We previously reported that lifetime consumption of soy proteins or whey proteins reduced the incidence of azoxymethane (AOM)-induced colon tumors in rats. To obtain insights into these effects, global gene expression profiles of colons from rats with lifetime ingestion of casein (CAS, control diet), soy protein isolate (SPI), and whey protein hydrolysate (WPH) diets were determined.Male Sprague Dawley rats, fed one of the three purified diets, were studied at 40 weeks after AOM injection and when tumors had developed in some animals of each group. Total RNA, purified from non-tumor tissue within the proximal half of each colon, was used to prepare biotinylated probes, which were hybridized to Affymetrix RG_U34A rat microarrays containing probes sets for 8799 rat genes. Microarray data were analyzed using DMT (Affymetrix), SAM (Stanford) and pair-wise comparisons. Differentially expressed genes (SPI and/or WPH vs. CAS) were found. We identified 31 induced and 49 repressed genes in the proximal colons of the SPI-fed group and 44 induced and 119 repressed genes in the proximal colons of the WPH-fed group, relative to CAS. Hierarchical clustering identified the co-induction or co-repression of multiple genes by SPI and WPH. The differential expression of I-FABP , cyclin D1 , and the c-neu proto-oncogene  mRNAs were confirmed by real-time quantitative RT-PCR. SPI and WPH affected colonic neuro-endocrine gene expression: peptide YY (PYY) and glucagon mRNAs were down-regulated in WPH fed rats, whereas somatostatin mRNA and corresponding circulating protein levels, were enhanced by SPI and WPH.The identification of transcripts co- or differentially-regulated by SPI and WPH diets suggests common as well as unique anti-tumorigenesis mechanisms of action which may involve growth factor, neuroendocrine and immune system genes. SPI and WPH induction of somatostatin, a known anti-proliferative agent for colon cancer cells, would inhibit tumorigenesis. Colorectal cancer (CRC) is the third most common cancer and the third leading cause of cancer-related mortality in the U.S. ,2. Estimet al., among others, reported that genistein inhibits colon cancer cell proliferation and stimulates apoptosis in vitro [in vitro effects [et al. reported that soy sphingolipids inhibit colonic cell proliferation, and suggested that this may partially account for its anticancer benefits [et al. reported that dietary soy and whey proteins down-regulate expression of liver and mammary gland phase I enzymes involved in carcinogen activation [Soy foods and soybean constituents have received considerable attention for their potential role in reducing cancer risk ,7. Our lin vitro -15. Howe effects . Holly ebenefits . Other rtivation . Elevatetivation ,20. Suchtivation ,22. Moretivation . The anttivation .Despite extensive research, there is no consensus for anti-cancer mechanism(s) of soy and whey, which will undoubtedly involve multiple interrelated processes, pathways and many components. Many of the same molecular and biochemical changes underlying human colon cancer are observed in the azoxymethane (AOM)-induced rat colon cancer model . MoreoveQuality control steps ensured that the RNA used for microarray and real-time RT-PCR analysis was of high quality. These steps included evaluation of the RNA with the RNA 6000 Nano Assay and assessment of the cRNA hybridization to GeneChips by comparison of data obtained for probe sets representative of 5' and 3' ends of control genes. All RNA samples had an A260/280 absorbance ratio between 1.9 and 2.1. The ratio of 28S to 18S rRNA was very close to 2 on RNA electropherograms, and signal ratios below 3 were noted for 3' vs. 5' probe sets for β-actin and glyceraldehyde-3-phosphate dehydrogenase (per Affymetrix user guidelines) after hybridization.Total false change rates (TFC) were determined following Affymetrix-recommended guidelines , except Multiple filtering criteria were applied to the microarray data set so as to identify differentially expressed colon transcripts in rats fed SPI, WPH or CAS; results are reported only for transcripts that passed all three analytical filters used: DMT t-test, SAM and pair-wise comparison survival methods. Among the 8799 genes and ESTs examined with the rat U34A array, we identified 31 induced and 49 repressed genes in proximal colons of SPI-fed rats, whereas 44 induced and 119 repressed genes were detected in WPH-fed rats Tables , 3, 4, 5As based on Gene Ontology (GO) annotations, the 44 up-regulated and 119 down-regulated genes of the WPH group belong to multiple functional categories including cell adhesion (n = 10), cell cycle and growth control (n = 10), detoxification (n = 17), defense and immunity (n = 7), signal transduction n = 29), transcriptional regulation (n = 6), metabolism (n = 19), ligands and carriers (n = 27), cell death n = 3), structural proteins (n = 16), and others , and UDP-glucuronosyltransferase. Cytochrome P450 enzymes and ADH are considered to play key roles in activation of the proximate carcinogen from AOM . Down-rein vitro , lifetimColon genes, whose mRNA expression was affected by ingestion of SPI, fell into multiple functional categories including cell adhesion n = 4), cell cycle and growth control (n = 6), detoxification (n = 18), defense and immunity (n = 6), signal transduction (n = 4), transcriptional regulation (n = 1), metabolism (n = 8), ligands and carrier proteins (n = 17), cell death proteins n = 5), and structural proteins (n = 3)  can markedly affect the onset and/or progression of CRC . EpidemiThe ability to simultaneously analyze a large number of different mRNAs makes microarrays very appealing for identifying genes and gene families whose expression is altered by diet ,37. We fOnly two transcripts were induced by both SPI and WPH; whereas 37 transcripts were repressed by both SPI and WPH. This suggests that the cancer-protective actions of the two diets are generally associated with repression of colonic genes that facilitate tumorigenesis. An alternative explanation is that CAS induces genes that facilitate colon cancer development when compared to SPI and WPH. It is also likely that SPI and WPH diets act in unique ways to inhibit tumorigenesis. Irregardless, our results indicate that the nature of the dietary protein can profoundly affect colon gene expression profiles. Thus, gene expression profiling studies of colons should account for potential confounding effects of diet.Dietary factors in SPI or WPH inhibit cell proliferation and induce apoptosis among other biological actions ,13. In tGastrointestinal hormones regulate a myriad of intestinal functions including motility, absorption, digestion, cell proliferation and death, and immune response . The micOur data highlighted other aspects of diet and colon gene expression that warrant further study. For example, the B7 antigen  mRNA was strongly down-regulated by SPI or WPH. The corresponding protein is normally expressed at high levels on cell membranes of T and B lymphocytes and monocytes; infusion with anti-CD52 antibody leads to systemic depletion of T cells . The lowSeveral microarray studies of human paired normal colon vs. colon tumors have been published -64. CompWe have identified genes in rat colon that are differentially expressed, as a consequence of altered dietary protein, during AOM-induced oncogenesis. These are candidates for genes that sub-serve the anti-cancer effects of dietary SPI and WPH in this tissue.ad libitum access to water and pelleted food. Rats were randomly assigned to one of three semi-purified isocaloric diets made according to the AIN-93G diet formula [The animals whose colons were used in the present study have been previously described ,9. Time- formula  and whic formula ,9]. A re24 primer and Superscript II (Life Technologies, Inc., Gaithersburg, MD). The resulting cDNA was used with the ENZO BioArray High Yield RNA Transcript labeling kit  to synthesize biotin-labeled cRNA. The cRNA was purified on RNeasy spin columns (QIAGEN) and subjected to chemical fragmentation (size range of 35 to 200 bp). Three replicate cRNA targets were made in parallel starting from each RNA pool.Total RNA was isolated from rat proximal colons (n = 7 for each of CAS, SPI and WPH diets) using TRIzol reagent (Invitrogen, Carlsbad, CA), and further purified with the RNeasy Mini Kit . To remove contaminating DNA, on-column DNA digestion with RNase-Free DNase (QIAGEN) was performed. Integrity of isolated RNAs was confirmed using the RNA 6000 Nano LabChip kit with the Agilent 2100 Bioanalyzer System . To reduce errors due to biological variability, RNA samples were pooled as proposed by Bakay et al . Pooled Ten ug of cRNA was hybridized for 16 hours to an Affymetrix (Santa Clara, CA) rat U34A GeneChip (3 chips used per diet group), followed by incubations with streptavidin-conjugated phycoerythrin, and then with polyclonal anti-streptavidin antibody coupled to phycoerythrin. Following washing, GeneChips were scanned using an Agilent GeneArray laser scanner. Images were analyzed using Affymetrix MAS 5.0 software. Bacterial sequence-derived probes on the arrays served as external controls for hybridization, whereas the housekeeping genes β-actin and GAPDH served as endogenous controls and for monitoring the quality of the RNA target. To compare array data between GeneChips, we scaled the average of the fluorescent intensities of all probes on each array to a constant target intensity of 500.To validate the microarray procedure for our samples, unsupervised nearest-neighbor hierarchical clustering (Spotfire, Somerville, MA) was performed on gene expression data. The inter-chip variability test also was performed as specified in the Affymetrix data analysis manual . To idenP < 0.05).One μg of total RNA from each of the 21 individual proximal colons  was reverse-transcribed using random hexamers and MultiScribe Reverse Transcriptase in a two-step RT-PCR reaction (Applied Biosystems, Foster City, CA). Primers Table  were desRat serum Sst content  was determined using the somatostatin-28 EIA kit purchased from Phoenix Pharmaceuticals Corporation .RX performed the microarray and real-time PCR experiments, conducted the data analysis, and participated in drafting the manuscript. TMB designed and oversaw the animal component of the study. FAS designed the analytical and overall approaches to the study, supervised the project, and drafted the manuscript. All authors read and approved the final manuscript.


Although many proteins -- both damaged and normal -- have a tendency to aggregate, only some are capable of dividing and propagating. What does it take to turn a protein aggregate into an infectious prion? Sc), which makes it protease-resistant and initiates its aggregation  controls termination of translation, and Ure2 (forming [URE3+] prion) controls some membrane transporter systems. Aggregation of these proteins leads to phenotypes (e.g., suppression of nonsense mutations or transport defects) inherited in a non-Mendelian fashion owing to the nonchromosomal basis of the inheritance.While so far only one prion protein is known in mammals, several prion-like proteins capable of forming self-propagating aggregates have been found in various yeast species. The common structural feature of yeast prion proteins is the so-called prion domain, characterized by the high content of glutamines (Q) and asparagines (N) , also kner cells   contain Q/N-rich domains  or polyQin yeast . [RNQ+] Aplysia neurons  from  neurons . The neuble form . These d+] yeast cells have about 60 seeds of this prion , and maintenance of about this number of seeds after cell divisions requires functional Hsp104 (What makes protein aggregates in yeast propagate? The key cellular element that is critical for this process is molecular chaperone Hsp104 . This fal Hsp104 . In factl Hsp104 . Since tl Hsp104 , it was l Hsp104 . InteresPLoS Biology by The fact that some proteins with Q/N-rich domains form self-propagating aggregates, while others can aggregate but cannot form prions, suggests that there should be some structural elements either within the Q/N-rich sequence or close to it that confer the ability to propagate. In an article in this issue of +]. Furthermore, when the oligopeptide repeat sequence was added to a polyQ sequence, this fusion polypeptide also acquired the ability to form self-propagating aggregates. This work, therefore, clarifies the architecture of prions by defining two structural motifs in prion proteins that have distinct functions in aggregation and propagation. Interestingly, not all yeast prions have similar oligopeptide repeat motifs, indicating that distinct structures could confer prion properties to polypeptides that can aggregate. It would be important to identify these structures in order to understand the mechanisms of aggregate propagation. The work of The likely possibility was that the oligopeptide repeats could be interchangeable between different prions, leading to creation of novel chimeric prions. In fact, the authors constructed an F chimera, a fusion protein having the N-rich domain of New1 and the oligopeptide repeat of Sup35. This fusion polypeptide efficiently formed prion [F


Methodological research to support searching for those doing systematic reviews of epidemiological studies is a relatively neglected area. Our aim was to determine how many databases it is necessary to search to ensure a comprehensive coverage of the literature in diabetes epidemiology, with the aim of examining the efficiency of searching in support of systematic reviews of the epidemiology of diabetesThree approaches were used. First, we defined a set of English language diabetes journals and examined their coverage in bibliographic databases. Second, we searched extensively for diabetes epidemiology articles  to determine which are the most useful databases; and third, we analysed the scattering of these articles to determine the core journals in the area.Diabetes Care contributing 14.3% of the articles, followed by Diabetic Medicine (5.0%); Diabetes Research & Clinical Practice (4.1%); Diabetologia (4.0%); Diabetes & Metabolism (2.4%) and Diabetes (2.0%).The overlap between MEDLINE and Embase for diabetes journals was 59%. A search for diabetes epidemiology articles across both MEDLINE and Embase, showed that MEDLINE alone retrieved about 94% of the total articles. Searching for diabetes epidemiology studies beyond MEDLINE and Embase retrieved no additional English language journal articles. The only diabetes epidemiology studies found by searching beyond MEDLINE and Embase were found in LILACS, and were Spanish or Portuguese language studies from Latin America; no additional English language studies were found. Only 30% of the meeting abstracts were converted to full publication after three years. One third of journal articles were published in just six journals, with Our results show that when searching for articles on diabetes epidemiology, MEDLINE and Embase would suffice for English language papers, with LILACS giving some additional non-English articles from Latin America. Although a MEDLINE-only search will retrieve the vast majority of the relevant literature, Embase and LILACs should also be searched to ensure the search is comprehensive. Searching for meeting abstracts is recommended to alert reviewers to unpublished work. The low rate of full publication of meeting abstracts has the danger of producing bias in reviews. Our findings on scattering show that the core literature in this field is concentrated in just six journals. Review articles have a valuable role in the medical literature, because the volume of journals and articles is such that keeping up to date is very difficult. Reviews are much more valuable if they are systematic reviews done to internationally agreed standards, as non-systematic reviews are known to be subject to bias ,2.Dickersin noted that there was a shortage of systematic reviews in epidemiology, and called for more reviews, and more research into the methodology relating to reviews in epidemiology . A studyOne of the key quality criteria for systematic reviews is the comprehensiveness of the searching, as failure to identify all relevant reports can result in selection bias. The usuUlrich's International Periodicals Directory as a gold standard, found that a MEDLINE search in psychiatry would retrieve only about half the relevant journals [A study, using journals . SimilarThe scattering of the journal literature in a subject area can provide a useful insight into the number and range of journals needed to capture the key literature in a field. Bradford's Law of Scattering states that on any one subject, a small group of 'core' journals (Zone 1) will provide about one third of the articles on that subject, a medium number of less-core journals (Zone 2) will provide another third, and a large number of peripheral journals (Zone 3) will provide the final third of the articles ,10.The aim of this study was to examine the efficiency of searching in support of systematic reviews of the incidence and prevalence of diabetes by providing empirical data to answer the following questions:1. Which databases index diabetes journals (restricted to English language)?2. Which databases outside MEDLINE and Embase index diabetes epidemiology journal articles and grey literature?3. How are diabetes epidemiology articles scattered across the journals, and what are the core journals in this area?Accordingly, this study was divided into three parts.(Note: This study is concerned only with searching for the epidemiology of diabetes itself, not with its complications).For the purposes of this study, epidemiology articles were defined as studies of incidence or prevalence of diabetes, or of factors affecting those . Basic science studies, e.g. biological mechanisms of disease, were not included.We started from a position that both MEDLINE and Embase should always both be searched, since the overlap between these databases has been estimated to range from 10% to 87% ,11-17. AHence, a three-part approach was used to investigate literature searching to support systematic reviews of diabetes epidemiology, and address each of our aims in turn.Ulrich's Periodical Directory 2003  [Diabetes journals were identified from the 'Medical Sciences – Endocrinology' section of e world) . This wae world)  for jourUlrich's, the contents pages for the past five years, and where necessary, abstracts or the full journal articles, were examined by a diabetes epidemiologist.The inclusion criteria for the journals were: i) the word stem 'diabet' in the title, ii) contains original scientific studies of an academic or scholarly nature, iii) currently in print, and iv) published in English. If inclusion could not be decided on the basis of the information provided in List of Serials Indexed for Online Users [Journals which fulfilled all the above criteria were then checked against the ne Users  to see iIf any journals were not indexed in either MEDLINE or Embase, searches of BIOSIS, BNI, CINAHL, SCI were done to determine if the journals were indexed in any of these databases.Databases searched were: AMED, ASSIA, BIOSIS (abstracts only), BNI, CINAHL, Conference Papers Index, Dissertation Abstracts US, Health Management Information Consortium (HMIC), Index to Theses UK, ISI Proceedings, PsycINFO, NLM Gateway, LILACS , National Research Register (NRR), SIGLE, SCI (abstracts only), SSCI, and Zetoc.The search strategy used was 'diabetes and ' in the Title (TI) field and restricted to publication years 1998 to 2003.The titles (and abstracts when available) of all records were checked by an expert in diabetes epidemiology, in order to determine their potential usefulness for those doing systematic reviews.MEDLINE and Embase were searched using the search strategy: 'Diabetes Mellitus as the major subject heading, with the sub-heading 'Epidemiology' assigned , and restricted to publication years 1998 to 2003'. All languages were included. Duplicates found in both MEDLINE and Embase were removed. The journal titles in which the articles were found were ranked according to the number of articles contributed by each journal. The cumulated numbers of articles and journals were calculated and plotted. This was used to identify Bradford zones; that is, the number of journals needed to cover about one third, two thirds or all the relevant articles in the field.Ulrich's Periodicals Directory and PubList.com initially identified four English language journals that were of potential interest but not indexed by MEDLINE or Embase. On closer inspection, three of these, Clinical Diabetes, Journal of Diabetes Nursing, and Diabetes and Primary Care were excluded as they did not appear to contain any primary research; the articles were mainly educational, professional news and views, opinions, or narrative reviews. The fourth journal, The Diabetic Foot, contained primary research, but did not appear to contain studies useful for epidemiological reviews of diabetes itself (as opposed to complications). It is indexed by CINAHL only.Searches of As shown in Table The four diabetes journals unique to MEDLINE were all published in the USA. By contrast, only one of the seven journals unique to Embase was published in the USA.The results are summarised below:No English language journals articles that were not also indexed in MEDLINE or Embase were identified.There were 23 Spanish and Portuguese language articles identified in LILACs. On the basis of the English translation of the titles, they all reported studies done in Latin America.We defined grey literature as any literature not published in a peer reviewed journal. After removing duplicates, there were 51 dissertations identified from searches of Dissertations Abstracts US, Index to Theses UK, and SIGLE. The research presented in the vast majority (92%) of the dissertations appeared to have been written up as articles in journals indexed in MEDLINE or Embase. No grey literature studies of any format other than dissertations were retrieved from SIGLE, so there was very little additional information gained by these searches.The National Research Register  gave brief details of 18 projects in progress that had not been otherwise identified. Searching the NRR might be useful if unpublished results could be included in the review, but its main value would be to indicate when the review was likely to need updating.The search of the Conference Proceedings Index retrieved 25 articles, none of which appeared to have been published as journal articles after five years. The Zetoc Conference Search found eight articles, of which 50% had been published as full journal articles in MEDLINE or Embase.The search of Science Citation Index (SCI), restricted to meeting abstracts only, found 171 relevant studies. The time to publication of the SCI abstracts was examined by checking how many had subsequently been published as journal articles indexed in MEDLINE or Embase. It was found that 30% had reached full publication after three years.Diabetes and Metabolism and Diabetes Research and Clinical Practice. Of these, 11 (12%) had been published in journals. The average time delay from the date of publication of the abstract to full publication was 1.4 years.A search of BIOSIS, restricted to meeting abstracts only, retrieved 71 additional relevant abstracts that were not in SCI. Most (65%) of these 71 abstracts came from the supplements of These included AMED, BNI, HMIC, NLM Gateway meeting abstracts, PsychINFO, and SSCI,In summary, the data indicate that when searching for English language journal articles on diabetes epidemiology, searches of MEDLINE and Embase would suffice. The exception would be for studies from Latin America, where LILACS should also be searched. Searching for meeting abstracts may alert reviewers to forthcoming or unpublished work.The searches for diabetes epidemiology articles in MEDLINE and Embase resulted in 2923 articles being found in 696 different journal titles; 39% were found to be in 'diabetes journals' and 14% were non-English language.Figure Applying Bradford's Law of Scattering gives three zones, each providing one-third of the articles.Diabetes Care (contained 14.3% of the articles); Diabetic Medicine (5.0%); Diabetes Research & Clinical Practice (4.1%); Diabetologia (4.0%); Diabetes & Metabolism (2.4%), Diabetes (2.0%) These six journals represent 0.9% (of the 696) total journals, and all are indexed in MEDLINE.The first one-third of articles were from six journals. They are in rank order: Practical Diabetes International; Diabetologia Polska; Diabetes und Stoffwechsel and Journal of the Japan Diabetes Society. Hence, 94% of Zone 2 journals are covered by MEDLINE.The second one-third of articles were from 62 journals, representing 9.1% of the total journals. The four journals in Embase only were: The final one-third of articles were in 628 journals, representing 90.2% of the total journals. MEDLINE indexed 88% of these journals.Overall, for the three zones, the search of MEDLINE and Embase for diabetes epidemiology articles revealed that MEDLINE indexed 89% of the total journals, and these contained 94% of the articles.Our results showed that there was an overlap of only 59% in current English language 'diabetes journals' indexed by both MEDLINE and Embase. Also, a search for diabetes epidemiology articles across both MEDLINE and Embase showed that MEDLINE alone retrieved about 94% of the total articles; therefore, both databases should be searched. Embase appears to index more diabetes journals published outside the USA. Therefore, if searching is limited to MEDLINE only (Embase being less accessible and more expensive than MEDLINE, which is free via PubMED) this could potentially introduce a bias. Also, duplication of searching can be useful, as due to differences in indexing practices, a search of one database may retrieve something missed by the other.We also found that despite a wide range of additional databases searched after MEDLINE and Embase, no additional English language journal articles on diabetes epidemiology were identified. The LILACS database was a useful source of Spanish and Portuguese language articles on the epidemiology of diabetes in Latin American countries.Meeting abstracts appeared to be valuable sources of information on forthcoming studies, but their inclusion in systematic reviews is contentious. Some reviewers exclude abstracts on the grounds that the quality of the study cannot be judged because of the inevitably limited detail. However, others include them on the grounds that abstracts provide the most up-to-date information.Nearly all the dissertations identified had been published as journal articles. However, it was found that only 30% of the meeting abstracts were converted to full publication after three years, which is considerably lower than the figure for RCTs, which is 56% . This haDiabetes Care alone containing about 14% of the articles. A similar concentration effect in journals was also shown in a study of 3400 science journals in the SCI database, where just 100 journals accounted for 22% of the published articles and 100 journals also accounted for 44% of cited articles [Scattering of the diabetes epidemiology articles revealed that the 'core' literature in this field is concentrated in just six journals, with articles .This study has a number of limitations. The search to identify diabetes journals was restricted to English language journals only, as we were unable to assess articles in other languages. We did not compare the quality of the articles identified from databases outside MEDLINE and Embase. Also, when searching for articles, we were necessarily limited to the range of databases available to us.Finally, there may be databases inaccessible or unknown to us that cover foreign language and regional journals not indexed in MEDLINE and Embase. Such journals may carry studies of incidence which may seem of primarily local interest, but which may be useful contributions to the international body of evidence because they may show large variations in incidence, or in its relationship to possible aetiological risk factors.It is often useful to study the epidemiology of a disease where it is rare, as well as where it is common. However it is likely that studies which report high incidence are more likely to be published than those which report low incidence. Similarly with risk factors; a study which finds no link between factor x and disease y may be less likely to be published than one which does show a correlation ,23.There is a need for further research to see whether our findings apply to searching for epidemiological reviews of other diseases, and on measuring the sensitivity and specificity of various search filters to retrieve epidemiological studies in MEDLINE and Embase.We endorse Dickersin's suggestion of an international collaborative effort to establish an 'epidemiological Cochrane-like database' to identify all relevant studies and to begin systematically reviewing available data for important epidemiological questions .Searching MEDLINE and Embase appears to provide comprehensive coverage of the English language journal literature in diabetes epidemiology. LILACs is a useful source of Spanish and Portuguese language articles on diabetes epidemiology done in Latin American countries and published in regional journals not indexed in MEDLINE and Embase. Searching for meeting abstracts is recommended to alert reviewers to unpublished work.The volume of literature on diabetes epidemiology makes it impossible for one person to read everything. However the provision of systematic reviews makes keeping up with research manageable, and more reviews are needed. Our findings on scattering shows that the core literature in diabetes epidemiology is concentrated in a small number of core journals, and that in the absence of reviews, one can follow the field by reading these journals. It may also be reassuring that a good MEDLINE-only search will retrieve the vast majority of the relevant literature.The author(s) declare that they have no competing interests.PR and NW conceived the study and drafted the initial manuscript. PR and LB collected and analysed the data. All authors contributed to and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Calcium makes up just 2% of the human body, and 99% of it is sequestered in bones and teeth. The remainder exists within and around cells, influencing a variety of cellular processes, from transcription and cell survival to nerve signaling and muscle contraction. Calcium inhabits the intra- and extracellular space as a gradient, with extracellular concentrations some 1,000 times greater than those inside the cell. These gradients are maintained by calcium pumps. Calcium signaling operates mostly through voltage- and ligand-gated calcium channels , both in the plasma membrane of the cell and in the membranes enclosing intracellular organelles. Signaling is typically initiated by an influx of calcium across the plasma membrane or by the release of calcium from an organelle, such as the endoplasmic reticulum. In neuron-to-neuron signaling, calcium signaling helps convert electrical signals into chemical signals in the form of neurotransmitters. The arrival of an electrical signal at a nerve terminal opens the many calcium channels in the nerve terminal, admitting a stream of calcium ions. The increased intracellular calcium concentration in turn releases the resident neurotransmitters accumulated in the nerve terminal, converting the electrical signal into a chemical signal. As neurotransmitters bind to their receptors in the next target neuron, they change the cell's membrane potential, prompting the neuron to generate an electrical signal, thereby converting the chemical signal back into an electrical one and completing the signaling circuit. Since calcium dynamics mediates most neuronal information flow, it can be used as a general measure of neural activity.Through an elaborate network of electrical activity, the brain encodes, combines, and interprets signals to process information about the world. Simultaneous measurement of this activity in multiple brain locations has provided valuable insight into how neural networks function. But since electrical recordings can't pinpoint activity in the fine branches of individual neurons or pick up biochemical  signals, researchers are increasingly turning to approaches that measure calcium concentrations as a proxy for neuron activity. Now Mazahir Hasan et al. have created mice engineered to stably express two different kinds of fluorescent calcium indicator proteins (FCIPs) in the brain (the fluorescence produced by these proteins can be seen when the brain is viewed with a two-photon microscope). Because the indicators are incorporated into the mouse genomes, this approach offers the possibility of targeting specific cells (by using promoters that specify which cells the genes should be activated in), allowing researchers to map the activity of select neuron populations.Fluorescent proteins can be incorporated into a gene of interest to help researchers track that gene's protein in living tissue. FCIPs report calcium concentrations by changing fluorescence when they bind to calcium. Introducing calcium indicators into neural tissues was largely impractical, often failing to target specific cell types, until these genetically engineered indicators were developed in the late 1990s, allowing the desired specificity. While FCIPs have been used to good effect in worms, fruitflies, and zebrafish—and just recently in mouse muscle—they had not been stably and functionally expressed in the mammalian brain until now.To deliver the FCIPs to the mouse brain, Hasan et al. used a regulatory promoter called Ptet (in the tetracycline system), which offers the possibility of targeting the expression of the FCIPs in different neural populations. To test the functionality of the proteins, they used fluorescence microscopy to analyze neurons from mouse lines and found high levels of FCIP expression. The real test, however, was whether the FCIPs could fulfill their promise as a probe for calcium activity. When the authors electrically stimulated brain slices from a mouse line expressing moderate to high levels of FCIP , fluorescence increased rapidly following the stimulus. Significant changes in FCIP fluorescence were also observed when live mice responded to odor stimulation. That “fast and robust” FCIP signals were detected in live animals responding to sensory stimulation, the authors argue, proves the promise of FCIPs as a reporter on the activity of select neural populations in living systems. And since these indicator proteins retain stable functional expression over time (8- to 12-week-old mice continued to express the proteins), they could help researchers track neuronal activity over extended periods.While a variety of bugs remain to be worked out with FCIPs—it's unclear, for example, why only the Ptet promoter generates high levels of FCIP expression in the brain and why not all neurons in a given population express the proteins—Hasan et al. demonstrate that the tetracycline system supports stable expression of the calcium indicators. The FCIP approach avoids the complications of invasive techniques like surgically administering dyes and produces a more interpretable signal, since the cell populations are already known. Because FCIPS can be used in living animals, they can reveal where and when neurons are firing. And because FCIP mice can be crossed with mice containing mutations in genes important for neural function, this method could reveal how specific genes contribute to the construction of neural networks.


Fusobacterium necrophorum may cause a number of clinical syndromes, collectively known as necrobacillosis. Meningitis is a significant cause of mortality, rarely reported in the adult population.Fusobacterium necrophorum, secondary to otitis media in an alcoholic male. Diagnosis was delayed due to the typical slow growth of the organism. The clinical course was complicated by encephalitis and by hydrocephalus. The patient failed to respond to metronidazole and penicillin. The patient died on day 12 from increased intracranial pressure and brain stem infarction.We report a fatal case of meningitis, caused by Fusobacterium necrophorum meningitis. We recommend the use of appropriate anaerobic culture techniques and antimicrobial coverage for anaerobic organisms when the gram stain shows gram negative bacilli.This case emphasizes the need for a high index of clinical suspicion to make the diagnosis of  Fusobacterium necrophorum may cause a number of clinical syndromes, collectively known as necrobacillosis. Meningitis is a significant cause of mortality, rarely reported in the adult population. Diagnosis is often delayed by difficulties encountered in isolating the organism. Here, we report a fatal case of meningitis from complicated otitis media caused by this organism.A 51 year male was brought to the emergency department (ED) by his family for confusion and shaking episodes. The patient was very lethargic in the ED and was intubated for airway protection. Family reported that he had not been well for several months, but the family was not able to define any specific symptoms until the past few days when he reported right ear pain. The patient had been given a prescription for erythromycin within the previous week for a diagnosis of otitis media; the patient had not taken any doses for at least two days. The only significant past medical history was of ongoing alcohol abuse without intravenous drug use. The family reported that he had no alcohol intake over the two days prior to presentation. Vitals on presentation: temperature 98.5°F (36.9°C), heart rate 125/min, respirations 25/min, blood pressure 219/121, Oxygen saturation 96 % on room air. On physical exam he was lethargic, the right tympanic membrane was erythematous with decreased movement on pneumatic otoscopy. The neurological exam revealed no focal deficits.F. necrophorum. Penicillin G was added with discontinuation of ceftazidime and levofloxacin. The patient further deteriorated and the neurological exam revealed absent corneal reflexes, and no response to painful stimulus. EEG was done to assess brain function, which revealed evidence of brain death. The caloric test also confirmed brain death.A non-contrasted head CT scan showed no abnormalities; there was no evidence of an intracranial bleed or elevated intracranial pressure. Lumbar puncture revealed markedly purulent CSF with 338,400 white cells per cubic millimeter (94% neutrophils), glucose was less than 2 mg/dl and protein was 3544 mg/dl. Gram stain showed numerous gram negative bacilli. He was initially treated with high dose dexamethasone, along with ceftriaxone, ampicillin and vancomycin. When gram negative bacilli were identified in the CSF, the antibiotic regimen was revised to ceftazidime, levofloxacin and metronidazole. There was a decline in mental status despite aggressive antibiotic therapy. He continued to require complete ventilatory and pressor support to maintain adequate blood pressure and oxygenation. Physical examination revealed up-going plantar reflexes on day 6. MRI brain showed extensive infarction of the brain stem, obstructive hydrocephalus, severe basilar meningitis, and ventriculitis (see figures Fusobacterium is an anaerobic, non spore forming gram negative rod which belongs to the family of Bacteroidaceae. It is a part of normal flora which is found in mouth, upper respiratory tract, gastrointestinal tract and vagina. It can cause local infections like pharyngitis, tonsillitis, mastoiditis or can cause severe bacteremic illness like meningitis. Other central nervous system complications caused by Fusobacterium include cranial nerve palsy, sinus venous thrombosis, and brain abscess [F. necrophorum was the most common anaerobe isolated, found in 33% of patients [F. necrophorum. Lemierre syndrome has been described to progress through three stages [ abscess ,2. In onF. necrophorum meningitis, middle ear infection was the source for 75% of the cases [Fusobacterium [In one series of he cases . Other pacterium ,4,6-19, acterium . Althougacterium . It has F. necrophorum. Anaerobic organisms should be considered as potential causative agents of meningitis when routine cultures are negative. Routine cultures of cerebrospinal fluid do not include the use of anaerobic growth media. Therefore, appropriate anaerobic culture techniques should be employed when sinus, otitic or mastoid symptoms precede or accompany the onset of meningitis in children or adults. The presence of irregularly stained gram negative rods in the CSF or meningitis unresponsive to empiric antibiotics should also raise the suspicion of anaerobic infection. The addition of metronidazole should be considered in these cases.This case shows the severity of illness that can result from infection with None declared.AI cared for the patient. SG drafted the manuscript. TH cared for the patient. All authors reviewed and approved the final manuscript.The pre-publication history for this paper can be accessed here:


There is considerable interest in the literature on disease mapping to interpolate estimates of disease occurrence or risk of disease from a regional database onto a continuous surface. In addition to many interpolation techniques available the geostatistical method of kriging has been used but also criticised.To circumvent these critics one may use kriging along with already smoothed regional estimates, where smoothing is based on empirical Bayes estimates, also known as shrinkage estimates. The empirical Bayes step has the advantage of shrinking the unstable and often extreme estimates to the global or local mean, and also has a stabilising effect on variance by borrowing strength, as well. Negative interpolates are prevented by choice of the appropriate kriging method. The proposed mapping method is applied to the North Carolina SIDS data example as well as to an example data set from veterinary epidemiology. The SIDS data are modelled without spatial trend. And spatial interpolation is based on ordinary kriging. The second example is included to demonstrate the method when the phenomenon under study exhibits a spatial trend and interpolation is based on universal kriging.Interpolation of the regional estimates overcomes the areal bias problem and the resulting isopleth maps are easier to read than choropleth maps. The empirical Bayesian estimate for smoothing is related to internal standardization in epidemiology. Therefore, the proposed concept is easily communicable to map users. As with the analysis of any set of data, it is always good practice to begin by producing and inspecting graphs. A feel for the data can then be obtained and any outstanding features identified. In spatial epidemiology this is called disease mapping. Bithel , Diggle There are three basic types of disease maps corresponding to certain types of data. These are dot maps for point (or case-event) data, choropleth maps for regional data , and lastly, isopleth maps for geostatistical data  representing spatially continuous phenomena at a limited number of sampling locations. Spatial epidemiology is mainly concerned with the analysis of two types of data: case-event data and regional summary data, generally leading to dot maps and choropleth maps, respectively. However, epidemiology is also concerned with the identification of unknown risk factors, which may be part of the environment such as air pollution, radiation or magnetic fields. And such factors vary spatially continuously. Thus it is important to produce an isopleth map of disease occurrence or risk. Furthermore, as has been often pointed out in the literature [, p. 131]For spatial point case-control data Bithell  introducRegional data arise from summarising individual information for administrative regions such as census tracts. The basic model for the individual data, i.e. spatial point data, is the spatial Poisson process. In case of a rare disease, aggregation of the data over distinct regions results again in Poisson distributed data. For a more common disease the regional counts may be binomially distributed.When spatial interpolation of regional data is the objective of the disease mapping study a grid of surface interpolant co-ordinates must be provided. Then a number of techniques can be used to automatically interpolate the data by use of deterministic methods or to predict the values statistically at the grid co-ordinates. Among the possible choices are kernel smoothing, splines, loess and running medians; see  for a diAnother approach for the interpolation of regional data onto a continuous surface is the geostatistical prediction method of kriging. Some implementations of kriging have been proposed to obtain a risk surface ,34,35,38The problems with the kriging method for generating isopleth maps of disease occurrence, i.e. the spatial risk function, are: (1) heterogeneous variances in the regional estimates and (2) the potential of negative interpolations. The first problem can be ameliorated by the use of empirical Bayes estimation to smooth the data prior to kriging. Appropriate geostatistical modelling can solve the second problem.N disjunctive sub regions indexed by i (i = 1,..., N). Define ni as the size of the 'at-risk' population of the i-th region, and denote the number of cases by mi. The proportion pi = mi/ ni is the crude estimate for the parameter of interest θi.Let the study area be divided into Mapping raw estimates of disease occurrence can lead to spurious spatial features. To overcome this problem Cressie and Read  have expClayton and Kaldor  proposedi are independently Poisson distributed with the unknown parameter θi which has an unknown prior distribution associated with expectation E(θi) = π and variance Var(θi) = φ2. Then the totals of cases from the i-th region, e.g. mi (i = 1,..., N), are distributed as followsAssume the cases in every region mi | θi, ni ~ Po(ni θi)θi ~ (π, φ2) for the prior mean and  for the prior variance, where  denotes the mean regional 'at-risk' population and the summation is over the range of i. The empirical Bayesian estimates then becomes  with shrinkage weights .The MME of the unknown hyperparameters are pi has the side-effect of stabilising the variances for the regional empirical Bayes estimates, i.e. . Heuristically this is clear, because the empirical Bayes estimates are based on the whole sample information and not just on the individual regional sample, and thus are more stable. But this is difficult to prove, because this requires an analytical expression for the corresponding variance or an approximate variance estimate. Morris [As stated above, the smoothing of the crude regional estimates . Morris  proposed. Morris , p. 80]., generally have a smaller associated variance than the corresponding frequentist estimates, i.e. pi [Therefore, two facts are used to claim the conjecture that the empirical Bayes estimates of the regional estimates show up the tendency of variance homogeneity. First, it should be noted that Bayes estimates, i.e.  i.e. pi . This me i.e. pi .The geostatistical method of kriging is widely accepted for the purpose of spatial prediction, i.e. interpolation and (moderate) extrapolation. It is proposed here to predict the smoothed regional data onto a fine meshed regular grid of points for isopleth mapping purposes.Z = (Z1,..., ZN)' at sample sites si, i = 1,..,N, takes place within the framework of the spatial linear model [Kriging of spatial data, say Z = Z,..., ZN'ar model Z = μ + δ,     E(Z) = μ,     δ ~ Gau(0, Σ)An integral part of this model is spatial correlation, which must be taken into account to draw valid scientific inferences. Here Σ is a variance-covariance matrix, spatially structured according to the position and direction between sampling sites. The basic assumption for spatial data is that near things are more related than distant things. This was neglected for smoothing of the regional epidemiological measures  via empirical Bayes estimators., where h denotes the translation between any two arbitrary sites si and sj within the study region. See [For geostatistical modelling, the structure of spatial variation will be estimated through the semivariogram ion. See ,37 for dμ(s). Ordinary kriging is concerned with an unknown but constant mean function, i.e. μ(s) = μ. Furthermore, universal kriging is based on a polynomial trend surface model which is to be removed prior to estimation of the semivariogram from the residuals, i.e. δ(s). This technique may be the most widely-used in practice. An outlier-resistant alternative is median polish kriging. This method starts by the robust and non-parametric estimation of the non-constant mean surface via median polishing followed by robust semivariogram estimation. Berke [In geostatistics kriging is used synonymously with optimal spatial prediction. However the optimality depends on the appropriateness of the spatial model. Cressie  gives a n. Berke  proposedμ(s) plus the kriging prediction for the residual process δ (s), formally .Generally, kriging surfaces are the sum of an estimate for the trend surface  are in absolute not larger than the model residuals δ(s) = Z(s) - μ(s), i.e. the variability of the predictions  around the estimated mean surface  is smaller than the variability of the observations Z(s). When the semivariogram is modelled without nugget effect, i.e. without small-scale variability at spatial scales smaller than the observational scale, then kriging leads to direct interpolation at the sampling sites. In this case the prediction equals the observation at the sample sites and thus the predicted residuals are equal to the model residuals, i.e. . Predictions at any other sites have the tendency to shrink towards the value of the estimated trend surface at that place. On the other hand, when a semivariogram with nugget effect is appropriate, than the prediction tends to be closer to the mean surface, which gives smaller residuals , i.e. a smoother prediction surface . Thus the prediction of invalid values for risk or for other epidemiologic measures, as has been criticised in the past, is a consequence of inappropriate spatial modelling. This could only happen when universal kriging is based on an estimated trend surface model , which exceeds the range of valid values.Kriging is sometimes termed a smoothing method. This is due to the fact that the predicted residuals The mapping technique proposed to generate isopleth risk maps from regional count data is now applied to two example data sets. Example 1 is based on the SIDS mortality rates in North Carolina that show no spatial trend. Example 2 is based on spatial trend contaminated data of tapeworm infections among red foxes in Lower Saxony.This by now classical spatial data set on SIDS mortality rates in North Carolina from 1974 to 1984 has been analysed by many researchers. Cressie  gives anFor the years 1974 to 1984 the number of live births ranges from 567 to 52345 over North Carolina's 100 counties. The total number of reported SIDS cases is 1503 out of 753354 live births, which results in an annual mortality rate of approximately 2 per 1000 live births. The mean of the counties boundary files coordinates in longitude and latitude were used here as the geographic coordinates for the regions centres. Figure The smoothed rates are more appropriate for disease mapping than the raw rates and hence used here for choropleth mapping in Figure Geostatistical prediction, i.e. kriging of the empirical Bayesian smoothed rates, is based on appropriate modelling of the data. Here, the constant mean assumption for the spatial mean surface is chosen and justified by visual inspection of the empirical semivariogram which levels out and reaches a sill. The spatial dependence structure is modelled by an isotropic exponential semivariogram without nugget effect, which is fitted by weighted least squares estimation to the robustly estimated empirical semivariogram; see  for techDue to the constant mean assumption ordinary kriging is the appropriate spatial prediction method and without nugget effect this leads to direct interpolation of the data at the counties centres and to smoothed values shrunken towards the global mean for the rest of the study area. The resulting isopleth map or risk surface map is given in Figure Cross-validation can be used to explore the predictive performance of the kriging model. The cross-validation residuals  should be approximately Gaussian distributed. Normal probability plots can be used to disclose grossly model inadequacies [, p. 498]Aside from some positive and negative extreme values the general appearance of the residual process is Gaussian. The potential outliers are regions with steep gradients in risk and hence part of disease clusters (or their surroundings) which were previously identified  and are Echinococcus multilocularis (E.m.) is a tapeworm occurring in the northern hemisphere, including endemic regions in central Europe, most of northern and central Eurasia and parts of North America. In central Europe the red fox is the main definitive host with rodents such as mice or muskrats serving as intermediate hosts [te hosts . The parThe federal state of Lower Saxony is part of northern Germany and contains the federal city-state of Bremen as an enclave. During the period from 1991 to 1997, 5365 red foxes were sampled in Lower Saxony and examined for infections with E.m.. The data are given in Table Figure The map indicates high period prevalences in the south and north of Lower Saxony. Extraordinarily high prevalences were observed in the southern regions, which indicate the presence of a positive disease cluster that is identified by use of the spatial scan statistic .Figure s = (x, y)' denote an arbitrary location in the study region, where x and y are the co-ordinates to the east and north. By inspection of the variogram cloud [μ(s) = β0 + β1y + β2y2, with , , . Instead of OLSE one could use iterated WLSE, but iteration may lead to biased estimates. The residuals of the trend surface fit to the empirical Bayes smoothed regional period prevalences were then used to model a spherical semivariogram by weighted least squares estimation of the robustly estimated empirical semivariogram. Figure Figure am cloud  based onThe normal probability plot of the kriging cross-validation residuals in Figure Geostatistical modelling of the empirical Bayesian smoothed regional prevalences allows the calculation of an error map representing the kriging standard errors. The darker the error map in Figure In this paper two sophisticated statistical methods were combined to solve an open problem in disease mapping. Mapping regional data using choropleth maps holds on to several problems that are overcome by isopleth mapping. To interpolate the regional, i.e. spatially discrete information, the geostatistical method of kriging is used here. Kriging requires variance homogeneous data. However, regional risk estimates are generally based on varying sample sizes and consequently turn up spatially varying standard errors. Therefore the spatial risk estimates are smoothed using linear empirical Bayes estimation. By borrowing-strength-from-the-ensemble, the impact of outliers is reduced and standard errors are stabilized over space. This cannot achieve variance homogeneity entirely nor could the tendency be shown analytically. Bootstrapping methods for spatially dependent data  promise The purpose of exploratory disease mapping is to provide insight, as opposed to precise estimates of location, spread or trends . EmphasiKriging is known to be a smoothing method and it may be argued that the proposed mapping method results in double or over-smoothing. But here kriging is based on a semivariogram model without nugget effect, which is known to be a direct interpolation method . TherefoThe proposed disease mapping approach is a two-step procedure. The corresponding error map Figure , howeverThe empirical Bayes method as described here does not take into account spatial autocorrelation, but a modification to overcome this lack has been proposed . The metInterpolation of the regional disease risk estimates overcomes the areal bias and related problems of choropleth disease mapping. Consequently, isopleth maps are easier to read and interpret than choropleth maps. The geostatistical method of kriging is appropriate for this task when based on linear empirical Bayesian smoothed data. Unlike non-parametric and mathematical interpolation methods, the spatial model underlying the exploratory spatial risk map offers ways of interpretation.Finally, the proposed concept for exploratory spatial risk mapping is easily communicable to map users. The Bayesian smoothing estimator is related to internal standardization in epidemiology. Also kriging can be viewed as weighted moving averaging.


Far more species exist in the fossil record than inhabit Earth today. An estimated 94% of all bird species that ever lived, for example, are now extinct. So why is species extinction of urgent concern today? Though species come and go over evolutionary time, mass extinctions are relatively rare. Biologists believe they have occurred only five times, arising from relatively short-lived cataclysmic natural forces like astronomical or volcanic events. We are now on the brink of the Sixth Great Extinction, and we humans are largely to blame. For thousands of years, humans have retooled the landscape, an endeavor that has rarely coincided with the life history needs of local flora and fauna: over 150 bird species alone have vanished since 1500.halt biodiversity loss by that time. To meet this goal, biologists need reliable metrics to monitor global trends in biodiversity. Stuart Butchart et al. describe a new model for generating such indices to measure trends in extinction risk for complete classes of organisms, starting with the world's 10,000 bird species. Their “Red List Index” measures changes in overall extinction risk over time for all bird species worldwide. Similar indices are already being developed to track other groups, including mammals and amphibians, and in the future will hopefully be developed for some plant and invertebrate groups.As our capacity to alter the landscape has mushroomed, species have started disappearing faster than biologists can identify and document them. Mindful of this crisis, nearly 200 countries  agreed to staunch the loss of biodiversity by 2010, with the European Union raising the bar to In 2002, the CBD proposed that efforts to monitor global trends in biodiversity start by developing indicators to evaluate trends in biodiversity components, such as ecosystems and habitats, abundance and distribution of selected species, and change in threat status of species. Butchart et al. focus on evaluating trends in changes in threat status (extinction risk), relying on categories developed by the World Conservation Union (IUCN) Red List. Species are placed in categories on the Red List ranging from extinct to “least concern,” according to criteria that take into account their population size, population trends, and range size. Thousands of scientists from around the world feed these assessments, which have been widely used to measure the degree of degradation of biodiversity.To use the Red List to track biodiversity trends over time, Butchart et al. collected data from four complete assessments of the world's birds over sixteen years, supplemented by other sources. The number of threatened and near threatened species increased from 1,664 species in 1988 to 1,990 species in 2004, but many species moved between categories. To calculate the real net increase in extinction risk for the world's birds over this time, the authors first identified reasons for these category switches to remove biases introduced by factors irrelevant to genuine changes in species status; category changes owing to better knowledge, for example, do not reflect real changes in conservation status. They also accounted for time lags between status changes, and category changes owing to delays in knowledge becoming available to Red List assessors.The authors argue that the Red List Index provides a simple measure of trends in the status of avian species worldwide, in terms of their overall extinction risk. Overall, the index shows “a steady and continuing deterioration in the threat status of the world's birds between 1988 and 2004.” Though the extinction risk has improved for some species, it has deteriorated for others, with “particularly steep declines” in recent years for Asian birds—resulting from massive deforestation in Indonesia—and for seabirds such as albatrosses and petrels, which drown on the hooks of commercial long-line fisheries.Butchart et al. argue that Red List Indices complement indicators based on population trends, because although the indices show coarser temporal resolution, they have much better geographic representation; they're based on nearly all species in a group worldwide rather than on a potentially biased subset. Both types of species-based indicators show finer ecological resolution in tracking biodiversity loss than indicators like habitat or biome trends. Thus, the Red List Index provides a baseline for tracking progress toward the 2010 target. But having a reliable indicator is only the first step. Without an international commitment to halt the advancing extinction crisis, biodiversity will continue to decrease. The United States is the only industrialized nation that has not signed on to this effort.


Pseudomonas aeruginosa (PA) is the most important bacterial pathogen in patients with cystic fibrosis (CF) patients. Currently, routine bacteriological culture on selective/non- selective culture media is the cornerstone of microbiological detection. The aim of this study was to compare isolation rates of PA by conventional culture and molecular (PCR) detection directly from sputum.Pseudomonas isolation agar, Blood agar & McConkey agar). In addition, from the same specimen, genomic bacterial DNA was extracted (1 ml) and was amplified employing two sequence-specific targets, namely (i) the outer membrane protein (oprL) gene locus and (ii) the exotoxin A (ETA) gene locus.Adult patients (n = 57) attending the regional adult CF centre in Northern Ireland, provided fresh sputum following airways clearance exercise. Following processing of the specimen with sputasol (1:1 vol), the specimen was examined for the presence of PA by plating onto a combination of culture media (oprL locus was more sensitive than the ETA locus, as the former was positive in 10 more patients and there were no patients where the ETA was positive and the oprL target negative. Where a PCR +ve/culture -ve result was recorded (10 patients), we followed these patients and recorded that 5 of these patients converted to being culture-positive at times ranging from 4–17 months later, with a mean lag time of 4.5 months.By sputum culture, there were 30 patients positive for PA, whereas by molecular techniques, there were 35 positive patients. In 39 patients (22 PA +ve & 17 PA -ve), there was complete agreement between molecular and conventional detection and with both PCR gene loci. The oprL gene target, is a useful technique in the early detection of PA, gaining on average 4.5 months over conventional culture. It now remains to be established whether aggressive antibiotic intervention at this earlier stage, based on PCR detection, has any significant benefits on clinical outcome.This study indicates that molecular detection of PA in sputum employing the  Pseudomonas aeruginosa, Burkholderia cepacia and Stenotrophomonas maltophilia. However, with modern antibiotic management with improved antimicrobial agents, such as the aminoglycosides and carbapenems, CF patients have an improved survival, resulting in more adults in employment.Cystic fibrosis [CF] is the most commonly inherited fatal disease in persons originating from a white and European background, currently affecting approximately 30,000 adults and children in the US . The defPseudomonas aeruginosa in CF patients.[P. aeruginosa,[P. aeruginosa in CF patients [P. aeruginosa directly from the sputum of CF patients in the UK and Ireland is uncommon. Most clinical microbiology laboratories in the UK and Ireland, which support a CF centre, have developed standard operating procedures (SOPs) for the isolation of this organism from patients' sputum. Hence, it was the aim of this study to compare the conventional and molecular detection of P. aeruginosa from the sputum of adult patients attending the adult centre in Northern Ireland, as well as to estimate the lag time of conventional culture to detection, compared with molecular (PCR) detection.Previous studies have described alternative laboratory markers to conventional bacteriological culture for the detection of patients.,5 These ruginosa, as well Pseudomonas aeruginosa. Processed sputa (10 μ l) were inoculated and incubated, onto several selective media for the isolation of Pseudomonas aeruginosa, including:- Columbia Blood Agar (Oxoid CM0331) supplemented with 5% (v/v) defribinated horse blood, MacConkey Agar (Oxoid CM0007) and Pseudomonas Isolation Agar (PIA) (Oxoid CM0559 + SR0102). All media were incubated aerobically at 37°C for 48 h, unless otherwise stated. The PIA plates were incubated at room temperature for a further three days following initial 48 hrs incubation. In addition, all different phenotypes from the sputum of each patient were identified phenotypically employing a combination of conventional identification methods (e.g. oxidase), as well as the API Identification schemes (API 20NE, API 20E) .Duplicate sputa (1 ml minimum) specimens were collected from 57 adult patients with a well characterized history of CF in sterile (100 ml) plastic disposable containers. Sputum was collected immediately after a standardized session of physiotherapy and was stored at ambient temperature and was processed within 4 h from collection. Fresh sputum (1 ml min) was mixed with an equal mass (1:1) of Sputasol (Oxoid SR089A, Oxoid Ltd., Poole, England) and was incubated in a water bath at 37°C for 15 min, before further qualitative processing for the detection of et al[Pseudomonas aeruginosa  ATCC 27853, by employment of the Roche High Purity PCR Template Preparation Kit (Roche, England), in accordance with the manufacturer's instructions. Extracted DNA was stored at -80°C prior to PCR amplification. For each batch of extractions, a negative extraction control containing all reagents minus sputum, was performed, as well as an extraction positive control with P. aeruginosa.All DNA isolation procedures were carried out in a Class II Biological Safety Cabinet (MicroFlow, England) in a room physically separated from that used to set up nucleic acid amplification reaction mixes and also from the "post-PCR" room in accordance with the Good Molecular Diagnostic Procedures (GMDP) guidelines of Millar et al, in orde2, 200 μM (each) dATP, dCTP, dGTP and dTTP; 1.25 U of Taq DNA polymerase , 0.1 μM (each) of the each set of primers (exoA &oprL) (Table P. aeruginosa ATCC 27853 DNA) and multiple negative (water) amplification controls were included in every set of PCR reactions. In addition, a broad-range/universal PCR was employed with each sputum to demonstrate successful extraction of bacterial DNA from the specimen, as well as lack of PCR inhibition, by employing the highly conserved 16S rDNA primers, PSL/PSR  were set up as follows:-10 mM Tris-HCl, pH 8.3, 50 mM KCl, 2.5 mM MgCl00 μl werexoA or oprL loci, the specimen was considered positive for P. aeruginosa.Following amplification, aliquots (10 μl) were removed from each reaction mixture and examined by electrophoresis (80 V, 45 min) in gels composed of 2% (w/v) agarose (Gibco, UK) in TAE buffer (40 mM Tris, 20 mM acetic acid, 1 mM EDTA, pH 8.3), stained with ethidium bromide (5 μg/100 ml). Gels were visualised under UV illumination using a gel image analysis system (UVP Products, England) and all images archived as digital graphic files (*.bmp). Where a band was visualized at the correct expected size for either P. aeruginosa in patients' sputum by conventional and molecular techniques is shown (Table P. aeruginosa, whereas by molecular techniques, there were 35 positive patients. In 39 patients (22 P aeruginosa [PA] positive [+ve] & 17 P. aeruginosa [PA] negative [-ve]), there was complete agreement between molecular and conventional detection techniques. The oprL locus was more sensitive than the ETA locus, as the former was positive in 10 more patients and there were no patients where the ETA was positive and the oprL target negative, However, there were five patients who were culture positive but PCR -ve by both gene targets. Given that the universal 16S rDNA PCR was positive for these sputum specimens and that the appropriate molecular controls were working optimally, this may accounted for by potential phenotypic misidentification of P. aeruginosa, which has been recently described.[P. aeruginosa colonization with a false-negative culture result due to sample overgrowth by other bacteria or to the presence of non-cultivable organisms or auxotrophic mutations in the organism. Where a PCR +ve/culture -ve result was recorded (10 patients), we followed these patients and recorded that five of these patients converted to being culture-positive at times ranging from 4–17 months later, with a mean lag time of 4.5 months, whereas the remaining five patients remained negative for P. aeruginosa.A comparison of the occurrence of wn Table . By cultP. aeruginosa directly from the sputum of patients with CF. Two gene loci were targeted, as specific markers of P. aeruginosa, namely the exotoxin A (ETA) gene and the outer membrane lipoprotein (oprL) gene. ETA is produced by the majority of P. aeruginosa strains and can inhibit eucaryotic protein biosynthesis at the level of polypeptide chain elongation factor 2, similarly to diphtheria toxin.[In this study, two PCR assays were performed individually for the molecular detection of ia toxin. OprL is ia toxin.Pseudomonas aeruginosa is the most important bacterial pathogen in patients with CF [Pseudomonas colonisation of the major airways leading to delibating exacerbations of pulmonary infection, is the major cause of morbidity and mortality in patients with CF, hence it is important to be able to reliably detect P. aeruginosa from patients' sputum. Emerson et al. [P. aeruginosa was a major predictor of morbidity and mortality, whereby the 8-year risk of death parameter was 2.6 times higher in patients who had positive sputum cultures for this organism, as well as having a significantly lower percent predicted forced expiratory volume (FEV1). These workers suggested that early interventions may help decrease associated morbidity and mortality of young patients with CF. with CF , as demon et al.  recentlyet al. [P. aeruginosa infection in CF. These workers suggested that chronic lower airway infection with P. aeruginosa is associated with significant morbidity and mortality among CF patients [P. aeruginosa does not appear to cause an immediate and rapid decline in lung function, as early isolates are generally non-mucoid, antibiotic-sensitive and present at low densities, suggesting a possible "window of opportunity" for early intervention, as they describe [More recently, Rosenfeld et al.  describepatients . Howeverdescribe . This stdescribe Pseudomonas colonization as early as possible, so that (i) aggressive antibiotic regimes may be considered,(ii). the patient is managed optimally, in an attempt to avoid early biofilm formation and chronic colonization with Pseudomonas and (iii) appropriate infection control precautions are considered. More recently, West et al. showed by a combination of serum IgG, IgA and IgM anti P. aeruginosa antibodies, in conjunction with these authors' Wisconsis Cystic Fibrosis Radiograph score, that P. aeruginosa infections occurred approximately 6–12 months before the organism was recovered from respiratory secretions.[P. aeruginosa acquisition.It is therefore important that primary diagnostic bacteriology laboratories have the ability to detect transient and early cretions. In additPseudomonas aeruginosa, either from culture plates or patients' sputum, small numbers of Pseudomonas colonies (n = 1–2) may therefore be missed when present in the early stages of colonization preceding infection of the patient's airways, particularly where such single colonies are mixed alongside other phenotypically similar genera on the primary culture plate.[Pseudomonas colonization. Although such assays are not generally available in most clinical diagnostic laboratories, these laboratories generally do have access to such technology at regional specialist microbiology centres and it may therefore be prudent to establish routine analysis of children's sputum, particularly from patients with no or an intermittent history by culture of Pseudomonas colonization, at annual review. Furthermore, it is very important to follow up any such patients, which demonstrate a PCR +ve/culture -ve finding from their sputum, in order to establish whether there is transient infection in patients, which never result in established colonization leading to chronic infection. Additionally, it is important to monitor such PCR +ve/culture -ve patients in terms of optimal antibiotic management and infection control.Given that not all laboratories employ molecular detection methods for re plate. PragmatiP. aeruginosa were able to detect P. aeruginosa at an early stage, on average 4.5 months sooner than culture detection. However, it remains unclear what this "window of opportunity" potentially offers in terms of a decrease in P. aeruginosa-associated morbidity and mortality.Overall, our study demonstrated that molecular assays for the detection of JEM, PGM and JSE jointly conceived the study and designed the experiments. JX executed and analyzed all practical aspects of the study, as well as analyzing the data. JEM executed certain molecular components of experimentation, analyzed the data and prepared the manuscript. BCM helped guide the molecular aspects of experimentation and was involved in drafting of the manuscript. PGM provided expert microbiological analysis and interpretation of data. JSE provided clinical expertise, interacted with the CF patients and was involved in interpretation of data, as well as critically reviewing the final manuscript. All authors read and approved the final manuscript.


Changes in cellular functions in response to drug therapy are mediated by specific transcriptional profiles resulting from the induction or repression in the activity of a number of genes, thereby modifying the preexisting gene activity pattern of the drug-targeted cell(s). Recombinant human interferon beta (rIFNβ) is routinely used to control exacerbations in multiple sclerosis patients with only partial success, mainly because of adverse effects and a relatively large proportion of nonresponders. We applied advanced data-mining and predictive modeling tools to a longitudinal 70-gene expression dataset generated by kinetic reverse-transcription PCR from 52 multiple sclerosis patients treated with rIFNβ to discover higher-order predictive patterns associated with treatment outcome and to define the molecular footprint that rIFNβ engraves on peripheral blood mononuclear cells. We identified nine sets of gene triplets whose expression, when tested before the initiation of therapy, can predict the response to interferon beta with up to 86% accuracy. In addition, time-series analysis revealed potential key players involved in a good or poor response to interferon beta. Statistical testing of a random outcome class and tolerance to noise was carried out to establish the robustness of the predictive models. Large-scale kinetic reverse-transcription PCR, coupled with advanced data-mining efforts, can effectively reveal preexisting and drug-induced gene expression signatures associated with therapeutic effects. By studying gene expression in patients with multiple sclerosis before and after therapy with beta interferon, it is possible to identify gene expression signatures that are associated with therapeutic effects Interferons are small, inducible proteins secreted by nucleated cells in response to viral infection and other stimuli. They act in a paracrine fashion on other cells in their immediate vicinity, triggering a state of growth arrest, so that infected cells cannot be forced to produce viral proteins, and activating the process of programmed cell death, so that infected cells can be removed . InterfeWe generated and analyzed longitudinal patterns of gene expression from interferon beta (IFNβ)–treated patients suffering from MS with the aim of identifying preexisting and drug-induced signatures that would predict or explain the clinical response to the drug.Fifty-two patients with relapsing-remitting MS were followed for at least 2 y after initiation of therapy with IFNβ. Clinical follow-up included a neurological examination every 3 mo and at the time of relapse. At each visit, a blood sample was obtained by venipuncture. After the 2-y endpoint, patients were classified as either good or poor responders based on strict criteria, as described in MX1 as the single best discriminating variable between samples obtained at baseline (T = 0) and at 3 mo after initiation of therapy (T = 3) with a classification accuracy of 79% (data not shown). Given that MX1 is a known marker of IFN bioavailability [Using linear discriminant analysis–based integrated Bayesian inference system (IBIS), we were able to detect the gene lability , this reT = 0 correlated with a good or poor outcome of therapy at the 2-y endpoint. This process exhaustively carried out sample classification by searching through all 54,740 possible three-gene combinations of 70 genes. Higher-order combinatorial searches beyond combinations of three genes are possible using IBIS and are currently under investigation. However, higher-order predictive variable combinations do require the support of many more samples to prevent overfitting of the model.To search for expression signatures associated with therapeutic outcome (good or poor responder), we conducted clustering of samples using normalized data for all 70 genes at each time point . DespiteWe implemented a stringent method for examining the statistical validity of our classification results that consisted in testing the obtained classifier in an independent set of samples not previously “seen” by the program. All of the following statistical analyses were thus performed on split datasets, namely, training (75% of the samples) and test (25%), each reflecting the same relative proportion of classes (63% good and 37% poor responders). We started by conducting 3D IBIS searches using expression data from only the training set and selected the top nine scoring triplets . For each gene triplet, and using the training data only, a committee of classifiers was built based on an internal cross-validation scheme. Subsequently, the classifiers were used to predict the outcome of an independent test set of samples. Gene triplets were ranked on the basis of the prediction accuracies of the classifiers on this independent test set. We identified nine gene triplets with a predictive accuracy of at least 80% . We consCaspase 2, Caspase 10, and FLIP, which yielded a predictive accuracy of 86% in the original split. The bell-shaped distribution resulting from 100 tests for this triplet displayed a mean accuracy of 87.8% and a tenth percentile of 78.6%, meaning that if the prediction were performed multiple times, in 90% of these instances an accuracy of almost 79% or better would be obtained. This histogram only reflects the range of accuracies obtained, should the initial data split be different. Notably, the genes in the top-scoring triplet were Caspase 2, Caspase 10, and FLIP—three apoptosis-related molecules. The second-highest-scoring triplet was that of Caspase 2, Caspase 3, and IRF4 (86.8% mean accuracy after 100 splits). Other high-scoring triplets included IL4Ra and MAP3K1, in addition to other apoptotic molecules (F-test, the obtained mean accuracy was 64% (tenth percentile at 50%) (Consequently, for the nine top-scoring gene triplets and their corresponding classifiers, we generated 100 random splits and built classifiers for each new resulting training set. Next, we tested how well the classifiers predicted therapeutic outcome in the corresponding test datasets. olecules . When we at 50%) .Caspase 2, Caspase 10, and FLIP; 3D model) was compared with those obtained with the single-gene (1D) and gene-pair (2D) models. We observed that the classification accuracy improves as more genes are added to the classifier. We next plotted the samples of a test dataset (25% of samples) on the predictive probability model shown in Caspase 2/Caspase 10 and Caspase 10/FLIP gene pairs show significant predictive capability, but that all three genes are required to provide the highest level of model accuracy and robustness.In To validate the specificity and predictive capability of the top-scoring gene triplet (for the good and poor responding classes) and its associated classifiers, we examined the model exhibiting the best performance on a “default” expression dataset. This null dataset was built keeping the original gene expression data and randomly permuting the class labels of the outcomes 1,000 times . The prediction accuracies for all the gene triplets obtained with this dataset dropped dramatically as the means ranged from 49.2% to 53.6% (data not shown), emphasizing the specificity of the classifiers. In addition, for the top-scoring gene triplet (for good and poor responder classes), we calculated the probability of achieving, under the null hypothesis, an equal or better accuracy than that obtained in the original prediction (86%), as previously described . This acFinally, we tested the robustness of each of these gene sets as predictors of IFNβ response by simulating experimental measurement error. To accomplish this, we first calculated the standard deviation of the expression measurements for all genes as an estimation of the overall experimental error. We then added a fixed amount of Gaussian noise corresponding to one standard deviation (taken from 20 random deviations) to each expression value and repeated the classification/prediction in 30 different splits of the data . Notably, the mean drop in predictive accuracy after the addition of noise was less than 10%, denoting a significant tolerance to reasonable measurement errors .Because all the patients in this study were systematically followed up for a period of 2 y, we were able to perform a longitudinal analysis. Using a repeated-measures analysis of variance (ANOVA), we searched for genes with significantly different expression patterns based on models that tested for responder effect, time effect, and interaction effect (time × response).IRF4 (p = 0.03), IL4Ra (p = 0.01), Caspase 10 (p = 0.0008), Caspase 7 (p = 0.01), IRF2 (p = 0.02), and IRF6 (p = 0.03) are among the 12 genes that best predict response at T = 0 (shown in bold in TRADD, and BAX) was observed for the poor responders.Significant responder effect for 20 genes  and signAlthough we successfully identified informative, combinatorial relationships, establishing the causality of the association between gene expression and outcome to therapy is beyond of the scope of this work, and these genes are therefore considered surrogate markers. Moreover, although extensive in vivo and in vitro experiments have been conducted, the full mechanism of action of IFNβ in MS remains unknown.Transcription profiling experiments have involved IFNβ in the regulation of apoptosis in both cancer and MS ,16,17,18IRF4, a gene essential for mature T and B lymphocyte function and homeostasis, and a transcription factor with dual function (activator/repressor) that regulates transcription of IL4 through physical interaction with NFATc2 [IRF4 is a repressor of other IFN-induced genes [IRF4 observed in the poor responders before initiation of therapy.Other differentially expressed transcripts included h NFATc2 . Remarkaed genes , an obseMX1 showed a significant time effect independent of clinical response (p = 0.01). This result is in agreement with previous findings indicating substantial MX1 upregulation in response to type I IFNs [MX1, which occurs minutes after IFN stimulation [MX1 as the best single classifier for samples from patients before (T = 0) and after (T = 3) initiation of therapy. In fact, as MX1 comes from the difference between T = 0 and T = 3.As expected, the gene e I IFNs . Interesmulation , is sustIFNAR1 and STAT2 , their upregulation on administration of rIFNβ is likely related to mechanistic aspects of IFN signaling. Our results suggest that poor response is associated with downstream signaling events rather than deficient recognition or metabolism of the drug. Our previous finding that IFN receptor polymorphisms do not affect therapeutic response in this same set of patients partially supports this hypothesis [Caspase 10 (p = 0.01) and MAP3K1 (p = 0.01) were part of any predictor set . These results highlight the involvement of these genes in the response to IFN both at T = 0 and once therapy has started.Also of interest, a significant time effect (but not responder effect) was observed for nd STAT2 B. Becauspothesis . Only twctor set B. In addHere we combined large-scale, function-oriented gene expression with advanced data mining to identify a set of markers that accurately and robustly predict the response to rIFNβ therapy. Although larger, prospective studies are needed to confirm these findings, our results suggest that the underlying gene activity profile of an individual at the verge of therapy harbors sufficient information to allow investigators to estimate the chances of experiencing satisfactory therapeutic effects. As analytical tools to predict clinical outcomes based on molecular evidence evolve, these types of studies are likely to become a substantial aid to the physician, taking the paradigm of personalized medicine one step further.All studies were approved by the respective Committees of Human Research at Hospital Vall d'Hebron, Barcelona, Spain, and the University of California, San Francisco, United States. Informed consent was obtained for all study participants. All patients were examined by a trained neurologist at the CNI Unit, Vall d'Hebron Hospital. Inclusion criteria for this study were clinically definite MS (Poser's criteria), disease in relapsing-remitting phase, age between 18 and 65 y, recorded history of at least two clearly identified relapses within the preceding 24 mo, and expanded disability status scale between zero and 5.5 (inclusive). Detailed information about clinical aspects of these patients has been recently reported elsewhere [n = 33) if they experienced a total suppression of relapses and no increase in the expanded disability status scale after a 2-y follow-up period. Poor responders (n = 19) were defined as having suffered two or more relapses or having a confirmed increase of one point in the expanded disability status scale score. Patients with intermediate phenotypes were excluded from this study. Blood specimens were taken following institutional guidelines at approximately the same time of the day just before the administration of the first dose of rIFNβ and every 3 mo thereafter during the neurological examination, with the exception of T = 15 and T = 21 mo. Altogether, 336 samples were tested . Immediately after being drawn, all blood samples were spun over Ficoll-Paque (Amersham Biosciences, Piscataway, New Jersey, United States) gradients to enrich the sample for mononuclear cells. After three washes with PBS, aliquots of 5 × 106 cells were frozen in RPMI1640 containing 20% DMSO and 20% fetal calf serum.Patients were categorized as good responders . Plates were kept frozen at −70 °C until needed.Peripheral blood mononuclear cells were thawed at 37 °C for 1 min, and RNA lysis buffer was added immediately. RNA was purified with the Strataprep kit  and finally resuspended in nuclease-free water (Promega, Madison, Wisconsin, United States). One-microliter RNA aliquots were subjected to fluorescence-based quantitation (in duplicate) using the Ribogreen reagent (Molecular Probes, Eugene, Oregon, United States) in a Spectra Max Gemini fluorometer . Samples were diluted to 1 ng μlA master mix was prepared essentially as described previously , with thA custom Microsoft Excel (Microsoft, Redmond, Washington, United States) worksheet was prepared for handling reaction data import and performing initial statistics. Normalized data were imported as a .csv file into GeneLinker Platinum (Predictive Patterns Software, Kingston, Ontario, Canada) for preprocessing and clustering analysis. Quadratic discriminant analysis–based IBIS implemented for 3D searches was carried out at the School of Computing, Queen's University, Kingston, Ontario, Canada, and at Biosystemix, Sydenham, Ontario, Canada. IBIS is a data-mining algorithm that searches through the gene space for a single gene (or group of genes) that can predict the outcome class (in this case, good and poor response to rIFNβ therapy). This algorithm incorporates a complete 10-fold cross-validation method with several independently trained classifiers to predict an outcome on the basis of a voting scheme (see below). We used MSE and classification accuracy to assess how well the classification predictions matched the true response of the patients to therapy. The top-performing gene triplets were selected on the basis of a mixed threshold for low MSE levels and high accuracy rates.i,g two Gaussian functions are fitted to the distributions of the observed expression levels in good responding and poor responding patients . Our fitted distribution, Dg(x), denotes the probability of a patient having an expression level of i = x,g given that this patient is a good responder. The fundamental question we are aiming to answer using data-mining methods (here using IBIS particularly) is as follows: what is the probability of a patient being a good responder given the observed expression level of a gene for that patient? Taking advantage of the fitted distributions, a classifier applies Bayes' formula to answer the fundamental question. According to this formula:IBIS identifies genes (or gene pairs or groups of genes) that are highly predictive of the outcome based on probability distributions of those genes in different outcome classes. For example, for a given gene P1= P((i = xg)|Pagood_responder)P(Pagood_responder). In fact, P((i = xg)|Pagood_responder) is the distribution function fitted to the observed gene expression values of ig above (Dg), and P(Pagood_responder), the probability of a patient being a good responder, can be easily calculated using the total and good responding patient counts. The term P2 is strictly analogous to P1 but applies to poor responders. Therefore, according to where To obtain a reliable classifier that is generalizable to all patient samples obtained under similar conditions, the Gaussian distributions and the classifier were only trained on a subsample of the patient data (training set). The results of the classification (i.e., probability of a patient being a good or poor responder), however, were tested on patient samples never seen by the classifier before (test set). This ensures limitation of the classifier overfitting. Further, a complete 10-fold cross-validation scheme was built into the training phase. In IBIS, linear and quadratic classifiers correspond to classifiers built using Gaussian distributions with equal or different covariance matrices, respectively. The prediction results of IBIS are visualized graphically within the observed gene expression space by presenting the probabilities of a patient being a good or poor responder as a background color see  and 5. Tiobserved − responseiexpected)2 averaged over all patients. For a given patient, the clinical response determined by the end of the 2-y monitoring period is denoted by responseiobserved and responseiexpected and represents the probability of that patient being a good responder to rIFNβ therapy, using the Bayes' formula above. Classification accuracy simply expresses the percentage of patients that were correctly predicted as being good or poor responders.Several measures were used to assess how well the calculated probabilities matched the true patient responses to therapy. Two of these measures were MSE and classification accuracy. MSE was calculated as the sum of . Only the training set was used for identifying the best predictive gene triplets with the IBIS method, as well as for building the classifier. A committee of classifiers was then generated using a 10-fold cross-validation scheme during training. The training data themselves were divided into ten parts, and each time, a classifier was built using only nine parts of the data. That classifier's predictive capability was determined by its accuracy over the one-tenth of the data withheld. A committee of ten classifiers was assembled from the results of this training stage; this committee was then applied to the test data (which have thus far been hidden from the classifiers). For a patient sample in the test data, each classifier in the committee made a prediction. A majority voting scheme then decided as to which class the sample would be assigned.Given the initial data split into training and test sets, it was important to rule out the role of fortuitous idiosyncrasies in this split and the resulting accuracy rates. To address this point, we created 100 random splits of the data into training and test subgroups. A committee of classifiers was trained on the training set for each data split, and the accuracies were calculated over the blind test set. A histogram of the test set accuracies was then built, representing the expected ranges of accuracies had the initial data split been different. This histogram is not representative of the estimated or idealized distribution of the accuracies for a gene triplet in a machine learning sense. Rather, it is a coarse approximation of the possible range of gene triplet outcome–prediction accuracies that could be expected.p-value, and indicates the number of times in 1,000 trials for which accuracies of 86% or higher can be achieved under the “no predictive capability” null hypothesis.To assess the significance and specificity of the top-scoring gene triplets and their corresponding trained committee of classifiers, a null dataset was created by keeping the same expression levels of genes in the dataset and randomly permuting the class labels of the patients 1,000 times . Classifiers were built using the training null data, and accuracies were calculated on the corresponding test sets. The mean of these accuracies for all the top-performing gene triplets was around 50%. The achieved significance level, which represents the probability of achieving accuracy levels better than or equal to that of the nonpermuted classification, was calculated to be 0.009. This value can be considered a significance level, or Time-series analysis was performed using SAS version 8.0 (SAS Institute, Cary, North Carolina, United States). Permutation analysis and histogram graphic outputs were produced with Matlab (The Mathworks, Natick, Massachusetts, United States).Dataset S1Gene expression values for all samples at all time points. This is the raw file from which all analyses were performed.(491 KB XLS).Click here for additional data file.Table S1Gene names, symbols, and LocusLink and GenBank accession numbers, as well as primer sequences, are listed for all targets.(160 KB DOC).Click here for additional data file.http://www.ncbi.nlm.nih.gov/projects/LocusLink/) accession numbers for the genes discussed in this paper are BAX (LLID 581), Caspase 10 (LLID 843), Caspase 2 (LLID 835), Caspase 3 (LLID 836), Caspase 7 (LLID 840), FLIP (LLID 8837), IFNAR1 (LLID 3454), IL4 (LLID 3565), IL4Ra (LLID 3566), IRF2 (LLID 3660), IRF4 (LLID 3662), IRF6 (LLID 3664), MAP3K1 (LLID 4214), MX1 (LLID 4599), NFATc2 (LLID 4773), STAT2 (LLID 6773), and TRADD (LLID 8717).The LocusLink (