Characterization of anti-malarial drug concentration profiles is necessary to optimize dosing, and thereby optimize cure rates and reduce both toxicity and the emergence of resistance. Population pharmacokinetic studies determine the drug concentration time profiles in the target patient populations, including children who have limited sampling options. Currently, population pharmacokinetic studies of anti-malarial drugs are designed based on logistical, financial and ethical constraints, and prior knowledge of the drug concentration time profile. Although these factors are important, the proposed design may be unable to determine the desired pharmacokinetic profile because there was no formal consideration of the complex statistical models used to analyse the drug concentration data.Optimal design methods incorporate prior knowledge of the pharmacokinetic profile of the drug, the statistical methods used to analyse data from population pharmacokinetic studies, and also the practical constraints of sampling the patient population. The methods determine the statistical efficiency of the design by evaluating the information of the candidate study design prior to the pharmacokinetic study being conducted.In a hypothetical population pharmacokinetic study of intravenous artesunate, where the number of patients and blood samples to be assayed was constrained to be 50 and 200 respectively, an evaluation of varying elementary designs using optimal design methods found that the designs with more patients and less samples per patient improved the precision of the pharmacokinetic parameters and inter-patient variability, and the overall statistical efficiency by at least 50%.Optimal design methods ensure that the proposed study designs for population pharmacokinetic studies are robust and efficient. It is unethical to continue conducting population pharmacokinetic studies when the sampling schedule may be insufficient to estimate precisely the pharmacokinetic profile. Despite significant progress in malaria control over the last few years, malaria-related morbidity and mortality are still considerable. The conOver the last two decades developments in statistical methodology have allowed drug concentration-time profiles to be derived from patients providing only "sparse" samples at random times,5. Data Although there are now statistical methods to analyse sparse data from population PK studies, the design of these population PK studies is not straightforward. Many questions arise about how best to conduct these studies including: how many patients should be included in the study, what sampling scheme should be used, how many different sampling schemes are required, and how many patients need to be allocated to each of these sampling schemes. Answers to these questions require prior knowledge of the PK profile of the drug, the statistical methods used to analyse data from population PK studies, and also the practical constraints of sampling the patient population. Examples of such constraints include the timing of clinic visit, the maximum number of blood samples per patient that can be collected, logistical constraints of clinic workload, and whether a venous or capillary sample is being collected. Currently population PK studies are designed based on logistical, financial and ethical constraints, and the investigator's knowledge of the drug concentration-time profile. Although these factors are important, they may result in a study of 'insufficient design', which is unable to determine the desired PK parameters and, therefore, unable to answer the fundamental questions that the population PK model was intended to answer. In such a case the study is wasted. Thus, there is both a scientific and an ethical imperative to ensure PK studies are designed in such a way that they are most likely to provide useable results.et al [A prime example of the applicability, but also the difficulty of a population approach is seen with PK studies of the artemisinin derivatives. These compounds are rapidly absorbed and have very short elimination half-lives, typically less than one-hour , thus liet al , the autThese various problems could be overcome by designing population PK studies using both information regarding the practical constraints of sampling and optimal design methods. OptimalThis paper will provide an introduction to optimal design methods, including the prior information required for optimal design methods, followed by a theoretical application of the method for designing a population PK study of intravenous artesunate.Optimal design methods  for designing population PK studies have only been available in the last decade -17. The The methods require prior specification of:• structural PK model (e.g. one-compartment PK model with first-order absorption and elimination),a), clearance (CL), apparent volume of distribution (Vd)),• PK parameter values ,• study design space determined from sampling constraints .Figure et al [It is important to note that optimal design methods, when applied to PK studies, are dependent on the prior choice of a particular set of parameters and model. The influence of this dependence can be diluted by considering and accounting for uncertainty in both the parameter values as well as the plausible models. For example, Roos et al  used optIn addition to consideration of uncertainty in the prior information on the models, it is also important to consider that the optimum design may not necessarily be achievable in the clinic. Hence, it is important to consider practical constraints such that an optimum design is achieved conditioned on logistical components. An example using constraints on optimal design methods is given in . In thisOptimal design methods have not been used for designing population PK studies of anti-malarial treatments.To derive the optimum design, several candidate designs within the study constraints are considered, where the amount of "information" (i.e. the precision of the parameters to be estimated) in each design is estimated. Thus the design that contains the most "information" (i.e. the design with the most precision) is considered optimal. This is done mathematically by computing the Fisher information matrix based on parameters of the structural PK model, the inter-patient variability, and the residual variability. A brief explanation of the Fisher information matrix is given in Additional file There are currently five open source software packages that derive optimal designs for population PK studies using the above D-optimal criteria, PFIM, PopDes, PopED2, and POPBesides searching for the D-optimal design, POPT can evaluate the performance of any particular design. This informs researchers of the expected precision of the PK parameters and any limitations of their proposed population PK design.The following hypothetical example is a population PK study to determine the PK of dihydroartemisinin  following a single intravenous infusion (given as a rapid push) of 120 mg of artesunate (circa 2.4 mg/kg). It is assumed that artesunate is rapidly and completely converted to dihydroartemisinin. The study population are adults requiring intravenous treatment for malaria.Prior information regarding the structural PK model and PK parameters is required. For this example a one-compartment model with first-order elimination was selected to describe the PK profile of dihyroartemisinin:Vd and CL were 38 L and 37 L.hr-1[The mean values chosen for 37 L.hr-1, and theVd and CL, and covariance of Vd and CL equal to zero. Residual variability was assumed to be additive with a standard deviation of 200 ng.mL-1.Inter-patient variability was assumed to be log-normally distributed with the inter-patient standard deviation equal to 0.5 for both Sampling constraints were based on preliminary findings of a survey of 16 experts, who currently conduct anti-malarial PK studies in Asia and Africa. The constraints identified for venous sampling of an adult were: minimum time of 15 minutes between consecutive blood samples of a patient; and no more than 10 samples in the first 8 hours post drug administration.The total number of patients to be recruited for our hypothetical population PK study is constrained to be 50 and the total number of blood samples that could be transported to a laboratory and assayed was fixed at 200.Table These findings demonstrate the advantage of population PK studies compared with traditional PK studies of a small group of patients. The elementary designs presented have fixed sampling times for each of the patients. For a study in the field, which requires flexibility with blood sampling (for logistic reasons), POPT can generate optimal sampling windows around the fixed time points. Based on the fixed sampling times of 0.25, 0.75, 2 and 4 hrs for 50 patients presented in Table The above elementary designs were evaluated and compared based on the expected precision  of the parameters, inter-patient and residual variability. Once a final design has been selected the design should also be evaluated by a population PK analysis of simulated data. At leasPopulation PK studies determine the drug concentration-time profile in the target patient population and the patient factors that cause changes in the drug concentration-time profile. These studies might be easier than traditional detailed PK studies to conduct, but they are complex to design and analyse. The design of a population PK study is very different to a sample size calculation for the number of participants required for a randomized controlled trial which can be performed using a hand calculator. Optimal design is necessary to ensure we design robust and efficient population PK studies that are able to estimate precise PK parameters. This is the necessary prerequisite for optimizing dose regimens. It is especially important for population PK studies of anti-malarials in pregnant women and young children in whom blood sampling cannot be extensive. Optimal design is a collaborative process involving clinicians, pharmacologists and statisticians. This research team plans to provide optimal designs for population PK studies of the artemisinin derivatives and their partner drugs. Prior specification of the structural PK model and parameters will be determined from an extensive literature review. Study design constraints for all malaria patients will be identified from the survey of clinicians and field workers actively involved in anti-malarial pharmacokinetic studies.The authors declare that they have no competing interests.JAS, SBD, RNP and NJW conceived the project. JAS wrote the first draft of the manuscript. KMJ, RNP, NJW, NL, JT and SBD revised the manuscript critically for important intellectual content. All authors read and approved the final manuscript.Explanation of the Fisher information matrix.Click here for file


In metagenomic sequence data, majority of sequences/reads originate from new or partially characterized genomes, the corresponding sequences of which are absent in existing reference databases. Since taxonomic assignment of reads is based on their similarity to sequences from known organisms, the presence of reads originating from new organisms poses a major challenge to taxonomic binning methods. The recently published SOrt-ITEMS algorithm uses an elaborate work-flow to assign reads originating from hitherto unknown genomes with significant accuracy and specificity. Nevertheless, a significant proportion of reads still get misclassified. Besides, the use of an alignment-based orthology step (for improving the specificity of assignments) increases the total binning time of SOrt-ITEMS.Distance Score Ratio for Improved Binning And Taxonomic Estimation). DiScRIBinATE replaces the orthology approach of SOrt-ITEMS with a quicker 'alignment-free' approach. We demonstrate that incorporating this approach reduces binning time by half without any loss in the specificity and accuracy of assignments. Besides, a novel reclassification strategy incorporated in DiScRIBinATE results in reducing the overall misclassification rate to around 3 - 7%. This misclassification rate is 1.5 - 3 times lower as compared to that by SOrt-ITEMS, and 3 - 30 times lower as compared to that by MEGAN.In this paper, we introduce a rapid binning approach called DiScRIBinATE , indicates the immense applicability of the proposed algorithm in rapidly mapping the taxonomic diversity of large metagenomic samples with high accuracy and specificity.The program is available on request from the authors. The enormous microbial diversity prevalent in natural ecosystems represents a rich resource for discovery of hitherto unknown microbes and the novel genes/proteins they encompass. Estimates reveal that 99% of these microbes cannot be easily cultured in the laboratory . The rapAn important aspect in metagenomic analysis is the identification of the source organism of each fragment (read/contig). This process, called binning, helps in identifying and enumerating the various taxa present in a given metagenomic sample. Binning approaches can be broadly classified into two categories, namely, composition-based and similarity-based. Composition-based approaches cluster and classify sequences based on their compositional characteristics, such as GC percent, codon usage, oligo-nucleotide frequency distribution, etc. ,3. On thLowest Common Ancestor (LCA) of the taxa which correspond to these organisms. The specificity of assignment using the LCA approach is thus dependent on the spatial distribution of hits in the phylogenetic tree.In similarity-based approaches, sequences in the database having significant similarity to the input reads are referred to as hits. While a read with hit(s) originating from a single organism is assigned to the organism corresponding to the hit(s), a read with hits originating from multiple organisms is assigned to the vice-versa. Once this level is identified, SOrt-ITEMS employs the 'orthology step' in the second phase to finally assign the read to the LCA of the taxa which correspond to the subset of hits that share a true orthologous relationship with the read sequence. While it is seen that the first phase of SOrt-ITEMS helps in significantly reducing the number of false positive assignments, the second phase ensures the specificity of assignments i.e. assignment of reads at relatively lower taxonomic levels .To improve the specificity of assignment of reads, similarity-based methods like MEGAN use bit-score of an alignment to identify a subset of hits which are significant, and subsequently assign the read to the LCA of this subset . HoweverIn spite of having significantly higher binning specificity and accuracy, the SOrt-ITEMS approach has the following two limitations. Firstly, since SOrt-ITEMS involves performing a reciprocal BLAST search, an additional time is spent (for every read) in this 'orthology step'. Summing this additional time for all reads in a typical metagenomic data set (having approximately 1-10 million sequences) results in a significant increase in the total computation time. Secondly, about 7-10% of input sequences still get misclassified by SOrt-ITEMS. This misclassification rate is significant since this will result in several thousands of sequences being classified wrongly. For example, if one analyzes the Sargasso sea metagenome data set consisting of greater than 7 million sequences , approxii.e the minimum number of sequences to be assigned to a taxon for a bin to be created for that respective taxon). In this process, assignments to isolated taxa (i.e. taxa to which the number of sequences assigned is less than the minimum bin-size threshold) are discarded. Increasing the value of this threshold parameter, will thus reduce the number of false positive assignments. However this strategy will result in an increased number of unassigned sequences.One way of reducing the number of misclassified sequences is by increasing the threshold of the 'minimum bin-size' parameter (Distance Score Ratio for Improved Binning And Taxonomic Estimation) which attempts to address the limitations associated with SOrt-ITEMS. To maintain the accuracy of assignments, DiScRIBinATE retains the steps followed in the first phase of SOrt-ITEMS i.e. for finding an appropriate taxonomic level where the assignment of the read is to be restricted.In this paper, we propose a new approach termed as DiScRIBinATE  as the input [e.g Burkholderia ambifaria AMMD) is substituted by the name of the corresponding family . Similarly, taxon names of the remaining hits are also substituted with their corresponding taxa names that occur at the family level. These substituted names are used in all subsequent steps. Therefore, the taxonomic level of the final assignment of the read can only occur either at the TL or at levels above TL, i.e. to a taxon in the phylogenetic path from the TL to the root. For tracing the path between the root to the taxon name corresponding to each hit, the NCBI taxonomy tree has been utilized as the reference tree.The DiScRIBinATE approach takes the BLASTx output obtained for all the reads against a sequence database  are referred to as 'child nodes'. All child nodes unidirectionally descend from the 'root node'. Intermediate nodes, which lie in the path from the root to the child, are referred to as 'ancestor nodes' for the corresponding child node. The set of unique candidate ancestors corresponding to each read is progressively obtained by finding the LCA of each of the substituted taxa names as explained below.Among all the hits obtained for a given read, first the LCA of the taxon names corresponding to the top two scoring hits is obtained. This LCA is identified as the first candidate ancestor. The hit with the next highest score is then added to the previous hits considered, and the combined LCA of the corresponding taxa names is obtained from these hits. If this LCA is not the same as the one obtained earlier, it is added to the existing set of candidate ancestors. In this way a set of candidate ancestors for a given read is obtained by traversing the entire list of hits till the last hit is reached.In this step, distances are calculated between the candidate ancestors and the set of taxa corresponding to the hits. The level of a taxon in the NCBI taxonomy tree denotes the distance traversed in terms of the number of edges from the root to that taxon. This level information is used for obtaining distances between any two taxa. If the taxon corresponding to a hit is a child node of a candidate ancestor, then the distance between the candidate ancestor and that taxon is equal to difference of levels between them. If the taxon corresponding to a hit is not a child node of a candidate ancestor, then the LCA of the candidate ancestor and that taxon is obtained, and the distance between them is subsequently calculated using the following formula:In this step, the 'bit-score/distance' ratio between a hit and a candidate ancestor is obtained by dividing the bit-score of the hit by the distance of its corresponding taxon from the candidate ancestor (obtained as described in the previous step). These ratios are obtained considering all the hits and the candidate ancestors. For each candidate ancestor, the 'bit-score/distance' ratios obtained with each of the hits are then summed. The candidate ancestor with the highest sum, referred to as the 'Most Probable Ancestor' (MPA), is selected, and the read is assigned to the taxon corresponding to this candidate ancestor.The objective of this step is to reduce the number of misclassified sequences. For this purpose, all assignments to isolated taxa are re-analyzed using the work-flow illustrated in Figure In this step, reads assigned to each isolated taxon are reassigned to the taxon corresponding to their immediate phylogenetic ancestor, if the cumulative number of reads assigned to the latter taxon is greater than the Isolated Taxon Size. If this cumulative number does not exceed the Isolated Taxon Size, the above mentioned step is iterated for progressively higher taxonomic levels (up to the level of super-kingdom). This iteration is performed till an ancestor taxon is found for which the cumulative number of reads assigned exceeds the Isolated Taxon Size. However, if the cumulative number of reads for any of the ancestor taxa does not exceed the 'Isolated Taxon Size', reads assigned to the isolated taxon are categorized as 'unassigned'.In the present study, an 'Isolated Taxon Size' of 300 or 1% of the total number of reads (whichever is less) was used. A value of 300 was used keeping in mind the presence of rare organisms in typical metagenomic samples. This value ensured that reads originating from rare organisms or organisms with small genome sizes are not unnecessarily reassigned at non-specific taxonomic levels. For example, the value of 300 ensures that an organism having a genome length as low as 0.6 Mb and also with extremely low coverage  is not picked up as an isolated taxon.Consequently, reads belonging to this organism are not re-assigned at non-specific taxonomic levels during the reclassification step.In order to evaluate the binning time achieved using the 'bit-score/distance ratio approach' (adopted by DiScRIBinATE) as compared to the 'orthology based approach' (used by SOrt-ITEMS) and standard LCA-based approach (used by MEGAN), the time taken by DiScRIBinATE, SOrt-ITEMS and MEGAN for binning 100000, 200000, 500000 and 1000000 sequences was determined. These tests were performed on a work station with Intel(R) Xeon(R) CPU, 1.86 GHz processor.ftp://ftp.ncbi.nih.gov/genomes/Bacteria/. Using MetaSim [Thirty-five completely sequenced bacterial genomes belonging to diverse taxonomic clades were downloaded from NCBI website  MetaSim , four daTo validate the performance of DiScRIBinATE, with respect to the assignment of reads originating from 'known' as well as 'unknown' organisms, input reads were queried against the following three variants of the nr database.nr database where sequences belonging to the query species are absent. This scenario mimics a scenario wherein the read sequences belong to an unknown species.nr database where sequences belonging to the query genus are absent. This scenario simulates a situation wherein read sequences originate from an unknown genus.nr database where sequences belonging to the query family are absent. This scenario simulates a situation wherein read sequences originate from an unknown family.To quantify the accuracy and specificity of assignment of reads, the results obtained for the four validation data sets against the three database variants (mentioned above) were categorized as follows:Burkholderia ambifaria AMMD, then its assignment to any of the taxa mentioned below was categorized as a correct assignment.Assignment of a read to a taxon that lies in the path between the root and the taxon corresponding to the source organism of the read was categorized as 'correct assignment'. For example, if the read originated from Burkholderia; Burkholderia cepacia complex; Burkholderia ambifaria; Burkholderia ambifaria AMMD.root; cellular organisms; Bacteria; Proteobacteria; Betaproteobacteria; Burkholderiales; Burkholderiaceae; To quantify the specificity, the 'Correct assignments' were sub-grouped into the following categories:1. Higher (or Non-Specific) level: Correct assignments of the reads at the level of root or cellular organisms or super-kingdom.2. Intermediate levels: Correct assignments of the reads at the level of phylum or class or order.3. Specific levels: Assignments of the reads at the level of family or below.Assignment of a read to a taxon that does not lie in the path between the root and the taxon corresponding to the source organism of the read was categorized as 'Wrong assignment'.Those reads for which none of the hits had a bit-score greater than or equal to 35 and/or an alignment length of greater than 25 were classified as 'Unassigned'.All reads with no BLAST hits were categorized as 'No hits'.Results obtained using DiScRIBinATE were categorized into the above classes and were compared with corresponding results generated by the SOrt-ITEMS and the MEGAN program. Both SOrt-ITEMS and MEGAN were run using a "minimum bin-size" threshold of two. Results obtained for DiScRIBinATE after applying the reclassification step were compared with those obtained with SOrt-ITEMS and MEGAN.As shown in Figure Figure As seen from Figure The percentage of reads correctly assigned by DiScRIBinATE is seen to be 2-18% higher as compared to SOrt-ITEMS, and 11 - 30% higher as compared to MEGAN. Interestingly, this quantum of difference becomes higher and noticeable as sequences belonging to progressively higher clade levels are removed from the reference database (e.g. results with 'Genus/Family unknown' database variants). Given the composition of typical metagenomic data sets (where majority of sequences originate from hitherto unknown taxa/clades), results obtained with DiScRIBinATE  indicate its suitability for use with metagenomic sequence data sets.Results  wherein MEGAN, in spite of having a higher specificity in the case of SANGER reads, has a mis-classification rate which is 3 - 30 times higher than DiScRIBinATE and SOrt-ITEMS.It is also observed that the binning specificity of DiScRIBinATE is slightly lower as compared to SOrt-ITEMS. This marginal decrease of specificity (1-2%) can be attributed to the reclassification step used in DiScRIBinATE, wherein assignments to isolated taxa are moved to relatively higher taxonomic levels.The percentage of reads assigned by DiScRIBinATE at intermediate levels is around 4-10% higher than SOrt-ITEMS, and around 9-40% higher than MEGAN , with incorrect  taxa present in reference databases. It is due to this reason that even an algorithm like SOrt-ITEMS  assigns such reads to incorrect taxa. The latter reads (assigned to incorrect taxa) are manifested in the output as isolated assignments. One way of reducing the number of misclassified sequences is by discarding such isolated assignments. However, adopting this strategy will results in increased number of sequences categorized as unassigned.Evaluation of similarity-based binning approaches (using simulated metagenomic data sets) have indicated that a significant percentage of reads get misclassified or are categorized as unassigned ,5. This In the current study, a novel reclassification strategy has been devised that attempts to reclassify assignments to isolated taxa instead of simply discarding them. The premise of this strategy is the following. The SOrt-ITEMS algorithm  revealedHence, it is likely that most of the assignments to isolated taxa could also be a result of over-binning. Therefore to improve binning accuracy, the method presented in this paper attempts to re-classify assignments to isolated taxa at progressively higher taxonomic levels, rather than simply discarding them. The results obtained in this study have revealed that adopting this strategy significantly reduces the percentage of wrong assignments without increasing the percentage of unassigned reads.The method described in this paper also incorporates a quicker 'alternative approach' (as compared to the time consuming orthology approach used by SOrt-ITEMS) based on the ratio of bit-score and distance information. The use of such a ratio (for improving specificity) is based on the following premise. Bit-score is one of the parameters in the BLAST output which reflects the overall quality of an alignment. Higher bit-scores indicate higher similarity between the query and the hit sequences. Furthermore, higher similarity also indicates that the organism corresponding to the query and hit sequences are taxonomically close to each other (i.e. the phylogenetic distance between them is low). Hence a ratio of bit-score/distance is directly indicative of the taxonomic relatedness.DiScRIBinATE uses the above principle to improve the specificity of assignments. For each query sequence, DiScRIBinATE identifies a set of candidate ancestors (from amongst the set of significant hits) and assigns the query to a candidate ancestor having the highest bit-score/distance ratio. Using this approach ensures that the query sequence is assigned to a taxon that is taxonomically closest to it (thereby improving specificity). The specificity of assignments using this alternate approach is seen to be comparable to those obtained using the orthology based approach. Figure Results indicate that, as compared to the orthology based approach, this alternative 'bit-score/distance approach', coupled with the reclassification step reduces the binning time by half and also significantly reduces the percentage of wrong assignments. As demonstrated in the validation results, the scale of reduction is as high as 30 times.The three taxonomic binning methods described in this study use the output of a BLAST search as input. However, it is important to note that the time spent on the taxonomic assignment from BLAST results is really a tiny proportion as compared to the time spent on BLAST search. Besides developing efficient methods that can rapidly and accurately derive taxonomic inferences from BLAST outputs, it is also necessary to develop approaches that can reduce the time spent on performing the actual BLAST search. Our current research focus is on development of novel methods that reduce the computational time associated with typical BLAST searches.A significant reduction in binning time, coupled with a superior assignment accuracy (as compared to existing binning methods), indicates the immense applicability of the proposed algorithm in rapidly mapping the taxonomic diversity of large metagenomic samples with high accuracy and specificity.SOrt-ITEMS: Sequence orthology approach for Identification and Taxonomic estimation of metagenomic sequences; LCA: Lowest Common Ancestor; MPA: Most Probable Ancestor; DiScRIBinATE: Distance Score Ratio for Improved Binning And Taxonomic Estimation; MEGAN: Metagenomic analyzer.The authors declare that they have no competing interests.TSG, MMH and SSM have conceived the idea and designed the detailed methodology. TSG and MMH have implemented the algorithm, created validation datasets and carried out detailed validation and testing of the algorithm. TSG, MMH and SSM have analyzed the data and finally drafted the complete paper.Table S1: Detailed summary of the percentage of assignments by DiScRIBinATE, SOrt-ITEMS and MEGAN under the various bin categories.Click here for file


Tissue MicroArray technology aims to perform immunohistochemical staining on hundreds of different tissue samples simultaneously. It allows faster analysis, considerably reducing costs incurred in staining. A time consuming phase of the methodology is the selection of tissue areas within paraffin blocks: no utilities have been developed for the identification of areas to be punched from the donor block and assembled in the recipient block.The presented work supports, in the specific case of a primary subtype of breast cancer (tubular breast cancer), the semi-automatic discrimination and localization between normal and pathological regions within the tissues. The diagnosis is performed by analysing specific morphological features of the sample such as the absence of a double layer of cells around the lumen and the decay of a regular glands-and-lobules structure. These features are analysed using an algorithm which performs the extraction of morphological parameters from images and compares them to experimentally validated threshold values. Results are satisfactory since in most of the cases the automatic diagnosis matches the response of the pathologists. In particular, on a total of 1296 sub-images showing normal and pathological areas of breast specimens, algorithm accuracy, sensitivity and specificity are respectively 89%, 84% and 94%.The proposed work is a first attempt to demonstrate that automation in the Tissue MicroArray field is feasible and it can represent an important tool for scientists to cope with this high-throughput technique. One of the most powerful high-throughput histology related analysis applications of recent years in medical research is the Tissue MicroArray (TMA) technique . This teThe intrinsic high parallelism of a TMA block, due to the matrix structure that allows the examination of many different tissues at the same time, leads to several advantages, such as a decrease in the time taken to perform one assay instead of hundreds of separate assays, and a limited reagents cost, as around 500 separate experiments can be completed on a single TMA. On the other hand the technique presents particular challenges in the input/output data evaluation, including the investigation of the results of hundreds of different tissues at the same time. In addition, TMA construction is highly time-consuming because it requires a pathologist to identify, on every donor block, the area suitable to be punched-out and inserted into the array block. This aspect is often underestimated.Since the technique is relatively new, up to now few specific software tools have been developed to support TMA construction. To carry out image evaluation, scientists require specific support in two important phases. First, in the evaluation of hematoxylin-eosin (H&E) stained slides of donor block tissues (pre-array images), to highlight the interesting areas for TMA building. This is a highly time consuming task and in most pathology institutes evaluations are performed completely by hand for each of the hundreds of tissues that will compose the matrix. Secondly, the scoring phase on TMA samples (post-array images) may be improved by the use of automatic algorithms. Considering that a single TMA block may theoretically yield around a hundred of stained sections and that each section may contain hundreds of samples (cores), the need for support tools is evident. Therefore, it is clear how the impact of the TMA technique may be significantly enhanced by suitable and efficient algorithms to analyse tissue images.To accomplish this task a number of applications exist. Some software tools are commercially available and generally these allow the user to capture spots in images, organize them into a sorted matrix, annotate and analyse them with quantitative imaging, including nuclei counting and signal quantification or some morphology measurements. In addition, for most of these solutions the image software cannot be released as standalone and is usually distributed together with a virtual microscope system. However, these software packages focus on the final analysis phase and are not suitable for aiding automation in the pre-array phase. Moreover, they are not very flexible for keeping up with ongoing experimental research and they have not been developed for automatically identifying variations in morphologies, which is the crucial feature in the pre-array phase. The most used tools, listed in Figure Useful highly customizable software can be found as open source applications -5. Most All of these packages take into account the post-array images, used in the analysis phase, providing semi-quantitative evaluation tools for the stained core images and assigning to them reaction scores, or exploiting extracted parameters to generate statistical results. Nevertheless the identification and localization of suitable areas for extracting tissue cores that are to be used for TMA construction represent a major bottleneck in a TMA experiment.In this paper we present an algorithm for efficient TMA construction by automatic identification of areas of interest in a TMA donor block, which has been included in the TMARepDB  web siteOther works exist which describe automatic approaches to H&E stained tissue slides, to support specifically the field of histopathology and to improve the potentiality of telepathology and virtual pathology. An interesting approach for detecting cancers from digital tissue images has been proposed  concerniFor analysing breast tissue, two state-of-the-art algorithms are presented ,10. BothThe MAGIC system  represenEven other valuable commercial software exists, mainly oriented to tissue analysis in TMA and the pathology analysis context: among them the tools implemented by TissueGnostics , BeecherTubular carcinoma was chosen as pilot subject for the development of the presented tool. It is a type of invasive ductal carcinoma of the breast. It takes its name from its microscopic appearance, in which the cancer cells resemble small tubes. In some cases, tubular cancer cells are mixed with ductal or lobular cancer cells, giving a mixed-tumor diagnosis. Tubular carcinomas are often very small, but may show up on a mammogram, as an irregularly shaped mass with a spiky, or starry outline.It shows morphological features that differentiate it from other types of breast cancer, and that can be used to define parameters for developing an automatic approach for its recognition.The first feature that has been considered is the presence of a single layer of cells around the lumen of the glands Figure . In normμm H&E sections were prepared from the FFPE tissue blocks and digitalized using a Virtual Microscope  [X and having the same dimension (6720 × 4200). All the H&E images have been valued by two experts in tissues morphology recognition, which discriminate pathological regions from normal ones. In case of contrasting evaluation, which occurs in about 10% of the analysis, the corresponding images have been removed from the dataset. To allow a suitable sampling of the tissue (coherently with the pathologists defined sampling criterion) and according to the defined magnification and dimension, the algorithm automatically subdivided these images using a 8 × 9 punching grid which results in a reference dataset composed by 1296 sub-images. Using a classical approach for classification problems, the whole dataset has been separated into three parts: the training set, the tuning set and the validation set. The first set, composed of around 50% of the available sub-images (650), is used to build the model, in particular to evaluate the parameters suitable for the pipeline implementation; the second set, composed of 25% (323) of the available sub-images, is exploited to tune the model; the third set, composed of 25% (323) of the available sub-images, is necessary to measure the performance of the algorithm, holding the identified parameters as constant values. In order to guarantee the reliability of the results a cross-validation is performed, by repeating the classification four times, randomly selecting the images belonging to each group. During each step of the crossvalidation test parameters have been optimized in order to achieve the best possible results. Remarkably, considering for each algorithm parameter the interval in which best values are placed for each experiment the range is quite narrow and the variance quite low. In agreement to the achieved results the algorithm default parameters have been selected according to the experiment that provided the best performance in terms of accuracy: data are reported in the following sections. In order to test the effective flexibility of our algorithm, two more datasets of images originated with different resolution and different pixel intensity have been exploited. One dataset includes 5 images presenting a resolution of 0.23 μm/px and a pixel intensity similar to the reference dataset. According to our system, images from this dataset are converted in the preliminary algorithm step in order to obtain images presenting 200X of magnification and 0.46 μm/px of resolution .The algorithm has been developed using a primary set of 20 tubular breast cancer biopsies provided by the Erasmus MC tissue bank hosted at the Department of Pathology of the Erasmus MC in Rotterdam (NL). Standard 4 , Japan) , which aREF P= 170 . For both sets, images that encountered contrasting tissue evaluation among pathologists (around 10% of the whole sets) have been removed from the datasets. As reported in the performance section, prediction capabilities of our algorithm was good in comparison with the primary dataset . In both cases by providing a fine-tuning of the algorithm parameters (e.g. setting the effective resolution for the first dataset and the effective pixel intensity for the second dataset) the performance showed improvements.The other dataset includes 5 images showing the same resolution of the reference dataset but different pixel intensity. The first step when analyzing these images consists in normalizing to the average pixel intensity of the training set The workflow for the identification of pathological regions is presented in Figure R Bfor background, R Nfor nuclei, R Cfor cytoplasm and their average value REF P), in order to eliminate the differences in brightness among images acquired at different times or with different instruments. The transformation is performed according to a well-established colour normalization approach [As a preliminary step, each whole image of an H&E stained section acquired through a virtual microscope is normalized with respect to image intensity and magnification, in order to make the images comparable to each other and thus providing algorithm thresholds suitable for all the considered images. Each image has then been scaled to specific reference pixel intensity values  represents the background average value of the image to be calibratediy,(RB) represents the background average value of reference image;ix,(NN) represents the nuclei area average value of the image to be calibratediy,(RN) represents the nuclei area average value of reference image;ix,(NC) represents the cytoplasm area average value of the image to be calibratedi y,(RC) represents the cytoplasm area average value of reference image.For each image, A, B, and C transfer coefficients are automatically calculated based on the previous data. All pixels of images were mathematically converted according to the mathematical transfer function and after the standardization step, images of the same dataset showed the same average colour levels on the background area, on the nuclei regions and on the cytoplasm areas.REF R. It must be noticed that the user can intervene on the image normalization phase, in order to better adapt the software to manage images acquired through different technologies, by providing both customized REF Rand REF Pvalues before running the algorithm. This fine-tuning possibility allows the user to redefine the default values coming from the dataset on which this work relies, since performance could slightly suffer the preliminary conversion step of the algorithm. Concerning REF Ra simple re-dimensioning is performed, while regarding REF P, even new values of RB, R Nand R Ccomponents are calculated, maintaining the same proportions existing in the original dataset.When dealing with resolution normalization, images were always scaled to the reference value Before enabling image classification using the designed algorithm, the image is divided into a number of independent sub-images, to be individually analysed and labelled according to the result of the computed diagnosis: the generated grid allows a tissue division where from each area corresponding to one sub-image one tissue punching can be performed randomly.n objects based on attributes into k partitions (k <n).The successive step of the algorithm consists in discovering if either (a) the image contains significant information for the segregation of un-affected and affected areas, or (b) it represents an area where no tissue is present at all, or the tissue does not have the characteristics of affected or unaffected tissue (i.e. fat cells). To perform this classification the average intensity of each image is calculated and compared with a threshold value. If the image is consistent with case (a), the algorithm must proceed, if the image is consistent with case (b) the algorithm stops. If the selected image is labelled as informative, the work flow evaluates the first discriminating feature among normal and pathological tissue, which is the random distribution of cells. The basic idea for performing the step arises by observing the whole tissue organization: in affected areas cell agglomerates are randomly distributed within the tissue and are much smaller and more numerous than in unaffected areas. Therefore, the designed algorithm relies on object detection and area estimation: areas values are successively evaluated in order to distinguish if they belong to a well organized or a random distributed structure. At first a pre-processing step is required: each image is filtered in a suitable way to obtain a black and white mask where only gland nuclei are highlighted. Firstly tissue components had to be separated virtually. A robust and largely used method for performing tissue image segmentation -23 is k-Practically, k-means clustering takes pixel intensity values as inputs and considers three random values as starting points. Using the Euclidean distance it creates three groups that include pixels whose intensity values are close to the three values chosen as starting points. For each group the centroid is calculated, and the three groups are modified considering the centroids as the new starting points.By applying this procedure to the H&E image and by iterating it, the algorithm approaches the local minimum of the function. When the group variance converges to the minimum each pixel group that has one distinct class label represents one segmented object component. In the studied context it separates the three main different colours of the image generating three result images:• the white based image, showing the mucus presence• the pink based image, indicating the basic areas (such as cytoplasm)• the blue based image, reporting the acid regions (such as nuclei)This method even enables the isolation of the dark blue intensities that characterize nucleic substance images. This is possible by ranking the pixel values by their intensity and just considering the darker ones, that correspond to nuclei, which are the most highly acid substance within the cell. Such a processed image is then converted from an RGB (Red-Green-Blue) map to gray-scale and the agglomerate shape is highlighted using two morphological filters: dilation and hole filling.After the pre-processing phase, area detection is performed by including each unknown-shaped agglomerate into fitting ellipses, defined by their semimajor and semiminor axes. If the analysed sub-image can be classified as random-distributed pathological tissue in this step, the work flow comes to an end and labels the image as affected.thickness algorithm based on Hildebrand and Ruegsegger approach [p), where p is an arbitrary point in the structure Ω ⊂ R3, can be defined as:If the work flow does not reach an endpoint the presence of a double or a single layer of cells is investigated, using a approach . The chosph is the set of points inside a sphere with center x and radius r. The maximum local thickness is equivalent to the diameter of the largest sphere that completely ts inside the structure:where The algorithm works on the segmented image, by creating a distance map which assigns the Euclidean distance to the nearest edge point to every point within each segmented object. This is equivalent to the radius of the largest sphere centred at the considered point and still completely inside the structure. The inclusion tests lead to the identification of non-redundant spheres, that will define the distance ridge. The thickness algorithm provides a false-colour image, which for each point shows the thickness of the structures by assigning different colours to disks of different diameter: in particular brighter colours are associated to larger disks. The output of the phase is the separation between single-layer pathological tissue and normal tissue. Each sub-image is analysed with the described work flow, giving as final output a diagnosis of unaffected, affected, or fat area, indicated by the colour of the border that is overlaid on the original image in the last step of the analysis. The red border means that the considered area is affected, the green border designates unaffected areas. Both the green and the red areas may be of importance for selecting cores for the TMA. The grey border represents areas without tissue or tissue that is mainly fat. Since each image is only a part of the whole slide, in the final step the image is reassembled, thus providing a view that allows the correct choice of the areas to be punched for building the TMA block. Labelled areas may differ from one region to another, due to the complexity of the tumour tissue mixed with normal tissue. The two biological aspects described, the layers of cells forming the lumen and the morphological organization of the tubular structures, were first considered in separate algorithms, and then combined in the unified work flow.The algorithm has been implemented using Perl language, embedding external functions based on different software, among which Matlab , a commeA crucial role for a successful application of this algorithm is the selection of the parameters that characterize the examined features and the related threshold values for decision making. In the following section the whole work flow will be retraced in order to analyse the parameters used for each conditional step.REF Rand is defined in order to guarantee a correct tile size with respect to real biological object size: the grid must be large enough to provide morphological consistency within each tile, and thick enough to always allow the correct distance between the selected punching areas.Pre-processing step includes, other than the normalization phase, the detection of the correct sub-image size, and the identification of a suitable grid to cut the original image. The sub-image dimension is strictly connected to the aim of the proposed work, that is to select punching areas for Tissue Microarray experiments. Therefore, the grid spacing and the sub image size cannot be arbitrarily varied. It depends on image resolution REF P.Concerning the first phase, related to the presence of interesting information inside the considered sub-image, the discriminating factor is identified in the average value of the pixel intensity of the sub-image itself. The threshold value to distinguish between interesting and not interesting sub-images is set to an intensity value identified by analysing a training set of images including significant, void and fat areas. The value correspondent to the intensity that discriminates between brighter and darker images, thus defining those that do not contain informative tissue areas, is 1.2*X - Y axis a significant difference is observed between tissues with random organization of elements and tissue with the characteristic breast tissue structure. Figure Proceeding with the model investigation, from empirically analysing results achieved on the training set the most reliable parameter for the analysis of the cell distribution appears to be the standard deviation of ellipse area values. When plotting this data in To avoid false positive results which may be caused by the presence of single T and B lymphocytes, which are frequently seen in inflamed tissues but that are not specifically involved in tumour proliferation, area values lower than a fixed threshold were rejected before plotting the data. This size filter has been established to The considered signal is represented by the distribution of the ellipses areas within an image. The signal standard deviation is computed and, repeating the process for each image of the training set, a biologically consistent value of this parameter has been obtained, which distinguishes between the well organized and the random distributed tissues, thus representing a threshold value. This discriminating value is set to * REF P) in the histogram plot. In fact, according to given representation of the Hildebrand and Ruegsegger, higher intensity values correspond to major thickness of image objects. The value of this parameter in unaffected areas is always higher than in pathological ones, as shown in Figure To answer the final conditional block of the work flow, concerning the thickness of the cell layer around the lumen, a histogram profile is produced for each image resulting from the thickness algorithm application. Converting the image from RGB to gray scale, major thickness, related to unaffected double-cell surrounded structures, are dyed in bright colours, while thin objects, mostly present in pathological tissues, show dark colours. In fact, histogram comparison showed differences between the normal and the pathological tissues especially concerning the highest values of intensity, as expected. In this case the analytical parameter chosen to express the observed differences is the sum of the quantity of pixels estimated for brightest colours  retrieved an accuracy value of around 90%, a sensitivity of 85% and a specificity of 94%. Algorithm performance is better when enabling fine tuning than when using default parameters. Even regarding the pixel intensity test five images have been used. The test performed with default parameters provided an accuracy value of 75%, a sensitivity value of 71% and a specificity value of 82%. The test which foresees the parameters fine tuning  provided an accuracy of 90%, a sensitivity of 82% and a specificity of 97%. Even for what concerns software flexibility about pixel intensity, this second test showed better performances than the previous one which implies the use of the default parameters values.In conclusion, the algorithm shows to be robust since parameters vary slightly in the cross-validation. Moreover, the software presents good flexibility whereas it works receiving images of different resolution and pixel intensity as input. Finally, the algorithm shows a high level of adaptability, due to the possibility given to users to exploit information about the dataset images to set the correct parameters and to optimize the performance.The time performance primarily depends on the image size: typical size range goes from 3MB to 8MB. It must be noticed that the time spent to cut the global image in a set of sub-images, and the mirror phase of resembling, have to be considered as an intrinsic sequential part of the code, while the rest of the code  is parallelizable. Cutting times go from 45 s to 57 s, while reassembling times go from 63 s to 125 s, according to image size. Average sub-image processing time is 49 s, with a σ = 15 s, which takes into account not only the image size but even the variability of the last algorithm step.In the following part an example is presented. The whole image was acquired with the previously defined procedure. The result is an image as wide as one third of the whole slide, with pixel dimensions of 6720 × 4200. Using a suitable grid dimension the image was divided into 72 sub-images.The pipeline is applied to each sub-image using the parameters and the thresholds described above. Finally all the sub-images are collated, enriched with the computed information, as shown in Figure On the other hand, the classification of image 24 among the normal ones has to be considered a false positive, enforcing the idea that the implemented software represents a first screening and support for the pathologist and therefore should be followed by human evaluation.The pilot experiment described here, involving TMA support tools, shows that it is feasible to design a program able to distinguish areas in a tissue slide as affected, unaffected and non-informative and localized them. It is currently integrated into the web based TMARepDB database, together with some other basic image processing methods, like Sobel edge detection filter and dilation morphological filter Figure . This soThe aim of this work is to show that, despite the fact that automation in this context is a very challenging task due to morphological complexity of human tissues, the implementation of a pathologist's support tool, in the TMA context, is feasible and the product is reliable. The strength of the computerized method presented here is that once it has been developed and fine-tuned it may be used to analyze numerous images in parallel in a short time.Future developments mainly concerns the possibility of switching the final evaluation of a sub-picture, that should be given to the user to enable modifying the process output. This would result in a completely reliable evaluation of the image, which could be further overlapped to the donor block to map the punching areas on it, with the possibility of performing automatic cores picking. In the authors' plan the described application could be part of a fully TMA analysis pipeline: after the tissues collection and the staining process, the procedure would be carried out by automatic or semiautomatic arrayers, guided by software able to detect suitable punching areas, thus allowing a fast tissue microarray building.In conclusion, although the algorithms can only approximate human competencies and experience, the developed tool represents an important aid for scientists through automation of a laborious and time-consuming job.ftp://fileserver.itb.cnr.it/federica/BMCTubMaterial is available at URL: Software is archived in BMCTub.tar.gz; requirements and documentation are provided in the README file; the exploited datasets are contained in folder Datasets.TMA: Tissue MicroArray; H&E: Hematoxylin-Eosin; SVM: Support Vector Machine; VM: Virtual Microscope.The authors declare that they have no competing interests.FV and IM designed and implemented the algorithm, MT and MdB gave the biological perspective, FB, PR and LM supervised the work from biological and technical points of view. All authors read and approved the final manuscript.


A 66-year-old female patient was admitted to our department with a large tumor of the tongue measuring 10 cm in diameter. The tumor occupied nearly the entire oral cavity and showed exophytic and ulcerative areas. Histological analysis revealed a low grade squamous cell carcinoma (SCC) of the tongue. Bilateral enlarged cervical lymphatic masses were also present. The extent of the tumor infiltration was assessed by fluoro-2-deoxy-glucose-positron emission tomography (PET) scans showing an elevated activity of the tracer corresponding to the assumed cervical metastases. Additionally, pulmonary metastases were identified. Contrast enhanced computed tomography (CT) scans showed metastases in the soft tissues of the abdomen, legs and arms. Foci of distant metastases were found in the left upper anterior thoracal wall, near the intraabdominal portion of the aorta, near the right iliac crest and in both the right vastus medialis- and adductor magnus muscles. The final diagnosis was a T4N3M1(G3)(C3) SCC of the tongue with multiple distant thoracal, abdominal and intramuscular metastases. The survival expectancy was five weeks, and the patient finally deceased by cardiopulmonary complications. Patients suffering from a squamous cell carcinoma (SCC) of the head and neck region with distant metastases generally have a very poor prognosis . DistantIn summary, we report on a 66-year-old female patient who suffered from a terminal SCC of the tongue with multiple distant metastases which were localized mainly in the peripheral skeletal muscles and adjacent soft tissues of the lower extremities. Furthermore, we show an overview on the current literature on metastases originating from head and neck tumors Table .A 66-year-old woman was admitted with the symptoms of acute dyspnoe, orthopnoe and progressive dysphagia. The inspection of the oral cavity revealed an extensive lesion of the tongue Fig.  with areAltogether, one suspect soft tissue metastasis could be identified near the left upper ventral thoracal wall Fig. . A furthIn summary, the tumor staging according to the UICC  revealed a terminal cT4cN3cM1(G3)(C3) tumor. At this advanced stage, surgical therapy with curative intention was not indicated. At the same time, the patient strictly refused a surgical reduction of the tumor, a palliative radiochemotherapy as well as the application of a PEG-device. Therefore, she was transferred to the palliative care unit. The patient finally deceased after 5 weeks of hospitalization by a cardiopulmonary failure as a consequence of the tumor progression. The histologic confirmation of the suspect radiologic diagnoses, which included the spreading of the neoplasia, could be made only for the intraoral findings within the limits of the primary diagnostics, because the patient's relatives did not consent to perform an autopsy despite our request.Importantly, in the case presented here, the pattern of an advanced hematogenous spreading of metastases is particular. The metastases were located mainly in the soft tissues of the lower extremities, in particular in the skeletal muscular system. A few small metastasis-like lesions were also found in the cardiopulmonary system, as shown by 18F-FDG-PET imaging and CT scans, but they were clearly of minor extent if compared to the prominent distant metastases in the peripheral skeletal muscles and adjacent soft tissues.The patient did not show any cardiac lesion in the CT scans. Thus, in case of the presence of masses of unknown origin in the lower extremities in patients who once suffered or are still suffering from a SCC of the oral cavity, a possible spreading of metastases should be taken into consideration. We would like to point out that the examination for such findings should be performed during the continuous tumor recall and control appointments. In the case presented here, a native computertomography of the neck was initially performed. It showed no obstruction or imminent obstruction of the airways. Therefore a tracheotomy was abstained from. In general and in accordance with the current literature, the authors prefer a preoperative tracheotomy in order to avoid postoperative airway difficulties and a tracheotomy in an emergeny setting .In the case of the patient presented here, the quality of life was limited mainly by the tumor of the tongue causing impaired ability to swallow, reduced function of speech and impairment by esthetic and functional disfigurement, three factors that contributed to a low quality of life in a considerable degree. Not the distant metastases were the life-limiting factor, but rather the extensive local findings in the oral cavity which caused considerable respiratory and nutritional limitations. A valuable alternative for patients with a stage IV tumor disease with distant metastases, where the primary tumor is mostly considered to be the survival-limiting factor, is to offer them both an immediate therapy of the primary tumor and an integral palliative care in order to improve the quality of life. Other authors also support these recommendations; e.g., it has recently been shown that patients whose loco-regional and remote metastases were operated had a high two-year surviving rate . This kiIt has recently been reported that some patients who suffered an untreated SCC of the head and neck region survived for more than five years. Studies in which the outcome of patients with a SCC who were not treated at all were compared to the outcome of patients who underwent a palliative therapy showed that the mean survival rate was 8.4 months larger in patients who underwent a palliative therapy . HoweverRapoport et al. (1975) compared recurring head and neck tumors in relation to the outcome . One groThe studies cited here support the ruling opinion that a meticulous staging is an essential aspect within the control of survival rates and quality of life. If the patient, as in the case presented here, refuses biopsy of suspected peripheral metastases, further costs can evolve later when other apparative examination means become necessary. This should be avoided, if possible, by an appropriate early education and guidance of the patient. However, the clinician is often confronted with the delicate task to choose a reasonable balance between minimal and maximal diagnostic means that are available and treat the patient with respect, which is a basic requirement. A PET-CT Scan could have been a cost saving, sufficient alternative. According to Nahmias et al. (2007), it shows a specificity of 99% and a sensitivity of 48% in a preoperative lymph node staging of the neck with oral squamous cell carcinoma. A positive PET-CT scan has a high positive predictive value . AlthougIn conclusion, we point out that distant metastases of SCC's of the head and neck region can manifest themselves in the peripheral skeletal muscles and adjacent soft tissues. Therefore, during performing a tumor staging and during aftercare, this possible localization of metastases should be taken into consideration if suspect lesions were found in the peripheral skeletal muscles and nearby soft tissues in a patient who suffers from or who had once suffered from a SCC. It has to be considered that distant metastases of head and neck tumors are probably not the limiting factor for the patient's survival prognosis since the local primary tumors in the oral cavity can importantly impair nutrition and breathing. Therefore, an early operative treatment should be an option for patients suffering from a progressive and terminal head and neck tumor, as a considerable good life expectancy can generally result and as the quality of life can be improved or at least upheld on an acceptable level as long as possible.All authors have declared that no competing interests, whether of financial or non-financial nature exist.RS and OM designed the study. MBG and MH analyzed the data. RS, OM, MBG and MH contributed to writing the paper. DR supervised the clinical treatment and data collection. RS and OM wrote the main part of the paper. All authors gave useful comment on the analysis of data and text of the manuscript.Written informed consent was obtained from the patient for publication of this case report and accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.


Lillehei and colleagues are credited with the first successful repair of a complete atrioventricular septal defect (CAVSD) in 1954.[survival. Details The purported benefits and pitfalls of both single- and two-patch techniques have been touted by multiple authors. Subsequently, Wilcox and Anderson described the repair of a ventricular component of the CAVSD by directly approximating the left atrioventricular (AV) valve component to the scooped out intraventricular septum. Nunn andRepair of CAVSD at the Children's Hospital of New York (CHONY) is usually undertaken with a two-patch technique, essentially as outlined by Dr. Backer in one of the groups' earlier publications. Bicaval The VSD component of the defect is then closed, followed by suturing of the top of the patch to the crest of the LAVV component and a pericardial patch. The “cleft” in the LAVV is closed with interrupted horizontal mattress sutures, without pledgets. Of note, the VSD patch is undersized slightly in both width and height, to avoid separating the LAVV components or elevating the valve leaflets above the natural height of the annulus of the common AV valve, both of which can promote regurgitation. This undersizing of the patch creates a type of “septal annuloplasty”. Occasionally, “Wooler type” commissuroplasty stitches are placed between the LSL and the left lateral leaflet or between the left inferior bridging leaflet and the left lateral leaflet. Closure of the cleft is done routinely unless the LAVV is inherently small or a parachute or double orifice type anatomy is encountered. The coronary sinus (CS) is left to drain into the right atrium, unless the “coronary sinus septum” that is between the mouth of the CS and the primum ASD is significantly deficient.At CHONY we now have a limited experience (approximately 10 patients) with the modified single-patch technique also showing good results. Due to surgeon preference, the modified single-patch technique has been used in those instances in which the VSD component has been nonrestrictive, but the height of the defect has been limited. In other words, the degree of scooping out of the ventricular septum has been mild. Dr. Backer notes that the VSDs in his patients are about 9 mm, however, it is not clear what dimension this is describing. As one would expect, we have also noted a decrease in the aortic cross clamp and total cardiopulmonary bypass times when using the modified single-patch technique. Early results reveal no mortalities, essentially minimal LAAV regurgitation, no instances of complete heart block or subaortic stenosis, and mild-to-moderate right atrioventricular valve (RAVV) regurgitation.When examining the two techniques, the major components of repair and possible complications need to be assessed and compared.Key points for achieving competency of the LAVV, by whichever technique is employed, include, accurately evaluating and aligning the limits of the LSL and LIL bridging leaflets, suturing these leaflets to one another as well as to the appropriate point along the scooped out portion of the interventricular septum, and avoiding prolapse of the valve.A benefit of the use of a VSD patch is that the LAVV can be floated and sutured directly to the point where it abuts the VSD patch. Care must be taken to avoid “oversizing” the patch so as not to stretch the annulus open, distort the valve chordae or cause the LAVV to rise above the level of the annulus. When no VSD patch is used, one must rely upon the “minds eye” to identify the correct point on the septum .The current reported incidence of reoperation for “LAAV” insufficiency after the modified single-patch technique, in this series, is low. This may be a significant improvement over previously reported methods or a result of limited follow-up.Closure of the atrial component of the defect is similar regardless of the method used. The decision as to whether or not to leave the CS draining into the right atrium or incising it and allowing the coronary venous flow to drain to the left atrium should be individualized. The incidence of heart block has been quite low even when sewing in the vicinity of the conduction pathway, as it travels toward the ventricular septum. Cases in which there is very deficient tissue between the coronary sinus and the primum ASD may leave the conduction system more vulnerable and may be best treated by incorporating the CS into the left atrium. As a caveat, some surgeons have been concerned that the coronary sinus connections to the atrial septum and to the LAVV annulus contribute to the support of the LAVV, and incising it may allow the annulus to stretch open and increase the insufficiency.When using the “two-patch technique” the VSD is closed by filling the defect with a prosthetic patch. This is often easier to do when dealing with a Rastelli C type defect  rather than a Rastelli type A defect. The usual VSD in CAVSD is larger in its midportion and in the subaortic area than it is in the more inferior area of the coronary sinus. The patch must be trimmed accurately so as to avoid distortion of the chords and LAVV. In general, it is better to undersize rather than oversize this patch.Closure of the VSD by the “modified single-patch” technique is accomplished by bringing the septal components of the LSL and LIL down to the intraventricular septum.In instances with multiple chords crossing the defect, this method may be easier than placing a patch around these chordae.In CAVSD the LVOT is relatively narrow when compared to that of a normal heart as a result of: (i) anterior displacement of the outflow tract (ii) apical displacement of the AV valve orifices (iii) accentuated area of direct fibrous continuity between the aortic valve and LSL (iv) prominence of the muscle of Moulaert protruding into the LVOT, and (v) tendency for fibromuscular obstruction in the subaortic area due to accessory LAVV tissue and abnormal papillary muscle insertions.Given the above, one might predict that primary closure of the VSD would lead to left ventricular outflow tract obstruction (LVOTO). Thus far, this appears not to be the case, at least in short-term follow-up. Instances in which the LVOT appears unusually small, prior to the operation, and those in which the VSD extends into the subpulmonary area, that is, Tetralogy of Fallot with CAVSD, may still require a prosthetic patch to minimize postoperative LVOTO. These details will be borne out with further follow up.Following repair with the two-patch technique, tricuspid valve competency is accomplished by the septal component of the RAVV abutting the VSD patch-LAVV suture line. The tricuspid valve closes at the same level as the LAVV and if necessary can be sutured to the above.As mentioned above, the modified single-patch technique draws the LAVV component down to the IV septum. The RAVV portion, however still has its closure point “higher” in the right atrium. This may lead to more significant RAVV regurgitation following this method.Repair of CAVSD continues to evolve. Better understanding of the anatomy of the atrial and ventricular septal defects, conduction system, atrioventricular valve leaflets, and LVOT have led to improved results over time. Each technique; single-patch technique, two-patch technique, and modified single-patch technique may offer advantages in certain situations. When critically examined, the modified single-patch technique is essentially the same as the two-patch technique, except for the method of VSD closure. At least in the short-intermediate follow-up, the modified single-patch technique performs much better than many predicted, when pertaining to the function of the LAVV and the LVOT. The utility of the modified single-patch technique in the presence of a very large outlet component of the VSD remains unclear. It is likely that in the future it will behoove the surgeon to be experienced with each method and apply each when most appropriate.


Prunus persica and Prunus dulcis EST sequences that in its current version encompasses 75,404 sequences from 3 almond and 19 peach libraries. Nine peach genotypes and four peach tissues are represented, from four fruit developmental stages. The aim of this work was to implement the already existing ESTree db by adding new sequences and analysis programs. Particular care was given to the implementation of the web interface, that allows querying each of the database features.The ESTree database (db) is a collection of A Perl modular pipeline is the backbone of sequence analysis in the ESTree db project. Outputs obtained during the pipeline steps are automatically arrayed into the fields of a MySQL database. Apart from standard clustering and annotation analyses, version VI of the ESTree db encompasses new tools for tandem repeat identification, annotation against genomic Rosaceae sequences, and positioning on the database of oligomer sequences that were used in a peach microarray study. Furthermore, known protein patterns and motifs were identified by comparison to PROSITE. Based on data retrieved from sequence annotation against the UniProtKB database, a script was prepared to track positions of homologous hits on the GO tree and build statistics on the ontologies distribution in GO functional categories. EST mapping data were also integrated in the database. The PHP-based web interface was upgraded and extended. The aim of the authors was to enable querying the database according to all the biological aspects that can be investigated from the analysis of data available in the ESTree db. This is achieved by allowing multiple searches on logical subsets of sequences that represent different biological situations or features.The version VI of ESTree db offers a broad overview on peach gene expression. Sequence analyses results contained in the database, extensively linked to external related resources, represent a large amount of information that can be queried via the tools offered in the web interface. Flexibility and modularity of the ESTree analysis pipeline and of the web interface allowed the authors to set up similar structures for different datasets, with limited manual intervention. The ESTree db  is an ExPrunus persica, but 3,864 Prunus dulcis sequences  were also added. The resulting dataset was composed of sequences obtained from twenty-two libraries , seven of which were added to Version VI for the first time. The peach dataset, in particular, represented nine genotypes and four tissues, and mesocarp sequences were obtained from the four developmental stages describing the peach ripening process. The availability of a more extended sequence dataset allowed the authors to explore in more detail the differences in gene expression in the various tissues and developmental stages, making use of an extended version of the ESTree db pipeline that integrates a more comprehensive collection of sequence analysis programs.The first version of the ESTree db released in 2004 encompassed a restricted number of peach sequences, derived from four peach mesocarp in-house prepared libraries. In the following versions, public peach sequences were added to the collection, and in version III (released on April 2005)  the numbThe aim of this work is to describe the major changes and improvements brought to the ESTree db, and to inform users of the availability of this extended tool, for an easy exploration of peach and related species functional genomics.The ESTree db encompasses peach and almond sequences. The few available almond sequences were introduced in the database to support mapping data produced on the TxE Prunus reference map based on an almond x peach F2 progeny. Almond sequences downloaded from GenBank were from pistils and embryo, plus a third library of unknown origin. The collection of peach sequences represents nine genotypes , four tissues  and four mesocarp developmental stages . Distribution of sequences among libraries or developmental stages was unequal, the mesocarp post-climateric stage being the most represented. In program outputs displaying sequences from different libraries , a colour code was assigned to relate sequences to the tissue and developmental stage of origin. Sequence names themselves were library-declarative, as library identifiers were used as sequence name prefixes.et al., 2005 [The ESTree sequence analysis pipeline Figure  has a mol., 2005 , but newThe MySQL database where data are stored has expanded to contain all the new programs' outputs and all the tables that are queried to produce dynamic outputs upon users' requests.The ESTree db pipeline input file was the multifasta file containing the complete dataset of 19 libraries. Electropherogram reading, vector clipping and poor region removal were performed with Phred  and Lucye−10. The master annotation was performed by BLASTx against the GenBank nr db, in order to retrieve the highest number of annotations; a second annotation was performed by BLASTn against a custom-prepared database composed of 7,829 Rosaceae genomic DNA/RNA sequences downloaded from the GenBank CoreNucleotide db in October 2006. The already existing so-called “GO annotation” procedure - aimed to the identification of proteins homologs annotated according to the Gene Ontology (GO) project [In the ESTree db, version VI, ESTs and contig consensus sequences were annotated against three different databases, with an E-value cutoff of ≤  project ,12 - was project  program According to the adopted E-value cutoff, 78.6% of the sequences contained in the ESTree db were annotated against the GenBank nr db, 69.7% against the UniProtKB db, and 26.8% against the genomic Rosaceae db Table .Considering the high number of sequences that had to be annotated and the need to perform annotation against there different databases, a parallel approach was adopted to perform BLAST searches on a computer cluster , giving The GO DAG was the source of information from which paths of GO identifiers were retrieved for each significantly annotated sequence: the global statistics were created using each found identifier to increase GO category-specific counters. In the “Statistics on GO annotations” page of the web interface Figure , GO statA repeat analysis feature was introduced in the ESTree db and the Tandem Repeats Finder program  was usedIn order to allow users to choose sets of repeats of given characteristics , a search engine was prepared.The aim of the ESTree Mapping Project is to map approximately 200 ESTs on a genetic map , as a prA program that allows the automatic BIN assignment based on a TxE subset of plants  was prepThe ESTate package programs  were useThe PHP-based web interface was upgraded and modified in Version VI. The Sequence Report and Contig Report tables represent the standard format for data presentation in the ESTree db, and query outputs or selectable sequence subsets are presented with the same table structure. To integrate new data, more columns were added to the tables, so that each sequence feature is presented in table fields and linked to dedicated pages, internal to the ESTree db or belonging to related web resources. A column was added to associate the 4,699 oligomer sequences used in a peach microarray study  to the cThe ESTree and the GDR databases represent the only existing online resources dedicated to peach EST analysis. The two databases are very similar in terms of entry number , but quite different in terms of information and its retrieval. The ESTree db clustering procedure produced a dataset of 27,097 unigenes, 4,303 of which were derived from our in-house prepared libraries.From the bioinformatic point of view, flexibility and modularity were the keywords in structuring the database. The authors prepared an EST analysis pipeline that could be conveniently applied to the analysis of data derived from other projects. The ESTree pipeline invokes standard public programs as well as custom prepared Perl modules. Adding or removing modules requires little manual intervention, making the pipeline easily adaptable to the specific requirements of different datasets. Nonetheless, because of the progressive introduction of new data and of the significant amount of data updating needed to compile the database tables, an effort was made to reduce the time required to complete the whole procedure. BLAST analyses were not included into the pipeline to allow a parallel approach on a computer cluster. Updates in the GO blast procedure call for the updating of the corresponding in-house prepared table upon which GO statistics are created, and this is another example of time-consuming task that is carried out independently from the main pipeline. In the whole ESTree db structure, the PHP-based web interface plays a fundamental role. All the graphical outputs that are presented in the web site are prepared on-the-fly by this interface, and are not stored in the database.The ESTree project is still underway, and additional data are being produced. The integration of microarray-derived data and of EST frequency analyses in different tissues and developmental stages into the ESTree db will make it an exhaustive tool for interpretation of expression differences among the represented biological situations. Furthermore, new graphical interfaces to access sequences belonging to specific metabolic pathways will add value to the already available links to KEGG .et al., in preparation).The version VI of ESTree db offers the possibility to access data concerning peach gene expression in an easy and complete way. ESTs multiple origins with respect to tissues and developmental stages allow a broad study of gene expression in a fruit tree. Ontologies analysis allows the comparison of the transcriptomes from different biological sets, and the creation of expression profiles, to be used in the study of patterns of gene expression (Vecchietti Additional features of version VI of the ESTree db are the increased, more user-friendly, data retrieval systems according to custom-selected aspects implemented via the web interface. The continuous data production by the ESTree Interuniversitary Centre units will result in a growth of the ESTree db, that will be updated and upgraded according to the future peach genomic projects requirements..The ESTree db is available without restrictions at the following URL: The authors declare that they have no competing interests.BL participated to library preparation, defined the pipeline structure and parameters and drafted the manuscript. AC structured the database and wrote all the accessory programs. AV was responsible for library preparation and sequence management. IM was the match point between the ESTree db and GRID technology. FB was responsible for the ESTree mapping project. LM coordinated the integration of bioinformatical resources. AS participated in the design of the study and critically revised the manuscript. CP guided and coordinated the execution of the project. All authors read and approved the final manuscript.