patient population. Measures were deemed eligible for comparison if at least 10 minority patients were included in the rate calculations. For example, if practice A only had 8 minority patients with a diagnosis of having had myocardial infarction (MI), then the measure of the percentage of MI patients who had been prescribed a beta blocker could not be compared between the minority and white patient population. The proportion of eligible measures in which minority patients exhibited greater improvement was calculated for each practice and for all 3 practices combined. A Wilcoxon signed rank test  was used to test the hypotheses that minority patients exhibited changes similar to those of the non-minority patients. This study had approximately 80% power (2-sided hypothesis testing, α = 0.05) to detect a 6.6 percentage point difference between average improvement in the study indicators among all minority and non-minority patients.Baseline characteristics of the patients from the 3 practices are listed in table In these 3 physician practices, all of which were in the intervention arm of a randomized trial aimed at improving primary and secondary prevention of cardiovascular disease and stroke, we found that results for minorities were relatively similar to the results experienced by the overall practice populations. Change from baseline was greater among minority patients than among white patients for 48.3% of the 29 eligible study indicators, and the average changes in the study indicators among all minority patients were not significantly different from the changes observed among the white patients.There are some limitations of this study which should be noted. As noted earlier, the ethnicity status was only available on 42% of patients within the practices of interest; thus the results may not truly represent what occurred in these practices overall during the study. Given the relatively small number of eligible indicators for comparisons across ethnicities, this statistical power to detect subtle differences was not optimal. Nevertheless, the overall findings suggest that any true differences in this intervention's effectiveness across ethnicities are small.These findings are encouraging, and they suggest that the quality improvement strategies that have been developed to date for physician practices that use electronic medical records have a similar impact on minorities and non-minorities. Future studies should continue to address whether the effectiveness of interventions such as ours is cross-cultural, and whether interventions tailored to be more culturally appropriate can improve the overall effectiveness of interventions.IOM: Institute of MedicineHIV: Human Immunodeficiency VirusAIDS: Acquired Immunodeficiency SyndromeQI: Quality ImprovementLDL: Low-Density LipoproteinMI: Myocardial InfarctionThe author(s) declare that they have no competing interests.PJN helped design the study, perform the analyses, and write the manuscript. SMO helped design the study, perform the site visits, and edit the manuscript. RGJ helped design the study, perform the analyses, and write the manuscript. LFR helped design the study, assisted with data acquisition, and edited the manuscript. LMD helped design the study, perform the site visits, and edit the manuscript. CF helped perform site visits and edit the manuscript. All authors read and approved the final manuscript.Study indicators as measured at baseline (B) and the end (E) of the study for all patients and minority patients within each of the 3 practices.Click here for file


The newly published strategic plan for developing an HIV vaccine is crucially important, say the PLoS Medicine editors, but it must be followed by clear milestones and a process for monitoring progress The new global plan is exciting, but now needs clear milestones In 1997, United States President Bill Clinton announced the challenge to develop an AIDS vaccine by 2007. Since 1997, the AIDS Vaccine Advocacy Coalition (AVAC) has published annual reports on the global status of the effort to meet Clinton's deadline. Last year's report, entitled “AIDS Vaccine Trials—Getting the Global House in Order,” officially ends the countdown. Saying that “we are on a long term mission,” AVAC concludes that there will not be a safe and efficient vaccine in 2007, and that we need to “focus on the long haul and set an agenda for sustained and sustainable action that stretches well beyond 2007.” It is not that there are no vaccine candidates in clinical trials, but there is little hope that any of the current candidates will turn out to be a cheap and safe vaccine that affords long-term protection.PLoS Medicine .Among notable developments over the past 12 months, the AVAC report highlights the Global HIV/AIDS Vaccine Enterprise as an effort to improve coordination within the AIDS vaccine field. The Enterprise was announced in June 2003 and now shares its scientific strategic plan with everyone affected by the AIDS pandemic—that is, all of us—by publishing it in In its plan the Enterprise presents itself as a global endeavor and emphasizes the need for integration and capacity building around the world. It is not “a discrete organization with a pool of money” but a “coordinating group of individual funding agencies that will support specific areas of research using their own mechanisms, according to their own practices and policies, and following the Enterprise's principles.” These principles include collaboration, standardization, and coordination among international researchers and agencies. The plan focuses on specific scientific roadblocks that need to be overcome, but also looks ahead and mentions the need to build capacity for product manufacturing and clinical trials, and to address regulatory issues.10.1371/journal.pmed.0020036), mentions the danger of “group think,” and the Enterprise must not fall into that trap.These are noble goals, and the fact that they are stipulated jointly by many of the leaders in the field will generate excitement and expectations, even though much of what is said has been said before. The plan stresses collaboration and coordination; there are clear benefits from a concerted effort. But might a level of competition, rather than collaboration, be healthy, and, if so, what level of competition would work best? The Enterprise members seem to have wrestled with that question. The plan mentions an “appropriate balance between productive competition and effective collaboration,” and suggests that certain incentives could be provided by “the funders with greatest flexibility.” As long as it remains unclear where scientific breakthroughs will come from, diversity and flexibility should be encouraged and not stifled. David Ho, in his Perspective on the plan  Moreover, provided it can be done, it is impossible to predict when the necessary scientific advances will happen. That said, without a list of specific projects, project leaders, and a time frame for achieving or at least evaluating specific goals, it will be impossible to define success and failure, review progress, and assure internal and external accountability.There is another reason why a best-guess timeline is essential: realistic expectations about an AIDS vaccine would stress the urgency of combating the AIDS pandemic over the next decade—and maybe longer—in the absence of an effective vaccine. The potential benefits of a vaccine cannot be overestimated, and its development has to be one top priority for the global scientific community. But its success cannot be taken for granted and will come too late for millions. Therefore, parallel efforts to prevent or reduce transmission and to treat infected individuals need to be accelerated now.The Enterprise's plan should be hailed as a crucially important outline for vaccine development, but the goodwill surrounding it won't last unless it is quickly followed up with a set of milestones, and a transparent process by which progress will be measured and course corrections implemented.


Id2 mutant animals are deficient in lobulo-alveolar development. This failure of development is believed to be due to a proliferation defect.During pregnancy, the mammary glands from Id2 expression is necessary for Wnt induced mammary hyperplasia, side branching, and cancer, by generating mice expressing a Wnt1 transgene in an Id2 mutant background.We have asked whether functional Id2. We also show that Wnt1 expression is able to cause mammary tumors in an Id2 mutant background.We show in this work that forced expression of Wnt1 in the mammary gland is capable of overcoming the block to proliferation caused by the absence of Id2 expression is not required for Wnt1 to induce mammary hyperplasia and mammary tumors.We conclude that functional  Basic helix-loop-helix (bHLH) transcription factors such as MyoD, E12, and E47 are key regulators of gene expression and control many differentiation events during development . These tId2, is expressed in glandular and ductal epithelium of the mouse mammary gland and has also been implicated in its development. Mammary glands of female mice that are homozygous mutant for Id2 have impaired lobulo-alveolar development [Id2 is regulated by Wnt-β catenin signaling [Id2 expression, thereby leading to cancer. We have asked therefore whether functional Id2 expression is necessary for Wnt induced mammary hyperplasia, side branching and cancer, by generating mice expressing a Wnt1 transgene in an Id2 mutant background.There are 4 mammalian Id genes, which show differences in their patterns of expression and function ,3. One oignaling ,6. It haId2 males and females on a 129/Sv background. Id2 genotyping was done by PCR  using primers Id2-S (5'-tctgagcttatgtcgaatgatagc-3'), Id-2AS (5'-cgtgttctcctggtgaaatggctg-3'), and neo 1 (5'-tcgtgctttacggtatcgccgctc-3").We used heterozygous Hemizygous transgenic MMTV-Wnt1 males on a mixed FVB/BL6/SJL background were obtained from Yi Li in the H.Varmus laboratory. Genotyping was done by PCR  using Wnt1 (5'-gaacttgcttctcttctcatagcc-3') and SV40 (5'-ccacacaggcatagagtgtctgc-3') primers that produce a 350 bp product in transgenic mice.4, dehydrated, cleared in xylene, and mounted in Permount.Five mammary glands per mouse were removed and fat and muscle were dissected away. The glands were flattened between two slides and flooded with Carnoy's fixative  and fixed overnight. They were then de-fatted in 3 changes of acetone, rehydrated, stained overnight in 0.2% carmine and 0.5%KSOId2 loss of function mutant mice [Id2 -/- or Wnt1 transgenic mothers, as these animals cannot feed their young [Id2 +/- females were crossed with MMTV-Wnt1 hemizygous transgenic males, producing 7 MMTV-Wnt1 Tg; Id2+/- males   and we cant mice ,8. Crossir young ,7. Id2 +Id2 -/- mice have an immunologic defect. Even with this care, 50% die before maturity [Id2 -/- mice were born in sub-Mendelian ratios, they were smaller than litter-mates, and several died of unknown causes.The subject animals were kept in mixed groups in autoclaved cages because maturity . Id2 -/-Id2 +/- and Id2 -/- females glands were similar to those of WT females [Id2 +/- and MMTV-Wnt1 Tg ; Id2 -/- mice had branching patterns resembling those of MMTV-Wnt1 Tg females at 3 months , the mechanism through which hyperplasia and side branching is promoted by Wnt1 expression in the virgin mammary gland is unknown. Our results demonstrate that Wnt1 is not operating solely through Id2 or that it is not operating through Id2 at all.The lack of pithelia . We showId2-/- mice [Id2-/- phenotype, suggesting that Wnt1 signaling is independent of both Cyclin D1 and Id2 in the mammary gland.Another known Wnt target with a similar loss of function phenotype is Cyclin D1 , a prote-/- mice , a resulId2, we conclude that functional Id2 expression is not required for Wnt1 to induce mammary hyperplasia and mammary tumors.By showing that forced expression of Wnt1 in the mammary gland is capable of overcoming the block to proliferation caused by the absence of The author(s) declare that they have no competing interests.SM and CR carried out the experiments. YY participated in the design of the study. SM and RN conceived of the study, participated in its design and coordination and wrote the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here: